CRAN Package Check Results for Package mlr

Last updated on 2017-01-16 17:49:21.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 2.9 18.51 414.00 432.51 ERROR
r-devel-linux-x86_64-debian-gcc 2.9 18.29 439.48 457.77 ERROR
r-devel-linux-x86_64-fedora-clang 2.9 811.90 ERROR --no-stop-on-test-error
r-devel-linux-x86_64-fedora-gcc 2.9 790.23 ERROR --no-stop-on-test-error
r-devel-macos-x86_64-clang 2.9 621.28 ERROR --no-stop-on-test-error
r-devel-windows-ix86+x86_64 2.9 54.00 1060.00 1114.00 ERROR
r-patched-linux-x86_64 2.9 17.11 407.74 424.86 ERROR
r-patched-solaris-sparc 2.9 1280.20 NOTE --no-examples --no-tests
r-patched-solaris-x86 2.9 264.80 NOTE --no-examples --no-tests
r-release-linux-x86_64 2.9 18.55 412.79 431.35 ERROR
r-release-osx-x86_64-mavericks 2.9 ERROR
r-release-windows-ix86+x86_64 2.9 53.00 986.00 1039.00 ERROR
r-oldrel-windows-ix86+x86_64 2.9 40.00 943.00 983.00 ERROR

Check Details

Version: 2.9
Check: dependencies in R code
Result: NOTE
    Missing or unexported object: ‘irace::removeCandidatesMetaData’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-osx-x86_64-mavericks, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 2.9
Check: tests
Result: ERROR
     Running ‘run-base.R’ [290s/286s]
    Running the tests in ‘tests/run-base.R’ failed.
    Last 13 lines of output:
     testthat results ================================================================
     OK: 2349 SKIPPED: 1 FAILED: 15
     1. Error: downsample wrapper works with xgboost, we had issue #492 (@test_base_downsample.R#38)
     2. Failure: generateCalibrationData (@test_base_generateCalibration.R#55)
     3. Failure: generateCalibrationData (@test_base_generateCalibration.R#57)
     4. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#72)
     5. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#74)
     6. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#46)
     7. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#48)
     8. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#216)
     9. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#219)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 2.9
Check: tests
Result: ERROR
     Running ‘run-base.R’ [317s/314s]
    Running the tests in ‘tests/run-base.R’ failed.
    Last 13 lines of output:
     testthat results ================================================================
     OK: 2349 SKIPPED: 1 FAILED: 15
     1. Error: downsample wrapper works with xgboost, we had issue #492 (@test_base_downsample.R#38)
     2. Failure: generateCalibrationData (@test_base_generateCalibration.R#55)
     3. Failure: generateCalibrationData (@test_base_generateCalibration.R#57)
     4. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#72)
     5. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#74)
     6. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#46)
     7. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#48)
     8. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#216)
     9. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#219)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 2.9
Flags: --no-stop-on-test-error
Check: dependencies in R code
Result: NOTE
    Missing or unexported object: ‘irace::removeCandidatesMetaData’
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-macos-x86_64-clang

Version: 2.9
Flags: --no-stop-on-test-error
Check: tests
Result: ERROR
     Running ‘run-base.R’ [9m/13m]
     Running ‘run-classif.R’
     Running ‘run-cluster.R’
     Running ‘run-featsel.R’
     Running ‘run-learners-classif.R’
     Running ‘run-learners-classiflabelswitch.R’
     Running ‘run-learners-cluster.R’
     Running ‘run-learners-general.R’
     Running ‘run-learners-multilabel.R’
     Running ‘run-learners-regr.R’
     Running ‘run-learners-surv.R’
     Running ‘run-parallel.R’
     Running ‘run-regr.R’
     Running ‘run-stack.R’
     Running ‘run-surv.R’
     Running ‘run-tune.R’
    Running the tests in ‘tests/run-base.R’ failed.
    Complete output:
     > library(testthat)
     > test_check("mlr", filter = "base")
     Loading required package: mlr
     Loading required package: BBmisc
     Loading required package: ggplot2
     Loading required package: ParamHelpers
     Loading required package: stringi
     OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead.
     OMP: Hint: Consider unsetting KMP_ALL_THREADS and OMP_THREAD_LIMIT (if either is set).
     1. Error: downsample wrapper works with xgboost, we had issue #492 (@test_base_downsample.R#38)
     'predict' is not an exported object from 'namespace:xgboost'
     1: resample(lrn, binaryclass.task, rdesc) at testthat/test_base_downsample.R:38
     2: parallelMap(doResampleIteration, seq_len(rin$desc$iters), level = "mlr.resample",
     more.args = more.args)
     3: mapply(fun2, ..., MoreArgs = more.args, SIMPLIFY = FALSE, USE.NAMES = FALSE)
     4: (function (learner, task, rin, i, measures, weights, model, extract, show.info)
     {
     setSlaveOptions()
     if (show.info)
     messagef("[Resample] %s iter: %i", rin$desc$id, i)
     train.i = rin$train.inds[[i]]
     test.i = rin$test.inds[[i]]
     err.msgs = c(NA_character_, NA_character_)
     m = train(learner, task, subset = train.i, weights = weights[train.i])
     if (isFailureModel(m))
     err.msgs[1L] = getFailureModelMsg(m)
     ms.train = rep(NA, length(measures))
     ms.test = rep(NA, length(measures))
     pred.train = NULL
     pred.test = NULL
     pp = rin$desc$predict
     if (pp == "train") {
     pred.train = predict(m, task, subset = train.i)
     if (!is.na(pred.train$error))
     err.msgs[2L] = pred.train$error
     ms.train = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.train, measures = pm))
     }
     else if (pp == "test") {
     pred.test = predict(m, task, subset = test.i)
     if (!is.na(pred.test$error))
     err.msgs[2L] = pred.test$error
     ms.test = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.test, measures = pm))
     }
     else {
     pred.train = predict(m, task, subset = train.i)
     if (!is.na(pred.train$error))
     err.msgs[2L] = pred.train$error
     ms.train = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.train, measures = pm))
     pred.test = predict(m, task, subset = test.i)
     if (!is.na(pred.test$error))
     err.msgs[2L] = paste(err.msgs[2L], pred.test$error)
     ms.test = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.test, measures = pm))
     }
     ex = extract(m)
     list(measures.test = ms.test, measures.train = ms.train, model = if (model) m else NULL,
     pred.test = pred.test, pred.train = pred.train, err.msgs = err.msgs, extract = ex)
     })(dots[[1L]][[1L]], learner = structure(list(id = "classif.xgboost.downsampled",
     type = "classif", package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(
     pars = structure(list(dw.perc = structure(list(id = "dw.perc", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), dw.stratify = structure(list(id = "dw.stratify", type = "logical",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE,
     `FALSE` = FALSE), .Names = c("TRUE", "FALSE")), cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = FALSE, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("dw.perc", "dw.stratify")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     dw.perc = 0.5), .Names = "dw.perc"), predict.type = "response", fix.factors.prediction = FALSE,
     next.learner = structure(list(id = "classif.xgboost", type = "classif", package = "xgboost",
     properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"
     ), par.set = structure(list(pars = structure(list(booster = structure(list(
     id = "booster", type = "discrete", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type",
     "package", "properties", "par.set", "par.vals", "predict.type", "name", "short.name",
     "note", "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), model.subclass = "DownsampleModel"), .Names = c("id",
     "type", "package", "properties", "par.set", "par.vals", "predict.type", "fix.factors.prediction",
     "next.learner", "model.subclass"), class = c("DownsampleWrapper", "BaseWrapper",
     "Learner")), task = structure(list(type = "classif", env = <environment>, weights = NULL,
     blocking = NULL, task.desc = structure(list(id = "binary", type = "classif",
     target = "Class", size = 208L, n.feat = structure(c(60L, 0L, 0L), .Names = c("numerics",
     "factors", "ordered")), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE,
     class.levels = c("M", "R"), positive = "M", negative = "R"), .Names = c("id",
     "type", "target", "size", "n.feat", "has.missings", "has.weights", "has.blocking",
     "class.levels", "positive", "negative"), class = c("TaskDescClassif", "TaskDescSupervised",
     "TaskDesc"))), .Names = c("type", "env", "weights", "blocking", "task.desc"), class = c("ClassifTask",
     "SupervisedTask", "Task")), rin = structure(list(desc = structure(list(id = "cross-validation",
     iters = 2L, predict = "test", stratify = FALSE), .Names = c("id", "iters", "predict",
     "stratify"), class = c("CVDesc", "ResampleDesc")), size = 208L, train.inds = list(
     c(45L, 137L, 82L, 56L, 188L, 133L, 11L, 76L, 91L, 106L, 132L, 48L, 72L, 207L,
     149L, 28L, 143L, 97L, 186L, 198L, 122L, 127L, 27L, 65L, 75L, 203L, 157L, 146L,
     79L, 51L, 205L, 128L, 1L, 24L, 155L, 144L, 89L, 187L, 174L, 8L, 86L, 38L, 130L,
     109L, 99L, 125L, 12L, 2L, 200L, 134L, 42L, 6L, 165L, 199L, 84L, 177L, 14L, 4L,
     190L, 129L, 185L, 62L, 70L, 40L, 196L, 150L, 32L, 171L, 17L, 160L, 112L, 16L,
     33L, 147L, 41L, 197L, 136L, 105L, 58L, 167L, 23L, 57L, 31L, 181L, 22L, 113L,
     119L, 96L, 103L, 151L, 178L, 21L, 115L, 102L, 107L, 121L, 34L, 5L, 126L, 117L,
     15L, 67L, 153L, 60L), c(36L, 208L, 100L, 52L, 10L, 141L, 71L, 163L, 182L, 142L,
     172L, 116L, 80L, 206L, 192L, 30L, 110L, 54L, 124L, 68L, 164L, 43L, 37L, 98L,
     44L, 87L, 145L, 104L, 88L, 3L, 74L, 183L, 173L, 154L, 159L, 201L, 19L, 179L,
     9L, 193L, 7L, 13L, 93L, 118L, 94L, 92L, 140L, 83L, 18L, 156L, 49L, 53L, 108L,
     158L, 35L, 184L, 101L, 29L, 66L, 202L, 90L, 111L, 25L, 26L, 152L, 191L, 39L,
     180L, 69L, 189L, 175L, 63L, 138L, 61L, 85L, 135L, 139L, 73L, 81L, 123L, 20L,
     170L, 176L, 46L, 47L, 78L, 162L, 120L, 194L, 95L, 168L, 148L, 64L, 195L, 77L,
     131L, 169L, 204L, 59L, 161L, 55L, 166L, 114L, 50L)), test.inds = list(c(3L, 7L,
     9L, 10L, 13L, 18L, 19L, 20L, 25L, 26L, 29L, 30L, 35L, 36L, 37L, 39L, 43L, 44L, 46L,
     47L, 49L, 50L, 52L, 53L, 54L, 55L, 59L, 61L, 63L, 64L, 66L, 68L, 69L, 71L, 73L, 74L,
     77L, 78L, 80L, 81L, 83L, 85L, 87L, 88L, 90L, 92L, 93L, 94L, 95L, 98L, 100L, 101L,
     104L, 108L, 110L, 111L, 114L, 116L, 118L, 120L, 123L, 124L, 131L, 135L, 138L, 139L,
     140L, 141L, 142L, 145L, 148L, 152L, 154L, 156L, 158L, 159L, 161L, 162L, 163L, 164L,
     166L, 168L, 169L, 170L, 172L, 173L, 175L, 176L, 179L, 180L, 182L, 183L, 184L, 189L,
     191L, 192L, 193L, 194L, 195L, 201L, 202L, 204L, 206L, 208L), c(1L, 2L, 4L, 5L, 6L,
     8L, 11L, 12L, 14L, 15L, 16L, 17L, 21L, 22L, 23L, 24L, 27L, 28L, 31L, 32L, 33L, 34L,
     38L, 40L, 41L, 42L, 45L, 48L, 51L, 56L, 57L, 58L, 60L, 62L, 65L, 67L, 70L, 72L, 75L,
     76L, 79L, 82L, 84L, 86L, 89L, 91L, 96L, 97L, 99L, 102L, 103L, 105L, 106L, 107L, 109L,
     112L, 113L, 115L, 117L, 119L, 121L, 122L, 125L, 126L, 127L, 128L, 129L, 130L, 132L,
     133L, 134L, 136L, 137L, 143L, 144L, 146L, 147L, 149L, 150L, 151L, 153L, 155L, 157L,
     160L, 165L, 167L, 171L, 174L, 177L, 178L, 181L, 185L, 186L, 187L, 188L, 190L, 196L,
     197L, 198L, 199L, 200L, 203L, 205L, 207L)), group = structure(integer(0), .Label = character(0), class = "factor")), .Names = c("desc",
     "size", "train.inds", "test.inds", "group"), class = "ResampleInstance"), weights = NULL,
     measures = list(structure(list(id = "mmce", minimize = TRUE, properties = c("classif",
     "classif.multi", "req.pred", "req.truth"), fun = function (task, model, pred,
     feats, extra.args)
     {
     measureMMCE(pred$data$truth, pred$data$response)
     }, extra.args = list(), best = 0, worst = 1, name = "Mean misclassification error",
     note = "", aggr = structure(list(id = "test.mean", name = "Test mean", fun = function (task,
     perf.test, perf.train, measure, group, pred)
     mean(perf.test)), .Names = c("id", "name", "fun"), class = "Aggregation")), .Names = c("id",
     "minimize", "properties", "fun", "extra.args", "best", "worst", "name", "note",
     "aggr"), class = "Measure")), model = FALSE, extract = function (model)
     {
     }, show.info = FALSE)
     5: predict(m, task, subset = test.i)
     6: predict.WrappedModel(m, task, subset = test.i)
     7: system.time(fun1(p <- fun2(do.call(predictLearner2, pars))), gcFirst = FALSE)
     8: fun1(p <- fun2(do.call(predictLearner2, pars)))
     9: evalVis(expr)
     10: withVisible(eval(expr, pf))
     11: eval(expr, pf)
     12: eval(expr, pf)
     13: fun2(do.call(predictLearner2, pars))
     14: do.call(predictLearner2, pars)
     15: (function (.learner, .model, .newdata, ...)
     {
     if (.learner$fix.factors.prediction) {
     fls = .model$factor.levels
     ns = names(fls)
     ns = intersect(colnames(.newdata), ns)
     fls = fls[ns]
     if (length(ns) > 0L)
     .newdata[ns] = mapply(factor, x = .newdata[ns], levels = fls, SIMPLIFY = FALSE)
     }
     p = predictLearner(.learner, .model, .newdata, ...)
     p = checkPredictLearnerOutput(.learner, .model, p)
     return(p)
     })(.learner = structure(list(id = "classif.xgboost.downsampled", type = "classif",
     package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(pars = structure(list(
     dw.perc = structure(list(id = "dw.perc", type = "numeric", len = 1L, lower = 0,
     upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 1, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), dw.stratify = structure(list(
     id = "dw.stratify", type = "logical", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = FALSE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("dw.perc",
     "dw.stratify")), forbidden = NULL), .Names = c("pars", "forbidden"), class = c("LearnerParamSet",
     "ParamSet")), par.vals = structure(list(dw.perc = 0.5), .Names = "dw.perc"),
     predict.type = "response", fix.factors.prediction = FALSE, next.learner = structure(list(
     id = "classif.xgboost", type = "classif", package = "xgboost", properties = c("twoclass",
     "multiclass", "numerics", "factors", "prob", "weights"), par.set = structure(list(
     pars = structure(list(booster = structure(list(id = "booster", type = "discrete",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(gbtree = "gbtree",
     gblinear = "gblinear"), .Names = c("gbtree", "gblinear")), cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "gbtree", trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), silent = structure(list(id = "silent", type = "integer", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eta = structure(list(id = "eta", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.3, trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), gamma = structure(list(id = "gamma", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight",
     type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), subsample = structure(list(id = "subsample", type = "numeric", len = 1L,
     lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree",
     type = "numeric", len = 1L, lower = 0, upper = 1, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "binary:logistic",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "error", trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = 0.5, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer",
     len = 1L, lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round",
     type = "integer", len = 1L, lower = 1, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE,
     `FALSE` = FALSE), .Names = c("TRUE", "FALSE")), cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("booster", "silent", "eta", "gamma", "max_depth",
     "min_child_weight", "subsample", "colsample_bytree", "num_parallel_tree",
     "lambda", "lambda_bias", "alpha", "objective", "eval_metric", "base_score",
     "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     nrounds = 1), .Names = "nrounds"), predict.type = "response", name = "eXtreme Gradient Boosting",
     short.name = "xgboost", note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type",
     "package", "properties", "par.set", "par.vals", "predict.type", "name", "short.name",
     "note", "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), model.subclass = "DownsampleModel"), .Names = c("id",
     "type", "package", "properties", "par.set", "par.vals", "predict.type", "fix.factors.prediction",
     "next.learner", "model.subclass"), class = c("DownsampleWrapper", "BaseWrapper",
     "Learner")), .model = structure(list(learner = structure(list(id = "classif.xgboost.downsampled",
     type = "classif", package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(
     pars = structure(list(dw.perc = structure(list(id = "dw.perc", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), dw.stratify = structure(list(id = "dw.stratify", type = "logical",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE,
     `FALSE` = FALSE), .Names = c("TRUE", "FALSE")), cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = FALSE, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("dw.perc", "dw.stratify")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     dw.perc = 0.5), .Names = "dw.perc"), predict.type = "response", fix.factors.prediction = FALSE,
     next.learner = structure(list(id = "classif.xgboost", type = "classif", package = "xgboost",
     properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"
     ), par.set = structure(list(pars = structure(list(booster = structure(list(
     id = "booster", type = "discrete", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type",
     "package", "properties", "par.set", "par.vals", "predict.type", "name", "short.name",
     "note", "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), model.subclass = "DownsampleModel"), .Names = c("id",
     "type", "package", "properties", "par.set", "par.vals", "predict.type", "fix.factors.prediction",
     "next.learner", "model.subclass"), class = c("DownsampleWrapper", "BaseWrapper",
     "Learner")), learner.model = structure(list(next.model = structure(list(learner = structure(list(
     id = "classif.xgboost", type = "classif", package = "xgboost", properties = c("twoclass",
     "multiclass", "numerics", "factors", "prob", "weights"), par.set = structure(list(
     pars = structure(list(booster = structure(list(id = "booster", type = "discrete",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(gbtree = "gbtree",
     gblinear = "gblinear"), .Names = c("gbtree", "gblinear")), cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "gbtree", trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), silent = structure(list(id = "silent", type = "integer", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eta = structure(list(id = "eta", type = "numeric", len = 1L, lower = 0,
     upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0.3, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type", "package",
     "properties", "par.set", "par.vals", "predict.type", "name", "short.name", "note",
     "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), learner.model = structure(list(handle = <pointer: 0xed45620>,
     raw = as.raw(c(0x00, 0x00, 0x00, 0x80, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x62, 0x69, 0x6e, 0x61, 0x72, 0x79, 0x3a, 0x6c, 0x6f, 0x67,
     0x69, 0x73, 0x74, 0x69, 0x63, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x67, 0x62, 0x74, 0x72, 0x65, 0x65, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00,
     0x00, 0x14, 0x00, 0x00, 0x80, 0x6e, 0xc5, 0x2e, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x80, 0xdf,
     0x4f, 0x2d, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00,
     0x00, 0x00, 0x3b, 0x00, 0x00, 0x80, 0x82, 0xe2, 0x47, 0x3b, 0x01, 0x00, 0x00,
     0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,
     0x9a, 0x99, 0x99, 0xbe, 0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x32, 0xa4, 0xf3, 0x3e, 0x02, 0x00,
     0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x8c, 0xaf, 0xf8, 0xbe, 0xc7,
     0x92, 0xac, 0x41, 0x00, 0x00, 0x50, 0x41, 0x25, 0x49, 0x92, 0x3d, 0x00, 0x00,
     0x00, 0x00, 0xef, 0xd4, 0x14, 0x41, 0x00, 0x00, 0xe8, 0x40, 0xd9, 0x64, 0x93,
     0x3f, 0x02, 0x00, 0x00, 0x00, 0x90, 0xb9, 0x43, 0x40, 0x00, 0x00, 0xb8, 0x40,
     0x68, 0x2f, 0xa1, 0xbf, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x80, 0x3f, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0xc8, 0x40, 0xd4, 0x08, 0xcb, 0x3f, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0x40, 0xf4,
     0x3c, 0xcf, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x6e, 0x69, 0x74, 0x65, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x30)), niter = 1, evaluation_log = structure(list(iter = 1, train_error = 0.076923), .Names = c("iter",
     "train_error"), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x23d24a8>),
     call = xgb.train(params = params, data = dtrain, nrounds = nrounds, watchlist = watchlist,
     verbose = verbose, print_every_n = print_every_n, early_stopping_rounds = early_stopping_rounds,
     maximize = maximize, save_period = save_period, save_name = save_name, xgb_model = xgb_model,
     callbacks = callbacks, objective = ..1), params = structure(list(objective = "binary:logistic",
     silent = 1), .Names = c("objective", "silent")), callbacks = structure(list(
     cb.print.evaluation = structure(function (env = parent.frame())
     {
     if (length(env$bst_evaluation) == 0 || period == 0 || NVL(env$rank, 0) !=
     0)
     return()
     i <- env$iteration
     if ((i - 1)%%period == 0 || i == env$begin_iteration || i == env$end_iteration) {
     msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
     cat(msg, "\n")
     }
     }, call = cb.print.evaluation(period = print_every_n), name = "cb.print.evaluation"),
     cb.evaluation.log = structure(function (env = parent.frame(), finalize = FALSE)
     {
     if (is.null(mnames))
     init(env)
     if (finalize)
     return(finalizer(env))
     ev <- env$bst_evaluation
     if (!is.null(env$bst_evaluation_err))
     ev <- c(ev, env$bst_evaluation_err)
     env$evaluation_log <- c(env$evaluation_log, list(c(iter = env$iteration,
     ev)))
     }, call = cb.evaluation.log(), name = "cb.evaluation.log"), cb.save.model = structure(function (env = parent.frame())
     {
     if (is.null(env$bst))
     stop("'save_model' callback requires the 'bst' booster object in its calling frame")
     if ((save_period > 0 && (env$iteration - env$begin_iteration)%%save_period ==
     0) || (save_period == 0 && env$iteration == env$end_iteration))
     xgb.save(env$bst, sprintf(save_name, env$iteration))
     }, call = cb.save.model(save_period = save_period, save_name = save_name), name = "cb.save.model")), .Names = c("cb.print.evaluation",
     "cb.evaluation.log", "cb.save.model"))), .Names = c("handle", "raw", "niter",
     "evaluation_log", "call", "params", "callbacks"), class = "xgb.Booster"), task.desc = structure(list(
     id = "binary", type = "classif", target = "Class", size = 52L, n.feat = structure(c(60L,
     0L, 0L), .Names = c("numerics", "factors", "ordered")), has.missings = FALSE,
     has.weights = FALSE, has.blocking = FALSE, class.levels = c("M", "R"), positive = "M",
     negative = "R"), .Names = c("id", "type", "target", "size", "n.feat", "has.missings",
     "has.weights", "has.blocking", "class.levels", "positive", "negative"), class = c("TaskDescClassif",
     "TaskDescSupervised", "TaskDesc")), subset = 1:52, features = c("V1", "V2", "V3",
     "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
     "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28",
     "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52",
     "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), factor.levels = structure(list(
     Class = c("M", "R")), .Names = "Class"), time = 0.13300000000001), .Names = c("learner",
     "learner.model", "task.desc", "subset", "features", "factor.levels", "time"), class = "WrappedModel")), .Names = "next.model", class = c("DownsampleModel",
     "ChainModel", "WrappedModel")), task.desc = structure(list(id = "binary", type = "classif",
     target = "Class", size = 208L, n.feat = structure(c(60L, 0L, 0L), .Names = c("numerics",
     "factors", "ordered")), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE,
     class.levels = c("M", "R"), positive = "M", negative = "R"), .Names = c("id",
     "type", "target", "size", "n.feat", "has.missings", "has.weights", "has.blocking",
     "class.levels", "positive", "negative"), class = c("TaskDescClassif", "TaskDescSupervised",
     "TaskDesc")), subset = c(45L, 137L, 82L, 56L, 188L, 133L, 11L, 76L, 91L, 106L, 132L,
     48L, 72L, 207L, 149L, 28L, 143L, 97L, 186L, 198L, 122L, 127L, 27L, 65L, 75L, 203L,
     157L, 146L, 79L, 51L, 205L, 128L, 1L, 24L, 155L, 144L, 89L, 187L, 174L, 8L, 86L,
     38L, 130L, 109L, 99L, 125L, 12L, 2L, 200L, 134L, 42L, 6L, 165L, 199L, 84L, 177L,
     14L, 4L, 190L, 129L, 185L, 62L, 70L, 40L, 196L, 150L, 32L, 171L, 17L, 160L, 112L,
     16L, 33L, 147L, 41L, 197L, 136L, 105L, 58L, 167L, 23L, 57L, 31L, 181L, 22L, 113L,
     119L, 96L, 103L, 151L, 178L, 21L, 115L, 102L, 107L, 121L, 34L, 5L, 126L, 117L, 15L,
     67L, 153L, 60L), features = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9",
     "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21",
     "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33",
     "V34", "V35", "V36", "V37", "V38", "V39", "V40", "V41", "V42", "V43", "V44", "V45",
     "V46", "V47", "V48", "V49", "V50", "V51", "V52", "V53", "V54", "V55", "V56", "V57",
     "V58", "V59", "V60"), factor.levels = structure(list(Class = c("M", "R")), .Names = "Class"),
     time = 0.159999999999997), .Names = c("learner", "learner.model", "task.desc",
     "subset", "features", "factor.levels", "time"), class = c("DownsampleModel", "BaseWrapperModel",
     "WrappedModel")), .newdata = structure(list(V1 = c(0.0262, 0.0317, 0.0223, 0.0164,
     0.0079, 0.0192, 0.027, 0.0126, 0.0293, 0.0201, 0.01, 0.0189, 0.0311, 0.0206, 0.0094,
     0.0123, 0.0211, 0.0093, 0.0408, 0.0308, 0.019, 0.0119, 0.0131, 0.0087, 0.0293, 0.0132,
     0.0225, 0.013, 0.0086, 0.0067, 0.0176, 0.0368, 0.0195, 0.0065, 0.0208, 0.0139, 0.0239,
     0.0336, 0.0108, 0.0229, 0.0409, 0.0378, 0.0188, 0.0856, 0.0235, 0.0253, 0.026, 0.0459,
     0.0025, 0.0491, 0.0201, 0.0629, 0.0162, 0.0428, 0.0264, 0.021, 0.0283, 0.0414, 0.0228,
     0.0261, 0.0249, 0.027, 0.0443, 0.1083, 0.043, 0.0731, 0.0164, 0.0412, 0.0707, 0.0299,
     0.0654, 0.0231, 0.0233, 0.0211, 0.0201, 0.0107, 0.0258, 0.0305, 0.0217, 0.0072, 0.0221,
     0.0137, 0.0015, 0.013, 0.0179, 0.018, 0.0191, 0.0294, 0.0197, 0.0394, 0.0423, 0.0095,
     0.0096, 0.0089, 0.0156, 0.0315, 0.0056, 0.0203, 0.0392, 0.0131, 0.0335, 0.0187, 0.0522,
     0.026), V2 = c(0.0582, 0.0956, 0.0375, 0.0173, 0.0086, 0.0607, 0.0092, 0.0149, 0.0644,
     0.0026, 0.0275, 0.0308, 0.0491, 0.0132, 0.0166, 0.0022, 0.0319, 0.0269, 0.0653, 0.0339,
     0.0038, 0.0582, 0.0068, 0.0046, 0.0378, 0.008, 0.0019, 6e-04, 0.0215, 0.0096, 0.0172,
     0.0403, 0.0142, 0.0122, 0.0186, 0.0222, 0.0189, 0.0294, 0.0086, 0.0369, 0.0421, 0.0318,
     0.037, 0.0454, 0.0291, 0.0808, 0.0192, 0.0437, 0.0309, 0.0279, 0.0423, 0.1065, 0.0253,
     0.0555, 0.0071, 0.0121, 0.0599, 0.0436, 0.0106, 0.0266, 0.0119, 0.0163, 0.0446, 0.107,
     0.0902, 0.1249, 0.0627, 0.1135, 0.1252, 0.0688, 0.0649, 0.0315, 0.0394, 0.0128, 0.0178,
     0.0453, 0.0433, 0.0363, 0.0152, 0.0027, 0.0065, 0.0297, 0.0186, 0.012, 0.0136, 0.0444,
     0.0173, 0.0123, 0.0394, 0.042, 0.0321, 0.0308, 0.0404, 0.0274, 0.021, 0.0252, 0.0267,
     0.0121, 0.0108, 0.0387, 0.0258, 0.0346, 0.0437, 0.0363), V3 = c(0.1099, 0.1321, 0.0484,
     0.0347, 0.0055, 0.0378, 0.0145, 0.0641, 0.039, 0.0138, 0.019, 0.0197, 0.0692, 0.0533,
     0.0398, 0.0196, 0.0415, 0.0217, 0.0397, 0.0202, 0.0642, 0.0623, 0.0308, 0.0081, 0.0257,
     0.0188, 0.0075, 0.0088, 0.0242, 0.0024, 0.0501, 0.0317, 0.0181, 0.0068, 0.0131, 0.0089,
     0.0466, 0.0476, 0.0058, 0.004, 0.0573, 0.0423, 0.0953, 0.0382, 0.0749, 0.0507, 0.0254,
     0.0347, 0.0171, 0.0592, 0.0554, 0.1526, 0.0262, 0.0708, 0.0342, 0.0203, 0.0656, 0.0447,
     0.013, 0.0223, 0.0277, 0.0341, 0.0235, 0.0257, 0.0833, 0.1665, 0.0738, 0.0518, 0.1447,
     0.0992, 0.0737, 0.017, 0.0416, 0.0015, 0.0274, 0.0289, 0.0547, 0.0214, 0.0346, 0.0089,
     0.0164, 0.0116, 0.0289, 0.0436, 0.0408, 0.0476, 0.0291, 0.0117, 0.0384, 0.0446, 0.0709,
     0.0539, 0.0682, 0.0248, 0.0282, 0.0167, 0.0221, 0.038, 0.0267, 0.0329, 0.0398, 0.0168,
     0.018, 0.0136), V4 = c(0.1083, 0.1408, 0.0475, 0.007, 0.025, 0.0774, 0.0278, 0.1732,
     0.0173, 0.0062, 0.0371, 0.0622, 0.0831, 0.0569, 0.0359, 0.0206, 0.0286, 0.0339, 0.0604,
     0.0889, 0.0452, 0.06, 0.0311, 0.023, 0.0062, 0.0141, 0.0097, 0.0456, 0.0445, 0.0058,
     0.0285, 0.0293, 0.0406, 0.0108, 0.0211, 0.0108, 0.044, 0.0539, 0.046, 0.0375, 0.013,
     0.035, 0.0824, 0.0203, 0.0519, 0.0244, 0.0061, 0.0456, 0.0228, 0.127, 0.0783, 0.1229,
     0.0386, 0.0618, 0.0793, 0.1036, 0.0229, 0.0844, 0.0842, 0.0749, 0.076, 0.0247, 0.1008,
     0.0837, 0.0813, 0.1496, 0.0608, 0.0232, 0.1644, 0.1021, 0.1132, 0.0226, 0.0547, 0.045,
     0.0232, 0.0713, 0.0681, 0.0227, 0.0346, 0.0061, 0.0487, 0.0082, 0.0195, 0.0624, 0.0633,
     0.0698, 0.0301, 0.0113, 0.0076, 0.0551, 0.0108, 0.0411, 0.0688, 0.0237, 0.0596, 0.0479,
     0.0561, 0.0128, 0.0257, 0.0078, 0.057, 0.0177, 0.0292, 0.0272), V5 = c(0.0974, 0.1674,
     0.0647, 0.0187, 0.0344, 0.1388, 0.0412, 0.2565, 0.0476, 0.0133, 0.0416, 0.008, 0.0079,
     0.0647, 0.0681, 0.018, 0.0121, 0.0305, 0.0496, 0.157, 0.0333, 0.1397, 0.0085, 0.0586,
     0.013, 0.0436, 0.0445, 0.0525, 0.0667, 0.0197, 0.0262, 0.082, 0.0391, 0.0217, 0.061,
     0.0215, 0.0657, 0.0794, 0.0752, 0.0455, 0.0183, 0.1787, 0.0249, 0.0385, 0.0227, 0.1724,
     0.0352, 0.0067, 0.0434, 0.1772, 0.062, 0.1437, 0.0645, 0.1215, 0.1043, 0.1675, 0.0839,
     0.0419, 0.1117, 0.1364, 0.1218, 0.0822, 0.2252, 0.0748, 0.0165, 0.1443, 0.0233, 0.0646,
     0.1693, 0.08, 0.2482, 0.041, 0.0993, 0.0711, 0.0724, 0.1075, 0.0784, 0.0456, 0.0484,
     0.042, 0.0519, 0.0241, 0.0515, 0.0428, 0.0596, 0.1615, 0.0463, 0.0497, 0.0251, 0.0597,
     0.107, 0.0613, 0.0887, 0.0224, 0.0462, 0.0902, 0.0936, 0.0537, 0.041, 0.0721, 0.0529,
     0.0393, 0.0351, 0.0214), V6 = c(0.228, 0.171, 0.0591, 0.0671, 0.0546, 0.0809, 0.0757,
     0.2559, 0.0816, 0.0151, 0.0201, 0.0789, 0.02, 0.1432, 0.0706, 0.0492, 0.0438, 0.1172,
     0.1817, 0.175, 0.069, 0.1883, 0.0767, 0.0682, 0.0612, 0.0668, 0.0906, 0.0778, 0.0771,
     0.0618, 0.0351, 0.1342, 0.0249, 0.0284, 0.0613, 0.0136, 0.0742, 0.0804, 0.0887, 0.1452,
     0.1019, 0.1635, 0.0488, 0.0534, 0.0834, 0.3823, 0.0701, 0.089, 0.1224, 0.1908, 0.0871,
     0.119, 0.0472, 0.1524, 0.0783, 0.0418, 0.1673, 0.1215, 0.1506, 0.1513, 0.1538, 0.1256,
     0.2611, 0.1125, 0.0277, 0.277, 0.1048, 0.1124, 0.0844, 0.0629, 0.1257, 0.0116, 0.1515,
     0.1563, 0.0833, 0.1019, 0.125, 0.0665, 0.0526, 0.0865, 0.0849, 0.0253, 0.0817, 0.0349,
     0.0808, 0.0887, 0.069, 0.0998, 0.0629, 0.1416, 0.0973, 0.1039, 0.0932, 0.0845, 0.0779,
     0.1057, 0.1146, 0.0874, 0.0491, 0.1341, 0.1091, 0.163, 0.1171, 0.0338), V7 = c(0.2431,
     0.0731, 0.0753, 0.1056, 0.0528, 0.0568, 0.1026, 0.2947, 0.0993, 0.0541, 0.0314, 0.144,
     0.0981, 0.1344, 0.102, 0.0033, 0.1299, 0.145, 0.1178, 0.092, 0.0901, 0.1422, 0.0771,
     0.0993, 0.0895, 0.0609, 0.0889, 0.0931, 0.0499, 0.0432, 0.0362, 0.1161, 0.0892, 0.0527,
     0.0612, 0.0659, 0.138, 0.1136, 0.1015, 0.2211, 0.1054, 0.0887, 0.1424, 0.214, 0.0677,
     0.3729, 0.1263, 0.1798, 0.1947, 0.2217, 0.1201, 0.0884, 0.1056, 0.1543, 0.1417, 0.0723,
     0.1154, 0.2002, 0.1776, 0.1316, 0.1192, 0.1323, 0.2061, 0.3322, 0.0569, 0.2555, 0.1338,
     0.1787, 0.0715, 0.013, 0.1797, 0.0223, 0.1674, 0.1518, 0.1232, 0.1606, 0.1296, 0.0939,
     0.0773, 0.1182, 0.0812, 0.0279, 0.1005, 0.0384, 0.209, 0.0596, 0.0576, 0.1326, 0.0747,
     0.0956, 0.0961, 0.1016, 0.0955, 0.1488, 0.1365, 0.1024, 0.0706, 0.1021, 0.1053, 0.1626,
     0.1709, 0.2028, 0.1257, 0.0655), V8 = c(0.3771, 0.1401, 0.0098, 0.0697, 0.0958, 0.0219,
     0.1138, 0.411, 0.0315, 0.021, 0.0651, 0.1451, 0.1016, 0.2041, 0.0893, 0.0398, 0.139,
     0.0638, 0.1024, 0.1353, 0.1454, 0.1447, 0.064, 0.0717, 0.1107, 0.0131, 0.0655, 0.0941,
     0.0906, 0.0951, 0.0535, 0.0663, 0.0973, 0.0575, 0.0506, 0.0954, 0.1099, 0.1228, 0.0494,
     0.1188, 0.107, 0.0817, 0.1972, 0.311, 0.2002, 0.3583, 0.108, 0.1741, 0.1661, 0.0768,
     0.2707, 0.0907, 0.1388, 0.0391, 0.1176, 0.0828, 0.1098, 0.1516, 0.0997, 0.1654, 0.1229,
     0.1584, 0.1668, 0.459, 0.2057, 0.1712, 0.0644, 0.2407, 0.0947, 0.0813, 0.0989, 0.0805,
     0.1513, 0.1206, 0.1298, 0.2119, 0.1729, 0.0972, 0.0862, 0.0999, 0.1833, 0.013, 0.0124,
     0.0446, 0.3465, 0.1071, 0.1103, 0.1117, 0.0578, 0.0802, 0.1323, 0.1394, 0.214, 0.1224,
     0.078, 0.1209, 0.0996, 0.0852, 0.169, 0.1902, 0.1684, 0.1694, 0.1178, 0.14), V9 = c(0.5598,
     0.2083, 0.0684, 0.0962, 0.1009, 0.1037, 0.0794, 0.4983, 0.0736, 0.0505, 0.1896, 0.1789,
     0.2025, 0.1571, 0.0381, 0.0791, 0.0695, 0.074, 0.0583, 0.1593, 0.074, 0.0487, 0.0726,
     0.0576, 0.0973, 0.0899, 0.1624, 0.1711, 0.1229, 0.0836, 0.0258, 0.0155, 0.084, 0.1054,
     0.0989, 0.0786, 0.1384, 0.1235, 0.0472, 0.075, 0.2302, 0.1779, 0.1873, 0.2837, 0.2876,
     0.3429, 0.1523, 0.1598, 0.1368, 0.1246, 0.1206, 0.2107, 0.0598, 0.061, 0.0453, 0.0494,
     0.137, 0.0818, 0.1428, 0.1864, 0.2119, 0.2017, 0.1801, 0.5526, 0.3887, 0.0466, 0.1522,
     0.2682, 0.1583, 0.1761, 0.246, 0.2365, 0.1723, 0.1666, 0.2085, 0.3061, 0.2794, 0.2535,
     0.1451, 0.1976, 0.2228, 0.0489, 0.1168, 0.1318, 0.5276, 0.3175, 0.2423, 0.2984, 0.1357,
     0.1618, 0.2462, 0.2592, 0.2546, 0.1569, 0.1038, 0.1241, 0.1673, 0.1136, 0.2105, 0.261,
     0.1865, 0.2328, 0.1258, 0.1843), V10 = c(0.6194, 0.3513, 0.1487, 0.0251, 0.124, 0.1186,
     0.152, 0.592, 0.086, 0.1097, 0.2668, 0.2522, 0.0767, 0.1573, 0.1328, 0.0475, 0.0568,
     0.136, 0.2176, 0.2795, 0.0349, 0.0864, 0.0901, 0.0818, 0.0751, 0.0922, 0.1452, 0.1483,
     0.1185, 0.118, 0.0474, 0.0506, 0.1191, 0.1109, 0.1093, 0.1015, 0.1376, 0.0842, 0.0393,
     0.1631, 0.2259, 0.2053, 0.1806, 0.2751, 0.3674, 0.2197, 0.163, 0.1408, 0.143, 0.2028,
     0.0279, 0.3597, 0.1334, 0.0113, 0.0945, 0.0686, 0.1767, 0.1975, 0.2227, 0.2013, 0.2531,
     0.2122, 0.3083, 0.5966, 0.7106, 0.1114, 0.078, 0.2058, 0.1247, 0.0998, 0.3422, 0.2461,
     0.2078, 0.1345, 0.272, 0.2936, 0.2954, 0.3127, 0.211, 0.2318, 0.181, 0.0874, 0.1476,
     0.1375, 0.5965, 0.2918, 0.3134, 0.3473, 0.1695, 0.2558, 0.2696, 0.3745, 0.2952, 0.2119,
     0.1567, 0.1533, 0.1859, 0.1747, 0.2471, 0.3193, 0.266, 0.2684, 0.2529, 0.2354), V11 = c(0.6333,
     0.1786, 0.1156, 0.0801, 0.1097, 0.1237, 0.1675, 0.5832, 0.0414, 0.0841, 0.3376, 0.2607,
     0.1767, 0.2327, 0.1303, 0.1152, 0.0869, 0.2132, 0.2459, 0.3336, 0.1459, 0.2143, 0.075,
     0.1315, 0.0528, 0.1445, 0.1442, 0.1532, 0.0775, 0.0978, 0.0526, 0.0906, 0.1522, 0.0937,
     0.1063, 0.1261, 0.0938, 0.0357, 0.1106, 0.2709, 0.2373, 0.3135, 0.2139, 0.2707, 0.2974,
     0.2653, 0.103, 0.2693, 0.0994, 0.0947, 0.2251, 0.5466, 0.2969, 0.1255, 0.1132, 0.1125,
     0.1995, 0.2309, 0.2621, 0.289, 0.2855, 0.221, 0.3794, 0.5304, 0.7342, 0.1739, 0.1791,
     0.1546, 0.234, 0.0523, 0.2128, 0.2245, 0.1239, 0.0785, 0.2188, 0.3104, 0.2506, 0.2192,
     0.2343, 0.2472, 0.2549, 0.11, 0.2118, 0.2026, 0.6254, 0.3273, 0.4786, 0.4231, 0.1734,
     0.3078, 0.3412, 0.4229, 0.4025, 0.3003, 0.2476, 0.2128, 0.2481, 0.2198, 0.268, 0.3468,
     0.3188, 0.3108, 0.2716, 0.272), V12 = c(0.706, 0.0658, 0.1654, 0.1056, 0.1215, 0.1601,
     0.137, 0.5419, 0.0472, 0.0942, 0.3282, 0.371, 0.2555, 0.1785, 0.0273, 0.052, 0.1935,
     0.3738, 0.3332, 0.294, 0.3473, 0.372, 0.0844, 0.1862, 0.1209, 0.1475, 0.0948, 0.11,
     0.1101, 0.0909, 0.1854, 0.2545, 0.1322, 0.0827, 0.1179, 0.0828, 0.0259, 0.0689, 0.1412,
     0.3358, 0.3323, 0.3118, 0.1523, 0.0946, 0.0837, 0.3223, 0.2187, 0.3259, 0.225, 0.2497,
     0.2615, 0.5205, 0.4754, 0.2473, 0.084, 0.1741, 0.2869, 0.3025, 0.3109, 0.365, 0.2961,
     0.2399, 0.5364, 0.2251, 0.5033, 0.316, 0.2681, 0.2671, 0.1764, 0.0904, 0.1377, 0.152,
     0.0236, 0.0367, 0.3037, 0.3431, 0.2601, 0.2621, 0.2087, 0.288, 0.2984, 0.1084, 0.2575,
     0.2389, 0.4507, 0.3035, 0.5239, 0.5044, 0.247, 0.3404, 0.4292, 0.4499, 0.5148, 0.3094,
     0.2783, 0.2536, 0.2712, 0.2721, 0.3049, 0.3738, 0.3553, 0.2933, 0.2374, 0.2442),
     V13 = c(0.5544, 0.0513, 0.3833, 0.1266, 0.1874, 0.352, 0.1361, 0.5472, 0.0835,
     0.1204, 0.2432, 0.3906, 0.2812, 0.1507, 0.0644, 0.1192, 0.1478, 0.3738, 0.3087,
     0.1608, 0.3197, 0.2665, 0.1226, 0.2789, 0.1763, 0.2087, 0.0618, 0.089, 0.1042,
     0.0656, 0.104, 0.1464, 0.1434, 0.092, 0.1291, 0.0493, 0.1499, 0.1705, 0.2202,
     0.4091, 0.3827, 0.3686, 0.1975, 0.102, 0.1912, 0.5582, 0.1542, 0.4545, 0.2444,
     0.2209, 0.177, 0.5127, 0.5677, 0.3011, 0.0717, 0.271, 0.3275, 0.3938, 0.2859,
     0.351, 0.3341, 0.2964, 0.6173, 0.2402, 0.3, 0.3249, 0.1788, 0.3141, 0.2284, 0.2655,
     0.4032, 0.1732, 0.1771, 0.1227, 0.2959, 0.2456, 0.2249, 0.2419, 0.1645, 0.2126,
     0.2624, 0.1094, 0.2354, 0.2112, 0.3693, 0.3033, 0.4393, 0.5237, 0.3141, 0.34,
     0.3682, 0.5404, 0.4901, 0.2743, 0.2896, 0.2686, 0.2934, 0.2105, 0.2863, 0.3055,
     0.3116, 0.2275, 0.1878, 0.1665), V14 = c(0.532, 0.3752, 0.3598, 0.089, 0.3383,
     0.4479, 0.1345, 0.5314, 0.0938, 0.042, 0.1268, 0.2672, 0.2722, 0.1916, 0.0712,
     0.1943, 0.1871, 0.2673, 0.2613, 0.3335, 0.2823, 0.2113, 0.1619, 0.2579, 0.2039,
     0.2558, 0.1641, 0.1236, 0.0853, 0.0593, 0.0948, 0.1272, 0.1244, 0.0911, 0.1591,
     0.0848, 0.2851, 0.3257, 0.2976, 0.44, 0.484, 0.3885, 0.4844, 0.4519, 0.504, 0.6916,
     0.263, 0.5785, 0.3239, 0.3195, 0.3709, 0.5395, 0.569, 0.3747, 0.1968, 0.3087,
     0.3769, 0.505, 0.3316, 0.3495, 0.4287, 0.4061, 0.7842, 0.2689, 0.1951, 0.2164,
     0.1039, 0.2904, 0.3115, 0.3099, 0.5684, 0.3099, 0.3115, 0.2614, 0.2059, 0.1887,
     0.2115, 0.2179, 0.1689, 0.0708, 0.1893, 0.1023, 0.1334, 0.1444, 0.2864, 0.2587,
     0.344, 0.4398, 0.3297, 0.3951, 0.394, 0.4303, 0.4127, 0.2547, 0.2956, 0.2803,
     0.2637, 0.1727, 0.2294, 0.1926, 0.1965, 0.0994, 0.0983, 0.0336), V15 = c(0.6479,
     0.5419, 0.1713, 0.0198, 0.3227, 0.3769, 0.2144, 0.4981, 0.1466, 0.0031, 0.1278,
     0.2716, 0.3227, 0.2061, 0.1204, 0.184, 0.1994, 0.2333, 0.3232, 0.4985, 0.0166,
     0.1103, 0.2317, 0.224, 0.2727, 0.2603, 0.0708, 0.1197, 0.0456, 0.0832, 0.0912,
     0.1223, 0.0653, 0.1487, 0.168, 0.1514, 0.5743, 0.4602, 0.4116, 0.5485, 0.6812,
     0.585, 0.7298, 0.6737, 0.6352, 0.7943, 0.294, 0.4471, 0.3039, 0.334, 0.4533,
     0.6558, 0.6421, 0.452, 0.2633, 0.3575, 0.4169, 0.5872, 0.3755, 0.4325, 0.5205,
     0.5095, 0.8392, 0.6646, 0.2767, 0.2031, 0.198, 0.3531, 0.4725, 0.352, 0.2398,
     0.438, 0.499, 0.428, 0.0906, 0.1184, 0.127, 0.1159, 0.165, 0.1194, 0.0668, 0.0601,
     0.0092, 0.0742, 0.1635, 0.1682, 0.2869, 0.3236, 0.2759, 0.3352, 0.2965, 0.3333,
     0.3575, 0.187, 0.3189, 0.1886, 0.188, 0.204, 0.1165, 0.1385, 0.178, 0.1801, 0.0683,
     0.1302), V16 = c(0.6931, 0.544, 0.1136, 0.1133, 0.2723, 0.5761, 0.5354, 0.6985,
     0.0809, 0.0162, 0.4441, 0.4183, 0.3463, 0.2307, 0.0717, 0.2077, 0.3283, 0.5367,
     0.3731, 0.7295, 0.0572, 0.1136, 0.2934, 0.2568, 0.2321, 0.1985, 0.0844, 0.1145,
     0.1304, 0.1297, 0.1688, 0.1669, 0.089, 0.1666, 0.1918, 0.1396, 0.8278, 0.6225,
     0.4754, 0.7213, 0.7555, 0.7868, 0.7807, 0.6699, 0.6804, 0.7152, 0.2978, 0.2231,
     0.241, 0.3323, 0.5553, 0.8705, 0.7487, 0.5392, 0.4191, 0.4998, 0.5036, 0.661,
     0.4499, 0.5398, 0.6087, 0.5512, 0.9016, 0.6632, 0.3737, 0.258, 0.3234, 0.5079,
     0.5543, 0.3892, 0.4331, 0.5595, 0.6707, 0.6122, 0.161, 0.208, 0.1193, 0.1237,
     0.1967, 0.2808, 0.2666, 0.0906, 0.1951, 0.1533, 0.0422, 0.1308, 0.3889, 0.2956,
     0.2056, 0.2252, 0.3172, 0.3496, 0.3447, 0.1452, 0.1892, 0.1485, 0.1405, 0.1786,
     0.2127, 0.2122, 0.2794, 0.22, 0.1503, 0.1708), V17 = c(0.6759, 0.515, 0.0349,
     0.2826, 0.3943, 0.6426, 0.683, 0.8292, 0.1179, 0.0624, 0.6795, 0.6988, 0.5395,
     0.236, 0.1224, 0.1956, 0.6861, 0.7312, 0.4203, 0.735, 0.2164, 0.1934, 0.3526,
     0.2933, 0.2676, 0.2394, 0.259, 0.2137, 0.269, 0.2038, 0.1568, 0.1424, 0.1226,
     0.1268, 0.1615, 0.1066, 0.8669, 0.7327, 0.539, 0.8137, 0.9522, 0.9739, 0.7906,
     0.7066, 0.7505, 0.3512, 0.0699, 0.2164, 0.0367, 0.278, 0.4616, 0.9786, 0.8999,
     0.6588, 0.505, 0.6011, 0.618, 0.7417, 0.4765, 0.6237, 0.7236, 0.6613, 1, 0.1674,
     0.2507, 0.1796, 0.3748, 0.4639, 0.5386, 0.3962, 0.5954, 0.682, 0.7655, 0.7435,
     0.18, 0.2736, 0.1794, 0.0886, 0.2934, 0.4221, 0.4274, 0.1313, 0.3685, 0.3052,
     0.1785, 0.2803, 0.442, 0.3286, 0.1162, 0.2086, 0.2825, 0.3426, 0.3068, 0.1457,
     0.173, 0.216, 0.2028, 0.1318, 0.2062, 0.2758, 0.287, 0.2732, 0.1723, 0.2177),
     V18 = c(0.7551, 0.4262, 0.3796, 0.3234, 0.6432, 0.679, 0.56, 0.7839, 0.2179,
     0.2127, 0.7051, 0.5733, 0.7911, 0.1299, 0.2349, 0.163, 0.5814, 0.7659, 0.5364,
     0.8253, 0.4563, 0.4142, 0.3657, 0.2991, 0.2934, 0.3134, 0.2679, 0.2838, 0.2947,
     0.3811, 0.0375, 0.1285, 0.1846, 0.1374, 0.1647, 0.1923, 0.8131, 0.7843, 0.6279,
     0.9185, 0.9826, 1, 0.6122, 0.5632, 0.6595, 0.2008, 0.1401, 0.3201, 0.1672, 0.2975,
     0.3797, 0.9335, 1, 0.7113, 0.6711, 0.647, 0.8025, 0.8006, 0.6254, 0.6876, 0.7577,
     0.6804, 0.8911, 0.0837, 0.2507, 0.2422, 0.2586, 0.1859, 0.3746, 0.2449, 0.5772,
     0.6164, 0.8485, 0.813, 0.218, 0.3274, 0.2185, 0.1755, 0.3709, 0.5279, 0.6291,
     0.2758, 0.4646, 0.4116, 0.4394, 0.4519, 0.3892, 0.3231, 0.1884, 0.2248, 0.305,
     0.2851, 0.2945, 0.2429, 0.2226, 0.2417, 0.2613, 0.226, 0.2222, 0.4576, 0.3969,
     0.2862, 0.2339, 0.3175), V19 = c(0.8929, 0.2024, 0.7401, 0.3238, 0.7271, 0.7157,
     0.3093, 0.8215, 0.3326, 0.3436, 0.7966, 0.2226, 0.9064, 0.3812, 0.3684, 0.1218,
     0.25, 0.6271, 0.7062, 0.8793, 0.3819, 0.3279, 0.3221, 0.3924, 0.3295, 0.4077,
     0.3094, 0.364, 0.3669, 0.4451, 0.1316, 0.1857, 0.388, 0.1095, 0.1397, 0.2991,
     0.9045, 0.7988, 0.706, 1, 0.8871, 0.9843, 0.42, 0.3785, 0.4509, 0.2676, 0.299,
     0.2915, 0.3038, 0.2948, 0.345, 0.7917, 0.969, 0.7602, 0.7922, 0.8067, 0.9333,
     0.8456, 0.7304, 0.7329, 0.7726, 0.652, 0.8753, 0.4331, 0.3292, 0.3609, 0.368,
     0.4474, 0.4583, 0.2355, 0.8176, 0.6803, 0.9805, 0.9006, 0.2026, 0.2344, 0.1646,
     0.1758, 0.4309, 0.5857, 0.7782, 0.366, 0.5418, 0.5466, 0.695, 0.6641, 0.4088,
     0.4528, 0.339, 0.3382, 0.2408, 0.4062, 0.4351, 0.3259, 0.2427, 0.2989, 0.2778,
     0.2358, 0.3241, 0.6487, 0.5599, 0.2034, 0.1962, 0.3714), V20 = c(0.8619, 0.4233,
     0.9925, 0.4333, 0.8673, 0.5466, 0.3226, 0.9363, 0.3258, 0.3813, 0.9401, 0.2631,
     0.8701, 0.5858, 0.3918, 0.1017, 0.1734, 0.4395, 0.8196, 0.9657, 0.5627, 0.6222,
     0.3093, 0.4691, 0.491, 0.4529, 0.4678, 0.543, 0.4948, 0.5224, 0.2086, 0.1136,
     0.3658, 0.1286, 0.1426, 0.3247, 0.9046, 0.8261, 0.7918, 0.9418, 0.8268, 0.861,
     0.2807, 0.2721, 0.2964, 0.4299, 0.3915, 0.4235, 0.4069, 0.1729, 0.2665, 0.7383,
     0.9032, 0.8672, 0.8381, 0.9008, 0.9399, 0.7939, 0.8702, 0.8107, 0.8098, 0.6788,
     0.7886, 0.8718, 0.4871, 0.181, 0.3508, 0.4079, 0.5961, 0.3045, 0.8835, 0.8435,
     1, 0.9603, 0.1506, 0.126, 0.074, 0.154, 0.4161, 0.6153, 0.7686, 0.5269, 0.626,
     0.5933, 0.8097, 0.7683, 0.5006, 0.6339, 0.3926, 0.4578, 0.542, 0.6833, 0.7264,
     0.3679, 0.3149, 0.3341, 0.3346, 0.3107, 0.433, 0.7154, 0.6936, 0.174, 0.1395,
     0.4552), V21 = c(0.7974, 0.7723, 0.9802, 0.6068, 0.9674, 0.5399, 0.443, 1, 0.2111,
     0.3825, 0.9857, 0.7473, 0.7672, 0.4497, 0.4925, 0.1354, 0.3363, 0.433, 0.8835,
     1, 0.6484, 0.7468, 0.4084, 0.5665, 0.5402, 0.4893, 0.5958, 0.6673, 0.6275, 0.5911,
     0.1976, 0.2069, 0.2297, 0.2146, 0.2429, 0.3797, 1, 1, 0.9493, 0.9116, 0.7561,
     0.8443, 0.5148, 0.5297, 0.4019, 0.528, 0.3598, 0.446, 0.3613, 0.3264, 0.2395,
     0.6908, 0.7685, 0.8416, 0.8759, 0.8906, 0.9275, 0.8804, 0.9349, 0.8396, 0.8995,
     0.7811, 0.7156, 0.7992, 0.6527, 0.2604, 0.5606, 0.54, 0.7464, 0.3112, 0.5248,
     0.9921, 1, 0.9162, 0.0521, 0.0576, 0.0625, 0.0512, 0.5116, 0.6753, 0.8099, 0.581,
     0.742, 0.6663, 0.855, 0.696, 0.7271, 0.7044, 0.4282, 0.6474, 0.6802, 0.765, 0.8147,
     0.3355, 0.4102, 0.3786, 0.383, 0.3906, 0.5071, 0.801, 0.7969, 0.413, 0.3164,
     0.57), V22 = c(0.6737, 0.9735, 0.889, 0.7652, 0.9847, 0.6362, 0.5573, 0.9224,
     0.2302, 0.4764, 0.8193, 0.7263, 0.2957, 0.4876, 0.8793, 0.3157, 0.5588, 0.4326,
     0.8299, 0.8707, 0.7235, 0.7676, 0.4285, 0.6464, 0.6257, 0.5666, 0.7245, 0.7979,
     0.8162, 0.6566, 0.0946, 0.0219, 0.261, 0.2889, 0.2816, 0.5658, 0.9976, 0.9814,
     1, 0.9349, 0.8217, 0.9061, 0.7569, 0.7697, 0.6794, 0.3489, 0.2403, 0.238, 0.1994,
     0.3834, 0.1127, 0.385, 0.6998, 0.7974, 0.9422, 0.9338, 0.945, 0.8384, 0.9614,
     0.8632, 0.9247, 0.8369, 0.7581, 0.3712, 0.8454, 0.6572, 0.5231, 0.4786, 0.7644,
     0.4698, 0.6373, 1, 0.9992, 0.914, 0.2143, 0.1241, 0.2381, 0.1805, 0.6501, 0.7873,
     0.8493, 0.6181, 0.8257, 0.7333, 0.8717, 0.4393, 0.9385, 0.8314, 0.5418, 0.6708,
     0.632, 0.667, 0.8103, 0.31, 0.3808, 0.3956, 0.4003, 0.3631, 0.5944, 0.7924, 0.7452,
     0.6879, 0.5888, 0.7397), V23 = c(0.4293, 0.939, 0.6712, 0.9203, 0.948, 0.7849,
     0.5782, 0.7839, 0.3361, 0.6313, 0.5789, 0.3393, 0.4148, 1, 0.9606, 0.4645, 0.6592,
     0.5544, 0.7609, 0.6471, 0.8242, 0.7867, 0.4663, 0.6774, 0.6826, 0.6234, 0.8773,
     0.9273, 0.9237, 0.6308, 0.1965, 0.24, 0.4193, 0.4238, 0.429, 0.7483, 0.9872,
     0.962, 0.9645, 0.7484, 0.6967, 0.5847, 0.8596, 0.8643, 0.8297, 0.143, 0.4208,
     0.6415, 0.4611, 0.3523, 0.2556, 0.0671, 0.6644, 0.8385, 1, 1, 0.8328, 0.7852,
     0.9126, 0.8747, 0.9365, 0.8969, 0.6372, 0.1703, 0.9739, 0.9734, 0.5469, 0.4332,
     0.5711, 0.5534, 0.8375, 0.7983, 0.9067, 0.7851, 0.4333, 0.3239, 0.4824, 0.4039,
     0.7717, 0.8974, 0.944, 0.5875, 0.8609, 0.7136, 0.8601, 0.2432, 1, 0.8449, 0.6448,
     0.7007, 0.5824, 0.5703, 0.6665, 0.3914, 0.4896, 0.5232, 0.5114, 0.4809, 0.7078,
     0.8793, 0.8203, 0.812, 0.7631, 0.8062), V24 = c(0.3648, 0.5559, 0.4286, 0.9719,
     0.8036, 0.7756, 0.6173, 0.547, 0.4259, 0.7523, 0.6394, 0.2824, 0.6043, 0.8675,
     0.8786, 0.5906, 0.7012, 0.736, 0.7605, 0.5973, 0.8766, 0.8253, 0.5956, 0.7577,
     0.7527, 0.6741, 0.9214, 0.9027, 0.871, 0.5998, 0.1242, 0.2547, 0.5848, 0.6168,
     0.6443, 0.8757, 0.9761, 0.9601, 0.9432, 0.5146, 0.6444, 0.4033, 1, 0.9304, 1,
     0.5453, 0.5675, 0.8966, 0.6849, 0.541, 0.5169, 0.0502, 0.5964, 0.9317, 0.9931,
     0.9102, 0.7773, 0.8479, 0.9443, 0.9607, 0.9853, 0.9856, 0.321, 0.1611, 1, 0.9757,
     0.6954, 0.6113, 0.6257, 0.4532, 0.6699, 0.5426, 0.6803, 0.5134, 0.5943, 0.4357,
     0.6372, 0.5697, 0.8491, 0.9828, 0.945, 0.4639, 0.84, 0.7014, 0.9201, 0.2886,
     0.9831, 0.8512, 0.7223, 0.7619, 0.6805, 0.5995, 0.6958, 0.528, 0.6292, 0.6913,
     0.686, 0.6531, 0.7641, 1, 0.9261, 0.8453, 0.8473, 0.8837), V25 = c(0.5331, 0.5268,
     0.3374, 0.9207, 0.6833, 0.578, 0.8132, 0.4562, 0.4609, 0.8675, 0.7043, 0.6053,
     0.3178, 0.4718, 0.6905, 0.6776, 0.8099, 0.8589, 0.8367, 0.8218, 1, 1, 0.6948,
     0.8856, 0.8504, 0.8282, 0.9282, 0.9192, 0.8052, 0.4958, 0.0616, 0.024, 0.5643,
     0.8167, 0.9061, 0.9048, 0.9009, 0.9118, 0.8658, 0.4106, 0.6948, 0.5946, 0.8457,
     0.9372, 0.824, 0.6338, 0.6094, 0.8918, 0.7272, 0.5228, 0.3779, 0.2717, 0.3711,
     0.8555, 0.9575, 0.8496, 0.7007, 0.7434, 1, 0.9716, 0.9776, 1, 0.2076, 0.2086,
     0.6665, 0.8079, 0.6352, 0.5091, 0.6695, 0.4464, 0.7756, 0.3952, 0.5103, 0.3439,
     0.6926, 0.5734, 0.7531, 0.6577, 0.9104, 1, 0.9655, 0.5424, 0.8949, 0.7758, 0.8729,
     0.4974, 0.9932, 0.9138, 0.7853, 0.7745, 0.5984, 0.6484, 0.7748, 0.6409, 0.7519,
     0.7868, 0.749, 0.7812, 0.8878, 0.9865, 0.881, 0.8919, 0.9424, 0.9432), V26 = c(0.2413,
     0.6826, 0.7366, 0.7545, 0.5136, 0.4862, 0.9819, 0.5922, 0.2606, 0.8788, 0.6875,
     0.5897, 0.3482, 0.5341, 0.6937, 0.8119, 0.8901, 0.8989, 0.8905, 0.7755, 0.8582,
     0.9481, 0.8386, 0.9419, 0.8938, 0.8823, 0.9942, 1, 0.8756, 0.5647, 0.2141, 0.1923,
     0.5448, 0.9622, 1, 0.7511, 0.9724, 0.9086, 0.7895, 0.3443, 0.8014, 0.6793, 0.6797,
     0.6247, 0.7115, 0.7712, 0.6323, 0.7529, 0.7152, 0.4475, 0.4082, 0.2839, 0.0921,
     0.6162, 0.8647, 0.7867, 0.6154, 0.6433, 0.9455, 0.9121, 1, 0.9395, 0.2279, 0.2847,
     0.5323, 0.6521, 0.6757, 0.4606, 0.7131, 0.467, 0.875, 0.5179, 0.4716, 0.329,
     0.7576, 0.7825, 0.8959, 0.7474, 0.8912, 0.846, 0.8045, 0.7367, 0.9945, 0.9137,
     0.8084, 0.8172, 0.9161, 0.9985, 0.7984, 0.6767, 0.8412, 0.8614, 0.8688, 0.7707,
     0.7985, 0.8337, 0.7843, 0.8395, 0.9711, 0.9474, 0.8814, 0.93, 0.9986, 1), V27 = c(0.507,
     0.5713, 0.9611, 0.8289, 0.309, 0.4181, 0.9823, 0.5448, 0.0874, 0.7901, 0.4081,
     0.4967, 0.6158, 0.6197, 0.5674, 0.8594, 0.8745, 0.942, 0.7652, 0.6111, 0.6563,
     0.7539, 0.8875, 1, 0.9928, 0.9196, 1, 0.9821, 1, 0.6906, 0.4642, 0.4753, 0.4772,
     0.828, 0.8087, 0.6858, 0.9675, 0.7931, 0.6501, 0.6981, 0.6053, 0.6389, 0.6971,
     0.6024, 0.7726, 0.6838, 0.6549, 0.6838, 0.7102, 0.534, 0.5353, 0.2234, 0.0481,
     0.4139, 0.7215, 0.7688, 0.581, 0.5514, 0.8815, 0.8576, 0.9896, 0.8917, 0.3309,
     0.2211, 0.4024, 0.4915, 0.8499, 0.7243, 0.7567, 0.4621, 0.83, 0.565, 0.498, 0.2571,
     0.8787, 0.9252, 0.9941, 0.8543, 0.8189, 0.6055, 0.4969, 0.9089, 1, 0.9964, 0.8694,
     1, 0.8237, 1, 0.8847, 0.7373, 0.9911, 0.9819, 1, 0.8754, 0.883, 0.9199, 0.9021,
     0.918, 0.988, 0.9474, 0.9301, 0.9987, 0.9699, 0.9375), V28 = c(0.8533, 0.5429,
     0.7353, 0.8907, 0.0832, 0.2457, 0.9166, 0.3971, 0.2862, 0.8357, 0.1811, 0.8616,
     0.8049, 0.7143, 0.654, 0.9228, 0.7887, 0.9401, 0.5897, 0.4195, 0.5087, 0.6008,
     0.6404, 0.8564, 0.9134, 0.8965, 0.9071, 0.9092, 0.9858, 0.8513, 0.6471, 0.7003,
     0.6897, 0.5816, 0.6119, 0.7043, 0.7633, 0.5877, 0.4492, 0.8713, 0.6084, 0.5002,
     0.5843, 0.681, 0.6124, 0.8015, 0.7673, 0.839, 0.8516, 0.5323, 0.5116, 0.1911,
     0.0876, 0.3269, 0.5801, 0.7718, 0.4454, 0.3519, 0.752, 0.8798, 0.9076, 0.8105,
     0.2847, 0.6134, 0.3444, 0.5363, 0.8025, 0.8987, 0.8077, 0.6988, 0.6896, 0.3042,
     0.6196, 0.3685, 0.906, 0.9349, 0.9957, 0.9085, 0.6779, 0.3036, 0.396, 1, 0.9649,
     1, 0.8411, 0.9238, 0.6957, 0.7544, 0.9582, 0.7834, 0.9187, 0.938, 0.9941, 1,
     0.9915, 1, 1, 0.9769, 0.9812, 0.9315, 0.9955, 1, 1, 0.7603), V29 = c(0.6036,
     0.2177, 0.4856, 0.7309, 0.4019, 0.0716, 0.7423, 0.0882, 0.5606, 0.9631, 0.2064,
     0.8339, 0.6289, 0.5605, 0.7802, 0.8387, 0.8725, 0.9379, 0.3037, 0.299, 0.4817,
     0.5437, 0.3308, 0.679, 0.708, 0.7549, 0.8545, 0.8184, 0.9427, 1, 0.634, 0.6825,
     0.9797, 0.4667, 0.526, 0.5864, 0.4434, 0.3474, 0.4739, 0.9013, 0.8877, 0.5578,
     0.4772, 0.5047, 0.4936, 0.8073, 1, 1, 1, 0.3907, 0.4544, 0.0408, 0.104, 0.3108,
     0.4964, 0.6268, 0.3707, 0.3168, 0.7068, 0.772, 0.7306, 0.6828, 0.1949, 0.5807,
     0.4239, 0.7649, 0.6563, 0.8826, 0.8477, 0.7626, 0.3372, 0.1881, 0.7171, 0.5765,
     0.8528, 0.9348, 0.9328, 0.8668, 0.5368, 0.0144, 0.3856, 0.8247, 0.8747, 0.8881,
     0.5793, 0.8519, 0.4536, 0.4661, 0.899, 0.9619, 0.8005, 0.8435, 0.8793, 0.9806,
     0.9223, 0.899, 0.8888, 0.8937, 0.9464, 0.8326, 0.8576, 0.8104, 0.863, 0.7123),
     V30 = c(0.8514, 0.2149, 0.1594, 0.6896, 0.2344, 0.0613, 0.7736, 0.2385, 0.8344,
     0.9619, 0.3917, 0.4084, 0.4999, 0.3728, 0.7575, 0.7238, 0.9376, 0.8575, 0.0823,
     0.1354, 0.453, 0.5387, 0.3425, 0.5587, 0.6318, 0.6736, 0.7293, 0.6962, 0.8114,
     0.9166, 0.6107, 0.6443, 1, 0.3539, 0.3677, 0.3773, 0.3822, 0.4235, 0.6153, 0.8014,
     0.8557, 0.4831, 0.5201, 0.5775, 0.5648, 0.831, 0.8463, 0.8362, 0.769, 0.3456,
     0.4258, 0.2531, 0.1714, 0.2554, 0.4886, 0.4301, 0.2891, 0.3346, 0.5986, 0.5711,
     0.5758, 0.5572, 0.1671, 0.6925, 0.4182, 0.525, 0.8591, 0.9201, 0.9289, 0.7025,
     0.6405, 0.396, 0.6316, 0.619, 0.9087, 1, 0.9344, 0.8892, 0.5207, 0.2526, 0.5574,
     0.5441, 0.6257, 0.6585, 0.3754, 0.7722, 0.3281, 0.3924, 0.6831, 1, 0.6713, 0.6074,
     0.6482, 0.6969, 0.6981, 0.6456, 0.6511, 0.7022, 0.8542, 0.6213, 0.6069, 0.6199,
     0.6979, 0.8358), V31 = c(0.8512, 0.5811, 0.3007, 0.5829, 0.1905, 0.1816, 0.8473,
     0.2005, 0.8096, 0.9236, 0.3791, 0.2268, 0.583, 0.2481, 0.5836, 0.6292, 0.892,
     0.7284, 0.2787, 0.2438, 0.4521, 0.5619, 0.492, 0.4147, 0.6126, 0.6463, 0.6499,
     0.59, 0.6987, 0.7676, 0.7046, 0.7063, 0.9546, 0.2727, 0.2746, 0.2206, 0.4727,
     0.4633, 0.4929, 0.438, 0.5563, 0.4729, 0.4241, 0.4754, 0.4906, 0.7792, 0.5509,
     0.5427, 0.4841, 0.4091, 0.3869, 0.1979, 0.3264, 0.3367, 0.4079, 0.2077, 0.2185,
     0.2056, 0.3857, 0.4264, 0.4469, 0.4301, 0.1025, 0.3825, 0.4393, 0.5101, 0.6655,
     0.8005, 0.9513, 0.7382, 0.7138, 0.2286, 0.3554, 0.4613, 0.9657, 0.9308, 0.8854,
     0.9065, 0.5651, 0.4335, 0.7309, 0.3349, 0.2184, 0.2707, 0.3485, 0.5772, 0.2522,
     0.3849, 0.6108, 0.8086, 0.5632, 0.5403, 0.5876, 0.4973, 0.6167, 0.5967, 0.6083,
     0.65, 0.6457, 0.3772, 0.3934, 0.6041, 0.7717, 0.7622), V32 = c(0.5045, 0.6323,
     0.4096, 0.4935, 0.1235, 0.4493, 0.7352, 0.0587, 0.725, 0.8903, 0.2042, 0.1745,
     0.666, 0.1921, 0.6316, 0.5181, 0.7508, 0.67, 0.7241, 0.5624, 0.4532, 0.5141,
     0.4592, 0.2946, 0.4638, 0.5007, 0.6071, 0.5447, 0.681, 0.6177, 0.5376, 0.5373,
     0.8835, 0.141, 0.102, 0.2628, 0.4007, 0.341, 0.3195, 0.1319, 0.2897, 0.3318,
     0.1592, 0.24, 0.182, 0.5049, 0.4444, 0.4577, 0.3717, 0.4639, 0.3939, 0.1891,
     0.4612, 0.4465, 0.2443, 0.1198, 0.1711, 0.1032, 0.251, 0.286, 0.3719, 0.3339,
     0.1362, 0.4303, 0.1162, 0.4219, 0.5369, 0.6033, 0.7995, 0.7446, 0.8202, 0.3544,
     0.2897, 0.3615, 0.9306, 0.8478, 0.769, 0.8522, 0.5749, 0.4918, 0.8549, 0.0877,
     0.2945, 0.1746, 0.4639, 0.519, 0.3964, 0.4674, 0.548, 0.5558, 0.7332, 0.689,
     0.6408, 0.502, 0.5069, 0.4355, 0.4463, 0.5069, 0.3397, 0.2822, 0.2464, 0.5547,
     0.7305, 0.4567), V33 = c(0.1862, 0.2965, 0.317, 0.3101, 0.1717, 0.5976, 0.6671,
     0.2544, 0.8048, 0.9708, 0.2227, 0.0507, 0.4124, 0.1386, 0.8108, 0.4629, 0.6832,
     0.7547, 0.8032, 0.5555, 0.5385, 0.6084, 0.3034, 0.2025, 0.2797, 0.3663, 0.5588,
     0.5142, 0.6591, 0.5468, 0.5934, 0.6601, 0.7662, 0.1863, 0.1339, 0.2672, 0.3381,
     0.2849, 0.3735, 0.1709, 0.3638, 0.3969, 0.1668, 0.2779, 0.1811, 0.1413, 0.5169,
     0.8067, 0.6096, 0.558, 0.4661, 0.2433, 0.3939, 0.5, 0.1768, 0.166, 0.3578, 0.3168,
     0.2162, 0.3114, 0.2079, 0.2035, 0.2212, 0.7791, 0.4336, 0.416, 0.3118, 0.212,
     0.4362, 0.7927, 0.6657, 0.4187, 0.4316, 0.4434, 0.7774, 0.7605, 0.6865, 0.7204,
     0.525, 0.5409, 0.9425, 0.16, 0.3645, 0.2709, 0.6495, 0.6824, 0.4154, 0.4245,
     0.5058, 0.5409, 0.6038, 0.5977, 0.4972, 0.5359, 0.3921, 0.2997, 0.2948, 0.3903,
     0.3828, 0.2042, 0.1645, 0.416, 0.5197, 0.1715), V34 = c(0.2709, 0.1873, 0.3305,
     0.0306, 0.2351, 0.3785, 0.6083, 0.2009, 0.9435, 0.9647, 0.3341, 0.1588, 0.126,
     0.3325, 0.9039, 0.5255, 0.761, 0.8773, 0.805, 0.6963, 0.5308, 0.5621, 0.4366,
     0.0688, 0.1721, 0.2298, 0.5967, 0.5389, 0.6954, 0.5516, 0.8443, 0.8708, 0.6547,
     0.2176, 0.1582, 0.2907, 0.3172, 0.2847, 0.3336, 0.2484, 0.4786, 0.3894, 0.0588,
     0.1997, 0.1107, 0.2767, 0.4268, 0.6973, 0.511, 0.5727, 0.3974, 0.1956, 0.505,
     0.5111, 0.2472, 0.2618, 0.3947, 0.404, 0.0968, 0.2066, 0.0955, 0.0798, 0.1124,
     0.8703, 0.6553, 0.1906, 0.3763, 0.2866, 0.4048, 0.5227, 0.5254, 0.2398, 0.3791,
     0.3864, 0.6643, 0.704, 0.639, 0.62, 0.4255, 0.5961, 0.8726, 0.4169, 0.5012, 0.4853,
     0.6901, 0.622, 0.3308, 0.3095, 0.4476, 0.4988, 0.2575, 0.3244, 0.2755, 0.3842,
     0.3524, 0.2294, 0.1729, 0.3009, 0.3204, 0.219, 0.114, 0.1472, 0.1786, 0.1549),
     V35 = c(0.4232, 0.2969, 0.3408, 0.0244, 0.2489, 0.2495, 0.6239, 0.0329, 1, 0.7892,
     0.3984, 0.304, 0.2487, 0.2883, 0.8647, 0.5147, 0.9017, 0.9919, 0.7676, 0.7298,
     0.5356, 0.5956, 0.5175, 0.1171, 0.1665, 0.1362, 0.6275, 0.5531, 0.729, 0.5463,
     0.9481, 0.9518, 0.5447, 0.236, 0.1952, 0.1982, 0.2222, 0.1742, 0.1052, 0.3044,
     0.2908, 0.2314, 0.3967, 0.5305, 0.4603, 0.5084, 0.1802, 0.3915, 0.2586, 0.6355,
     0.2194, 0.2667, 0.4833, 0.5194, 0.3518, 0.3862, 0.2867, 0.4282, 0.1323, 0.1165,
     0.0488, 0.0809, 0.1677, 1, 0.6172, 0.0223, 0.2801, 0.4033, 0.4952, 0.3967, 0.296,
     0.1847, 0.2421, 0.3093, 0.6604, 0.7539, 0.6378, 0.6253, 0.333, 0.5248, 0.6673,
     0.6576, 0.7843, 0.7184, 0.5666, 0.5054, 0.1445, 0.0752, 0.2401, 0.3108, 0.0349,
     0.0516, 0.03, 0.1848, 0.2183, 0.1866, 0.1488, 0.1565, 0.1331, 0.2223, 0.0956,
     0.0849, 0.1098, 0.1641), V36 = c(0.3043, 0.5163, 0.2186, 0.1108, 0.3649, 0.5771,
     0.5972, 0.1547, 0.896, 0.5307, 0.5077, 0.1369, 0.4676, 0.3228, 0.6695, 0.3929,
     1, 0.9922, 0.7468, 0.7022, 0.5271, 0.6078, 0.5122, 0.2157, 0.2561, 0.2123, 0.5459,
     0.5318, 0.668, 0.5515, 0.9705, 0.9605, 0.4593, 0.1725, 0.1787, 0.2288, 0.0733,
     0.0549, 0.0671, 0.2312, 0.0899, 0.1036, 0.7147, 0.7409, 0.665, 0.4787, 0.0791,
     0.1558, 0.0916, 0.7563, 0.1816, 0.134, 0.3511, 0.4619, 0.3762, 0.3958, 0.2401,
     0.4538, 0.1344, 0.0185, 0.1406, 0.1525, 0.1039, 0.9212, 0.4373, 0.4219, 0.0875,
     0.2803, 0.1712, 0.3042, 0.0704, 0.376, 0.0944, 0.2138, 0.6884, 0.799, 0.6629,
     0.6848, 0.2331, 0.3777, 0.4694, 0.739, 0.9361, 0.8209, 0.5188, 0.3578, 0.1923,
     0.2885, 0.1405, 0.2897, 0.1799, 0.3157, 0.3356, 0.1149, 0.1245, 0.0922, 0.0801,
     0.0985, 0.044, 0.1327, 0.008, 0.0608, 0.1446, 0.1869), V37 = c(0.6116, 0.6153,
     0.2463, 0.1594, 0.3382, 0.8852, 0.5715, 0.1212, 0.5516, 0.2718, 0.5534, 0.1605,
     0.5382, 0.2607, 0.4027, 0.1279, 0.9123, 0.9419, 0.6253, 0.5468, 0.426, 0.5025,
     0.4746, 0.2216, 0.2735, 0.2395, 0.4786, 0.4826, 0.5917, 0.4561, 0.7766, 0.7712,
     0.4679, 0.0589, 0.0429, 0.3186, 0.2692, 0.1192, 0.0379, 0.1338, 0.2043, 0.1312,
     0.7319, 0.7775, 0.6423, 0.1356, 0.0535, 0.1598, 0.0947, 0.6903, 0.1023, 0.1073,
     0.2319, 0.4234, 0.2909, 0.3248, 0.3619, 0.3704, 0.225, 0.1302, 0.2554, 0.2626,
     0.2562, 0.9386, 0.4118, 0.5496, 0.3319, 0.3087, 0.3652, 0.1309, 0.097, 0.4331,
     0.0351, 0.1112, 0.6938, 0.7673, 0.5983, 0.7337, 0.1451, 0.2369, 0.1546, 0.7963,
     0.8195, 0.7536, 0.506, 0.3809, 0.3208, 0.4072, 0.1772, 0.2244, 0.3039, 0.359,
     0.3167, 0.157, 0.1592, 0.1829, 0.177, 0.22, 0.1234, 0.0521, 0.0702, 0.0969, 0.1066,
     0.2655), V38 = c(0.6756, 0.4283, 0.2726, 0.1371, 0.1589, 0.8409, 0.5242, 0.2446,
     0.3037, 0.1953, 0.3352, 0.2061, 0.315, 0.204, 0.237, 0.0411, 0.7388, 0.8388,
     0.173, 0.1421, 0.2436, 0.2829, 0.4902, 0.2776, 0.3209, 0.2673, 0.3965, 0.379,
     0.4899, 0.3466, 0.6313, 0.6772, 0.1987, 0.0621, 0.1096, 0.2871, 0.1888, 0.1154,
     0.0461, 0.2056, 0.1707, 0.0864, 0.3509, 0.4424, 0.2166, 0.2299, 0.1906, 0.2161,
     0.2287, 0.6176, 0.2108, 0.2023, 0.4029, 0.4372, 0.2311, 0.2302, 0.3314, 0.3741,
     0.3244, 0.248, 0.2054, 0.2456, 0.2624, 0.9303, 0.3641, 0.2483, 0.4237, 0.355,
     0.3763, 0.2408, 0.3941, 0.3626, 0.0844, 0.1386, 0.5932, 0.5955, 0.4565, 0.6281,
     0.1648, 0.172, 0.1748, 0.7493, 0.6207, 0.6496, 0.3885, 0.3813, 0.3367, 0.317,
     0.1742, 0.096, 0.476, 0.3881, 0.4133, 0.1311, 0.1626, 0.1743, 0.1382, 0.2243,
     0.203, 0.0618, 0.0936, 0.1411, 0.144, 0.1713), V39 = c(0.5375, 0.5479, 0.168,
     0.0696, 0.0989, 0.357, 0.2924, 0.3171, 0.2338, 0.1374, 0.2723, 0.0734, 0.2139,
     0.2396, 0.2685, 0.0859, 0.5915, 0.6605, 0.2916, 0.4738, 0.1205, 0.0477, 0.4603,
     0.2309, 0.2724, 0.2865, 0.2087, 0.1831, 0.3439, 0.3384, 0.576, 0.6431, 0.0699,
     0.1847, 0.1762, 0.2921, 0.0712, 0.0855, 0.1694, 0.2474, 0.0407, 0.2569, 0.0589,
     0.1416, 0.1951, 0.2789, 0.2561, 0.5178, 0.348, 0.5379, 0.3253, 0.1794, 0.3676,
     0.4277, 0.3168, 0.325, 0.3763, 0.3839, 0.3939, 0.1637, 0.1614, 0.198, 0.2236,
     0.7314, 0.4572, 0.2034, 0.1801, 0.2545, 0.2841, 0.178, 0.6028, 0.2519, 0.0436,
     0.1523, 0.5774, 0.4731, 0.3129, 0.5725, 0.2694, 0.1878, 0.3607, 0.6795, 0.4513,
     0.4708, 0.3762, 0.3359, 0.5683, 0.2863, 0.3326, 0.2287, 0.5756, 0.5716, 0.6281,
     0.1583, 0.2356, 0.2452, 0.2404, 0.2736, 0.1652, 0.1416, 0.0894, 0.1676, 0.1929,
     0.0959), V40 = c(0.4719, 0.6133, 0.2792, 0.0452, 0.1089, 0.3133, 0.1536, 0.3195,
     0.2382, 0.3105, 0.2278, 0.0202, 0.1848, 0.1319, 0.3662, 0.1131, 0.4057, 0.4816,
     0.5003, 0.641, 0.3845, 0.2811, 0.446, 0.1444, 0.188, 0.206, 0.1651, 0.175, 0.2366,
     0.2853, 0.6148, 0.672, 0.1493, 0.2452, 0.2481, 0.2806, 0.1062, 0.1811, 0.2169,
     0.279, 0.1286, 0.3179, 0.269, 0.3508, 0.4947, 0.3833, 0.2153, 0.4782, 0.2095,
     0.5622, 0.3697, 0.0227, 0.151, 0.4433, 0.3554, 0.4022, 0.4767, 0.3494, 0.3806,
     0.1103, 0.2232, 0.2412, 0.118, 0.4791, 0.4367, 0.2729, 0.3743, 0.1432, 0.0427,
     0.1598, 0.3521, 0.187, 0.113, 0.0996, 0.6223, 0.484, 0.4158, 0.6119, 0.373, 0.325,
     0.5208, 0.4713, 0.3004, 0.3482, 0.3738, 0.2771, 0.5505, 0.2634, 0.4021, 0.3228,
     0.4254, 0.4314, 0.4977, 0.2631, 0.2483, 0.2407, 0.2046, 0.2152, 0.1043, 0.146,
     0.1127, 0.12, 0.0325, 0.0768), V41 = c(0.4647, 0.5017, 0.2558, 0.062, 0.1043,
     0.6096, 0.2003, 0.3051, 0.3318, 0.379, 0.2044, 0.1638, 0.1679, 0.0683, 0.3267,
     0.1306, 0.3019, 0.2917, 0.522, 0.4375, 0.4107, 0.3422, 0.4196, 0.1513, 0.1552,
     0.1659, 0.1836, 0.1679, 0.1716, 0.2502, 0.545, 0.6035, 0.1713, 0.2984, 0.315,
     0.2682, 0.0694, 0.1264, 0.1677, 0.161, 0.1581, 0.2649, 0.42, 0.4482, 0.4925,
     0.2933, 0.2769, 0.2344, 0.1901, 0.6508, 0.2912, 0.1313, 0.0745, 0.37, 0.3741,
     0.4344, 0.4059, 0.438, 0.3258, 0.2144, 0.1773, 0.2409, 0.1103, 0.2087, 0.2964,
     0.2837, 0.4627, 0.5869, 0.5331, 0.5657, 0.3924, 0.1046, 0.2045, 0.1644, 0.5841,
     0.434, 0.4325, 0.5597, 0.4467, 0.2575, 0.5177, 0.2355, 0.2674, 0.3508, 0.2605,
     0.3648, 0.3231, 0.0541, 0.3009, 0.3454, 0.5046, 0.3051, 0.2613, 0.3103, 0.2437,
     0.2518, 0.197, 0.2438, 0.1066, 0.0846, 0.0873, 0.1201, 0.149, 0.0847), V42 = c(0.2587,
     0.2377, 0.174, 0.1421, 0.0839, 0.6378, 0.2031, 0.0836, 0.3821, 0.4105, 0.1986,
     0.1583, 0.2328, 0.0334, 0.22, 0.1757, 0.2331, 0.1769, 0.4824, 0.3178, 0.5067,
     0.5147, 0.2873, 0.1745, 0.2522, 0.2633, 0.0652, 0.0674, 0.1013, 0.1641, 0.4813,
     0.5155, 0.1654, 0.3041, 0.292, 0.2112, 0.03, 0.0799, 0.0644, 0.0056, 0.2191,
     0.2714, 0.3874, 0.4208, 0.4041, 0.1155, 0.2841, 0.3599, 0.2941, 0.4797, 0.301,
     0.1775, 0.1395, 0.3324, 0.4443, 0.4008, 0.3661, 0.4265, 0.3654, 0.2033, 0.2293,
     0.1901, 0.2831, 0.2016, 0.4312, 0.4463, 0.1614, 0.6431, 0.6952, 0.6443, 0.4808,
     0.2339, 0.1937, 0.1902, 0.4527, 0.3954, 0.4031, 0.4965, 0.4133, 0.2423, 0.3702,
     0.1704, 0.2241, 0.3181, 0.1591, 0.3834, 0.0448, 0.1874, 0.2075, 0.3882, 0.7179,
     0.4393, 0.4697, 0.4512, 0.2715, 0.3184, 0.2778, 0.3154, 0.211, 0.1055, 0.102,
     0.1036, 0.0328, 0.2076), V43 = c(0.2129, 0.1957, 0.2121, 0.1597, 0.1391, 0.2709,
     0.2207, 0.1266, 0.1575, 0.3355, 0.0835, 0.183, 0.1015, 0.0716, 0.2996, 0.2648,
     0.2931, 0.1136, 0.4004, 0.2377, 0.4216, 0.4372, 0.2296, 0.1756, 0.2121, 0.2552,
     0.0758, 0.0609, 0.0766, 0.1605, 0.3406, 0.3802, 0.26, 0.2275, 0.1902, 0.1513,
     0.0893, 0.0378, 0.0159, 0.0351, 0.1701, 0.1713, 0.244, 0.3054, 0.2402, 0.1705,
     0.1733, 0.2785, 0.2211, 0.3736, 0.2563, 0.1549, 0.1552, 0.2564, 0.3261, 0.337,
     0.232, 0.2854, 0.2983, 0.1887, 0.2521, 0.2077, 0.2385, 0.1669, 0.4155, 0.3178,
     0.2494, 0.5826, 0.4288, 0.4241, 0.4602, 0.1991, 0.0834, 0.1313, 0.4911, 0.4837,
     0.4201, 0.5027, 0.3743, 0.2706, 0.224, 0.2728, 0.3141, 0.3524, 0.1875, 0.3453,
     0.3131, 0.3459, 0.1206, 0.324, 0.6163, 0.4302, 0.4806, 0.3785, 0.1184, 0.1685,
     0.1377, 0.2112, 0.2417, 0.1639, 0.1964, 0.1977, 0.0537, 0.2505), V44 = c(0.2222,
     0.1749, 0.1099, 0.1384, 0.0819, 0.1419, 0.1778, 0.1381, 0.2228, 0.2998, 0.0908,
     0.1886, 0.0713, 0.0976, 0.2205, 0.1955, 0.2298, 0.0701, 0.3877, 0.2808, 0.2479,
     0.247, 0.0949, 0.1424, 0.1801, 0.1696, 0.0486, 0.0375, 0.0845, 0.1491, 0.1916,
     0.2278, 0.3846, 0.148, 0.0696, 0.1789, 0.1459, 0.1268, 0.0778, 0.1148, 0.0971,
     0.0584, 0.2, 0.2235, 0.1392, 0.1294, 0.0815, 0.1807, 0.1524, 0.2804, 0.1927,
     0.1626, 0.0377, 0.2527, 0.1963, 0.2518, 0.145, 0.2808, 0.1779, 0.137, 0.1464,
     0.1767, 0.0255, 0.2872, 0.1824, 0.0807, 0.3202, 0.4286, 0.3063, 0.4567, 0.4164,
     0.11, 0.1502, 0.1776, 0.5762, 0.5379, 0.4557, 0.5772, 0.3021, 0.2323, 0.0816,
     0.4016, 0.3693, 0.3659, 0.2267, 0.2096, 0.3387, 0.4646, 0.0255, 0.0926, 0.5663,
     0.4831, 0.4921, 0.1269, 0.1157, 0.0675, 0.0685, 0.0991, 0.1631, 0.1916, 0.2256,
     0.1339, 0.1309, 0.1862), V45 = c(0.2111, 0.1304, 0.0985, 0.0372, 0.0678, 0.126,
     0.1353, 0.1136, 0.1582, 0.2748, 0.138, 0.1008, 0.0615, 0.0787, 0.1163, 0.0656,
     0.2391, 0.1578, 0.1651, 0.1374, 0.1586, 0.1708, 0.0095, 0.0908, 0.1473, 0.1467,
     0.0353, 0.0533, 0.026, 0.1326, 0.1134, 0.1522, 0.3754, 0.1102, 0.0758, 0.185,
     0.1348, 0.1125, 0.0653, 0.1331, 0.2217, 0.123, 0.2307, 0.2611, 0.1779, 0.0909,
     0.0335, 0.0352, 0.0746, 0.1982, 0.2062, 0.0708, 0.0636, 0.2137, 0.0864, 0.2101,
     0.1017, 0.2395, 0.1535, 0.1376, 0.0673, 0.1119, 0.1967, 0.4374, 0.1487, 0.1192,
     0.2265, 0.4894, 0.5835, 0.576, 0.5438, 0.0684, 0.1675, 0.2, 0.5013, 0.4485, 0.3955,
     0.5907, 0.2069, 0.1724, 0.0395, 0.4125, 0.2986, 0.2846, 0.1577, 0.1031, 0.413,
     0.4366, 0.0298, 0.1173, 0.5749, 0.5084, 0.5294, 0.1459, 0.1449, 0.1186, 0.0664,
     0.0594, 0.0769, 0.2085, 0.1814, 0.0902, 0.091, 0.1439), V46 = c(0.0176, 0.0597,
     0.1271, 0.0688, 0.0663, 0.1288, 0.1373, 0.0516, 0.1433, 0.2024, 0.1948, 0.0663,
     0.0779, 0.0522, 0.0635, 0.058, 0.191, 0.1938, 0.0442, 0.1136, 0.1124, 0.1343,
     0.0527, 0.0138, 0.0681, 0.1286, 0.0297, 0.0278, 0.0333, 0.0687, 0.064, 0.0801,
     0.2414, 0.1178, 0.091, 0.1717, 0.0391, 0.0505, 0.021, 0.0276, 0.2732, 0.22, 0.1886,
     0.2798, 0.1946, 0.08, 0.0933, 0.0473, 0.0606, 0.2438, 0.1751, 0.0129, 0.0443,
     0.1789, 0.1688, 0.1181, 0.1111, 0.0369, 0.1199, 0.0307, 0.0965, 0.0779, 0.1483,
     0.3097, 0.0138, 0.2134, 0.1146, 0.5777, 0.5692, 0.5293, 0.5649, 0.0303, 0.1058,
     0.0765, 0.4042, 0.2674, 0.2966, 0.4803, 0.179, 0.1457, 0.0785, 0.347, 0.2226,
     0.1714, 0.1211, 0.0798, 0.3639, 0.2581, 0.0691, 0.0566, 0.3593, 0.1952, 0.2216,
     0.1092, 0.1883, 0.1833, 0.1665, 0.194, 0.0723, 0.2335, 0.2012, 0.1085, 0.0757,
     0.147), V47 = c(0.1348, 0.1124, 0.1459, 0.0867, 0.1202, 0.079, 0.0749, 0.0073,
     0.1634, 0.1043, 0.1211, 0.0183, 0.0761, 0.05, 0.0465, 0.0319, 0.1096, 0.1106,
     0.0663, 0.1034, 0.0651, 0.0838, 0.0383, 0.0469, 0.1091, 0.0926, 0.0241, 0.0179,
     0.0205, 0.0602, 0.0911, 0.0804, 0.1077, 0.0608, 0.0441, 0.0898, 0.0546, 0.0949,
     0.0509, 0.0763, 0.1874, 0.2198, 0.196, 0.2392, 0.1723, 0.0567, 0.1018, 0.0322,
     0.0692, 0.1789, 0.0841, 0.0795, 0.0264, 0.101, 0.1991, 0.115, 0.0655, 0.0805,
     0.0959, 0.0373, 0.1492, 0.1344, 0.0434, 0.1578, 0.1164, 0.3241, 0.0476, 0.4315,
     0.263, 0.3287, 0.3195, 0.0674, 0.1111, 0.0727, 0.3123, 0.1541, 0.2095, 0.3877,
     0.1689, 0.1175, 0.1052, 0.2739, 0.0849, 0.0694, 0.0883, 0.0701, 0.2069, 0.1319,
     0.0781, 0.0766, 0.2526, 0.1539, 0.1401, 0.1485, 0.1954, 0.1878, 0.1807, 0.1937,
     0.0912, 0.1964, 0.1688, 0.1521, 0.1059, 0.0991), V48 = c(0.0744, 0.1047, 0.1164,
     0.0513, 0.0692, 0.0829, 0.0472, 0.0278, 0.1133, 0.0453, 0.0843, 0.0404, 0.0845,
     0.0231, 0.0422, 0.0301, 0.03, 0.0693, 0.0418, 0.0688, 0.0789, 0.0755, 0.0107,
     0.048, 0.0919, 0.0716, 0.0379, 0.0114, 0.0309, 0.0561, 0.098, 0.0752, 0.0224,
     0.0333, 0.0244, 0.0656, 0.0469, 0.0677, 0.0387, 0.0631, 0.1062, 0.1074, 0.1701,
     0.2021, 0.1522, 0.0198, 0.0309, 0.0408, 0.0446, 0.1706, 0.1035, 0.0762, 0.0223,
     0.0528, 0.1217, 0.055, 0.0271, 0.0541, 0.0765, 0.0606, 0.1128, 0.096, 0.0627,
     0.0553, 0.2052, 0.2945, 0.0943, 0.264, 0.1196, 0.1283, 0.2484, 0.0785, 0.0849,
     0.0749, 0.2232, 0.1359, 0.1558, 0.2779, 0.1341, 0.0868, 0.1034, 0.179, 0.0359,
     0.0303, 0.085, 0.0526, 0.0859, 0.0505, 0.0777, 0.0969, 0.2299, 0.2037, 0.1888,
     0.1385, 0.1492, 0.1114, 0.1245, 0.1082, 0.0812, 0.13, 0.1037, 0.1363, 0.1005,
     0.0041), V49 = c(0.013, 0.0507, 0.0777, 0.0092, 0.0152, 0.052, 0.0325, 0.0372,
     0.0567, 0.0337, 0.0589, 0.0108, 0.0592, 0.0221, 0.0174, 0.0272, 0.0171, 0.0176,
     0.0475, 0.0422, 0.0325, 0.0304, 0.0108, 0.0159, 0.0397, 0.0325, 0.0119, 0.0073,
     0.0101, 0.0306, 0.0563, 0.0566, 0.0155, 0.0276, 0.0265, 0.0445, 0.0201, 0.0259,
     0.0262, 0.0309, 0.0665, 0.0423, 0.1366, 0.1326, 0.0929, 0.0114, 0.0208, 0.0163,
     0.0344, 0.0762, 0.0641, 0.0117, 0.0187, 0.0453, 0.0628, 0.0293, 0.0244, 0.0177,
     0.0649, 0.0399, 0.0463, 0.0598, 0.0513, 0.0334, 0.1069, 0.1474, 0.0824, 0.1794,
     0.0983, 0.0698, 0.1299, 0.0455, 0.0596, 0.0449, 0.1085, 0.0941, 0.0884, 0.1427,
     0.0769, 0.0392, 0.0764, 0.0922, 0.0289, 0.0292, 0.0355, 0.0241, 0.06, 0.0112,
     0.0369, 0.0588, 0.1271, 0.1054, 0.0947, 0.0716, 0.0511, 0.031, 0.0516, 0.0336,
     0.0496, 0.0633, 0.0501, 0.0858, 0.0535, 0.0154), V50 = c(0.0106, 0.0159, 0.0439,
     0.0198, 0.0266, 0.0216, 0.0179, 0.0121, 0.0133, 0.0122, 0.0247, 0.0143, 0.0068,
     0.0144, 0.0172, 0.0074, 0.0383, 0.0205, 0.0235, 0.0117, 0.007, 0.0074, 0.0077,
     0.0045, 0.0093, 0.0258, 0.0073, 0.0116, 0.0095, 0.0154, 0.0187, 0.0175, 0.0187,
     0.01, 0.0095, 0.011, 0.0095, 0.017, 0.0101, 0.024, 0.0405, 0.0162, 0.0398, 0.0358,
     0.0179, 0.0151, 0.0318, 0.0088, 0.0082, 0.0238, 0.0153, 0.0061, 0.0077, 0.0118,
     0.0323, 0.0183, 0.0179, 0.0065, 0.0313, 0.0169, 0.0193, 0.033, 0.0473, 0.0209,
     0.0199, 0.0211, 0.0171, 0.0772, 0.0374, 0.0334, 0.0825, 0.0246, 0.0201, 0.0134,
     0.0414, 0.0261, 0.0265, 0.0424, 0.0222, 0.0131, 0.0216, 0.0276, 0.0122, 0.0116,
     0.0219, 0.0117, 0.0267, 0.0059, 0.0057, 0.005, 0.0356, 0.0251, 0.0134, 0.0176,
     0.0155, 0.0143, 0.0044, 0.0177, 0.0101, 0.0183, 0.0136, 0.029, 0.0235, 0.0116
     ), V51 = c(0.0033, 0.0195, 0.0061, 0.0118, 0.0174, 0.036, 0.0045, 0.0153, 0.017,
     0.0072, 0.0118, 0.0091, 0.0089, 0.0307, 0.0134, 0.0149, 0.0053, 0.0309, 0.0066,
     0.007, 0.0026, 0.0069, 0.0109, 0.0015, 0.0076, 0.0136, 0.0051, 0.0092, 0.0047,
     0.0029, 0.0088, 0.0058, 0.0125, 0.0023, 0.014, 0.0024, 0.0155, 0.0033, 0.0161,
     0.0115, 0.0113, 0.0093, 0.0143, 0.0128, 0.0242, 0.0085, 0.0132, 0.0121, 0.0108,
     0.0268, 0.0081, 0.0257, 0.0137, 9e-04, 0.0253, 0.0104, 0.0109, 0.0222, 0.0185,
     0.0135, 0.014, 0.0197, 0.0248, 0.0172, 0.0208, 0.0361, 0.0244, 0.0798, 0.0291,
     0.0342, 0.0243, 0.0151, 0.0071, 0.0174, 0.0253, 0.0079, 0.0121, 0.0271, 0.0205,
     0.0092, 0.0167, 0.0169, 0.0045, 0.0024, 0.0086, 0.0122, 0.0125, 0.0041, 0.0091,
     0.0118, 0.0367, 0.0357, 0.031, 0.0199, 0.0189, 0.0138, 0.0185, 0.0209, 0.0089,
     0.0137, 0.013, 0.0203, 0.0155, 0.0181), V52 = c(0.0232, 0.0201, 0.0145, 0.009,
     0.0176, 0.0331, 0.0084, 0.0092, 0.0035, 0.0108, 0.0088, 0.0038, 0.0087, 0.0386,
     0.0141, 0.0125, 0.009, 0.0212, 0.0062, 0.0167, 0.0093, 0.0025, 0.0062, 0.0052,
     0.0065, 0.0044, 0.0034, 0.0078, 0.0072, 0.0048, 0.0042, 0.0091, 0.0028, 0.0069,
     0.0074, 0.0062, 0.0091, 0.015, 0.0029, 0.0064, 0.0028, 0.0046, 0.0093, 0.0172,
     0.0083, 0.0178, 0.0118, 0.0067, 0.0149, 0.0081, 0.0191, 0.0089, 0.0071, 0.0142,
     0.0214, 0.0117, 0.0147, 0.0045, 0.0098, 0.0222, 0.0027, 0.0189, 0.0274, 0.018,
     0.0176, 0.0444, 0.0258, 0.0376, 0.0156, 0.0459, 0.021, 0.0125, 0.0104, 0.0117,
     0.0131, 0.0164, 0.0091, 0.02, 0.0123, 0.0078, 0.0089, 0.0081, 0.0108, 0.0084,
     0.0123, 0.0122, 0.004, 0.0056, 0.0134, 0.0146, 0.0176, 0.0181, 0.0237, 0.0096,
     0.015, 0.0108, 0.0072, 0.0134, 0.0083, 0.015, 0.012, 0.0116, 0.016, 0.0146),
     V53 = c(0.0166, 0.0248, 0.0128, 0.0223, 0.0127, 0.0131, 0.001, 0.0035, 0.0052,
     0.007, 0.0104, 0.0096, 0.0032, 0.0147, 0.0191, 0.0134, 0.0042, 0.0091, 0.0129,
     0.0127, 0.0118, 0.0103, 0.0028, 0.0038, 0.0072, 0.0028, 0.0129, 0.0041, 0.0054,
     0.0023, 0.0175, 0.016, 0.0067, 0.0025, 0.0063, 0.0072, 0.0151, 0.0111, 0.0078,
     0.0022, 0.0036, 0.0044, 0.0033, 0.0138, 0.0037, 0.0073, 0.012, 0.0032, 0.0077,
     0.0129, 0.0182, 0.0262, 0.0082, 0.0179, 0.0262, 0.0101, 0.017, 0.0136, 0.0178,
     0.0175, 0.0068, 0.0204, 0.0205, 0.011, 0.0197, 0.023, 0.0143, 0.0143, 0.0197,
     0.0277, 0.0361, 0.0036, 0.0062, 0.0023, 0.0049, 0.012, 0.0062, 0.007, 0.0067,
     0.0071, 0.0051, 0.004, 0.0075, 0.01, 0.006, 0.0114, 0.0136, 0.0104, 0.0097, 0.004,
     0.0035, 0.0019, 0.0078, 0.0103, 0.006, 0.0062, 0.0055, 0.0094, 0.008, 0.0076,
     0.0039, 0.0098, 0.0029, 0.0129), V54 = c(0.0095, 0.0131, 0.0145, 0.0179, 0.0088,
     0.012, 0.0018, 0.0098, 0.0083, 0.0063, 0.0036, 0.0142, 0.013, 0.0018, 0.0145,
     0.0026, 0.0153, 0.0056, 0.0184, 0.0138, 0.0112, 0.0074, 0.004, 0.0079, 0.0108,
     0.0021, 0.01, 0.0013, 0.0022, 0.002, 0.0171, 0.016, 0.012, 0.0027, 0.0081, 0.0113,
     0.008, 0.0032, 0.0114, 0.0122, 0.0105, 0.0078, 0.0113, 0.0079, 0.0095, 0.0079,
     0.0051, 0.0109, 0.0036, 0.0161, 0.016, 0.0108, 0.0232, 0.0079, 0.0177, 0.0061,
     0.0158, 0.0113, 0.0077, 0.0127, 0.015, 0.0085, 0.0141, 0.0234, 0.021, 0.029,
     0.0226, 0.0272, 0.0135, 0.0172, 0.0239, 0.0123, 0.0026, 0.0047, 0.0104, 0.0113,
     0.0019, 0.007, 0.0011, 0.0081, 0.0015, 0.0025, 0.0089, 0.0018, 0.0187, 0.0098,
     0.0137, 0.0079, 0.0042, 0.0114, 0.0093, 0.0102, 0.0144, 0.0093, 0.0082, 0.0044,
     0.0074, 0.0047, 0.0026, 0.0032, 0.0053, 0.0199, 0.0051, 0.0047), V55 = c(0.018,
     0.007, 0.0058, 0.0084, 0.0098, 0.0108, 0.0068, 0.0121, 0.0078, 0.003, 0.0088,
     0.019, 0.0188, 0.01, 0.0065, 0.0038, 0.0106, 0.0086, 0.0069, 0.009, 0.0094, 0.0123,
     0.0075, 0.0114, 0.0051, 0.0022, 0.0044, 0.0011, 0.0016, 0.004, 0.0079, 0.0081,
     0.0012, 0.0052, 0.0087, 0.0012, 0.0018, 0.0035, 0.0083, 0.0151, 0.012, 0.0102,
     0.003, 0.0037, 0.0105, 0.0038, 0.007, 0.0164, 0.0114, 0.0063, 0.029, 0.0138,
     0.0198, 0.006, 0.0037, 0.0031, 0.0046, 0.0053, 0.0074, 0.0022, 0.0012, 0.0043,
     0.0185, 0.0276, 0.0141, 0.0141, 0.0187, 0.0127, 0.0127, 0.0087, 0.0447, 0.0043,
     0.0025, 0.0049, 0.0102, 0.0021, 0.0045, 0.0086, 0.0026, 0.0034, 0.0075, 0.0036,
     0.0036, 0.0035, 0.0111, 0.0027, 0.0172, 0.0014, 0.0058, 0.0032, 0.0121, 0.0133,
     0.017, 0.0025, 0.0091, 0.0072, 0.0068, 0.0045, 0.0079, 0.0037, 0.0062, 0.0033,
     0.0062, 0.0039), V56 = c(0.0244, 0.0138, 0.0049, 0.0068, 0.0019, 0.0024, 0.0039,
     6e-04, 0.0075, 0.0011, 0.0047, 0.014, 0.0101, 0.0096, 0.0129, 0.0018, 0.002,
     0.0092, 0.0198, 0.0051, 0.014, 0.0069, 0.0039, 0.005, 0.0102, 0.0048, 0.0057,
     0.0045, 0.0029, 0.0019, 0.005, 0.007, 0.0022, 0.0036, 0.0044, 0.0022, 0.0078,
     0.0169, 0.0058, 0.0056, 0.0087, 0.0065, 0.0057, 0.0051, 0.003, 0.0116, 0.0015,
     0.0151, 0.0085, 0.0119, 0.009, 0.0187, 0.0074, 0.0131, 0.0068, 0.0099, 0.0073,
     0.0165, 0.0095, 0.0124, 0.0133, 0.0092, 0.0055, 0.0032, 0.0049, 0.0161, 0.0185,
     0.0166, 0.0138, 0.0046, 0.0394, 0.0114, 0.0061, 0.0031, 0.0092, 0.0097, 0.0079,
     0.0089, 0.0049, 0.0064, 0.0058, 0.0058, 0.0029, 0.0058, 0.0126, 0.0025, 0.0132,
     0.0054, 0.0072, 0.0062, 0.0075, 0.004, 0.0012, 0.0044, 0.0038, 7e-04, 0.0084,
     0.0042, 0.0042, 0.0071, 0.0046, 0.0101, 0.0089, 0.0061), V57 = c(0.0316, 0.0092,
     0.0065, 0.0032, 0.0059, 0.0045, 0.012, 0.0181, 0.0105, 7e-04, 0.0117, 0.0099,
     0.0229, 0.0077, 0.0217, 0.0113, 0.0105, 0.007, 0.0199, 0.0029, 0.0072, 0.0076,
     0.0053, 0.003, 0.0041, 0.0138, 0.003, 0.0039, 0.0058, 0.0034, 0.0112, 0.0135,
     0.0058, 0.0026, 0.0028, 0.0025, 0.0045, 0.0137, 3e-04, 0.0026, 0.0061, 0.0061,
     0.009, 0.0258, 0.0132, 0.0033, 0.0035, 0.007, 0.0101, 0.0194, 0.0242, 0.023,
     0.0035, 0.0089, 0.0121, 0.008, 0.0054, 0.0141, 0.0055, 0.0054, 0.0048, 0.0138,
     0.0045, 0.0084, 0.0027, 0.0177, 0.011, 0.0095, 0.0133, 0.0203, 0.0355, 0.0052,
     0.0038, 0.0024, 0.0083, 0.0072, 0.0031, 0.0074, 0.0029, 0.0037, 0.0016, 0.0067,
     0.0013, 0.0011, 0.0081, 0.0026, 0.011, 0.0015, 0.0041, 0.0101, 0.0056, 0.0042,
     0.0109, 0.0021, 0.0056, 0.0054, 0.0037, 0.0028, 0.0071, 0.004, 0.0045, 0.0065,
     0.014, 0.004), V58 = c(0.0164, 0.0143, 0.0093, 0.0035, 0.0058, 0.0037, 0.0132,
     0.0094, 0.016, 0.0024, 0.002, 0.0092, 0.0182, 0.018, 0.0087, 0.0058, 0.0049,
     0.0116, 0.0102, 0.0122, 0.0022, 0.0073, 0.0013, 0.0064, 0.0055, 0.014, 0.0035,
     0.0022, 0.005, 0.0034, 0.0179, 0.0067, 0.0042, 0.0036, 0.0019, 0.0059, 0.0026,
     0.0015, 0.0023, 0.0029, 0.0061, 0.0062, 0.0057, 0.0102, 0.0068, 0.0039, 8e-04,
     0.0085, 0.0016, 0.014, 0.0224, 0.0057, 0.01, 0.0084, 0.0077, 0.0107, 0.0033,
     0.0077, 0.0045, 0.0021, 0.0244, 0.0094, 0.0115, 0.0122, 0.0162, 0.0194, 0.0094,
     0.0225, 0.0131, 0.013, 0.044, 0.0091, 0.0101, 0.0039, 0.002, 0.006, 0.0063, 0.0042,
     0.0022, 0.0036, 0.007, 0.0035, 0.001, 9e-04, 0.0155, 0.005, 0.0122, 6e-04, 0.0045,
     0.0068, 0.0021, 0.003, 0.0036, 0.0069, 0.0056, 0.0035, 0.0024, 0.0036, 0.0044,
     9e-04, 0.0022, 0.0115, 0.0138, 0.0036), V59 = c(0.0095, 0.0036, 0.0059, 0.0056,
     0.0059, 0.0112, 0.007, 0.0116, 0.0095, 0.0057, 0.0091, 0.0052, 0.0046, 0.0109,
     0.0077, 0.0047, 0.007, 0.006, 0.007, 0.0056, 0.0055, 0.003, 0.0052, 0.0058, 0.005,
     0.0028, 0.0021, 0.0023, 0.0024, 0.0051, 0.0294, 0.0078, 0.0067, 6e-04, 0.0049,
     0.0039, 0.0036, 0.0069, 0.0026, 0.0104, 0.003, 0.0043, 0.0068, 0.0037, 0.0108,
     0.0081, 0.0044, 0.0117, 0.0028, 0.0332, 0.019, 0.0113, 0.0048, 0.0113, 0.0078,
     0.0161, 0.0045, 0.0246, 0.0063, 0.0028, 0.0077, 0.0105, 0.0152, 0.0082, 0.0059,
     0.0207, 0.0078, 0.0098, 0.0154, 0.0115, 0.0243, 8e-04, 0.0078, 0.0051, 0.0048,
     0.0017, 0.0048, 0.0055, 0.0022, 0.0012, 0.0074, 0.0043, 0.0032, 0.0033, 0.016,
     0.0073, 0.0114, 0.0081, 0.0047, 0.0053, 0.0043, 0.0031, 0.0043, 0.006, 0.0048,
     1e-04, 0.0034, 0.0013, 0.0022, 0.0015, 5e-04, 0.0193, 0.0077, 0.0061), V60 = c(0.0078,
     0.0103, 0.0022, 0.004, 0.0032, 0.0075, 0.0088, 0.0063, 0.0011, 0.0044, 0.0058,
     0.0075, 0.0038, 0.007, 0.0122, 0.0071, 0.008, 0.011, 0.0055, 0.002, 0.0122, 0.0138,
     0.0023, 0.003, 0.0087, 0.0064, 0.0027, 0.0016, 0.003, 0.0031, 0.0063, 0.0068,
     0.0012, 0.0035, 0.0023, 0.0048, 0.0024, 0.0051, 0.0027, 0.0163, 0.0078, 0.0053,
     0.0024, 0.0037, 0.009, 0.0053, 0.0077, 0.0056, 0.0014, 0.0439, 0.0096, 0.0131,
     0.0019, 0.0049, 0.0066, 0.0133, 0.0079, 0.0198, 0.0039, 0.0023, 0.0074, 0.0093,
     0.01, 0.0143, 0.0021, 0.0057, 0.0112, 0.0085, 0.0218, 0.0015, 0.0098, 0.0092,
     6e-04, 0.0015, 0.0036, 0.0036, 0.005, 0.0021, 0.0032, 0.0037, 0.0038, 0.0033,
     0.0047, 0.0026, 0.0085, 0.0022, 0.0068, 0.0043, 0.0054, 0.0087, 0.0017, 0.0033,
     0.0018, 0.0018, 0.0024, 0.0055, 7e-04, 0.0016, 0.0014, 0.0085, 0.0031, 0.0157,
     0.0031, 0.0115)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8",
     "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20",
     "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32",
     "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40", "V41", "V42", "V43", "V44",
     "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52", "V53", "V54", "V55", "V56",
     "V57", "V58", "V59", "V60"), class = "data.frame", row.names = c("3", "7", "9", "10",
     "13", "18", "19", "20", "25", "26", "29", "30", "35", "36", "37", "39", "43", "44",
     "46", "47", "49", "50", "52", "53", "54", "55", "59", "61", "63", "64", "66", "68",
     "69", "71", "73", "74", "77", "78", "80", "81", "83", "85", "87", "88", "90", "92",
     "93", "94", "95", "98", "100", "101", "104", "108", "110", "111", "114", "116", "118",
     "120", "123", "124", "131", "135", "138", "139", "140", "141", "142", "145", "148",
     "152", "154", "156", "158", "159", "161", "162", "163", "164", "166", "168", "169",
     "170", "172", "173", "175", "176", "179", "180", "182", "183", "184", "189", "191",
     "192", "193", "194", "195", "201", "202", "204", "206", "208")))
     16: predictLearner(.learner, .model, .newdata, ...)
     17: predictLearner.BaseWrapper(.learner, .model, .newdata, ...)
     18: do.call(predictLearner, c(list(.learner = .learner$next.learner, .model = .model$learner.model$next.model,
     .newdata = .newdata), args))
     19: (function (.learner, .model, .newdata, ...)
     {
     lmod = getLearnerModel(.model)
     if (inherits(lmod, "NoFeaturesModel")) {
     predict_nofeatures(.model, .newdata)
     }
     else {
     assertDataFrame(.newdata, min.rows = 1L, min.cols = 1L)
     UseMethod("predictLearner")
     }
     })(.learner = structure(list(id = "classif.xgboost", type = "classif", package = "xgboost",
     properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"
     ), par.set = structure(list(pars = structure(list(booster = structure(list(id = "booster",
     type = "discrete", len = 1L, lower = NULL, upper = NULL, values = structure(list(
     gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree", "gblinear"
     )), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), silent = structure(list(id = "silent", type = "integer", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), eta = structure(list(id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), gamma = structure(list(id = "gamma", type = "numeric", len = 1L, lower = 0,
     upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), max_depth = structure(list(
     id = "max_depth", type = "integer", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 6, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), subsample = structure(list(id = "subsample", type = "numeric", len = 1L,
     lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 1, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
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     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x62, 0x69, 0x6e, 0x61, 0x72, 0x79, 0x3a, 0x6c, 0x6f, 0x67,
     0x69, 0x73, 0x74, 0x69, 0x63, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x67, 0x62, 0x74, 0x72, 0x65, 0x65, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00,
     0x00, 0x14, 0x00, 0x00, 0x80, 0x6e, 0xc5, 0x2e, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x80, 0xdf,
     0x4f, 0x2d, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00,
     0x00, 0x00, 0x3b, 0x00, 0x00, 0x80, 0x82, 0xe2, 0x47, 0x3b, 0x01, 0x00, 0x00,
     0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,
     0x9a, 0x99, 0x99, 0xbe, 0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x32, 0xa4, 0xf3, 0x3e, 0x02, 0x00,
     0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x8c, 0xaf, 0xf8, 0xbe, 0xc7,
     0x92, 0xac, 0x41, 0x00, 0x00, 0x50, 0x41, 0x25, 0x49, 0x92, 0x3d, 0x00, 0x00,
     0x00, 0x00, 0xef, 0xd4, 0x14, 0x41, 0x00, 0x00, 0xe8, 0x40, 0xd9, 0x64, 0x93,
     0x3f, 0x02, 0x00, 0x00, 0x00, 0x90, 0xb9, 0x43, 0x40, 0x00, 0x00, 0xb8, 0x40,
     0x68, 0x2f, 0xa1, 0xbf, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x80, 0x3f, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0xc8, 0x40, 0xd4, 0x08, 0xcb, 0x3f, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0x40, 0xf4,
     0x3c, 0xcf, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x6e, 0x69, 0x74, 0x65, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x30)), niter = 1, evaluation_log = structure(list(iter = 1, train_error = 0.076923), .Names = c("iter",
     "train_error"), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x23d24a8>),
     call = xgb.train(params = params, data = dtrain, nrounds = nrounds, watchlist = watchlist,
     verbose = verbose, print_every_n = print_every_n, early_stopping_rounds = early_stopping_rounds,
     maximize = maximize, save_period = save_period, save_name = save_name, xgb_model = xgb_model,
     callbacks = callbacks, objective = ..1), params = structure(list(objective = "binary:logistic",
     silent = 1), .Names = c("objective", "silent")), callbacks = structure(list(
     cb.print.evaluation = structure(function (env = parent.frame())
     {
     if (length(env$bst_evaluation) == 0 || period == 0 || NVL(env$rank, 0) !=
     0)
     return()
     i <- env$iteration
     if ((i - 1)%%period == 0 || i == env$begin_iteration || i == env$end_iteration) {
     msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
     cat(msg, "\n")
     }
     }, call = cb.print.evaluation(period = print_every_n), name = "cb.print.evaluation"),
     cb.evaluation.log = structure(function (env = parent.frame(), finalize = FALSE)
     {
     if (is.null(mnames))
     init(env)
     if (finalize)
     return(finalizer(env))
     ev <- env$bst_evaluation
     if (!is.null(env$bst_evaluation_err))
     ev <- c(ev, env$bst_evaluation_err)
     env$evaluation_log <- c(env$evaluation_log, list(c(iter = env$iteration,
     ev)))
     }, call = cb.evaluation.log(), name = "cb.evaluation.log"), cb.save.model = structure(function (env = parent.frame())
     {
     if (is.null(env$bst))
     stop("'save_model' callback requires the 'bst' booster object in its calling frame")
     if ((save_period > 0 && (env$iteration - env$begin_iteration)%%save_period ==
     0) || (save_period == 0 && env$iteration == env$end_iteration))
     xgb.save(env$bst, sprintf(save_name, env$iteration))
     }, call = cb.save.model(save_period = save_period, save_name = save_name), name = "cb.save.model")), .Names = c("cb.print.evaluation",
     "cb.evaluation.log", "cb.save.model"))), .Names = c("handle", "raw", "niter",
     "evaluation_log", "call", "params", "callbacks"), class = "xgb.Booster"), task.desc = structure(list(
     id = "binary", type = "classif", target = "Class", size = 52L, n.feat = structure(c(60L,
     0L, 0L), .Names = c("numerics", "factors", "ordered")), has.missings = FALSE,
     has.weights = FALSE, has.blocking = FALSE, class.levels = c("M", "R"), positive = "M",
     negative = "R"), .Names = c("id", "type", "target", "size", "n.feat", "has.missings",
     "has.weights", "has.blocking", "class.levels", "positive", "negative"), class = c("TaskDescClassif",
     "TaskDescSupervised", "TaskDesc")), subset = 1:52, features = c("V1", "V2", "V3",
     "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
     "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28",
     "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52",
     "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), factor.levels = structure(list(
     Class = c("M", "R")), .Names = "Class"), time = 0.13300000000001), .Names = c("learner",
     "learner.model", "task.desc", "subset", "features", "factor.levels", "time"), class = "WrappedModel"),
     .newdata = structure(list(V1 = c(0.0262, 0.0317, 0.0223, 0.0164, 0.0079, 0.0192,
     0.027, 0.0126, 0.0293, 0.0201, 0.01, 0.0189, 0.0311, 0.0206, 0.0094, 0.0123,
     0.0211, 0.0093, 0.0408, 0.0308, 0.019, 0.0119, 0.0131, 0.0087, 0.0293, 0.0132,
     0.0225, 0.013, 0.0086, 0.0067, 0.0176, 0.0368, 0.0195, 0.0065, 0.0208, 0.0139,
     0.0239, 0.0336, 0.0108, 0.0229, 0.0409, 0.0378, 0.0188, 0.0856, 0.0235, 0.0253,
     0.026, 0.0459, 0.0025, 0.0491, 0.0201, 0.0629, 0.0162, 0.0428, 0.0264, 0.021,
     0.0283, 0.0414, 0.0228, 0.0261, 0.0249, 0.027, 0.0443, 0.1083, 0.043, 0.0731,
     0.0164, 0.0412, 0.0707, 0.0299, 0.0654, 0.0231, 0.0233, 0.0211, 0.0201, 0.0107,
     0.0258, 0.0305, 0.0217, 0.0072, 0.0221, 0.0137, 0.0015, 0.013, 0.0179, 0.018,
     0.0191, 0.0294, 0.0197, 0.0394, 0.0423, 0.0095, 0.0096, 0.0089, 0.0156, 0.0315,
     0.0056, 0.0203, 0.0392, 0.0131, 0.0335, 0.0187, 0.0522, 0.026), V2 = c(0.0582,
     0.0956, 0.0375, 0.0173, 0.0086, 0.0607, 0.0092, 0.0149, 0.0644, 0.0026, 0.0275,
     0.0308, 0.0491, 0.0132, 0.0166, 0.0022, 0.0319, 0.0269, 0.0653, 0.0339, 0.0038,
     0.0582, 0.0068, 0.0046, 0.0378, 0.008, 0.0019, 6e-04, 0.0215, 0.0096, 0.0172,
     0.0403, 0.0142, 0.0122, 0.0186, 0.0222, 0.0189, 0.0294, 0.0086, 0.0369, 0.0421,
     0.0318, 0.037, 0.0454, 0.0291, 0.0808, 0.0192, 0.0437, 0.0309, 0.0279, 0.0423,
     0.1065, 0.0253, 0.0555, 0.0071, 0.0121, 0.0599, 0.0436, 0.0106, 0.0266, 0.0119,
     0.0163, 0.0446, 0.107, 0.0902, 0.1249, 0.0627, 0.1135, 0.1252, 0.0688, 0.0649,
     0.0315, 0.0394, 0.0128, 0.0178, 0.0453, 0.0433, 0.0363, 0.0152, 0.0027, 0.0065,
     0.0297, 0.0186, 0.012, 0.0136, 0.0444, 0.0173, 0.0123, 0.0394, 0.042, 0.0321,
     0.0308, 0.0404, 0.0274, 0.021, 0.0252, 0.0267, 0.0121, 0.0108, 0.0387, 0.0258,
     0.0346, 0.0437, 0.0363), V3 = c(0.1099, 0.1321, 0.0484, 0.0347, 0.0055, 0.0378,
     0.0145, 0.0641, 0.039, 0.0138, 0.019, 0.0197, 0.0692, 0.0533, 0.0398, 0.0196,
     0.0415, 0.0217, 0.0397, 0.0202, 0.0642, 0.0623, 0.0308, 0.0081, 0.0257, 0.0188,
     0.0075, 0.0088, 0.0242, 0.0024, 0.0501, 0.0317, 0.0181, 0.0068, 0.0131, 0.0089,
     0.0466, 0.0476, 0.0058, 0.004, 0.0573, 0.0423, 0.0953, 0.0382, 0.0749, 0.0507,
     0.0254, 0.0347, 0.0171, 0.0592, 0.0554, 0.1526, 0.0262, 0.0708, 0.0342, 0.0203,
     0.0656, 0.0447, 0.013, 0.0223, 0.0277, 0.0341, 0.0235, 0.0257, 0.0833, 0.1665,
     0.0738, 0.0518, 0.1447, 0.0992, 0.0737, 0.017, 0.0416, 0.0015, 0.0274, 0.0289,
     0.0547, 0.0214, 0.0346, 0.0089, 0.0164, 0.0116, 0.0289, 0.0436, 0.0408, 0.0476,
     0.0291, 0.0117, 0.0384, 0.0446, 0.0709, 0.0539, 0.0682, 0.0248, 0.0282, 0.0167,
     0.0221, 0.038, 0.0267, 0.0329, 0.0398, 0.0168, 0.018, 0.0136), V4 = c(0.1083,
     0.1408, 0.0475, 0.007, 0.025, 0.0774, 0.0278, 0.1732, 0.0173, 0.0062, 0.0371,
     0.0622, 0.0831, 0.0569, 0.0359, 0.0206, 0.0286, 0.0339, 0.0604, 0.0889, 0.0452,
     0.06, 0.0311, 0.023, 0.0062, 0.0141, 0.0097, 0.0456, 0.0445, 0.0058, 0.0285,
     0.0293, 0.0406, 0.0108, 0.0211, 0.0108, 0.044, 0.0539, 0.046, 0.0375, 0.013,
     0.035, 0.0824, 0.0203, 0.0519, 0.0244, 0.0061, 0.0456, 0.0228, 0.127, 0.0783,
     0.1229, 0.0386, 0.0618, 0.0793, 0.1036, 0.0229, 0.0844, 0.0842, 0.0749, 0.076,
     0.0247, 0.1008, 0.0837, 0.0813, 0.1496, 0.0608, 0.0232, 0.1644, 0.1021, 0.1132,
     0.0226, 0.0547, 0.045, 0.0232, 0.0713, 0.0681, 0.0227, 0.0346, 0.0061, 0.0487,
     0.0082, 0.0195, 0.0624, 0.0633, 0.0698, 0.0301, 0.0113, 0.0076, 0.0551, 0.0108,
     0.0411, 0.0688, 0.0237, 0.0596, 0.0479, 0.0561, 0.0128, 0.0257, 0.0078, 0.057,
     0.0177, 0.0292, 0.0272), V5 = c(0.0974, 0.1674, 0.0647, 0.0187, 0.0344, 0.1388,
     0.0412, 0.2565, 0.0476, 0.0133, 0.0416, 0.008, 0.0079, 0.0647, 0.0681, 0.018,
     0.0121, 0.0305, 0.0496, 0.157, 0.0333, 0.1397, 0.0085, 0.0586, 0.013, 0.0436,
     0.0445, 0.0525, 0.0667, 0.0197, 0.0262, 0.082, 0.0391, 0.0217, 0.061, 0.0215,
     0.0657, 0.0794, 0.0752, 0.0455, 0.0183, 0.1787, 0.0249, 0.0385, 0.0227, 0.1724,
     0.0352, 0.0067, 0.0434, 0.1772, 0.062, 0.1437, 0.0645, 0.1215, 0.1043, 0.1675,
     0.0839, 0.0419, 0.1117, 0.1364, 0.1218, 0.0822, 0.2252, 0.0748, 0.0165, 0.1443,
     0.0233, 0.0646, 0.1693, 0.08, 0.2482, 0.041, 0.0993, 0.0711, 0.0724, 0.1075,
     0.0784, 0.0456, 0.0484, 0.042, 0.0519, 0.0241, 0.0515, 0.0428, 0.0596, 0.1615,
     0.0463, 0.0497, 0.0251, 0.0597, 0.107, 0.0613, 0.0887, 0.0224, 0.0462, 0.0902,
     0.0936, 0.0537, 0.041, 0.0721, 0.0529, 0.0393, 0.0351, 0.0214), V6 = c(0.228,
     0.171, 0.0591, 0.0671, 0.0546, 0.0809, 0.0757, 0.2559, 0.0816, 0.0151, 0.0201,
     0.0789, 0.02, 0.1432, 0.0706, 0.0492, 0.0438, 0.1172, 0.1817, 0.175, 0.069, 0.1883,
     0.0767, 0.0682, 0.0612, 0.0668, 0.0906, 0.0778, 0.0771, 0.0618, 0.0351, 0.1342,
     0.0249, 0.0284, 0.0613, 0.0136, 0.0742, 0.0804, 0.0887, 0.1452, 0.1019, 0.1635,
     0.0488, 0.0534, 0.0834, 0.3823, 0.0701, 0.089, 0.1224, 0.1908, 0.0871, 0.119,
     0.0472, 0.1524, 0.0783, 0.0418, 0.1673, 0.1215, 0.1506, 0.1513, 0.1538, 0.1256,
     0.2611, 0.1125, 0.0277, 0.277, 0.1048, 0.1124, 0.0844, 0.0629, 0.1257, 0.0116,
     0.1515, 0.1563, 0.0833, 0.1019, 0.125, 0.0665, 0.0526, 0.0865, 0.0849, 0.0253,
     0.0817, 0.0349, 0.0808, 0.0887, 0.069, 0.0998, 0.0629, 0.1416, 0.0973, 0.1039,
     0.0932, 0.0845, 0.0779, 0.1057, 0.1146, 0.0874, 0.0491, 0.1341, 0.1091, 0.163,
     0.1171, 0.0338), V7 = c(0.2431, 0.0731, 0.0753, 0.1056, 0.0528, 0.0568, 0.1026,
     0.2947, 0.0993, 0.0541, 0.0314, 0.144, 0.0981, 0.1344, 0.102, 0.0033, 0.1299,
     0.145, 0.1178, 0.092, 0.0901, 0.1422, 0.0771, 0.0993, 0.0895, 0.0609, 0.0889,
     0.0931, 0.0499, 0.0432, 0.0362, 0.1161, 0.0892, 0.0527, 0.0612, 0.0659, 0.138,
     0.1136, 0.1015, 0.2211, 0.1054, 0.0887, 0.1424, 0.214, 0.0677, 0.3729, 0.1263,
     0.1798, 0.1947, 0.2217, 0.1201, 0.0884, 0.1056, 0.1543, 0.1417, 0.0723, 0.1154,
     0.2002, 0.1776, 0.1316, 0.1192, 0.1323, 0.2061, 0.3322, 0.0569, 0.2555, 0.1338,
     0.1787, 0.0715, 0.013, 0.1797, 0.0223, 0.1674, 0.1518, 0.1232, 0.1606, 0.1296,
     0.0939, 0.0773, 0.1182, 0.0812, 0.0279, 0.1005, 0.0384, 0.209, 0.0596, 0.0576,
     0.1326, 0.0747, 0.0956, 0.0961, 0.1016, 0.0955, 0.1488, 0.1365, 0.1024, 0.0706,
     0.1021, 0.1053, 0.1626, 0.1709, 0.2028, 0.1257, 0.0655), V8 = c(0.3771, 0.1401,
     0.0098, 0.0697, 0.0958, 0.0219, 0.1138, 0.411, 0.0315, 0.021, 0.0651, 0.1451,
     0.1016, 0.2041, 0.0893, 0.0398, 0.139, 0.0638, 0.1024, 0.1353, 0.1454, 0.1447,
     0.064, 0.0717, 0.1107, 0.0131, 0.0655, 0.0941, 0.0906, 0.0951, 0.0535, 0.0663,
     0.0973, 0.0575, 0.0506, 0.0954, 0.1099, 0.1228, 0.0494, 0.1188, 0.107, 0.0817,
     0.1972, 0.311, 0.2002, 0.3583, 0.108, 0.1741, 0.1661, 0.0768, 0.2707, 0.0907,
     0.1388, 0.0391, 0.1176, 0.0828, 0.1098, 0.1516, 0.0997, 0.1654, 0.1229, 0.1584,
     0.1668, 0.459, 0.2057, 0.1712, 0.0644, 0.2407, 0.0947, 0.0813, 0.0989, 0.0805,
     0.1513, 0.1206, 0.1298, 0.2119, 0.1729, 0.0972, 0.0862, 0.0999, 0.1833, 0.013,
     0.0124, 0.0446, 0.3465, 0.1071, 0.1103, 0.1117, 0.0578, 0.0802, 0.1323, 0.1394,
     0.214, 0.1224, 0.078, 0.1209, 0.0996, 0.0852, 0.169, 0.1902, 0.1684, 0.1694,
     0.1178, 0.14), V9 = c(0.5598, 0.2083, 0.0684, 0.0962, 0.1009, 0.1037, 0.0794,
     0.4983, 0.0736, 0.0505, 0.1896, 0.1789, 0.2025, 0.1571, 0.0381, 0.0791, 0.0695,
     0.074, 0.0583, 0.1593, 0.074, 0.0487, 0.0726, 0.0576, 0.0973, 0.0899, 0.1624,
     0.1711, 0.1229, 0.0836, 0.0258, 0.0155, 0.084, 0.1054, 0.0989, 0.0786, 0.1384,
     0.1235, 0.0472, 0.075, 0.2302, 0.1779, 0.1873, 0.2837, 0.2876, 0.3429, 0.1523,
     0.1598, 0.1368, 0.1246, 0.1206, 0.2107, 0.0598, 0.061, 0.0453, 0.0494, 0.137,
     0.0818, 0.1428, 0.1864, 0.2119, 0.2017, 0.1801, 0.5526, 0.3887, 0.0466, 0.1522,
     0.2682, 0.1583, 0.1761, 0.246, 0.2365, 0.1723, 0.1666, 0.2085, 0.3061, 0.2794,
     0.2535, 0.1451, 0.1976, 0.2228, 0.0489, 0.1168, 0.1318, 0.5276, 0.3175, 0.2423,
     0.2984, 0.1357, 0.1618, 0.2462, 0.2592, 0.2546, 0.1569, 0.1038, 0.1241, 0.1673,
     0.1136, 0.2105, 0.261, 0.1865, 0.2328, 0.1258, 0.1843), V10 = c(0.6194, 0.3513,
     0.1487, 0.0251, 0.124, 0.1186, 0.152, 0.592, 0.086, 0.1097, 0.2668, 0.2522, 0.0767,
     0.1573, 0.1328, 0.0475, 0.0568, 0.136, 0.2176, 0.2795, 0.0349, 0.0864, 0.0901,
     0.0818, 0.0751, 0.0922, 0.1452, 0.1483, 0.1185, 0.118, 0.0474, 0.0506, 0.1191,
     0.1109, 0.1093, 0.1015, 0.1376, 0.0842, 0.0393, 0.1631, 0.2259, 0.2053, 0.1806,
     0.2751, 0.3674, 0.2197, 0.163, 0.1408, 0.143, 0.2028, 0.0279, 0.3597, 0.1334,
     0.0113, 0.0945, 0.0686, 0.1767, 0.1975, 0.2227, 0.2013, 0.2531, 0.2122, 0.3083,
     0.5966, 0.7106, 0.1114, 0.078, 0.2058, 0.1247, 0.0998, 0.3422, 0.2461, 0.2078,
     0.1345, 0.272, 0.2936, 0.2954, 0.3127, 0.211, 0.2318, 0.181, 0.0874, 0.1476,
     0.1375, 0.5965, 0.2918, 0.3134, 0.3473, 0.1695, 0.2558, 0.2696, 0.3745, 0.2952,
     0.2119, 0.1567, 0.1533, 0.1859, 0.1747, 0.2471, 0.3193, 0.266, 0.2684, 0.2529,
     0.2354), V11 = c(0.6333, 0.1786, 0.1156, 0.0801, 0.1097, 0.1237, 0.1675, 0.5832,
     0.0414, 0.0841, 0.3376, 0.2607, 0.1767, 0.2327, 0.1303, 0.1152, 0.0869, 0.2132,
     0.2459, 0.3336, 0.1459, 0.2143, 0.075, 0.1315, 0.0528, 0.1445, 0.1442, 0.1532,
     0.0775, 0.0978, 0.0526, 0.0906, 0.1522, 0.0937, 0.1063, 0.1261, 0.0938, 0.0357,
     0.1106, 0.2709, 0.2373, 0.3135, 0.2139, 0.2707, 0.2974, 0.2653, 0.103, 0.2693,
     0.0994, 0.0947, 0.2251, 0.5466, 0.2969, 0.1255, 0.1132, 0.1125, 0.1995, 0.2309,
     0.2621, 0.289, 0.2855, 0.221, 0.3794, 0.5304, 0.7342, 0.1739, 0.1791, 0.1546,
     0.234, 0.0523, 0.2128, 0.2245, 0.1239, 0.0785, 0.2188, 0.3104, 0.2506, 0.2192,
     0.2343, 0.2472, 0.2549, 0.11, 0.2118, 0.2026, 0.6254, 0.3273, 0.4786, 0.4231,
     0.1734, 0.3078, 0.3412, 0.4229, 0.4025, 0.3003, 0.2476, 0.2128, 0.2481, 0.2198,
     0.268, 0.3468, 0.3188, 0.3108, 0.2716, 0.272), V12 = c(0.706, 0.0658, 0.1654,
     0.1056, 0.1215, 0.1601, 0.137, 0.5419, 0.0472, 0.0942, 0.3282, 0.371, 0.2555,
     0.1785, 0.0273, 0.052, 0.1935, 0.3738, 0.3332, 0.294, 0.3473, 0.372, 0.0844,
     0.1862, 0.1209, 0.1475, 0.0948, 0.11, 0.1101, 0.0909, 0.1854, 0.2545, 0.1322,
     0.0827, 0.1179, 0.0828, 0.0259, 0.0689, 0.1412, 0.3358, 0.3323, 0.3118, 0.1523,
     0.0946, 0.0837, 0.3223, 0.2187, 0.3259, 0.225, 0.2497, 0.2615, 0.5205, 0.4754,
     0.2473, 0.084, 0.1741, 0.2869, 0.3025, 0.3109, 0.365, 0.2961, 0.2399, 0.5364,
     0.2251, 0.5033, 0.316, 0.2681, 0.2671, 0.1764, 0.0904, 0.1377, 0.152, 0.0236,
     0.0367, 0.3037, 0.3431, 0.2601, 0.2621, 0.2087, 0.288, 0.2984, 0.1084, 0.2575,
     0.2389, 0.4507, 0.3035, 0.5239, 0.5044, 0.247, 0.3404, 0.4292, 0.4499, 0.5148,
     0.3094, 0.2783, 0.2536, 0.2712, 0.2721, 0.3049, 0.3738, 0.3553, 0.2933, 0.2374,
     0.2442), V13 = c(0.5544, 0.0513, 0.3833, 0.1266, 0.1874, 0.352, 0.1361, 0.5472,
     0.0835, 0.1204, 0.2432, 0.3906, 0.2812, 0.1507, 0.0644, 0.1192, 0.1478, 0.3738,
     0.3087, 0.1608, 0.3197, 0.2665, 0.1226, 0.2789, 0.1763, 0.2087, 0.0618, 0.089,
     0.1042, 0.0656, 0.104, 0.1464, 0.1434, 0.092, 0.1291, 0.0493, 0.1499, 0.1705,
     0.2202, 0.4091, 0.3827, 0.3686, 0.1975, 0.102, 0.1912, 0.5582, 0.1542, 0.4545,
     0.2444, 0.2209, 0.177, 0.5127, 0.5677, 0.3011, 0.0717, 0.271, 0.3275, 0.3938,
     0.2859, 0.351, 0.3341, 0.2964, 0.6173, 0.2402, 0.3, 0.3249, 0.1788, 0.3141, 0.2284,
     0.2655, 0.4032, 0.1732, 0.1771, 0.1227, 0.2959, 0.2456, 0.2249, 0.2419, 0.1645,
     0.2126, 0.2624, 0.1094, 0.2354, 0.2112, 0.3693, 0.3033, 0.4393, 0.5237, 0.3141,
     0.34, 0.3682, 0.5404, 0.4901, 0.2743, 0.2896, 0.2686, 0.2934, 0.2105, 0.2863,
     0.3055, 0.3116, 0.2275, 0.1878, 0.1665), V14 = c(0.532, 0.3752, 0.3598, 0.089,
     0.3383, 0.4479, 0.1345, 0.5314, 0.0938, 0.042, 0.1268, 0.2672, 0.2722, 0.1916,
     0.0712, 0.1943, 0.1871, 0.2673, 0.2613, 0.3335, 0.2823, 0.2113, 0.1619, 0.2579,
     0.2039, 0.2558, 0.1641, 0.1236, 0.0853, 0.0593, 0.0948, 0.1272, 0.1244, 0.0911,
     0.1591, 0.0848, 0.2851, 0.3257, 0.2976, 0.44, 0.484, 0.3885, 0.4844, 0.4519,
     0.504, 0.6916, 0.263, 0.5785, 0.3239, 0.3195, 0.3709, 0.5395, 0.569, 0.3747,
     0.1968, 0.3087, 0.3769, 0.505, 0.3316, 0.3495, 0.4287, 0.4061, 0.7842, 0.2689,
     0.1951, 0.2164, 0.1039, 0.2904, 0.3115, 0.3099, 0.5684, 0.3099, 0.3115, 0.2614,
     0.2059, 0.1887, 0.2115, 0.2179, 0.1689, 0.0708, 0.1893, 0.1023, 0.1334, 0.1444,
     0.2864, 0.2587, 0.344, 0.4398, 0.3297, 0.3951, 0.394, 0.4303, 0.4127, 0.2547,
     0.2956, 0.2803, 0.2637, 0.1727, 0.2294, 0.1926, 0.1965, 0.0994, 0.0983, 0.0336
     ), V15 = c(0.6479, 0.5419, 0.1713, 0.0198, 0.3227, 0.3769, 0.2144, 0.4981, 0.1466,
     0.0031, 0.1278, 0.2716, 0.3227, 0.2061, 0.1204, 0.184, 0.1994, 0.2333, 0.3232,
     0.4985, 0.0166, 0.1103, 0.2317, 0.224, 0.2727, 0.2603, 0.0708, 0.1197, 0.0456,
     0.0832, 0.0912, 0.1223, 0.0653, 0.1487, 0.168, 0.1514, 0.5743, 0.4602, 0.4116,
     0.5485, 0.6812, 0.585, 0.7298, 0.6737, 0.6352, 0.7943, 0.294, 0.4471, 0.3039,
     0.334, 0.4533, 0.6558, 0.6421, 0.452, 0.2633, 0.3575, 0.4169, 0.5872, 0.3755,
     0.4325, 0.5205, 0.5095, 0.8392, 0.6646, 0.2767, 0.2031, 0.198, 0.3531, 0.4725,
     0.352, 0.2398, 0.438, 0.499, 0.428, 0.0906, 0.1184, 0.127, 0.1159, 0.165, 0.1194,
     0.0668, 0.0601, 0.0092, 0.0742, 0.1635, 0.1682, 0.2869, 0.3236, 0.2759, 0.3352,
     0.2965, 0.3333, 0.3575, 0.187, 0.3189, 0.1886, 0.188, 0.204, 0.1165, 0.1385,
     0.178, 0.1801, 0.0683, 0.1302), V16 = c(0.6931, 0.544, 0.1136, 0.1133, 0.2723,
     0.5761, 0.5354, 0.6985, 0.0809, 0.0162, 0.4441, 0.4183, 0.3463, 0.2307, 0.0717,
     0.2077, 0.3283, 0.5367, 0.3731, 0.7295, 0.0572, 0.1136, 0.2934, 0.2568, 0.2321,
     0.1985, 0.0844, 0.1145, 0.1304, 0.1297, 0.1688, 0.1669, 0.089, 0.1666, 0.1918,
     0.1396, 0.8278, 0.6225, 0.4754, 0.7213, 0.7555, 0.7868, 0.7807, 0.6699, 0.6804,
     0.7152, 0.2978, 0.2231, 0.241, 0.3323, 0.5553, 0.8705, 0.7487, 0.5392, 0.4191,
     0.4998, 0.5036, 0.661, 0.4499, 0.5398, 0.6087, 0.5512, 0.9016, 0.6632, 0.3737,
     0.258, 0.3234, 0.5079, 0.5543, 0.3892, 0.4331, 0.5595, 0.6707, 0.6122, 0.161,
     0.208, 0.1193, 0.1237, 0.1967, 0.2808, 0.2666, 0.0906, 0.1951, 0.1533, 0.0422,
     0.1308, 0.3889, 0.2956, 0.2056, 0.2252, 0.3172, 0.3496, 0.3447, 0.1452, 0.1892,
     0.1485, 0.1405, 0.1786, 0.2127, 0.2122, 0.2794, 0.22, 0.1503, 0.1708), V17 = c(0.6759,
     0.515, 0.0349, 0.2826, 0.3943, 0.6426, 0.683, 0.8292, 0.1179, 0.0624, 0.6795,
     0.6988, 0.5395, 0.236, 0.1224, 0.1956, 0.6861, 0.7312, 0.4203, 0.735, 0.2164,
     0.1934, 0.3526, 0.2933, 0.2676, 0.2394, 0.259, 0.2137, 0.269, 0.2038, 0.1568,
     0.1424, 0.1226, 0.1268, 0.1615, 0.1066, 0.8669, 0.7327, 0.539, 0.8137, 0.9522,
     0.9739, 0.7906, 0.7066, 0.7505, 0.3512, 0.0699, 0.2164, 0.0367, 0.278, 0.4616,
     0.9786, 0.8999, 0.6588, 0.505, 0.6011, 0.618, 0.7417, 0.4765, 0.6237, 0.7236,
     0.6613, 1, 0.1674, 0.2507, 0.1796, 0.3748, 0.4639, 0.5386, 0.3962, 0.5954, 0.682,
     0.7655, 0.7435, 0.18, 0.2736, 0.1794, 0.0886, 0.2934, 0.4221, 0.4274, 0.1313,
     0.3685, 0.3052, 0.1785, 0.2803, 0.442, 0.3286, 0.1162, 0.2086, 0.2825, 0.3426,
     0.3068, 0.1457, 0.173, 0.216, 0.2028, 0.1318, 0.2062, 0.2758, 0.287, 0.2732,
     0.1723, 0.2177), V18 = c(0.7551, 0.4262, 0.3796, 0.3234, 0.6432, 0.679, 0.56,
     0.7839, 0.2179, 0.2127, 0.7051, 0.5733, 0.7911, 0.1299, 0.2349, 0.163, 0.5814,
     0.7659, 0.5364, 0.8253, 0.4563, 0.4142, 0.3657, 0.2991, 0.2934, 0.3134, 0.2679,
     0.2838, 0.2947, 0.3811, 0.0375, 0.1285, 0.1846, 0.1374, 0.1647, 0.1923, 0.8131,
     0.7843, 0.6279, 0.9185, 0.9826, 1, 0.6122, 0.5632, 0.6595, 0.2008, 0.1401, 0.3201,
     0.1672, 0.2975, 0.3797, 0.9335, 1, 0.7113, 0.6711, 0.647, 0.8025, 0.8006, 0.6254,
     0.6876, 0.7577, 0.6804, 0.8911, 0.0837, 0.2507, 0.2422, 0.2586, 0.1859, 0.3746,
     0.2449, 0.5772, 0.6164, 0.8485, 0.813, 0.218, 0.3274, 0.2185, 0.1755, 0.3709,
     0.5279, 0.6291, 0.2758, 0.4646, 0.4116, 0.4394, 0.4519, 0.3892, 0.3231, 0.1884,
     0.2248, 0.305, 0.2851, 0.2945, 0.2429, 0.2226, 0.2417, 0.2613, 0.226, 0.2222,
     0.4576, 0.3969, 0.2862, 0.2339, 0.3175), V19 = c(0.8929, 0.2024, 0.7401, 0.3238,
     0.7271, 0.7157, 0.3093, 0.8215, 0.3326, 0.3436, 0.7966, 0.2226, 0.9064, 0.3812,
     0.3684, 0.1218, 0.25, 0.6271, 0.7062, 0.8793, 0.3819, 0.3279, 0.3221, 0.3924,
     0.3295, 0.4077, 0.3094, 0.364, 0.3669, 0.4451, 0.1316, 0.1857, 0.388, 0.1095,
     0.1397, 0.2991, 0.9045, 0.7988, 0.706, 1, 0.8871, 0.9843, 0.42, 0.3785, 0.4509,
     0.2676, 0.299, 0.2915, 0.3038, 0.2948, 0.345, 0.7917, 0.969, 0.7602, 0.7922,
     0.8067, 0.9333, 0.8456, 0.7304, 0.7329, 0.7726, 0.652, 0.8753, 0.4331, 0.3292,
     0.3609, 0.368, 0.4474, 0.4583, 0.2355, 0.8176, 0.6803, 0.9805, 0.9006, 0.2026,
     0.2344, 0.1646, 0.1758, 0.4309, 0.5857, 0.7782, 0.366, 0.5418, 0.5466, 0.695,
     0.6641, 0.4088, 0.4528, 0.339, 0.3382, 0.2408, 0.4062, 0.4351, 0.3259, 0.2427,
     0.2989, 0.2778, 0.2358, 0.3241, 0.6487, 0.5599, 0.2034, 0.1962, 0.3714), V20 = c(0.8619,
     0.4233, 0.9925, 0.4333, 0.8673, 0.5466, 0.3226, 0.9363, 0.3258, 0.3813, 0.9401,
     0.2631, 0.8701, 0.5858, 0.3918, 0.1017, 0.1734, 0.4395, 0.8196, 0.9657, 0.5627,
     0.6222, 0.3093, 0.4691, 0.491, 0.4529, 0.4678, 0.543, 0.4948, 0.5224, 0.2086,
     0.1136, 0.3658, 0.1286, 0.1426, 0.3247, 0.9046, 0.8261, 0.7918, 0.9418, 0.8268,
     0.861, 0.2807, 0.2721, 0.2964, 0.4299, 0.3915, 0.4235, 0.4069, 0.1729, 0.2665,
     0.7383, 0.9032, 0.8672, 0.8381, 0.9008, 0.9399, 0.7939, 0.8702, 0.8107, 0.8098,
     0.6788, 0.7886, 0.8718, 0.4871, 0.181, 0.3508, 0.4079, 0.5961, 0.3045, 0.8835,
     0.8435, 1, 0.9603, 0.1506, 0.126, 0.074, 0.154, 0.4161, 0.6153, 0.7686, 0.5269,
     0.626, 0.5933, 0.8097, 0.7683, 0.5006, 0.6339, 0.3926, 0.4578, 0.542, 0.6833,
     0.7264, 0.3679, 0.3149, 0.3341, 0.3346, 0.3107, 0.433, 0.7154, 0.6936, 0.174,
     0.1395, 0.4552), V21 = c(0.7974, 0.7723, 0.9802, 0.6068, 0.9674, 0.5399, 0.443,
     1, 0.2111, 0.3825, 0.9857, 0.7473, 0.7672, 0.4497, 0.4925, 0.1354, 0.3363, 0.433,
     0.8835, 1, 0.6484, 0.7468, 0.4084, 0.5665, 0.5402, 0.4893, 0.5958, 0.6673, 0.6275,
     0.5911, 0.1976, 0.2069, 0.2297, 0.2146, 0.2429, 0.3797, 1, 1, 0.9493, 0.9116,
     0.7561, 0.8443, 0.5148, 0.5297, 0.4019, 0.528, 0.3598, 0.446, 0.3613, 0.3264,
     0.2395, 0.6908, 0.7685, 0.8416, 0.8759, 0.8906, 0.9275, 0.8804, 0.9349, 0.8396,
     0.8995, 0.7811, 0.7156, 0.7992, 0.6527, 0.2604, 0.5606, 0.54, 0.7464, 0.3112,
     0.5248, 0.9921, 1, 0.9162, 0.0521, 0.0576, 0.0625, 0.0512, 0.5116, 0.6753, 0.8099,
     0.581, 0.742, 0.6663, 0.855, 0.696, 0.7271, 0.7044, 0.4282, 0.6474, 0.6802, 0.765,
     0.8147, 0.3355, 0.4102, 0.3786, 0.383, 0.3906, 0.5071, 0.801, 0.7969, 0.413,
     0.3164, 0.57), V22 = c(0.6737, 0.9735, 0.889, 0.7652, 0.9847, 0.6362, 0.5573,
     0.9224, 0.2302, 0.4764, 0.8193, 0.7263, 0.2957, 0.4876, 0.8793, 0.3157, 0.5588,
     0.4326, 0.8299, 0.8707, 0.7235, 0.7676, 0.4285, 0.6464, 0.6257, 0.5666, 0.7245,
     0.7979, 0.8162, 0.6566, 0.0946, 0.0219, 0.261, 0.2889, 0.2816, 0.5658, 0.9976,
     0.9814, 1, 0.9349, 0.8217, 0.9061, 0.7569, 0.7697, 0.6794, 0.3489, 0.2403, 0.238,
     0.1994, 0.3834, 0.1127, 0.385, 0.6998, 0.7974, 0.9422, 0.9338, 0.945, 0.8384,
     0.9614, 0.8632, 0.9247, 0.8369, 0.7581, 0.3712, 0.8454, 0.6572, 0.5231, 0.4786,
     0.7644, 0.4698, 0.6373, 1, 0.9992, 0.914, 0.2143, 0.1241, 0.2381, 0.1805, 0.6501,
     0.7873, 0.8493, 0.6181, 0.8257, 0.7333, 0.8717, 0.4393, 0.9385, 0.8314, 0.5418,
     0.6708, 0.632, 0.667, 0.8103, 0.31, 0.3808, 0.3956, 0.4003, 0.3631, 0.5944, 0.7924,
     0.7452, 0.6879, 0.5888, 0.7397), V23 = c(0.4293, 0.939, 0.6712, 0.9203, 0.948,
     0.7849, 0.5782, 0.7839, 0.3361, 0.6313, 0.5789, 0.3393, 0.4148, 1, 0.9606, 0.4645,
     0.6592, 0.5544, 0.7609, 0.6471, 0.8242, 0.7867, 0.4663, 0.6774, 0.6826, 0.6234,
     0.8773, 0.9273, 0.9237, 0.6308, 0.1965, 0.24, 0.4193, 0.4238, 0.429, 0.7483,
     0.9872, 0.962, 0.9645, 0.7484, 0.6967, 0.5847, 0.8596, 0.8643, 0.8297, 0.143,
     0.4208, 0.6415, 0.4611, 0.3523, 0.2556, 0.0671, 0.6644, 0.8385, 1, 1, 0.8328,
     0.7852, 0.9126, 0.8747, 0.9365, 0.8969, 0.6372, 0.1703, 0.9739, 0.9734, 0.5469,
     0.4332, 0.5711, 0.5534, 0.8375, 0.7983, 0.9067, 0.7851, 0.4333, 0.3239, 0.4824,
     0.4039, 0.7717, 0.8974, 0.944, 0.5875, 0.8609, 0.7136, 0.8601, 0.2432, 1, 0.8449,
     0.6448, 0.7007, 0.5824, 0.5703, 0.6665, 0.3914, 0.4896, 0.5232, 0.5114, 0.4809,
     0.7078, 0.8793, 0.8203, 0.812, 0.7631, 0.8062), V24 = c(0.3648, 0.5559, 0.4286,
     0.9719, 0.8036, 0.7756, 0.6173, 0.547, 0.4259, 0.7523, 0.6394, 0.2824, 0.6043,
     0.8675, 0.8786, 0.5906, 0.7012, 0.736, 0.7605, 0.5973, 0.8766, 0.8253, 0.5956,
     0.7577, 0.7527, 0.6741, 0.9214, 0.9027, 0.871, 0.5998, 0.1242, 0.2547, 0.5848,
     0.6168, 0.6443, 0.8757, 0.9761, 0.9601, 0.9432, 0.5146, 0.6444, 0.4033, 1, 0.9304,
     1, 0.5453, 0.5675, 0.8966, 0.6849, 0.541, 0.5169, 0.0502, 0.5964, 0.9317, 0.9931,
     0.9102, 0.7773, 0.8479, 0.9443, 0.9607, 0.9853, 0.9856, 0.321, 0.1611, 1, 0.9757,
     0.6954, 0.6113, 0.6257, 0.4532, 0.6699, 0.5426, 0.6803, 0.5134, 0.5943, 0.4357,
     0.6372, 0.5697, 0.8491, 0.9828, 0.945, 0.4639, 0.84, 0.7014, 0.9201, 0.2886,
     0.9831, 0.8512, 0.7223, 0.7619, 0.6805, 0.5995, 0.6958, 0.528, 0.6292, 0.6913,
     0.686, 0.6531, 0.7641, 1, 0.9261, 0.8453, 0.8473, 0.8837), V25 = c(0.5331, 0.5268,
     0.3374, 0.9207, 0.6833, 0.578, 0.8132, 0.4562, 0.4609, 0.8675, 0.7043, 0.6053,
     0.3178, 0.4718, 0.6905, 0.6776, 0.8099, 0.8589, 0.8367, 0.8218, 1, 1, 0.6948,
     0.8856, 0.8504, 0.8282, 0.9282, 0.9192, 0.8052, 0.4958, 0.0616, 0.024, 0.5643,
     0.8167, 0.9061, 0.9048, 0.9009, 0.9118, 0.8658, 0.4106, 0.6948, 0.5946, 0.8457,
     0.9372, 0.824, 0.6338, 0.6094, 0.8918, 0.7272, 0.5228, 0.3779, 0.2717, 0.3711,
     0.8555, 0.9575, 0.8496, 0.7007, 0.7434, 1, 0.9716, 0.9776, 1, 0.2076, 0.2086,
     0.6665, 0.8079, 0.6352, 0.5091, 0.6695, 0.4464, 0.7756, 0.3952, 0.5103, 0.3439,
     0.6926, 0.5734, 0.7531, 0.6577, 0.9104, 1, 0.9655, 0.5424, 0.8949, 0.7758, 0.8729,
     0.4974, 0.9932, 0.9138, 0.7853, 0.7745, 0.5984, 0.6484, 0.7748, 0.6409, 0.7519,
     0.7868, 0.749, 0.7812, 0.8878, 0.9865, 0.881, 0.8919, 0.9424, 0.9432), V26 = c(0.2413,
     0.6826, 0.7366, 0.7545, 0.5136, 0.4862, 0.9819, 0.5922, 0.2606, 0.8788, 0.6875,
     0.5897, 0.3482, 0.5341, 0.6937, 0.8119, 0.8901, 0.8989, 0.8905, 0.7755, 0.8582,
     0.9481, 0.8386, 0.9419, 0.8938, 0.8823, 0.9942, 1, 0.8756, 0.5647, 0.2141, 0.1923,
     0.5448, 0.9622, 1, 0.7511, 0.9724, 0.9086, 0.7895, 0.3443, 0.8014, 0.6793, 0.6797,
     0.6247, 0.7115, 0.7712, 0.6323, 0.7529, 0.7152, 0.4475, 0.4082, 0.2839, 0.0921,
     0.6162, 0.8647, 0.7867, 0.6154, 0.6433, 0.9455, 0.9121, 1, 0.9395, 0.2279, 0.2847,
     0.5323, 0.6521, 0.6757, 0.4606, 0.7131, 0.467, 0.875, 0.5179, 0.4716, 0.329,
     0.7576, 0.7825, 0.8959, 0.7474, 0.8912, 0.846, 0.8045, 0.7367, 0.9945, 0.9137,
     0.8084, 0.8172, 0.9161, 0.9985, 0.7984, 0.6767, 0.8412, 0.8614, 0.8688, 0.7707,
     0.7985, 0.8337, 0.7843, 0.8395, 0.9711, 0.9474, 0.8814, 0.93, 0.9986, 1), V27 = c(0.507,
     0.5713, 0.9611, 0.8289, 0.309, 0.4181, 0.9823, 0.5448, 0.0874, 0.7901, 0.4081,
     0.4967, 0.6158, 0.6197, 0.5674, 0.8594, 0.8745, 0.942, 0.7652, 0.6111, 0.6563,
     0.7539, 0.8875, 1, 0.9928, 0.9196, 1, 0.9821, 1, 0.6906, 0.4642, 0.4753, 0.4772,
     0.828, 0.8087, 0.6858, 0.9675, 0.7931, 0.6501, 0.6981, 0.6053, 0.6389, 0.6971,
     0.6024, 0.7726, 0.6838, 0.6549, 0.6838, 0.7102, 0.534, 0.5353, 0.2234, 0.0481,
     0.4139, 0.7215, 0.7688, 0.581, 0.5514, 0.8815, 0.8576, 0.9896, 0.8917, 0.3309,
     0.2211, 0.4024, 0.4915, 0.8499, 0.7243, 0.7567, 0.4621, 0.83, 0.565, 0.498, 0.2571,
     0.8787, 0.9252, 0.9941, 0.8543, 0.8189, 0.6055, 0.4969, 0.9089, 1, 0.9964, 0.8694,
     1, 0.8237, 1, 0.8847, 0.7373, 0.9911, 0.9819, 1, 0.8754, 0.883, 0.9199, 0.9021,
     0.918, 0.988, 0.9474, 0.9301, 0.9987, 0.9699, 0.9375), V28 = c(0.8533, 0.5429,
     0.7353, 0.8907, 0.0832, 0.2457, 0.9166, 0.3971, 0.2862, 0.8357, 0.1811, 0.8616,
     0.8049, 0.7143, 0.654, 0.9228, 0.7887, 0.9401, 0.5897, 0.4195, 0.5087, 0.6008,
     0.6404, 0.8564, 0.9134, 0.8965, 0.9071, 0.9092, 0.9858, 0.8513, 0.6471, 0.7003,
     0.6897, 0.5816, 0.6119, 0.7043, 0.7633, 0.5877, 0.4492, 0.8713, 0.6084, 0.5002,
     0.5843, 0.681, 0.6124, 0.8015, 0.7673, 0.839, 0.8516, 0.5323, 0.5116, 0.1911,
     0.0876, 0.3269, 0.5801, 0.7718, 0.4454, 0.3519, 0.752, 0.8798, 0.9076, 0.8105,
     0.2847, 0.6134, 0.3444, 0.5363, 0.8025, 0.8987, 0.8077, 0.6988, 0.6896, 0.3042,
     0.6196, 0.3685, 0.906, 0.9349, 0.9957, 0.9085, 0.6779, 0.3036, 0.396, 1, 0.9649,
     1, 0.8411, 0.9238, 0.6957, 0.7544, 0.9582, 0.7834, 0.9187, 0.938, 0.9941, 1,
     0.9915, 1, 1, 0.9769, 0.9812, 0.9315, 0.9955, 1, 1, 0.7603), V29 = c(0.6036,
     0.2177, 0.4856, 0.7309, 0.4019, 0.0716, 0.7423, 0.0882, 0.5606, 0.9631, 0.2064,
     0.8339, 0.6289, 0.5605, 0.7802, 0.8387, 0.8725, 0.9379, 0.3037, 0.299, 0.4817,
     0.5437, 0.3308, 0.679, 0.708, 0.7549, 0.8545, 0.8184, 0.9427, 1, 0.634, 0.6825,
     0.9797, 0.4667, 0.526, 0.5864, 0.4434, 0.3474, 0.4739, 0.9013, 0.8877, 0.5578,
     0.4772, 0.5047, 0.4936, 0.8073, 1, 1, 1, 0.3907, 0.4544, 0.0408, 0.104, 0.3108,
     0.4964, 0.6268, 0.3707, 0.3168, 0.7068, 0.772, 0.7306, 0.6828, 0.1949, 0.5807,
     0.4239, 0.7649, 0.6563, 0.8826, 0.8477, 0.7626, 0.3372, 0.1881, 0.7171, 0.5765,
     0.8528, 0.9348, 0.9328, 0.8668, 0.5368, 0.0144, 0.3856, 0.8247, 0.8747, 0.8881,
     0.5793, 0.8519, 0.4536, 0.4661, 0.899, 0.9619, 0.8005, 0.8435, 0.8793, 0.9806,
     0.9223, 0.899, 0.8888, 0.8937, 0.9464, 0.8326, 0.8576, 0.8104, 0.863, 0.7123),
     V30 = c(0.8514, 0.2149, 0.1594, 0.6896, 0.2344, 0.0613, 0.7736, 0.2385, 0.8344,
     0.9619, 0.3917, 0.4084, 0.4999, 0.3728, 0.7575, 0.7238, 0.9376, 0.8575, 0.0823,
     0.1354, 0.453, 0.5387, 0.3425, 0.5587, 0.6318, 0.6736, 0.7293, 0.6962, 0.8114,
     0.9166, 0.6107, 0.6443, 1, 0.3539, 0.3677, 0.3773, 0.3822, 0.4235, 0.6153,
     0.8014, 0.8557, 0.4831, 0.5201, 0.5775, 0.5648, 0.831, 0.8463, 0.8362, 0.769,
     0.3456, 0.4258, 0.2531, 0.1714, 0.2554, 0.4886, 0.4301, 0.2891, 0.3346, 0.5986,
     0.5711, 0.5758, 0.5572, 0.1671, 0.6925, 0.4182, 0.525, 0.8591, 0.9201, 0.9289,
     0.7025, 0.6405, 0.396, 0.6316, 0.619, 0.9087, 1, 0.9344, 0.8892, 0.5207,
     0.2526, 0.5574, 0.5441, 0.6257, 0.6585, 0.3754, 0.7722, 0.3281, 0.3924, 0.6831,
     1, 0.6713, 0.6074, 0.6482, 0.6969, 0.6981, 0.6456, 0.6511, 0.7022, 0.8542,
     0.6213, 0.6069, 0.6199, 0.6979, 0.8358), V31 = c(0.8512, 0.5811, 0.3007,
     0.5829, 0.1905, 0.1816, 0.8473, 0.2005, 0.8096, 0.9236, 0.3791, 0.2268, 0.583,
     0.2481, 0.5836, 0.6292, 0.892, 0.7284, 0.2787, 0.2438, 0.4521, 0.5619, 0.492,
     0.4147, 0.6126, 0.6463, 0.6499, 0.59, 0.6987, 0.7676, 0.7046, 0.7063, 0.9546,
     0.2727, 0.2746, 0.2206, 0.4727, 0.4633, 0.4929, 0.438, 0.5563, 0.4729, 0.4241,
     0.4754, 0.4906, 0.7792, 0.5509, 0.5427, 0.4841, 0.4091, 0.3869, 0.1979, 0.3264,
     0.3367, 0.4079, 0.2077, 0.2185, 0.2056, 0.3857, 0.4264, 0.4469, 0.4301, 0.1025,
     0.3825, 0.4393, 0.5101, 0.6655, 0.8005, 0.9513, 0.7382, 0.7138, 0.2286, 0.3554,
     0.4613, 0.9657, 0.9308, 0.8854, 0.9065, 0.5651, 0.4335, 0.7309, 0.3349, 0.2184,
     0.2707, 0.3485, 0.5772, 0.2522, 0.3849, 0.6108, 0.8086, 0.5632, 0.5403, 0.5876,
     0.4973, 0.6167, 0.5967, 0.6083, 0.65, 0.6457, 0.3772, 0.3934, 0.6041, 0.7717,
     0.7622), V32 = c(0.5045, 0.6323, 0.4096, 0.4935, 0.1235, 0.4493, 0.7352,
     0.0587, 0.725, 0.8903, 0.2042, 0.1745, 0.666, 0.1921, 0.6316, 0.5181, 0.7508,
     0.67, 0.7241, 0.5624, 0.4532, 0.5141, 0.4592, 0.2946, 0.4638, 0.5007, 0.6071,
     0.5447, 0.681, 0.6177, 0.5376, 0.5373, 0.8835, 0.141, 0.102, 0.2628, 0.4007,
     0.341, 0.3195, 0.1319, 0.2897, 0.3318, 0.1592, 0.24, 0.182, 0.5049, 0.4444,
     0.4577, 0.3717, 0.4639, 0.3939, 0.1891, 0.4612, 0.4465, 0.2443, 0.1198, 0.1711,
     0.1032, 0.251, 0.286, 0.3719, 0.3339, 0.1362, 0.4303, 0.1162, 0.4219, 0.5369,
     0.6033, 0.7995, 0.7446, 0.8202, 0.3544, 0.2897, 0.3615, 0.9306, 0.8478, 0.769,
     0.8522, 0.5749, 0.4918, 0.8549, 0.0877, 0.2945, 0.1746, 0.4639, 0.519, 0.3964,
     0.4674, 0.548, 0.5558, 0.7332, 0.689, 0.6408, 0.502, 0.5069, 0.4355, 0.4463,
     0.5069, 0.3397, 0.2822, 0.2464, 0.5547, 0.7305, 0.4567), V33 = c(0.1862,
     0.2965, 0.317, 0.3101, 0.1717, 0.5976, 0.6671, 0.2544, 0.8048, 0.9708, 0.2227,
     0.0507, 0.4124, 0.1386, 0.8108, 0.4629, 0.6832, 0.7547, 0.8032, 0.5555, 0.5385,
     0.6084, 0.3034, 0.2025, 0.2797, 0.3663, 0.5588, 0.5142, 0.6591, 0.5468, 0.5934,
     0.6601, 0.7662, 0.1863, 0.1339, 0.2672, 0.3381, 0.2849, 0.3735, 0.1709, 0.3638,
     0.3969, 0.1668, 0.2779, 0.1811, 0.1413, 0.5169, 0.8067, 0.6096, 0.558, 0.4661,
     0.2433, 0.3939, 0.5, 0.1768, 0.166, 0.3578, 0.3168, 0.2162, 0.3114, 0.2079,
     0.2035, 0.2212, 0.7791, 0.4336, 0.416, 0.3118, 0.212, 0.4362, 0.7927, 0.6657,
     0.4187, 0.4316, 0.4434, 0.7774, 0.7605, 0.6865, 0.7204, 0.525, 0.5409, 0.9425,
     0.16, 0.3645, 0.2709, 0.6495, 0.6824, 0.4154, 0.4245, 0.5058, 0.5409, 0.6038,
     0.5977, 0.4972, 0.5359, 0.3921, 0.2997, 0.2948, 0.3903, 0.3828, 0.2042, 0.1645,
     0.416, 0.5197, 0.1715), V34 = c(0.2709, 0.1873, 0.3305, 0.0306, 0.2351, 0.3785,
     0.6083, 0.2009, 0.9435, 0.9647, 0.3341, 0.1588, 0.126, 0.3325, 0.9039, 0.5255,
     0.761, 0.8773, 0.805, 0.6963, 0.5308, 0.5621, 0.4366, 0.0688, 0.1721, 0.2298,
     0.5967, 0.5389, 0.6954, 0.5516, 0.8443, 0.8708, 0.6547, 0.2176, 0.1582, 0.2907,
     0.3172, 0.2847, 0.3336, 0.2484, 0.4786, 0.3894, 0.0588, 0.1997, 0.1107, 0.2767,
     0.4268, 0.6973, 0.511, 0.5727, 0.3974, 0.1956, 0.505, 0.5111, 0.2472, 0.2618,
     0.3947, 0.404, 0.0968, 0.2066, 0.0955, 0.0798, 0.1124, 0.8703, 0.6553, 0.1906,
     0.3763, 0.2866, 0.4048, 0.5227, 0.5254, 0.2398, 0.3791, 0.3864, 0.6643, 0.704,
     0.639, 0.62, 0.4255, 0.5961, 0.8726, 0.4169, 0.5012, 0.4853, 0.6901, 0.622,
     0.3308, 0.3095, 0.4476, 0.4988, 0.2575, 0.3244, 0.2755, 0.3842, 0.3524, 0.2294,
     0.1729, 0.3009, 0.3204, 0.219, 0.114, 0.1472, 0.1786, 0.1549), V35 = c(0.4232,
     0.2969, 0.3408, 0.0244, 0.2489, 0.2495, 0.6239, 0.0329, 1, 0.7892, 0.3984,
     0.304, 0.2487, 0.2883, 0.8647, 0.5147, 0.9017, 0.9919, 0.7676, 0.7298, 0.5356,
     0.5956, 0.5175, 0.1171, 0.1665, 0.1362, 0.6275, 0.5531, 0.729, 0.5463, 0.9481,
     0.9518, 0.5447, 0.236, 0.1952, 0.1982, 0.2222, 0.1742, 0.1052, 0.3044, 0.2908,
     0.2314, 0.3967, 0.5305, 0.4603, 0.5084, 0.1802, 0.3915, 0.2586, 0.6355, 0.2194,
     0.2667, 0.4833, 0.5194, 0.3518, 0.3862, 0.2867, 0.4282, 0.1323, 0.1165, 0.0488,
     0.0809, 0.1677, 1, 0.6172, 0.0223, 0.2801, 0.4033, 0.4952, 0.3967, 0.296,
     0.1847, 0.2421, 0.3093, 0.6604, 0.7539, 0.6378, 0.6253, 0.333, 0.5248, 0.6673,
     0.6576, 0.7843, 0.7184, 0.5666, 0.5054, 0.1445, 0.0752, 0.2401, 0.3108, 0.0349,
     0.0516, 0.03, 0.1848, 0.2183, 0.1866, 0.1488, 0.1565, 0.1331, 0.2223, 0.0956,
     0.0849, 0.1098, 0.1641), V36 = c(0.3043, 0.5163, 0.2186, 0.1108, 0.3649,
     0.5771, 0.5972, 0.1547, 0.896, 0.5307, 0.5077, 0.1369, 0.4676, 0.3228, 0.6695,
     0.3929, 1, 0.9922, 0.7468, 0.7022, 0.5271, 0.6078, 0.5122, 0.2157, 0.2561,
     0.2123, 0.5459, 0.5318, 0.668, 0.5515, 0.9705, 0.9605, 0.4593, 0.1725, 0.1787,
     0.2288, 0.0733, 0.0549, 0.0671, 0.2312, 0.0899, 0.1036, 0.7147, 0.7409, 0.665,
     0.4787, 0.0791, 0.1558, 0.0916, 0.7563, 0.1816, 0.134, 0.3511, 0.4619, 0.3762,
     0.3958, 0.2401, 0.4538, 0.1344, 0.0185, 0.1406, 0.1525, 0.1039, 0.9212, 0.4373,
     0.4219, 0.0875, 0.2803, 0.1712, 0.3042, 0.0704, 0.376, 0.0944, 0.2138, 0.6884,
     0.799, 0.6629, 0.6848, 0.2331, 0.3777, 0.4694, 0.739, 0.9361, 0.8209, 0.5188,
     0.3578, 0.1923, 0.2885, 0.1405, 0.2897, 0.1799, 0.3157, 0.3356, 0.1149, 0.1245,
     0.0922, 0.0801, 0.0985, 0.044, 0.1327, 0.008, 0.0608, 0.1446, 0.1869), V37 = c(0.6116,
     0.6153, 0.2463, 0.1594, 0.3382, 0.8852, 0.5715, 0.1212, 0.5516, 0.2718, 0.5534,
     0.1605, 0.5382, 0.2607, 0.4027, 0.1279, 0.9123, 0.9419, 0.6253, 0.5468, 0.426,
     0.5025, 0.4746, 0.2216, 0.2735, 0.2395, 0.4786, 0.4826, 0.5917, 0.4561, 0.7766,
     0.7712, 0.4679, 0.0589, 0.0429, 0.3186, 0.2692, 0.1192, 0.0379, 0.1338, 0.2043,
     0.1312, 0.7319, 0.7775, 0.6423, 0.1356, 0.0535, 0.1598, 0.0947, 0.6903, 0.1023,
     0.1073, 0.2319, 0.4234, 0.2909, 0.3248, 0.3619, 0.3704, 0.225, 0.1302, 0.2554,
     0.2626, 0.2562, 0.9386, 0.4118, 0.5496, 0.3319, 0.3087, 0.3652, 0.1309, 0.097,
     0.4331, 0.0351, 0.1112, 0.6938, 0.7673, 0.5983, 0.7337, 0.1451, 0.2369, 0.1546,
     0.7963, 0.8195, 0.7536, 0.506, 0.3809, 0.3208, 0.4072, 0.1772, 0.2244, 0.3039,
     0.359, 0.3167, 0.157, 0.1592, 0.1829, 0.177, 0.22, 0.1234, 0.0521, 0.0702,
     0.0969, 0.1066, 0.2655), V38 = c(0.6756, 0.4283, 0.2726, 0.1371, 0.1589,
     0.8409, 0.5242, 0.2446, 0.3037, 0.1953, 0.3352, 0.2061, 0.315, 0.204, 0.237,
     0.0411, 0.7388, 0.8388, 0.173, 0.1421, 0.2436, 0.2829, 0.4902, 0.2776, 0.3209,
     0.2673, 0.3965, 0.379, 0.4899, 0.3466, 0.6313, 0.6772, 0.1987, 0.0621, 0.1096,
     0.2871, 0.1888, 0.1154, 0.0461, 0.2056, 0.1707, 0.0864, 0.3509, 0.4424, 0.2166,
     0.2299, 0.1906, 0.2161, 0.2287, 0.6176, 0.2108, 0.2023, 0.4029, 0.4372, 0.2311,
     0.2302, 0.3314, 0.3741, 0.3244, 0.248, 0.2054, 0.2456, 0.2624, 0.9303, 0.3641,
     0.2483, 0.4237, 0.355, 0.3763, 0.2408, 0.3941, 0.3626, 0.0844, 0.1386, 0.5932,
     0.5955, 0.4565, 0.6281, 0.1648, 0.172, 0.1748, 0.7493, 0.6207, 0.6496, 0.3885,
     0.3813, 0.3367, 0.317, 0.1742, 0.096, 0.476, 0.3881, 0.4133, 0.1311, 0.1626,
     0.1743, 0.1382, 0.2243, 0.203, 0.0618, 0.0936, 0.1411, 0.144, 0.1713), V39 = c(0.5375,
     0.5479, 0.168, 0.0696, 0.0989, 0.357, 0.2924, 0.3171, 0.2338, 0.1374, 0.2723,
     0.0734, 0.2139, 0.2396, 0.2685, 0.0859, 0.5915, 0.6605, 0.2916, 0.4738, 0.1205,
     0.0477, 0.4603, 0.2309, 0.2724, 0.2865, 0.2087, 0.1831, 0.3439, 0.3384, 0.576,
     0.6431, 0.0699, 0.1847, 0.1762, 0.2921, 0.0712, 0.0855, 0.1694, 0.2474, 0.0407,
     0.2569, 0.0589, 0.1416, 0.1951, 0.2789, 0.2561, 0.5178, 0.348, 0.5379, 0.3253,
     0.1794, 0.3676, 0.4277, 0.3168, 0.325, 0.3763, 0.3839, 0.3939, 0.1637, 0.1614,
     0.198, 0.2236, 0.7314, 0.4572, 0.2034, 0.1801, 0.2545, 0.2841, 0.178, 0.6028,
     0.2519, 0.0436, 0.1523, 0.5774, 0.4731, 0.3129, 0.5725, 0.2694, 0.1878, 0.3607,
     0.6795, 0.4513, 0.4708, 0.3762, 0.3359, 0.5683, 0.2863, 0.3326, 0.2287, 0.5756,
     0.5716, 0.6281, 0.1583, 0.2356, 0.2452, 0.2404, 0.2736, 0.1652, 0.1416, 0.0894,
     0.1676, 0.1929, 0.0959), V40 = c(0.4719, 0.6133, 0.2792, 0.0452, 0.1089,
     0.3133, 0.1536, 0.3195, 0.2382, 0.3105, 0.2278, 0.0202, 0.1848, 0.1319, 0.3662,
     0.1131, 0.4057, 0.4816, 0.5003, 0.641, 0.3845, 0.2811, 0.446, 0.1444, 0.188,
     0.206, 0.1651, 0.175, 0.2366, 0.2853, 0.6148, 0.672, 0.1493, 0.2452, 0.2481,
     0.2806, 0.1062, 0.1811, 0.2169, 0.279, 0.1286, 0.3179, 0.269, 0.3508, 0.4947,
     0.3833, 0.2153, 0.4782, 0.2095, 0.5622, 0.3697, 0.0227, 0.151, 0.4433, 0.3554,
     0.4022, 0.4767, 0.3494, 0.3806, 0.1103, 0.2232, 0.2412, 0.118, 0.4791, 0.4367,
     0.2729, 0.3743, 0.1432, 0.0427, 0.1598, 0.3521, 0.187, 0.113, 0.0996, 0.6223,
     0.484, 0.4158, 0.6119, 0.373, 0.325, 0.5208, 0.4713, 0.3004, 0.3482, 0.3738,
     0.2771, 0.5505, 0.2634, 0.4021, 0.3228, 0.4254, 0.4314, 0.4977, 0.2631, 0.2483,
     0.2407, 0.2046, 0.2152, 0.1043, 0.146, 0.1127, 0.12, 0.0325, 0.0768), V41 = c(0.4647,
     0.5017, 0.2558, 0.062, 0.1043, 0.6096, 0.2003, 0.3051, 0.3318, 0.379, 0.2044,
     0.1638, 0.1679, 0.0683, 0.3267, 0.1306, 0.3019, 0.2917, 0.522, 0.4375, 0.4107,
     0.3422, 0.4196, 0.1513, 0.1552, 0.1659, 0.1836, 0.1679, 0.1716, 0.2502, 0.545,
     0.6035, 0.1713, 0.2984, 0.315, 0.2682, 0.0694, 0.1264, 0.1677, 0.161, 0.1581,
     0.2649, 0.42, 0.4482, 0.4925, 0.2933, 0.2769, 0.2344, 0.1901, 0.6508, 0.2912,
     0.1313, 0.0745, 0.37, 0.3741, 0.4344, 0.4059, 0.438, 0.3258, 0.2144, 0.1773,
     0.2409, 0.1103, 0.2087, 0.2964, 0.2837, 0.4627, 0.5869, 0.5331, 0.5657, 0.3924,
     0.1046, 0.2045, 0.1644, 0.5841, 0.434, 0.4325, 0.5597, 0.4467, 0.2575, 0.5177,
     0.2355, 0.2674, 0.3508, 0.2605, 0.3648, 0.3231, 0.0541, 0.3009, 0.3454, 0.5046,
     0.3051, 0.2613, 0.3103, 0.2437, 0.2518, 0.197, 0.2438, 0.1066, 0.0846, 0.0873,
     0.1201, 0.149, 0.0847), V42 = c(0.2587, 0.2377, 0.174, 0.1421, 0.0839, 0.6378,
     0.2031, 0.0836, 0.3821, 0.4105, 0.1986, 0.1583, 0.2328, 0.0334, 0.22, 0.1757,
     0.2331, 0.1769, 0.4824, 0.3178, 0.5067, 0.5147, 0.2873, 0.1745, 0.2522, 0.2633,
     0.0652, 0.0674, 0.1013, 0.1641, 0.4813, 0.5155, 0.1654, 0.3041, 0.292, 0.2112,
     0.03, 0.0799, 0.0644, 0.0056, 0.2191, 0.2714, 0.3874, 0.4208, 0.4041, 0.1155,
     0.2841, 0.3599, 0.2941, 0.4797, 0.301, 0.1775, 0.1395, 0.3324, 0.4443, 0.4008,
     0.3661, 0.4265, 0.3654, 0.2033, 0.2293, 0.1901, 0.2831, 0.2016, 0.4312, 0.4463,
     0.1614, 0.6431, 0.6952, 0.6443, 0.4808, 0.2339, 0.1937, 0.1902, 0.4527, 0.3954,
     0.4031, 0.4965, 0.4133, 0.2423, 0.3702, 0.1704, 0.2241, 0.3181, 0.1591, 0.3834,
     0.0448, 0.1874, 0.2075, 0.3882, 0.7179, 0.4393, 0.4697, 0.4512, 0.2715, 0.3184,
     0.2778, 0.3154, 0.211, 0.1055, 0.102, 0.1036, 0.0328, 0.2076), V43 = c(0.2129,
     0.1957, 0.2121, 0.1597, 0.1391, 0.2709, 0.2207, 0.1266, 0.1575, 0.3355, 0.0835,
     0.183, 0.1015, 0.0716, 0.2996, 0.2648, 0.2931, 0.1136, 0.4004, 0.2377, 0.4216,
     0.4372, 0.2296, 0.1756, 0.2121, 0.2552, 0.0758, 0.0609, 0.0766, 0.1605, 0.3406,
     0.3802, 0.26, 0.2275, 0.1902, 0.1513, 0.0893, 0.0378, 0.0159, 0.0351, 0.1701,
     0.1713, 0.244, 0.3054, 0.2402, 0.1705, 0.1733, 0.2785, 0.2211, 0.3736, 0.2563,
     0.1549, 0.1552, 0.2564, 0.3261, 0.337, 0.232, 0.2854, 0.2983, 0.1887, 0.2521,
     0.2077, 0.2385, 0.1669, 0.4155, 0.3178, 0.2494, 0.5826, 0.4288, 0.4241, 0.4602,
     0.1991, 0.0834, 0.1313, 0.4911, 0.4837, 0.4201, 0.5027, 0.3743, 0.2706, 0.224,
     0.2728, 0.3141, 0.3524, 0.1875, 0.3453, 0.3131, 0.3459, 0.1206, 0.324, 0.6163,
     0.4302, 0.4806, 0.3785, 0.1184, 0.1685, 0.1377, 0.2112, 0.2417, 0.1639, 0.1964,
     0.1977, 0.0537, 0.2505), V44 = c(0.2222, 0.1749, 0.1099, 0.1384, 0.0819,
     0.1419, 0.1778, 0.1381, 0.2228, 0.2998, 0.0908, 0.1886, 0.0713, 0.0976, 0.2205,
     0.1955, 0.2298, 0.0701, 0.3877, 0.2808, 0.2479, 0.247, 0.0949, 0.1424, 0.1801,
     0.1696, 0.0486, 0.0375, 0.0845, 0.1491, 0.1916, 0.2278, 0.3846, 0.148, 0.0696,
     0.1789, 0.1459, 0.1268, 0.0778, 0.1148, 0.0971, 0.0584, 0.2, 0.2235, 0.1392,
     0.1294, 0.0815, 0.1807, 0.1524, 0.2804, 0.1927, 0.1626, 0.0377, 0.2527, 0.1963,
     0.2518, 0.145, 0.2808, 0.1779, 0.137, 0.1464, 0.1767, 0.0255, 0.2872, 0.1824,
     0.0807, 0.3202, 0.4286, 0.3063, 0.4567, 0.4164, 0.11, 0.1502, 0.1776, 0.5762,
     0.5379, 0.4557, 0.5772, 0.3021, 0.2323, 0.0816, 0.4016, 0.3693, 0.3659, 0.2267,
     0.2096, 0.3387, 0.4646, 0.0255, 0.0926, 0.5663, 0.4831, 0.4921, 0.1269, 0.1157,
     0.0675, 0.0685, 0.0991, 0.1631, 0.1916, 0.2256, 0.1339, 0.1309, 0.1862),
     V45 = c(0.2111, 0.1304, 0.0985, 0.0372, 0.0678, 0.126, 0.1353, 0.1136, 0.1582,
     0.2748, 0.138, 0.1008, 0.0615, 0.0787, 0.1163, 0.0656, 0.2391, 0.1578, 0.1651,
     0.1374, 0.1586, 0.1708, 0.0095, 0.0908, 0.1473, 0.1467, 0.0353, 0.0533, 0.026,
     0.1326, 0.1134, 0.1522, 0.3754, 0.1102, 0.0758, 0.185, 0.1348, 0.1125, 0.0653,
     0.1331, 0.2217, 0.123, 0.2307, 0.2611, 0.1779, 0.0909, 0.0335, 0.0352, 0.0746,
     0.1982, 0.2062, 0.0708, 0.0636, 0.2137, 0.0864, 0.2101, 0.1017, 0.2395, 0.1535,
     0.1376, 0.0673, 0.1119, 0.1967, 0.4374, 0.1487, 0.1192, 0.2265, 0.4894, 0.5835,
     0.576, 0.5438, 0.0684, 0.1675, 0.2, 0.5013, 0.4485, 0.3955, 0.5907, 0.2069,
     0.1724, 0.0395, 0.4125, 0.2986, 0.2846, 0.1577, 0.1031, 0.413, 0.4366, 0.0298,
     0.1173, 0.5749, 0.5084, 0.5294, 0.1459, 0.1449, 0.1186, 0.0664, 0.0594, 0.0769,
     0.2085, 0.1814, 0.0902, 0.091, 0.1439), V46 = c(0.0176, 0.0597, 0.1271, 0.0688,
     0.0663, 0.1288, 0.1373, 0.0516, 0.1433, 0.2024, 0.1948, 0.0663, 0.0779, 0.0522,
     0.0635, 0.058, 0.191, 0.1938, 0.0442, 0.1136, 0.1124, 0.1343, 0.0527, 0.0138,
     0.0681, 0.1286, 0.0297, 0.0278, 0.0333, 0.0687, 0.064, 0.0801, 0.2414, 0.1178,
     0.091, 0.1717, 0.0391, 0.0505, 0.021, 0.0276, 0.2732, 0.22, 0.1886, 0.2798,
     0.1946, 0.08, 0.0933, 0.0473, 0.0606, 0.2438, 0.1751, 0.0129, 0.0443, 0.1789,
     0.1688, 0.1181, 0.1111, 0.0369, 0.1199, 0.0307, 0.0965, 0.0779, 0.1483, 0.3097,
     0.0138, 0.2134, 0.1146, 0.5777, 0.5692, 0.5293, 0.5649, 0.0303, 0.1058, 0.0765,
     0.4042, 0.2674, 0.2966, 0.4803, 0.179, 0.1457, 0.0785, 0.347, 0.2226, 0.1714,
     0.1211, 0.0798, 0.3639, 0.2581, 0.0691, 0.0566, 0.3593, 0.1952, 0.2216, 0.1092,
     0.1883, 0.1833, 0.1665, 0.194, 0.0723, 0.2335, 0.2012, 0.1085, 0.0757, 0.147
     ), V47 = c(0.1348, 0.1124, 0.1459, 0.0867, 0.1202, 0.079, 0.0749, 0.0073,
     0.1634, 0.1043, 0.1211, 0.0183, 0.0761, 0.05, 0.0465, 0.0319, 0.1096, 0.1106,
     0.0663, 0.1034, 0.0651, 0.0838, 0.0383, 0.0469, 0.1091, 0.0926, 0.0241, 0.0179,
     0.0205, 0.0602, 0.0911, 0.0804, 0.1077, 0.0608, 0.0441, 0.0898, 0.0546, 0.0949,
     0.0509, 0.0763, 0.1874, 0.2198, 0.196, 0.2392, 0.1723, 0.0567, 0.1018, 0.0322,
     0.0692, 0.1789, 0.0841, 0.0795, 0.0264, 0.101, 0.1991, 0.115, 0.0655, 0.0805,
     0.0959, 0.0373, 0.1492, 0.1344, 0.0434, 0.1578, 0.1164, 0.3241, 0.0476, 0.4315,
     0.263, 0.3287, 0.3195, 0.0674, 0.1111, 0.0727, 0.3123, 0.1541, 0.2095, 0.3877,
     0.1689, 0.1175, 0.1052, 0.2739, 0.0849, 0.0694, 0.0883, 0.0701, 0.2069, 0.1319,
     0.0781, 0.0766, 0.2526, 0.1539, 0.1401, 0.1485, 0.1954, 0.1878, 0.1807, 0.1937,
     0.0912, 0.1964, 0.1688, 0.1521, 0.1059, 0.0991), V48 = c(0.0744, 0.1047,
     0.1164, 0.0513, 0.0692, 0.0829, 0.0472, 0.0278, 0.1133, 0.0453, 0.0843, 0.0404,
     0.0845, 0.0231, 0.0422, 0.0301, 0.03, 0.0693, 0.0418, 0.0688, 0.0789, 0.0755,
     0.0107, 0.048, 0.0919, 0.0716, 0.0379, 0.0114, 0.0309, 0.0561, 0.098, 0.0752,
     0.0224, 0.0333, 0.0244, 0.0656, 0.0469, 0.0677, 0.0387, 0.0631, 0.1062, 0.1074,
     0.1701, 0.2021, 0.1522, 0.0198, 0.0309, 0.0408, 0.0446, 0.1706, 0.1035, 0.0762,
     0.0223, 0.0528, 0.1217, 0.055, 0.0271, 0.0541, 0.0765, 0.0606, 0.1128, 0.096,
     0.0627, 0.0553, 0.2052, 0.2945, 0.0943, 0.264, 0.1196, 0.1283, 0.2484, 0.0785,
     0.0849, 0.0749, 0.2232, 0.1359, 0.1558, 0.2779, 0.1341, 0.0868, 0.1034, 0.179,
     0.0359, 0.0303, 0.085, 0.0526, 0.0859, 0.0505, 0.0777, 0.0969, 0.2299, 0.2037,
     0.1888, 0.1385, 0.1492, 0.1114, 0.1245, 0.1082, 0.0812, 0.13, 0.1037, 0.1363,
     0.1005, 0.0041), V49 = c(0.013, 0.0507, 0.0777, 0.0092, 0.0152, 0.052, 0.0325,
     0.0372, 0.0567, 0.0337, 0.0589, 0.0108, 0.0592, 0.0221, 0.0174, 0.0272, 0.0171,
     0.0176, 0.0475, 0.0422, 0.0325, 0.0304, 0.0108, 0.0159, 0.0397, 0.0325, 0.0119,
     0.0073, 0.0101, 0.0306, 0.0563, 0.0566, 0.0155, 0.0276, 0.0265, 0.0445, 0.0201,
     0.0259, 0.0262, 0.0309, 0.0665, 0.0423, 0.1366, 0.1326, 0.0929, 0.0114, 0.0208,
     0.0163, 0.0344, 0.0762, 0.0641, 0.0117, 0.0187, 0.0453, 0.0628, 0.0293, 0.0244,
     0.0177, 0.0649, 0.0399, 0.0463, 0.0598, 0.0513, 0.0334, 0.1069, 0.1474, 0.0824,
     0.1794, 0.0983, 0.0698, 0.1299, 0.0455, 0.0596, 0.0449, 0.1085, 0.0941, 0.0884,
     0.1427, 0.0769, 0.0392, 0.0764, 0.0922, 0.0289, 0.0292, 0.0355, 0.0241, 0.06,
     0.0112, 0.0369, 0.0588, 0.1271, 0.1054, 0.0947, 0.0716, 0.0511, 0.031, 0.0516,
     0.0336, 0.0496, 0.0633, 0.0501, 0.0858, 0.0535, 0.0154), V50 = c(0.0106,
     0.0159, 0.0439, 0.0198, 0.0266, 0.0216, 0.0179, 0.0121, 0.0133, 0.0122, 0.0247,
     0.0143, 0.0068, 0.0144, 0.0172, 0.0074, 0.0383, 0.0205, 0.0235, 0.0117, 0.007,
     0.0074, 0.0077, 0.0045, 0.0093, 0.0258, 0.0073, 0.0116, 0.0095, 0.0154, 0.0187,
     0.0175, 0.0187, 0.01, 0.0095, 0.011, 0.0095, 0.017, 0.0101, 0.024, 0.0405,
     0.0162, 0.0398, 0.0358, 0.0179, 0.0151, 0.0318, 0.0088, 0.0082, 0.0238, 0.0153,
     0.0061, 0.0077, 0.0118, 0.0323, 0.0183, 0.0179, 0.0065, 0.0313, 0.0169, 0.0193,
     0.033, 0.0473, 0.0209, 0.0199, 0.0211, 0.0171, 0.0772, 0.0374, 0.0334, 0.0825,
     0.0246, 0.0201, 0.0134, 0.0414, 0.0261, 0.0265, 0.0424, 0.0222, 0.0131, 0.0216,
     0.0276, 0.0122, 0.0116, 0.0219, 0.0117, 0.0267, 0.0059, 0.0057, 0.005, 0.0356,
     0.0251, 0.0134, 0.0176, 0.0155, 0.0143, 0.0044, 0.0177, 0.0101, 0.0183, 0.0136,
     0.029, 0.0235, 0.0116), V51 = c(0.0033, 0.0195, 0.0061, 0.0118, 0.0174, 0.036,
     0.0045, 0.0153, 0.017, 0.0072, 0.0118, 0.0091, 0.0089, 0.0307, 0.0134, 0.0149,
     0.0053, 0.0309, 0.0066, 0.007, 0.0026, 0.0069, 0.0109, 0.0015, 0.0076, 0.0136,
     0.0051, 0.0092, 0.0047, 0.0029, 0.0088, 0.0058, 0.0125, 0.0023, 0.014, 0.0024,
     0.0155, 0.0033, 0.0161, 0.0115, 0.0113, 0.0093, 0.0143, 0.0128, 0.0242, 0.0085,
     0.0132, 0.0121, 0.0108, 0.0268, 0.0081, 0.0257, 0.0137, 9e-04, 0.0253, 0.0104,
     0.0109, 0.0222, 0.0185, 0.0135, 0.014, 0.0197, 0.0248, 0.0172, 0.0208, 0.0361,
     0.0244, 0.0798, 0.0291, 0.0342, 0.0243, 0.0151, 0.0071, 0.0174, 0.0253, 0.0079,
     0.0121, 0.0271, 0.0205, 0.0092, 0.0167, 0.0169, 0.0045, 0.0024, 0.0086, 0.0122,
     0.0125, 0.0041, 0.0091, 0.0118, 0.0367, 0.0357, 0.031, 0.0199, 0.0189, 0.0138,
     0.0185, 0.0209, 0.0089, 0.0137, 0.013, 0.0203, 0.0155, 0.0181), V52 = c(0.0232,
     0.0201, 0.0145, 0.009, 0.0176, 0.0331, 0.0084, 0.0092, 0.0035, 0.0108, 0.0088,
     0.0038, 0.0087, 0.0386, 0.0141, 0.0125, 0.009, 0.0212, 0.0062, 0.0167, 0.0093,
     0.0025, 0.0062, 0.0052, 0.0065, 0.0044, 0.0034, 0.0078, 0.0072, 0.0048, 0.0042,
     0.0091, 0.0028, 0.0069, 0.0074, 0.0062, 0.0091, 0.015, 0.0029, 0.0064, 0.0028,
     0.0046, 0.0093, 0.0172, 0.0083, 0.0178, 0.0118, 0.0067, 0.0149, 0.0081, 0.0191,
     0.0089, 0.0071, 0.0142, 0.0214, 0.0117, 0.0147, 0.0045, 0.0098, 0.0222, 0.0027,
     0.0189, 0.0274, 0.018, 0.0176, 0.0444, 0.0258, 0.0376, 0.0156, 0.0459, 0.021,
     0.0125, 0.0104, 0.0117, 0.0131, 0.0164, 0.0091, 0.02, 0.0123, 0.0078, 0.0089,
     0.0081, 0.0108, 0.0084, 0.0123, 0.0122, 0.004, 0.0056, 0.0134, 0.0146, 0.0176,
     0.0181, 0.0237, 0.0096, 0.015, 0.0108, 0.0072, 0.0134, 0.0083, 0.015, 0.012,
     0.0116, 0.016, 0.0146), V53 = c(0.0166, 0.0248, 0.0128, 0.0223, 0.0127, 0.0131,
     0.001, 0.0035, 0.0052, 0.007, 0.0104, 0.0096, 0.0032, 0.0147, 0.0191, 0.0134,
     0.0042, 0.0091, 0.0129, 0.0127, 0.0118, 0.0103, 0.0028, 0.0038, 0.0072, 0.0028,
     0.0129, 0.0041, 0.0054, 0.0023, 0.0175, 0.016, 0.0067, 0.0025, 0.0063, 0.0072,
     0.0151, 0.0111, 0.0078, 0.0022, 0.0036, 0.0044, 0.0033, 0.0138, 0.0037, 0.0073,
     0.012, 0.0032, 0.0077, 0.0129, 0.0182, 0.0262, 0.0082, 0.0179, 0.0262, 0.0101,
     0.017, 0.0136, 0.0178, 0.0175, 0.0068, 0.0204, 0.0205, 0.011, 0.0197, 0.023,
     0.0143, 0.0143, 0.0197, 0.0277, 0.0361, 0.0036, 0.0062, 0.0023, 0.0049, 0.012,
     0.0062, 0.007, 0.0067, 0.0071, 0.0051, 0.004, 0.0075, 0.01, 0.006, 0.0114,
     0.0136, 0.0104, 0.0097, 0.004, 0.0035, 0.0019, 0.0078, 0.0103, 0.006, 0.0062,
     0.0055, 0.0094, 0.008, 0.0076, 0.0039, 0.0098, 0.0029, 0.0129), V54 = c(0.0095,
     0.0131, 0.0145, 0.0179, 0.0088, 0.012, 0.0018, 0.0098, 0.0083, 0.0063, 0.0036,
     0.0142, 0.013, 0.0018, 0.0145, 0.0026, 0.0153, 0.0056, 0.0184, 0.0138, 0.0112,
     0.0074, 0.004, 0.0079, 0.0108, 0.0021, 0.01, 0.0013, 0.0022, 0.002, 0.0171,
     0.016, 0.012, 0.0027, 0.0081, 0.0113, 0.008, 0.0032, 0.0114, 0.0122, 0.0105,
     0.0078, 0.0113, 0.0079, 0.0095, 0.0079, 0.0051, 0.0109, 0.0036, 0.0161, 0.016,
     0.0108, 0.0232, 0.0079, 0.0177, 0.0061, 0.0158, 0.0113, 0.0077, 0.0127, 0.015,
     0.0085, 0.0141, 0.0234, 0.021, 0.029, 0.0226, 0.0272, 0.0135, 0.0172, 0.0239,
     0.0123, 0.0026, 0.0047, 0.0104, 0.0113, 0.0019, 0.007, 0.0011, 0.0081, 0.0015,
     0.0025, 0.0089, 0.0018, 0.0187, 0.0098, 0.0137, 0.0079, 0.0042, 0.0114, 0.0093,
     0.0102, 0.0144, 0.0093, 0.0082, 0.0044, 0.0074, 0.0047, 0.0026, 0.0032, 0.0053,
     0.0199, 0.0051, 0.0047), V55 = c(0.018, 0.007, 0.0058, 0.0084, 0.0098, 0.0108,
     0.0068, 0.0121, 0.0078, 0.003, 0.0088, 0.019, 0.0188, 0.01, 0.0065, 0.0038,
     0.0106, 0.0086, 0.0069, 0.009, 0.0094, 0.0123, 0.0075, 0.0114, 0.0051, 0.0022,
     0.0044, 0.0011, 0.0016, 0.004, 0.0079, 0.0081, 0.0012, 0.0052, 0.0087, 0.0012,
     0.0018, 0.0035, 0.0083, 0.0151, 0.012, 0.0102, 0.003, 0.0037, 0.0105, 0.0038,
     0.007, 0.0164, 0.0114, 0.0063, 0.029, 0.0138, 0.0198, 0.006, 0.0037, 0.0031,
     0.0046, 0.0053, 0.0074, 0.0022, 0.0012, 0.0043, 0.0185, 0.0276, 0.0141, 0.0141,
     0.0187, 0.0127, 0.0127, 0.0087, 0.0447, 0.0043, 0.0025, 0.0049, 0.0102, 0.0021,
     0.0045, 0.0086, 0.0026, 0.0034, 0.0075, 0.0036, 0.0036, 0.0035, 0.0111, 0.0027,
     0.0172, 0.0014, 0.0058, 0.0032, 0.0121, 0.0133, 0.017, 0.0025, 0.0091, 0.0072,
     0.0068, 0.0045, 0.0079, 0.0037, 0.0062, 0.0033, 0.0062, 0.0039), V56 = c(0.0244,
     0.0138, 0.0049, 0.0068, 0.0019, 0.0024, 0.0039, 6e-04, 0.0075, 0.0011, 0.0047,
     0.014, 0.0101, 0.0096, 0.0129, 0.0018, 0.002, 0.0092, 0.0198, 0.0051, 0.014,
     0.0069, 0.0039, 0.005, 0.0102, 0.0048, 0.0057, 0.0045, 0.0029, 0.0019, 0.005,
     0.007, 0.0022, 0.0036, 0.0044, 0.0022, 0.0078, 0.0169, 0.0058, 0.0056, 0.0087,
     0.0065, 0.0057, 0.0051, 0.003, 0.0116, 0.0015, 0.0151, 0.0085, 0.0119, 0.009,
     0.0187, 0.0074, 0.0131, 0.0068, 0.0099, 0.0073, 0.0165, 0.0095, 0.0124, 0.0133,
     0.0092, 0.0055, 0.0032, 0.0049, 0.0161, 0.0185, 0.0166, 0.0138, 0.0046, 0.0394,
     0.0114, 0.0061, 0.0031, 0.0092, 0.0097, 0.0079, 0.0089, 0.0049, 0.0064, 0.0058,
     0.0058, 0.0029, 0.0058, 0.0126, 0.0025, 0.0132, 0.0054, 0.0072, 0.0062, 0.0075,
     0.004, 0.0012, 0.0044, 0.0038, 7e-04, 0.0084, 0.0042, 0.0042, 0.0071, 0.0046,
     0.0101, 0.0089, 0.0061), V57 = c(0.0316, 0.0092, 0.0065, 0.0032, 0.0059,
     0.0045, 0.012, 0.0181, 0.0105, 7e-04, 0.0117, 0.0099, 0.0229, 0.0077, 0.0217,
     0.0113, 0.0105, 0.007, 0.0199, 0.0029, 0.0072, 0.0076, 0.0053, 0.003, 0.0041,
     0.0138, 0.003, 0.0039, 0.0058, 0.0034, 0.0112, 0.0135, 0.0058, 0.0026, 0.0028,
     0.0025, 0.0045, 0.0137, 3e-04, 0.0026, 0.0061, 0.0061, 0.009, 0.0258, 0.0132,
     0.0033, 0.0035, 0.007, 0.0101, 0.0194, 0.0242, 0.023, 0.0035, 0.0089, 0.0121,
     0.008, 0.0054, 0.0141, 0.0055, 0.0054, 0.0048, 0.0138, 0.0045, 0.0084, 0.0027,
     0.0177, 0.011, 0.0095, 0.0133, 0.0203, 0.0355, 0.0052, 0.0038, 0.0024, 0.0083,
     0.0072, 0.0031, 0.0074, 0.0029, 0.0037, 0.0016, 0.0067, 0.0013, 0.0011, 0.0081,
     0.0026, 0.011, 0.0015, 0.0041, 0.0101, 0.0056, 0.0042, 0.0109, 0.0021, 0.0056,
     0.0054, 0.0037, 0.0028, 0.0071, 0.004, 0.0045, 0.0065, 0.014, 0.004), V58 = c(0.0164,
     0.0143, 0.0093, 0.0035, 0.0058, 0.0037, 0.0132, 0.0094, 0.016, 0.0024, 0.002,
     0.0092, 0.0182, 0.018, 0.0087, 0.0058, 0.0049, 0.0116, 0.0102, 0.0122, 0.0022,
     0.0073, 0.0013, 0.0064, 0.0055, 0.014, 0.0035, 0.0022, 0.005, 0.0034, 0.0179,
     0.0067, 0.0042, 0.0036, 0.0019, 0.0059, 0.0026, 0.0015, 0.0023, 0.0029, 0.0061,
     0.0062, 0.0057, 0.0102, 0.0068, 0.0039, 8e-04, 0.0085, 0.0016, 0.014, 0.0224,
     0.0057, 0.01, 0.0084, 0.0077, 0.0107, 0.0033, 0.0077, 0.0045, 0.0021, 0.0244,
     0.0094, 0.0115, 0.0122, 0.0162, 0.0194, 0.0094, 0.0225, 0.0131, 0.013, 0.044,
     0.0091, 0.0101, 0.0039, 0.002, 0.006, 0.0063, 0.0042, 0.0022, 0.0036, 0.007,
     0.0035, 0.001, 9e-04, 0.0155, 0.005, 0.0122, 6e-04, 0.0045, 0.0068, 0.0021,
     0.003, 0.0036, 0.0069, 0.0056, 0.0035, 0.0024, 0.0036, 0.0044, 9e-04, 0.0022,
     0.0115, 0.0138, 0.0036), V59 = c(0.0095, 0.0036, 0.0059, 0.0056, 0.0059,
     0.0112, 0.007, 0.0116, 0.0095, 0.0057, 0.0091, 0.0052, 0.0046, 0.0109, 0.0077,
     0.0047, 0.007, 0.006, 0.007, 0.0056, 0.0055, 0.003, 0.0052, 0.0058, 0.005,
     0.0028, 0.0021, 0.0023, 0.0024, 0.0051, 0.0294, 0.0078, 0.0067, 6e-04, 0.0049,
     0.0039, 0.0036, 0.0069, 0.0026, 0.0104, 0.003, 0.0043, 0.0068, 0.0037, 0.0108,
     0.0081, 0.0044, 0.0117, 0.0028, 0.0332, 0.019, 0.0113, 0.0048, 0.0113, 0.0078,
     0.0161, 0.0045, 0.0246, 0.0063, 0.0028, 0.0077, 0.0105, 0.0152, 0.0082, 0.0059,
     0.0207, 0.0078, 0.0098, 0.0154, 0.0115, 0.0243, 8e-04, 0.0078, 0.0051, 0.0048,
     0.0017, 0.0048, 0.0055, 0.0022, 0.0012, 0.0074, 0.0043, 0.0032, 0.0033, 0.016,
     0.0073, 0.0114, 0.0081, 0.0047, 0.0053, 0.0043, 0.0031, 0.0043, 0.006, 0.0048,
     1e-04, 0.0034, 0.0013, 0.0022, 0.0015, 5e-04, 0.0193, 0.0077, 0.0061), V60 = c(0.0078,
     0.0103, 0.0022, 0.004, 0.0032, 0.0075, 0.0088, 0.0063, 0.0011, 0.0044, 0.0058,
     0.0075, 0.0038, 0.007, 0.0122, 0.0071, 0.008, 0.011, 0.0055, 0.002, 0.0122,
     0.0138, 0.0023, 0.003, 0.0087, 0.0064, 0.0027, 0.0016, 0.003, 0.0031, 0.0063,
     0.0068, 0.0012, 0.0035, 0.0023, 0.0048, 0.0024, 0.0051, 0.0027, 0.0163, 0.0078,
     0.0053, 0.0024, 0.0037, 0.009, 0.0053, 0.0077, 0.0056, 0.0014, 0.0439, 0.0096,
     0.0131, 0.0019, 0.0049, 0.0066, 0.0133, 0.0079, 0.0198, 0.0039, 0.0023, 0.0074,
     0.0093, 0.01, 0.0143, 0.0021, 0.0057, 0.0112, 0.0085, 0.0218, 0.0015, 0.0098,
     0.0092, 6e-04, 0.0015, 0.0036, 0.0036, 0.005, 0.0021, 0.0032, 0.0037, 0.0038,
     0.0033, 0.0047, 0.0026, 0.0085, 0.0022, 0.0068, 0.0043, 0.0054, 0.0087, 0.0017,
     0.0033, 0.0018, 0.0018, 0.0024, 0.0055, 7e-04, 0.0016, 0.0014, 0.0085, 0.0031,
     0.0157, 0.0031, 0.0115)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6",
     "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18",
     "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29",
     "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51",
     "V52", "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), class = "data.frame", row.names = c("3",
     "7", "9", "10", "13", "18", "19", "20", "25", "26", "29", "30", "35", "36", "37",
     "39", "43", "44", "46", "47", "49", "50", "52", "53", "54", "55", "59", "61",
     "63", "64", "66", "68", "69", "71", "73", "74", "77", "78", "80", "81", "83",
     "85", "87", "88", "90", "92", "93", "94", "95", "98", "100", "101", "104", "108",
     "110", "111", "114", "116", "118", "120", "123", "124", "131", "135", "138",
     "139", "140", "141", "142", "145", "148", "152", "154", "156", "158", "159",
     "161", "162", "163", "164", "166", "168", "169", "170", "172", "173", "175",
     "176", "179", "180", "182", "183", "184", "189", "191", "192", "193", "194",
     "195", "201", "202", "204", "206", "208")))
     20: predictLearner.classif.xgboost(.learner = structure(list(id = "classif.xgboost",
     type = "classif", package = "xgboost", properties = c("twoclass", "multiclass",
     "numerics", "factors", "prob", "weights"), par.set = structure(list(pars = structure(list(
     booster = structure(list(id = "booster", type = "discrete", len = 1L, lower = NULL,
     upper = NULL, values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
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     0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,
     0x9a, 0x99, 0x99, 0xbe, 0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x32, 0xa4, 0xf3, 0x3e, 0x02, 0x00,
     0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x8c, 0xaf, 0xf8, 0xbe, 0xc7,
     0x92, 0xac, 0x41, 0x00, 0x00, 0x50, 0x41, 0x25, 0x49, 0x92, 0x3d, 0x00, 0x00,
     0x00, 0x00, 0xef, 0xd4, 0x14, 0x41, 0x00, 0x00, 0xe8, 0x40, 0xd9, 0x64, 0x93,
     0x3f, 0x02, 0x00, 0x00, 0x00, 0x90, 0xb9, 0x43, 0x40, 0x00, 0x00, 0xb8, 0x40,
     0x68, 0x2f, 0xa1, 0xbf, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x80, 0x3f, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0xc8, 0x40, 0xd4, 0x08, 0xcb, 0x3f, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0x40, 0xf4,
     0x3c, 0xcf, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x6e, 0x69, 0x74, 0x65, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x30)), niter = 1, evaluation_log = structure(list(iter = 1, train_error = 0.076923), .Names = c("iter",
     "train_error"), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x23d24a8>),
     call = xgb.train(params = params, data = dtrain, nrounds = nrounds, watchlist = watchlist,
     verbose = verbose, print_every_n = print_every_n, early_stopping_rounds = early_stopping_rounds,
     maximize = maximize, save_period = save_period, save_name = save_name, xgb_model = xgb_model,
     callbacks = callbacks, objective = ..1), params = structure(list(objective = "binary:logistic",
     silent = 1), .Names = c("objective", "silent")), callbacks = structure(list(
     cb.print.evaluation = structure(function (env = parent.frame())
     {
     if (length(env$bst_evaluation) == 0 || period == 0 || NVL(env$rank, 0) !=
     0)
     return()
     i <- env$iteration
     if ((i - 1)%%period == 0 || i == env$begin_iteration || i == env$end_iteration) {
     msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
     cat(msg, "\n")
     }
     }, call = cb.print.evaluation(period = print_every_n), name = "cb.print.evaluation"),
     cb.evaluation.log = structure(function (env = parent.frame(), finalize = FALSE)
     {
     if (is.null(mnames))
     init(env)
     if (finalize)
     return(finalizer(env))
     ev <- env$bst_evaluation
     if (!is.null(env$bst_evaluation_err))
     ev <- c(ev, env$bst_evaluation_err)
     env$evaluation_log <- c(env$evaluation_log, list(c(iter = env$iteration,
     ev)))
     }, call = cb.evaluation.log(), name = "cb.evaluation.log"), cb.save.model = structure(function (env = parent.frame())
     {
     if (is.null(env$bst))
     stop("'save_model' callback requires the 'bst' booster object in its calling frame")
     if ((save_period > 0 && (env$iteration - env$begin_iteration)%%save_period ==
     0) || (save_period == 0 && env$iteration == env$end_iteration))
     xgb.save(env$bst, sprintf(save_name, env$iteration))
     }, call = cb.save.model(save_period = save_period, save_name = save_name), name = "cb.save.model")), .Names = c("cb.print.evaluation",
     "cb.evaluation.log", "cb.save.model"))), .Names = c("handle", "raw", "niter",
     "evaluation_log", "call", "params", "callbacks"), class = "xgb.Booster"), task.desc = structure(list(
     id = "binary", type = "classif", target = "Class", size = 52L, n.feat = structure(c(60L,
     0L, 0L), .Names = c("numerics", "factors", "ordered")), has.missings = FALSE,
     has.weights = FALSE, has.blocking = FALSE, class.levels = c("M", "R"), positive = "M",
     negative = "R"), .Names = c("id", "type", "target", "size", "n.feat", "has.missings",
     "has.weights", "has.blocking", "class.levels", "positive", "negative"), class = c("TaskDescClassif",
     "TaskDescSupervised", "TaskDesc")), subset = 1:52, features = c("V1", "V2", "V3",
     "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
     "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28",
     "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52",
     "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), factor.levels = structure(list(
     Class = c("M", "R")), .Names = "Class"), time = 0.13300000000001), .Names = c("learner",
     "learner.model", "task.desc", "subset", "features", "factor.levels", "time"), class = "WrappedModel"),
     .newdata = structure(list(V1 = c(0.0262, 0.0317, 0.0223, 0.0164, 0.0079, 0.0192,
     0.027, 0.0126, 0.0293, 0.0201, 0.01, 0.0189, 0.0311, 0.0206, 0.0094, 0.0123,
     0.0211, 0.0093, 0.0408, 0.0308, 0.019, 0.0119, 0.0131, 0.0087, 0.0293, 0.0132,
     0.0225, 0.013, 0.0086, 0.0067, 0.0176, 0.0368, 0.0195, 0.0065, 0.0208, 0.0139,
     0.0239, 0.0336, 0.0108, 0.0229, 0.0409, 0.0378, 0.0188, 0.0856, 0.0235, 0.0253,
     0.026, 0.0459, 0.0025, 0.0491, 0.0201, 0.0629, 0.0162, 0.0428, 0.0264, 0.021,
     0.0283, 0.0414, 0.0228, 0.0261, 0.0249, 0.027, 0.0443, 0.1083, 0.043, 0.0731,
     0.0164, 0.0412, 0.0707, 0.0299, 0.0654, 0.0231, 0.0233, 0.0211, 0.0201, 0.0107,
     0.0258, 0.0305, 0.0217, 0.0072, 0.0221, 0.0137, 0.0015, 0.013, 0.0179, 0.018,
     0.0191, 0.0294, 0.0197, 0.0394, 0.0423, 0.0095, 0.0096, 0.0089, 0.0156, 0.0315,
     0.0056, 0.0203, 0.0392, 0.0131, 0.0335, 0.0187, 0.0522, 0.026), V2 = c(0.0582,
     0.0956, 0.0375, 0.0173, 0.0086, 0.0607, 0.0092, 0.0149, 0.0644, 0.0026, 0.0275,
     0.0308, 0.0491, 0.0132, 0.0166, 0.0022, 0.0319, 0.0269, 0.0653, 0.0339, 0.0038,
     0.0582, 0.0068, 0.0046, 0.0378, 0.008, 0.0019, 6e-04, 0.0215, 0.0096, 0.0172,
     0.0403, 0.0142, 0.0122, 0.0186, 0.0222, 0.0189, 0.0294, 0.0086, 0.0369, 0.0421,
     0.0318, 0.037, 0.0454, 0.0291, 0.0808, 0.0192, 0.0437, 0.0309, 0.0279, 0.0423,
     0.1065, 0.0253, 0.0555, 0.0071, 0.0121, 0.0599, 0.0436, 0.0106, 0.0266, 0.0119,
     0.0163, 0.0446, 0.107, 0.0902, 0.1249, 0.0627, 0.1135, 0.1252, 0.0688, 0.0649,
     0.0315, 0.0394, 0.0128, 0.0178, 0.0453, 0.0433, 0.0363, 0.0152, 0.0027, 0.0065,
     0.0297, 0.0186, 0.012, 0.0136, 0.0444, 0.0173, 0.0123, 0.0394, 0.042, 0.0321,
     0.0308, 0.0404, 0.0274, 0.021, 0.0252, 0.0267, 0.0121, 0.0108, 0.0387, 0.0258,
     0.0346, 0.0437, 0.0363), V3 = c(0.1099, 0.1321, 0.0484, 0.0347, 0.0055, 0.0378,
     0.0145, 0.0641, 0.039, 0.0138, 0.019, 0.0197, 0.0692, 0.0533, 0.0398, 0.0196,
     0.0415, 0.0217, 0.0397, 0.0202, 0.0642, 0.0623, 0.0308, 0.0081, 0.0257, 0.0188,
     0.0075, 0.0088, 0.0242, 0.0024, 0.0501, 0.0317, 0.0181, 0.0068, 0.0131, 0.0089,
     0.0466, 0.0476, 0.0058, 0.004, 0.0573, 0.0423, 0.0953, 0.0382, 0.0749, 0.0507,
     0.0254, 0.0347, 0.0171, 0.0592, 0.0554, 0.1526, 0.0262, 0.0708, 0.0342, 0.0203,
     0.0656, 0.0447, 0.013, 0.0223, 0.0277, 0.0341, 0.0235, 0.0257, 0.0833, 0.1665,
     0.0738, 0.0518, 0.1447, 0.0992, 0.0737, 0.017, 0.0416, 0.0015, 0.0274, 0.0289,
     0.0547, 0.0214, 0.0346, 0.0089, 0.0164, 0.0116, 0.0289, 0.0436, 0.0408, 0.0476,
     0.0291, 0.0117, 0.0384, 0.0446, 0.0709, 0.0539, 0.0682, 0.0248, 0.0282, 0.0167,
     0.0221, 0.038, 0.0267, 0.0329, 0.0398, 0.0168, 0.018, 0.0136), V4 = c(0.1083,
     0.1408, 0.0475, 0.007, 0.025, 0.0774, 0.0278, 0.1732, 0.0173, 0.0062, 0.0371,
     0.0622, 0.0831, 0.0569, 0.0359, 0.0206, 0.0286, 0.0339, 0.0604, 0.0889, 0.0452,
     0.06, 0.0311, 0.023, 0.0062, 0.0141, 0.0097, 0.0456, 0.0445, 0.0058, 0.0285,
     0.0293, 0.0406, 0.0108, 0.0211, 0.0108, 0.044, 0.0539, 0.046, 0.0375, 0.013,
     0.035, 0.0824, 0.0203, 0.0519, 0.0244, 0.0061, 0.0456, 0.0228, 0.127, 0.0783,
     0.1229, 0.0386, 0.0618, 0.0793, 0.1036, 0.0229, 0.0844, 0.0842, 0.0749, 0.076,
     0.0247, 0.1008, 0.0837, 0.0813, 0.1496, 0.0608, 0.0232, 0.1644, 0.1021, 0.1132,
     0.0226, 0.0547, 0.045, 0.0232, 0.0713, 0.0681, 0.0227, 0.0346, 0.0061, 0.0487,
     0.0082, 0.0195, 0.0624, 0.0633, 0.0698, 0.0301, 0.0113, 0.0076, 0.0551, 0.0108,
     0.0411, 0.0688, 0.0237, 0.0596, 0.0479, 0.0561, 0.0128, 0.0257, 0.0078, 0.057,
     0.0177, 0.0292, 0.0272), V5 = c(0.0974, 0.1674, 0.0647, 0.0187, 0.0344, 0.1388,
     0.0412, 0.2565, 0.0476, 0.0133, 0.0416, 0.008, 0.0079, 0.0647, 0.0681, 0.018,
     0.0121, 0.0305, 0.0496, 0.157, 0.0333, 0.1397, 0.0085, 0.0586, 0.013, 0.0436,
     0.0445, 0.0525, 0.0667, 0.0197, 0.0262, 0.082, 0.0391, 0.0217, 0.061, 0.0215,
     0.0657, 0.0794, 0.0752, 0.0455, 0.0183, 0.1787, 0.0249, 0.0385, 0.0227, 0.1724,
     0.0352, 0.0067, 0.0434, 0.1772, 0.062, 0.1437, 0.0645, 0.1215, 0.1043, 0.1675,
     0.0839, 0.0419, 0.1117, 0.1364, 0.1218, 0.0822, 0.2252, 0.0748, 0.0165, 0.1443,
     0.0233, 0.0646, 0.1693, 0.08, 0.2482, 0.041, 0.0993, 0.0711, 0.0724, 0.1075,
     0.0784, 0.0456, 0.0484, 0.042, 0.0519, 0.0241, 0.0515, 0.0428, 0.0596, 0.1615,
     0.0463, 0.0497, 0.0251, 0.0597, 0.107, 0.0613, 0.0887, 0.0224, 0.0462, 0.0902,
     0.0936, 0.0537, 0.041, 0.0721, 0.0529, 0.0393, 0.0351, 0.0214), V6 = c(0.228,
     0.171, 0.0591, 0.0671, 0.0546, 0.0809, 0.0757, 0.2559, 0.0816, 0.0151, 0.0201,
     0.0789, 0.02, 0.1432, 0.0706, 0.0492, 0.0438, 0.1172, 0.1817, 0.175, 0.069, 0.1883,
     0.0767, 0.0682, 0.0612, 0.0668, 0.0906, 0.0778, 0.0771, 0.0618, 0.0351, 0.1342,
     0.0249, 0.0284, 0.0613, 0.0136, 0.0742, 0.0804, 0.0887, 0.1452, 0.1019, 0.1635,
     0.0488, 0.0534, 0.0834, 0.3823, 0.0701, 0.089, 0.1224, 0.1908, 0.0871, 0.119,
     0.0472, 0.1524, 0.0783, 0.0418, 0.1673, 0.1215, 0.1506, 0.1513, 0.1538, 0.1256,
     0.2611, 0.1125, 0.0277, 0.277, 0.1048, 0.1124, 0.0844, 0.0629, 0.1257, 0.0116,
     0.1515, 0.1563, 0.0833, 0.1019, 0.125, 0.0665, 0.0526, 0.0865, 0.0849, 0.0253,
     0.0817, 0.0349, 0.0808, 0.0887, 0.069, 0.0998, 0.0629, 0.1416, 0.0973, 0.1039,
     0.0932, 0.0845, 0.0779, 0.1057, 0.1146, 0.0874, 0.0491, 0.1341, 0.1091, 0.163,
     0.1171, 0.0338), V7 = c(0.2431, 0.0731, 0.0753, 0.1056, 0.0528, 0.0568, 0.1026,
     0.2947, 0.0993, 0.0541, 0.0314, 0.144, 0.0981, 0.1344, 0.102, 0.0033, 0.1299,
     0.145, 0.1178, 0.092, 0.0901, 0.1422, 0.0771, 0.0993, 0.0895, 0.0609, 0.0889,
     0.0931, 0.0499, 0.0432, 0.0362, 0.1161, 0.0892, 0.0527, 0.0612, 0.0659, 0.138,
     0.1136, 0.1015, 0.2211, 0.1054, 0.0887, 0.1424, 0.214, 0.0677, 0.3729, 0.1263,
     0.1798, 0.1947, 0.2217, 0.1201, 0.0884, 0.1056, 0.1543, 0.1417, 0.0723, 0.1154,
     0.2002, 0.1776, 0.1316, 0.1192, 0.1323, 0.2061, 0.3322, 0.0569, 0.2555, 0.1338,
     0.1787, 0.0715, 0.013, 0.1797, 0.0223, 0.1674, 0.1518, 0.1232, 0.1606, 0.1296,
     0.0939, 0.0773, 0.1182, 0.0812, 0.0279, 0.1005, 0.0384, 0.209, 0.0596, 0.0576,
     0.1326, 0.0747, 0.0956, 0.0961, 0.1016, 0.0955, 0.1488, 0.1365, 0.1024, 0.0706,
     0.1021, 0.1053, 0.1626, 0.1709, 0.2028, 0.1257, 0.0655), V8 = c(0.3771, 0.1401,
     0.0098, 0.0697, 0.0958, 0.0219, 0.1138, 0.411, 0.0315, 0.021, 0.0651, 0.1451,
     0.1016, 0.2041, 0.0893, 0.0398, 0.139, 0.0638, 0.1024, 0.1353, 0.1454, 0.1447,
     0.064, 0.0717, 0.1107, 0.0131, 0.0655, 0.0941, 0.0906, 0.0951, 0.0535, 0.0663,
     0.0973, 0.0575, 0.0506, 0.0954, 0.1099, 0.1228, 0.0494, 0.1188, 0.107, 0.0817,
     0.1972, 0.311, 0.2002, 0.3583, 0.108, 0.1741, 0.1661, 0.0768, 0.2707, 0.0907,
     0.1388, 0.0391, 0.1176, 0.0828, 0.1098, 0.1516, 0.0997, 0.1654, 0.1229, 0.1584,
     0.1668, 0.459, 0.2057, 0.1712, 0.0644, 0.2407, 0.0947, 0.0813, 0.0989, 0.0805,
     0.1513, 0.1206, 0.1298, 0.2119, 0.1729, 0.0972, 0.0862, 0.0999, 0.1833, 0.013,
     0.0124, 0.0446, 0.3465, 0.1071, 0.1103, 0.1117, 0.0578, 0.0802, 0.1323, 0.1394,
     0.214, 0.1224, 0.078, 0.1209, 0.0996, 0.0852, 0.169, 0.1902, 0.1684, 0.1694,
     0.1178, 0.14), V9 = c(0.5598, 0.2083, 0.0684, 0.0962, 0.1009, 0.1037, 0.0794,
     0.4983, 0.0736, 0.0505, 0.1896, 0.1789, 0.2025, 0.1571, 0.0381, 0.0791, 0.0695,
     0.074, 0.0583, 0.1593, 0.074, 0.0487, 0.0726, 0.0576, 0.0973, 0.0899, 0.1624,
     0.1711, 0.1229, 0.0836, 0.0258, 0.0155, 0.084, 0.1054, 0.0989, 0.0786, 0.1384,
     0.1235, 0.0472, 0.075, 0.2302, 0.1779, 0.1873, 0.2837, 0.2876, 0.3429, 0.1523,
     0.1598, 0.1368, 0.1246, 0.1206, 0.2107, 0.0598, 0.061, 0.0453, 0.0494, 0.137,
     0.0818, 0.1428, 0.1864, 0.2119, 0.2017, 0.1801, 0.5526, 0.3887, 0.0466, 0.1522,
     0.2682, 0.1583, 0.1761, 0.246, 0.2365, 0.1723, 0.1666, 0.2085, 0.3061, 0.2794,
     0.2535, 0.1451, 0.1976, 0.2228, 0.0489, 0.1168, 0.1318, 0.5276, 0.3175, 0.2423,
     0.2984, 0.1357, 0.1618, 0.2462, 0.2592, 0.2546, 0.1569, 0.1038, 0.1241, 0.1673,
     0.1136, 0.2105, 0.261, 0.1865, 0.2328, 0.1258, 0.1843), V10 = c(0.6194, 0.3513,
     0.1487, 0.0251, 0.124, 0.1186, 0.152, 0.592, 0.086, 0.1097, 0.2668, 0.2522, 0.0767,
     0.1573, 0.1328, 0.0475, 0.0568, 0.136, 0.2176, 0.2795, 0.0349, 0.0864, 0.0901,
     0.0818, 0.0751, 0.0922, 0.1452, 0.1483, 0.1185, 0.118, 0.0474, 0.0506, 0.1191,
     0.1109, 0.1093, 0.1015, 0.1376, 0.0842, 0.0393, 0.1631, 0.2259, 0.2053, 0.1806,
     0.2751, 0.3674, 0.2197, 0.163, 0.1408, 0.143, 0.2028, 0.0279, 0.3597, 0.1334,
     0.0113, 0.0945, 0.0686, 0.1767, 0.1975, 0.2227, 0.2013, 0.2531, 0.2122, 0.3083,
     0.5966, 0.7106, 0.1114, 0.078, 0.2058, 0.1247, 0.0998, 0.3422, 0.2461, 0.2078,
     0.1345, 0.272, 0.2936, 0.2954, 0.3127, 0.211, 0.2318, 0.181, 0.0874, 0.1476,
     0.1375, 0.5965, 0.2918, 0.3134, 0.3473, 0.1695, 0.2558, 0.2696, 0.3745, 0.2952,
     0.2119, 0.1567, 0.1533, 0.1859, 0.1747, 0.2471, 0.3193, 0.266, 0.2684, 0.2529,
     0.2354), V11 = c(0.6333, 0.1786, 0.1156, 0.0801, 0.1097, 0.1237, 0.1675, 0.5832,
     0.0414, 0.0841, 0.3376, 0.2607, 0.1767, 0.2327, 0.1303, 0.1152, 0.0869, 0.2132,
     0.2459, 0.3336, 0.1459, 0.2143, 0.075, 0.1315, 0.0528, 0.1445, 0.1442, 0.1532,
     0.0775, 0.0978, 0.0526, 0.0906, 0.1522, 0.0937, 0.1063, 0.1261, 0.0938, 0.0357,
     0.1106, 0.2709, 0.2373, 0.3135, 0.2139, 0.2707, 0.2974, 0.2653, 0.103, 0.2693,
     0.0994, 0.0947, 0.2251, 0.5466, 0.2969, 0.1255, 0.1132, 0.1125, 0.1995, 0.2309,
     0.2621, 0.289, 0.2855, 0.221, 0.3794, 0.5304, 0.7342, 0.1739, 0.1791, 0.1546,
     0.234, 0.0523, 0.2128, 0.2245, 0.1239, 0.0785, 0.2188, 0.3104, 0.2506, 0.2192,
     0.2343, 0.2472, 0.2549, 0.11, 0.2118, 0.2026, 0.6254, 0.3273, 0.4786, 0.4231,
     0.1734, 0.3078, 0.3412, 0.4229, 0.4025, 0.3003, 0.2476, 0.2128, 0.2481, 0.2198,
     0.268, 0.3468, 0.3188, 0.3108, 0.2716, 0.272), V12 = c(0.706, 0.0658, 0.1654,
     0.1056, 0.1215, 0.1601, 0.137, 0.5419, 0.0472, 0.0942, 0.3282, 0.371, 0.2555,
     0.1785, 0.0273, 0.052, 0.1935, 0.3738, 0.3332, 0.294, 0.3473, 0.372, 0.0844,
     0.1862, 0.1209, 0.1475, 0.0948, 0.11, 0.1101, 0.0909, 0.1854, 0.2545, 0.1322,
     0.0827, 0.1179, 0.0828, 0.0259, 0.0689, 0.1412, 0.3358, 0.3323, 0.3118, 0.1523,
     0.0946, 0.0837, 0.3223, 0.2187, 0.3259, 0.225, 0.2497, 0.2615, 0.5205, 0.4754,
     0.2473, 0.084, 0.1741, 0.2869, 0.3025, 0.3109, 0.365, 0.2961, 0.2399, 0.5364,
     0.2251, 0.5033, 0.316, 0.2681, 0.2671, 0.1764, 0.0904, 0.1377, 0.152, 0.0236,
     0.0367, 0.3037, 0.3431, 0.2601, 0.2621, 0.2087, 0.288, 0.2984, 0.1084, 0.2575,
     0.2389, 0.4507, 0.3035, 0.5239, 0.5044, 0.247, 0.3404, 0.4292, 0.4499, 0.5148,
     0.3094, 0.2783, 0.2536, 0.2712, 0.2721, 0.3049, 0.3738, 0.3553, 0.2933, 0.2374,
     0.2442), V13 = c(0.5544, 0.0513, 0.3833, 0.1266, 0.1874, 0.352, 0.1361, 0.5472,
     0.0835, 0.1204, 0.2432, 0.3906, 0.2812, 0.1507, 0.0644, 0.1192, 0.1478, 0.3738,
     0.3087, 0.1608, 0.3197, 0.2665, 0.1226, 0.2789, 0.1763, 0.2087, 0.0618, 0.089,
     0.1042, 0.0656, 0.104, 0.1464, 0.1434, 0.092, 0.1291, 0.0493, 0.1499, 0.1705,
     0.2202, 0.4091, 0.3827, 0.3686, 0.1975, 0.102, 0.1912, 0.5582, 0.1542, 0.4545,
     0.2444, 0.2209, 0.177, 0.5127, 0.5677, 0.3011, 0.0717, 0.271, 0.3275, 0.3938,
     0.2859, 0.351, 0.3341, 0.2964, 0.6173, 0.2402, 0.3, 0.3249, 0.1788, 0.3141, 0.2284,
     0.2655, 0.4032, 0.1732, 0.1771, 0.1227, 0.2959, 0.2456, 0.2249, 0.2419, 0.1645,
     0.2126, 0.2624, 0.1094, 0.2354, 0.2112, 0.3693, 0.3033, 0.4393, 0.5237, 0.3141,
     0.34, 0.3682, 0.5404, 0.4901, 0.2743, 0.2896, 0.2686, 0.2934, 0.2105, 0.2863,
     0.3055, 0.3116, 0.2275, 0.1878, 0.1665), V14 = c(0.532, 0.3752, 0.3598, 0.089,
     0.3383, 0.4479, 0.1345, 0.5314, 0.0938, 0.042, 0.1268, 0.2672, 0.2722, 0.1916,
     0.0712, 0.1943, 0.1871, 0.2673, 0.2613, 0.3335, 0.2823, 0.2113, 0.1619, 0.2579,
     0.2039, 0.2558, 0.1641, 0.1236, 0.0853, 0.0593, 0.0948, 0.1272, 0.1244, 0.0911,
     0.1591, 0.0848, 0.2851, 0.3257, 0.2976, 0.44, 0.484, 0.3885, 0.4844, 0.4519,
     0.504, 0.6916, 0.263, 0.5785, 0.3239, 0.3195, 0.3709, 0.5395, 0.569, 0.3747,
     0.1968, 0.3087, 0.3769, 0.505, 0.3316, 0.3495, 0.4287, 0.4061, 0.7842, 0.2689,
     0.1951, 0.2164, 0.1039, 0.2904, 0.3115, 0.3099, 0.5684, 0.3099, 0.3115, 0.2614,
     0.2059, 0.1887, 0.2115, 0.2179, 0.1689, 0.0708, 0.1893, 0.1023, 0.1334, 0.1444,
     0.2864, 0.2587, 0.344, 0.4398, 0.3297, 0.3951, 0.394, 0.4303, 0.4127, 0.2547,
     0.2956, 0.2803, 0.2637, 0.1727, 0.2294, 0.1926, 0.1965, 0.0994, 0.0983, 0.0336
     ), V15 = c(0.6479, 0.5419, 0.1713, 0.0198, 0.3227, 0.3769, 0.2144, 0.4981, 0.1466,
     0.0031, 0.1278, 0.2716, 0.3227, 0.2061, 0.1204, 0.184, 0.1994, 0.2333, 0.3232,
     0.4985, 0.0166, 0.1103, 0.2317, 0.224, 0.2727, 0.2603, 0.0708, 0.1197, 0.0456,
     0.0832, 0.0912, 0.1223, 0.0653, 0.1487, 0.168, 0.1514, 0.5743, 0.4602, 0.4116,
     0.5485, 0.6812, 0.585, 0.7298, 0.6737, 0.6352, 0.7943, 0.294, 0.4471, 0.3039,
     0.334, 0.4533, 0.6558, 0.6421, 0.452, 0.2633, 0.3575, 0.4169, 0.5872, 0.3755,
     0.4325, 0.5205, 0.5095, 0.8392, 0.6646, 0.2767, 0.2031, 0.198, 0.3531, 0.4725,
     0.352, 0.2398, 0.438, 0.499, 0.428, 0.0906, 0.1184, 0.127, 0.1159, 0.165, 0.1194,
     0.0668, 0.0601, 0.0092, 0.0742, 0.1635, 0.1682, 0.2869, 0.3236, 0.2759, 0.3352,
     0.2965, 0.3333, 0.3575, 0.187, 0.3189, 0.1886, 0.188, 0.204, 0.1165, 0.1385,
     0.178, 0.1801, 0.0683, 0.1302), V16 = c(0.6931, 0.544, 0.1136, 0.1133, 0.2723,
     0.5761, 0.5354, 0.6985, 0.0809, 0.0162, 0.4441, 0.4183, 0.3463, 0.2307, 0.0717,
     0.2077, 0.3283, 0.5367, 0.3731, 0.7295, 0.0572, 0.1136, 0.2934, 0.2568, 0.2321,
     0.1985, 0.0844, 0.1145, 0.1304, 0.1297, 0.1688, 0.1669, 0.089, 0.1666, 0.1918,
     0.1396, 0.8278, 0.6225, 0.4754, 0.7213, 0.7555, 0.7868, 0.7807, 0.6699, 0.6804,
     0.7152, 0.2978, 0.2231, 0.241, 0.3323, 0.5553, 0.8705, 0.7487, 0.5392, 0.4191,
     0.4998, 0.5036, 0.661, 0.4499, 0.5398, 0.6087, 0.5512, 0.9016, 0.6632, 0.3737,
     0.258, 0.3234, 0.5079, 0.5543, 0.3892, 0.4331, 0.5595, 0.6707, 0.6122, 0.161,
     0.208, 0.1193, 0.1237, 0.1967, 0.2808, 0.2666, 0.0906, 0.1951, 0.1533, 0.0422,
     0.1308, 0.3889, 0.2956, 0.2056, 0.2252, 0.3172, 0.3496, 0.3447, 0.1452, 0.1892,
     0.1485, 0.1405, 0.1786, 0.2127, 0.2122, 0.2794, 0.22, 0.1503, 0.1708), V17 = c(0.6759,
     0.515, 0.0349, 0.2826, 0.3943, 0.6426, 0.683, 0.8292, 0.1179, 0.0624, 0.6795,
     0.6988, 0.5395, 0.236, 0.1224, 0.1956, 0.6861, 0.7312, 0.4203, 0.735, 0.2164,
     0.1934, 0.3526, 0.2933, 0.2676, 0.2394, 0.259, 0.2137, 0.269, 0.2038, 0.1568,
     0.1424, 0.1226, 0.1268, 0.1615, 0.1066, 0.8669, 0.7327, 0.539, 0.8137, 0.9522,
     0.9739, 0.7906, 0.7066, 0.7505, 0.3512, 0.0699, 0.2164, 0.0367, 0.278, 0.4616,
     0.9786, 0.8999, 0.6588, 0.505, 0.6011, 0.618, 0.7417, 0.4765, 0.6237, 0.7236,
     0.6613, 1, 0.1674, 0.2507, 0.1796, 0.3748, 0.4639, 0.5386, 0.3962, 0.5954, 0.682,
     0.7655, 0.7435, 0.18, 0.2736, 0.1794, 0.0886, 0.2934, 0.4221, 0.4274, 0.1313,
     0.3685, 0.3052, 0.1785, 0.2803, 0.442, 0.3286, 0.1162, 0.2086, 0.2825, 0.3426,
     0.3068, 0.1457, 0.173, 0.216, 0.2028, 0.1318, 0.2062, 0.2758, 0.287, 0.2732,
     0.1723, 0.2177), V18 = c(0.7551, 0.4262, 0.3796, 0.3234, 0.6432, 0.679, 0.56,
     0.7839, 0.2179, 0.2127, 0.7051, 0.5733, 0.7911, 0.1299, 0.2349, 0.163, 0.5814,
     0.7659, 0.5364, 0.8253, 0.4563, 0.4142, 0.3657, 0.2991, 0.2934, 0.3134, 0.2679,
     0.2838, 0.2947, 0.3811, 0.0375, 0.1285, 0.1846, 0.1374, 0.1647, 0.1923, 0.8131,
     0.7843, 0.6279, 0.9185, 0.9826, 1, 0.6122, 0.5632, 0.6595, 0.2008, 0.1401, 0.3201,
     0.1672, 0.2975, 0.3797, 0.9335, 1, 0.7113, 0.6711, 0.647, 0.8025, 0.8006, 0.6254,
     0.6876, 0.7577, 0.6804, 0.8911, 0.0837, 0.2507, 0.2422, 0.2586, 0.1859, 0.3746,
     0.2449, 0.5772, 0.6164, 0.8485, 0.813, 0.218, 0.3274, 0.2185, 0.1755, 0.3709,
     0.5279, 0.6291, 0.2758, 0.4646, 0.4116, 0.4394, 0.4519, 0.3892, 0.3231, 0.1884,
     0.2248, 0.305, 0.2851, 0.2945, 0.2429, 0.2226, 0.2417, 0.2613, 0.226, 0.2222,
     0.4576, 0.3969, 0.2862, 0.2339, 0.3175), V19 = c(0.8929, 0.2024, 0.7401, 0.3238,
     0.7271, 0.7157, 0.3093, 0.8215, 0.3326, 0.3436, 0.7966, 0.2226, 0.9064, 0.3812,
     0.3684, 0.1218, 0.25, 0.6271, 0.7062, 0.8793, 0.3819, 0.3279, 0.3221, 0.3924,
     0.3295, 0.4077, 0.3094, 0.364, 0.3669, 0.4451, 0.1316, 0.1857, 0.388, 0.1095,
     0.1397, 0.2991, 0.9045, 0.7988, 0.706, 1, 0.8871, 0.9843, 0.42, 0.3785, 0.4509,
     0.2676, 0.299, 0.2915, 0.3038, 0.2948, 0.345, 0.7917, 0.969, 0.7602, 0.7922,
     0.8067, 0.9333, 0.8456, 0.7304, 0.7329, 0.7726, 0.652, 0.8753, 0.4331, 0.3292,
     0.3609, 0.368, 0.4474, 0.4583, 0.2355, 0.8176, 0.6803, 0.9805, 0.9006, 0.2026,
     0.2344, 0.1646, 0.1758, 0.4309, 0.5857, 0.7782, 0.366, 0.5418, 0.5466, 0.695,
     0.6641, 0.4088, 0.4528, 0.339, 0.3382, 0.2408, 0.4062, 0.4351, 0.3259, 0.2427,
     0.2989, 0.2778, 0.2358, 0.3241, 0.6487, 0.5599, 0.2034, 0.1962, 0.3714), V20 = c(0.8619,
     0.4233, 0.9925, 0.4333, 0.8673, 0.5466, 0.3226, 0.9363, 0.3258, 0.3813, 0.9401,
     0.2631, 0.8701, 0.5858, 0.3918, 0.1017, 0.1734, 0.4395, 0.8196, 0.9657, 0.5627,
     0.6222, 0.3093, 0.4691, 0.491, 0.4529, 0.4678, 0.543, 0.4948, 0.5224, 0.2086,
     0.1136, 0.3658, 0.1286, 0.1426, 0.3247, 0.9046, 0.8261, 0.7918, 0.9418, 0.8268,
     0.861, 0.2807, 0.2721, 0.2964, 0.4299, 0.3915, 0.4235, 0.4069, 0.1729, 0.2665,
     0.7383, 0.9032, 0.8672, 0.8381, 0.9008, 0.9399, 0.7939, 0.8702, 0.8107, 0.8098,
     0.6788, 0.7886, 0.8718, 0.4871, 0.181, 0.3508, 0.4079, 0.5961, 0.3045, 0.8835,
     0.8435, 1, 0.9603, 0.1506, 0.126, 0.074, 0.154, 0.4161, 0.6153, 0.7686, 0.5269,
     0.626, 0.5933, 0.8097, 0.7683, 0.5006, 0.6339, 0.3926, 0.4578, 0.542, 0.6833,
     0.7264, 0.3679, 0.3149, 0.3341, 0.3346, 0.3107, 0.433, 0.7154, 0.6936, 0.174,
     0.1395, 0.4552), V21 = c(0.7974, 0.7723, 0.9802, 0.6068, 0.9674, 0.5399, 0.443,
     1, 0.2111, 0.3825, 0.9857, 0.7473, 0.7672, 0.4497, 0.4925, 0.1354, 0.3363, 0.433,
     0.8835, 1, 0.6484, 0.7468, 0.4084, 0.5665, 0.5402, 0.4893, 0.5958, 0.6673, 0.6275,
     0.5911, 0.1976, 0.2069, 0.2297, 0.2146, 0.2429, 0.3797, 1, 1, 0.9493, 0.9116,
     0.7561, 0.8443, 0.5148, 0.5297, 0.4019, 0.528, 0.3598, 0.446, 0.3613, 0.3264,
     0.2395, 0.6908, 0.7685, 0.8416, 0.8759, 0.8906, 0.9275, 0.8804, 0.9349, 0.8396,
     0.8995, 0.7811, 0.7156, 0.7992, 0.6527, 0.2604, 0.5606, 0.54, 0.7464, 0.3112,
     0.5248, 0.9921, 1, 0.9162, 0.0521, 0.0576, 0.0625, 0.0512, 0.5116, 0.6753, 0.8099,
     0.581, 0.742, 0.6663, 0.855, 0.696, 0.7271, 0.7044, 0.4282, 0.6474, 0.6802, 0.765,
     0.8147, 0.3355, 0.4102, 0.3786, 0.383, 0.3906, 0.5071, 0.801, 0.7969, 0.413,
     0.3164, 0.57), V22 = c(0.6737, 0.9735, 0.889, 0.7652, 0.9847, 0.6362, 0.5573,
     0.9224, 0.2302, 0.4764, 0.8193, 0.7263, 0.2957, 0.4876, 0.8793, 0.3157, 0.5588,
     0.4326, 0.8299, 0.8707, 0.7235, 0.7676, 0.4285, 0.6464, 0.6257, 0.5666, 0.7245,
     0.7979, 0.8162, 0.6566, 0.0946, 0.0219, 0.261, 0.2889, 0.2816, 0.5658, 0.9976,
     0.9814, 1, 0.9349, 0.8217, 0.9061, 0.7569, 0.7697, 0.6794, 0.3489, 0.2403, 0.238,
     0.1994, 0.3834, 0.1127, 0.385, 0.6998, 0.7974, 0.9422, 0.9338, 0.945, 0.8384,
     0.9614, 0.8632, 0.9247, 0.8369, 0.7581, 0.3712, 0.8454, 0.6572, 0.5231, 0.4786,
     0.7644, 0.4698, 0.6373, 1, 0.9992, 0.914, 0.2143, 0.1241, 0.2381, 0.1805, 0.6501,
     0.7873, 0.8493, 0.6181, 0.8257, 0.7333, 0.8717, 0.4393, 0.9385, 0.8314, 0.5418,
     0.6708, 0.632, 0.667, 0.8103, 0.31, 0.3808, 0.3956, 0.4003, 0.3631, 0.5944, 0.7924,
     0.7452, 0.6879, 0.5888, 0.7397), V23 = c(0.4293, 0.939, 0.6712, 0.9203, 0.948,
     0.7849, 0.5782, 0.7839, 0.3361, 0.6313, 0.5789, 0.3393, 0.4148, 1, 0.9606, 0.4645,
     0.6592, 0.5544, 0.7609, 0.6471, 0.8242, 0.7867, 0.4663, 0.6774, 0.6826, 0.6234,
     0.8773, 0.9273, 0.9237, 0.6308, 0.1965, 0.24, 0.4193, 0.4238, 0.429, 0.7483,
     0.9872, 0.962, 0.9645, 0.7484, 0.6967, 0.5847, 0.8596, 0.8643, 0.8297, 0.143,
     0.4208, 0.6415, 0.4611, 0.3523, 0.2556, 0.0671, 0.6644, 0.8385, 1, 1, 0.8328,
     0.7852, 0.9126, 0.8747, 0.9365, 0.8969, 0.6372, 0.1703, 0.9739, 0.9734, 0.5469,
     0.4332, 0.5711, 0.5534, 0.8375, 0.7983, 0.9067, 0.7851, 0.4333, 0.3239, 0.4824,
     0.4039, 0.7717, 0.8974, 0.944, 0.5875, 0.8609, 0.7136, 0.8601, 0.2432, 1, 0.8449,
     0.6448, 0.7007, 0.5824, 0.5703, 0.6665, 0.3914, 0.4896, 0.5232, 0.5114, 0.4809,
     0.7078, 0.8793, 0.8203, 0.812, 0.7631, 0.8062), V24 = c(0.3648, 0.5559, 0.4286,
     0.9719, 0.8036, 0.7756, 0.6173, 0.547, 0.4259, 0.7523, 0.6394, 0.2824, 0.6043,
     0.8675, 0.8786, 0.5906, 0.7012, 0.736, 0.7605, 0.5973, 0.8766, 0.8253, 0.5956,
     0.7577, 0.7527, 0.6741, 0.9214, 0.9027, 0.871, 0.5998, 0.1242, 0.2547, 0.5848,
     0.6168, 0.6443, 0.8757, 0.9761, 0.9601, 0.9432, 0.5146, 0.6444, 0.4033, 1, 0.9304,
     1, 0.5453, 0.5675, 0.8966, 0.6849, 0.541, 0.5169, 0.0502, 0.5964, 0.9317, 0.9931,
     0.9102, 0.7773, 0.8479, 0.9443, 0.9607, 0.9853, 0.9856, 0.321, 0.1611, 1, 0.9757,
     0.6954, 0.6113, 0.6257, 0.4532, 0.6699, 0.5426, 0.6803, 0.5134, 0.5943, 0.4357,
     0.6372, 0.5697, 0.8491, 0.9828, 0.945, 0.4639, 0.84, 0.7014, 0.9201, 0.2886,
     0.9831, 0.8512, 0.7223, 0.7619, 0.6805, 0.5995, 0.6958, 0.528, 0.6292, 0.6913,
     0.686, 0.6531, 0.7641, 1, 0.9261, 0.8453, 0.8473, 0.8837), V25 = c(0.5331, 0.5268,
     0.3374, 0.9207, 0.6833, 0.578, 0.8132, 0.4562, 0.4609, 0.8675, 0.7043, 0.6053,
     0.3178, 0.4718, 0.6905, 0.6776, 0.8099, 0.8589, 0.8367, 0.8218, 1, 1, 0.6948,
     0.8856, 0.8504, 0.8282, 0.9282, 0.9192, 0.8052, 0.4958, 0.0616, 0.024, 0.5643,
     0.8167, 0.9061, 0.9048, 0.9009, 0.9118, 0.8658, 0.4106, 0.6948, 0.5946, 0.8457,
     0.9372, 0.824, 0.6338, 0.6094, 0.8918, 0.7272, 0.5228, 0.3779, 0.2717, 0.3711,
     0.8555, 0.9575, 0.8496, 0.7007, 0.7434, 1, 0.9716, 0.9776, 1, 0.2076, 0.2086,
     0.6665, 0.8079, 0.6352, 0.5091, 0.6695, 0.4464, 0.7756, 0.3952, 0.5103, 0.3439,
     0.6926, 0.5734, 0.7531, 0.6577, 0.9104, 1, 0.9655, 0.5424, 0.8949, 0.7758, 0.8729,
     0.4974, 0.9932, 0.9138, 0.7853, 0.7745, 0.5984, 0.6484, 0.7748, 0.6409, 0.7519,
     0.7868, 0.749, 0.7812, 0.8878, 0.9865, 0.881, 0.8919, 0.9424, 0.9432), V26 = c(0.2413,
     0.6826, 0.7366, 0.7545, 0.5136, 0.4862, 0.9819, 0.5922, 0.2606, 0.8788, 0.6875,
     0.5897, 0.3482, 0.5341, 0.6937, 0.8119, 0.8901, 0.8989, 0.8905, 0.7755, 0.8582,
     0.9481, 0.8386, 0.9419, 0.8938, 0.8823, 0.9942, 1, 0.8756, 0.5647, 0.2141, 0.1923,
     0.5448, 0.9622, 1, 0.7511, 0.9724, 0.9086, 0.7895, 0.3443, 0.8014, 0.6793, 0.6797,
     0.6247, 0.7115, 0.7712, 0.6323, 0.7529, 0.7152, 0.4475, 0.4082, 0.2839, 0.0921,
     0.6162, 0.8647, 0.7867, 0.6154, 0.6433, 0.9455, 0.9121, 1, 0.9395, 0.2279, 0.2847,
     0.5323, 0.6521, 0.6757, 0.4606, 0.7131, 0.467, 0.875, 0.5179, 0.4716, 0.329,
     0.7576, 0.7825, 0.8959, 0.7474, 0.8912, 0.846, 0.8045, 0.7367, 0.9945, 0.9137,
     0.8084, 0.8172, 0.9161, 0.9985, 0.7984, 0.6767, 0.8412, 0.8614, 0.8688, 0.7707,
     0.7985, 0.8337, 0.7843, 0.8395, 0.9711, 0.9474, 0.8814, 0.93, 0.9986, 1), V27 = c(0.507,
     0.5713, 0.9611, 0.8289, 0.309, 0.4181, 0.9823, 0.5448, 0.0874, 0.7901, 0.4081,
     0.4967, 0.6158, 0.6197, 0.5674, 0.8594, 0.8745, 0.942, 0.7652, 0.6111, 0.6563,
     0.7539, 0.8875, 1, 0.9928, 0.9196, 1, 0.9821, 1, 0.6906, 0.4642, 0.4753, 0.4772,
     0.828, 0.8087, 0.6858, 0.9675, 0.7931, 0.6501, 0.6981, 0.6053, 0.6389, 0.6971,
     0.6024, 0.7726, 0.6838, 0.6549, 0.6838, 0.7102, 0.534, 0.5353, 0.2234, 0.0481,
     0.4139, 0.7215, 0.7688, 0.581, 0.5514, 0.8815, 0.8576, 0.9896, 0.8917, 0.3309,
     0.2211, 0.4024, 0.4915, 0.8499, 0.7243, 0.7567, 0.4621, 0.83, 0.565, 0.498, 0.2571,
     0.8787, 0.9252, 0.9941, 0.8543, 0.8189, 0.6055, 0.4969, 0.9089, 1, 0.9964, 0.8694,
     1, 0.8237, 1, 0.8847, 0.7373, 0.9911, 0.9819, 1, 0.8754, 0.883, 0.9199, 0.9021,
     0.918, 0.988, 0.9474, 0.9301, 0.9987, 0.9699, 0.9375), V28 = c(0.8533, 0.5429,
     0.7353, 0.8907, 0.0832, 0.2457, 0.9166, 0.3971, 0.2862, 0.8357, 0.1811, 0.8616,
     0.8049, 0.7143, 0.654, 0.9228, 0.7887, 0.9401, 0.5897, 0.4195, 0.5087, 0.6008,
     0.6404, 0.8564, 0.9134, 0.8965, 0.9071, 0.9092, 0.9858, 0.8513, 0.6471, 0.7003,
     0.6897, 0.5816, 0.6119, 0.7043, 0.7633, 0.5877, 0.4492, 0.8713, 0.6084, 0.5002,
     0.5843, 0.681, 0.6124, 0.8015, 0.7673, 0.839, 0.8516, 0.5323, 0.5116, 0.1911,
     0.0876, 0.3269, 0.5801, 0.7718, 0.4454, 0.3519, 0.752, 0.8798, 0.9076, 0.8105,
     0.2847, 0.6134, 0.3444, 0.5363, 0.8025, 0.8987, 0.8077, 0.6988, 0.6896, 0.3042,
     0.6196, 0.3685, 0.906, 0.9349, 0.9957, 0.9085, 0.6779, 0.3036, 0.396, 1, 0.9649,
     1, 0.8411, 0.9238, 0.6957, 0.7544, 0.9582, 0.7834, 0.9187, 0.938, 0.9941, 1,
     0.9915, 1, 1, 0.9769, 0.9812, 0.9315, 0.9955, 1, 1, 0.7603), V29 = c(0.6036,
     0.2177, 0.4856, 0.7309, 0.4019, 0.0716, 0.7423, 0.0882, 0.5606, 0.9631, 0.2064,
     0.8339, 0.6289, 0.5605, 0.7802, 0.8387, 0.8725, 0.9379, 0.3037, 0.299, 0.4817,
     0.5437, 0.3308, 0.679, 0.708, 0.7549, 0.8545, 0.8184, 0.9427, 1, 0.634, 0.6825,
     0.9797, 0.4667, 0.526, 0.5864, 0.4434, 0.3474, 0.4739, 0.9013, 0.8877, 0.5578,
     0.4772, 0.5047, 0.4936, 0.8073, 1, 1, 1, 0.3907, 0.4544, 0.0408, 0.104, 0.3108,
     0.4964, 0.6268, 0.3707, 0.3168, 0.7068, 0.772, 0.7306, 0.6828, 0.1949, 0.5807,
     0.4239, 0.7649, 0.6563, 0.8826, 0.8477, 0.7626, 0.3372, 0.1881, 0.7171, 0.5765,
     0.8528, 0.9348, 0.9328, 0.8668, 0.5368, 0.0144, 0.3856, 0.8247, 0.8747, 0.8881,
     0.5793, 0.8519, 0.4536, 0.4661, 0.899, 0.9619, 0.8005, 0.8435, 0.8793, 0.9806,
     0.9223, 0.899, 0.8888, 0.8937, 0.9464, 0.8326, 0.8576, 0.8104, 0.863, 0.7123),
     V30 = c(0.8514, 0.2149, 0.1594, 0.6896, 0.2344, 0.0613, 0.7736, 0.2385, 0.8344,
     0.9619, 0.3917, 0.4084, 0.4999, 0.3728, 0.7575, 0.7238, 0.9376, 0.8575, 0.0823,
     0.1354, 0.453, 0.5387, 0.3425, 0.5587, 0.6318, 0.6736, 0.7293, 0.6962, 0.8114,
     0.9166, 0.6107, 0.6443, 1, 0.3539, 0.3677, 0.3773, 0.3822, 0.4235, 0.6153,
     0.8014, 0.8557, 0.4831, 0.5201, 0.5775, 0.5648, 0.831, 0.8463, 0.8362, 0.769,
     0.3456, 0.4258, 0.2531, 0.1714, 0.2554, 0.4886, 0.4301, 0.2891, 0.3346, 0.5986,
     0.5711, 0.5758, 0.5572, 0.1671, 0.6925, 0.4182, 0.525, 0.8591, 0.9201, 0.9289,
     0.7025, 0.6405, 0.396, 0.6316, 0.619, 0.9087, 1, 0.9344, 0.8892, 0.5207,
     0.2526, 0.5574, 0.5441, 0.6257, 0.6585, 0.3754, 0.7722, 0.3281, 0.3924, 0.6831,
     1, 0.6713, 0.6074, 0.6482, 0.6969, 0.6981, 0.6456, 0.6511, 0.7022, 0.8542,
     0.6213, 0.6069, 0.6199, 0.6979, 0.8358), V31 = c(0.8512, 0.5811, 0.3007,
     0.5829, 0.1905, 0.1816, 0.8473, 0.2005, 0.8096, 0.9236, 0.3791, 0.2268, 0.583,
     0.2481, 0.5836, 0.6292, 0.892, 0.7284, 0.2787, 0.2438, 0.4521, 0.5619, 0.492,
     0.4147, 0.6126, 0.6463, 0.6499, 0.59, 0.6987, 0.7676, 0.7046, 0.7063, 0.9546,
     0.2727, 0.2746, 0.2206, 0.4727, 0.4633, 0.4929, 0.438, 0.5563, 0.4729, 0.4241,
     0.4754, 0.4906, 0.7792, 0.5509, 0.5427, 0.4841, 0.4091, 0.3869, 0.1979, 0.3264,
     0.3367, 0.4079, 0.2077, 0.2185, 0.2056, 0.3857, 0.4264, 0.4469, 0.4301, 0.1025,
     0.3825, 0.4393, 0.5101, 0.6655, 0.8005, 0.9513, 0.7382, 0.7138, 0.2286, 0.3554,
     0.4613, 0.9657, 0.9308, 0.8854, 0.9065, 0.5651, 0.4335, 0.7309, 0.3349, 0.2184,
     0.2707, 0.3485, 0.5772, 0.2522, 0.3849, 0.6108, 0.8086, 0.5632, 0.5403, 0.5876,
     0.4973, 0.6167, 0.5967, 0.6083, 0.65, 0.6457, 0.3772, 0.3934, 0.6041, 0.7717,
     0.7622), V32 = c(0.5045, 0.6323, 0.4096, 0.4935, 0.1235, 0.4493, 0.7352,
     0.0587, 0.725, 0.8903, 0.2042, 0.1745, 0.666, 0.1921, 0.6316, 0.5181, 0.7508,
     0.67, 0.7241, 0.5624, 0.4532, 0.5141, 0.4592, 0.2946, 0.4638, 0.5007, 0.6071,
     0.5447, 0.681, 0.6177, 0.5376, 0.5373, 0.8835, 0.141, 0.102, 0.2628, 0.4007,
     0.341, 0.3195, 0.1319, 0.2897, 0.3318, 0.1592, 0.24, 0.182, 0.5049, 0.4444,
     0.4577, 0.3717, 0.4639, 0.3939, 0.1891, 0.4612, 0.4465, 0.2443, 0.1198, 0.1711,
     0.1032, 0.251, 0.286, 0.3719, 0.3339, 0.1362, 0.4303, 0.1162, 0.4219, 0.5369,
     0.6033, 0.7995, 0.7446, 0.8202, 0.3544, 0.2897, 0.3615, 0.9306, 0.8478, 0.769,
     0.8522, 0.5749, 0.4918, 0.8549, 0.0877, 0.2945, 0.1746, 0.4639, 0.519, 0.3964,
     0.4674, 0.548, 0.5558, 0.7332, 0.689, 0.6408, 0.502, 0.5069, 0.4355, 0.4463,
     0.5069, 0.3397, 0.2822, 0.2464, 0.5547, 0.7305, 0.4567), V33 = c(0.1862,
     0.2965, 0.317, 0.3101, 0.1717, 0.5976, 0.6671, 0.2544, 0.8048, 0.9708, 0.2227,
     0.0507, 0.4124, 0.1386, 0.8108, 0.4629, 0.6832, 0.7547, 0.8032, 0.5555, 0.5385,
     0.6084, 0.3034, 0.2025, 0.2797, 0.3663, 0.5588, 0.5142, 0.6591, 0.5468, 0.5934,
     0.6601, 0.7662, 0.1863, 0.1339, 0.2672, 0.3381, 0.2849, 0.3735, 0.1709, 0.3638,
     0.3969, 0.1668, 0.2779, 0.1811, 0.1413, 0.5169, 0.8067, 0.6096, 0.558, 0.4661,
     0.2433, 0.3939, 0.5, 0.1768, 0.166, 0.3578, 0.3168, 0.2162, 0.3114, 0.2079,
     0.2035, 0.2212, 0.7791, 0.4336, 0.416, 0.3118, 0.212, 0.4362, 0.7927, 0.6657,
     0.4187, 0.4316, 0.4434, 0.7774, 0.7605, 0.6865, 0.7204, 0.525, 0.5409, 0.9425,
     0.16, 0.3645, 0.2709, 0.6495, 0.6824, 0.4154, 0.4245, 0.5058, 0.5409, 0.6038,
     0.5977, 0.4972, 0.5359, 0.3921, 0.2997, 0.2948, 0.3903, 0.3828, 0.2042, 0.1645,
     0.416, 0.5197, 0.1715), V34 = c(0.2709, 0.1873, 0.3305, 0.0306, 0.2351, 0.3785,
     0.6083, 0.2009, 0.9435, 0.9647, 0.3341, 0.1588, 0.126, 0.3325, 0.9039, 0.5255,
     0.761, 0.8773, 0.805, 0.6963, 0.5308, 0.5621, 0.4366, 0.0688, 0.1721, 0.2298,
     0.5967, 0.5389, 0.6954, 0.5516, 0.8443, 0.8708, 0.6547, 0.2176, 0.1582, 0.2907,
     0.3172, 0.2847, 0.3336, 0.2484, 0.4786, 0.3894, 0.0588, 0.1997, 0.1107, 0.2767,
     0.4268, 0.6973, 0.511, 0.5727, 0.3974, 0.1956, 0.505, 0.5111, 0.2472, 0.2618,
     0.3947, 0.404, 0.0968, 0.2066, 0.0955, 0.0798, 0.1124, 0.8703, 0.6553, 0.1906,
     0.3763, 0.2866, 0.4048, 0.5227, 0.5254, 0.2398, 0.3791, 0.3864, 0.6643, 0.704,
     0.639, 0.62, 0.4255, 0.5961, 0.8726, 0.4169, 0.5012, 0.4853, 0.6901, 0.622,
     0.3308, 0.3095, 0.4476, 0.4988, 0.2575, 0.3244, 0.2755, 0.3842, 0.3524, 0.2294,
     0.1729, 0.3009, 0.3204, 0.219, 0.114, 0.1472, 0.1786, 0.1549), V35 = c(0.4232,
     0.2969, 0.3408, 0.0244, 0.2489, 0.2495, 0.6239, 0.0329, 1, 0.7892, 0.3984,
     0.304, 0.2487, 0.2883, 0.8647, 0.5147, 0.9017, 0.9919, 0.7676, 0.7298, 0.5356,
     0.5956, 0.5175, 0.1171, 0.1665, 0.1362, 0.6275, 0.5531, 0.729, 0.5463, 0.9481,
     0.9518, 0.5447, 0.236, 0.1952, 0.1982, 0.2222, 0.1742, 0.1052, 0.3044, 0.2908,
     0.2314, 0.3967, 0.5305, 0.4603, 0.5084, 0.1802, 0.3915, 0.2586, 0.6355, 0.2194,
     0.2667, 0.4833, 0.5194, 0.3518, 0.3862, 0.2867, 0.4282, 0.1323, 0.1165, 0.0488,
     0.0809, 0.1677, 1, 0.6172, 0.0223, 0.2801, 0.4033, 0.4952, 0.3967, 0.296,
     0.1847, 0.2421, 0.3093, 0.6604, 0.7539, 0.6378, 0.6253, 0.333, 0.5248, 0.6673,
     0.6576, 0.7843, 0.7184, 0.5666, 0.5054, 0.1445, 0.0752, 0.2401, 0.3108, 0.0349,
     0.0516, 0.03, 0.1848, 0.2183, 0.1866, 0.1488, 0.1565, 0.1331, 0.2223, 0.0956,
     0.0849, 0.1098, 0.1641), V36 = c(0.3043, 0.5163, 0.2186, 0.1108, 0.3649,
     0.5771, 0.5972, 0.1547, 0.896, 0.5307, 0.5077, 0.1369, 0.4676, 0.3228, 0.6695,
     0.3929, 1, 0.9922, 0.7468, 0.7022, 0.5271, 0.6078, 0.5122, 0.2157, 0.2561,
     0.2123, 0.5459, 0.5318, 0.668, 0.5515, 0.9705, 0.9605, 0.4593, 0.1725, 0.1787,
     0.2288, 0.0733, 0.0549, 0.0671, 0.2312, 0.0899, 0.1036, 0.7147, 0.7409, 0.665,
     0.4787, 0.0791, 0.1558, 0.0916, 0.7563, 0.1816, 0.134, 0.3511, 0.4619, 0.3762,
     0.3958, 0.2401, 0.4538, 0.1344, 0.0185, 0.1406, 0.1525, 0.1039, 0.9212, 0.4373,
     0.4219, 0.0875, 0.2803, 0.1712, 0.3042, 0.0704, 0.376, 0.0944, 0.2138, 0.6884,
     0.799, 0.6629, 0.6848, 0.2331, 0.3777, 0.4694, 0.739, 0.9361, 0.8209, 0.5188,
     0.3578, 0.1923, 0.2885, 0.1405, 0.2897, 0.1799, 0.3157, 0.3356, 0.1149, 0.1245,
     0.0922, 0.0801, 0.0985, 0.044, 0.1327, 0.008, 0.0608, 0.1446, 0.1869), V37 = c(0.6116,
     0.6153, 0.2463, 0.1594, 0.3382, 0.8852, 0.5715, 0.1212, 0.5516, 0.2718, 0.5534,
     0.1605, 0.5382, 0.2607, 0.4027, 0.1279, 0.9123, 0.9419, 0.6253, 0.5468, 0.426,
     0.5025, 0.4746, 0.2216, 0.2735, 0.2395, 0.4786, 0.4826, 0.5917, 0.4561, 0.7766,
     0.7712, 0.4679, 0.0589, 0.0429, 0.3186, 0.2692, 0.1192, 0.0379, 0.1338, 0.2043,
     0.1312, 0.7319, 0.7775, 0.6423, 0.1356, 0.0535, 0.1598, 0.0947, 0.6903, 0.1023,
     0.1073, 0.2319, 0.4234, 0.2909, 0.3248, 0.3619, 0.3704, 0.225, 0.1302, 0.2554,
     0.2626, 0.2562, 0.9386, 0.4118, 0.5496, 0.3319, 0.3087, 0.3652, 0.1309, 0.097,
     0.4331, 0.0351, 0.1112, 0.6938, 0.7673, 0.5983, 0.7337, 0.1451, 0.2369, 0.1546,
     0.7963, 0.8195, 0.7536, 0.506, 0.3809, 0.3208, 0.4072, 0.1772, 0.2244, 0.3039,
     0.359, 0.3167, 0.157, 0.1592, 0.1829, 0.177, 0.22, 0.1234, 0.0521, 0.0702,
     0.0969, 0.1066, 0.2655), V38 = c(0.6756, 0.4283, 0.2726, 0.1371, 0.1589,
     0.8409, 0.5242, 0.2446, 0.3037, 0.1953, 0.3352, 0.2061, 0.315, 0.204, 0.237,
     0.0411, 0.7388, 0.8388, 0.173, 0.1421, 0.2436, 0.2829, 0.4902, 0.2776, 0.3209,
     0.2673, 0.3965, 0.379, 0.4899, 0.3466, 0.6313, 0.6772, 0.1987, 0.0621, 0.1096,
     0.2871, 0.1888, 0.1154, 0.0461, 0.2056, 0.1707, 0.0864, 0.3509, 0.4424, 0.2166,
     0.2299, 0.1906, 0.2161, 0.2287, 0.6176, 0.2108, 0.2023, 0.4029, 0.4372, 0.2311,
     0.2302, 0.3314, 0.3741, 0.3244, 0.248, 0.2054, 0.2456, 0.2624, 0.9303, 0.3641,
     0.2483, 0.4237, 0.355, 0.3763, 0.2408, 0.3941, 0.3626, 0.0844, 0.1386, 0.5932,
     0.5955, 0.4565, 0.6281, 0.1648, 0.172, 0.1748, 0.7493, 0.6207, 0.6496, 0.3885,
     0.3813, 0.3367, 0.317, 0.1742, 0.096, 0.476, 0.3881, 0.4133, 0.1311, 0.1626,
     0.1743, 0.1382, 0.2243, 0.203, 0.0618, 0.0936, 0.1411, 0.144, 0.1713), V39 = c(0.5375,
     0.5479, 0.168, 0.0696, 0.0989, 0.357, 0.2924, 0.3171, 0.2338, 0.1374, 0.2723,
     0.0734, 0.2139, 0.2396, 0.2685, 0.0859, 0.5915, 0.6605, 0.2916, 0.4738, 0.1205,
     0.0477, 0.4603, 0.2309, 0.2724, 0.2865, 0.2087, 0.1831, 0.3439, 0.3384, 0.576,
     0.6431, 0.0699, 0.1847, 0.1762, 0.2921, 0.0712, 0.0855, 0.1694, 0.2474, 0.0407,
     0.2569, 0.0589, 0.1416, 0.1951, 0.2789, 0.2561, 0.5178, 0.348, 0.5379, 0.3253,
     0.1794, 0.3676, 0.4277, 0.3168, 0.325, 0.3763, 0.3839, 0.3939, 0.1637, 0.1614,
     0.198, 0.2236, 0.7314, 0.4572, 0.2034, 0.1801, 0.2545, 0.2841, 0.178, 0.6028,
     0.2519, 0.0436, 0.1523, 0.5774, 0.4731, 0.3129, 0.5725, 0.2694, 0.1878, 0.3607,
     0.6795, 0.4513, 0.4708, 0.3762, 0.3359, 0.5683, 0.2863, 0.3326, 0.2287, 0.5756,
     0.5716, 0.6281, 0.1583, 0.2356, 0.2452, 0.2404, 0.2736, 0.1652, 0.1416, 0.0894,
     0.1676, 0.1929, 0.0959), V40 = c(0.4719, 0.6133, 0.2792, 0.0452, 0.1089,
     0.3133, 0.1536, 0.3195, 0.2382, 0.3105, 0.2278, 0.0202, 0.1848, 0.1319, 0.3662,
     0.1131, 0.4057, 0.4816, 0.5003, 0.641, 0.3845, 0.2811, 0.446, 0.1444, 0.188,
     0.206, 0.1651, 0.175, 0.2366, 0.2853, 0.6148, 0.672, 0.1493, 0.2452, 0.2481,
     0.2806, 0.1062, 0.1811, 0.2169, 0.279, 0.1286, 0.3179, 0.269, 0.3508, 0.4947,
     0.3833, 0.2153, 0.4782, 0.2095, 0.5622, 0.3697, 0.0227, 0.151, 0.4433, 0.3554,
     0.4022, 0.4767, 0.3494, 0.3806, 0.1103, 0.2232, 0.2412, 0.118, 0.4791, 0.4367,
     0.2729, 0.3743, 0.1432, 0.0427, 0.1598, 0.3521, 0.187, 0.113, 0.0996, 0.6223,
     0.484, 0.4158, 0.6119, 0.373, 0.325, 0.5208, 0.4713, 0.3004, 0.3482, 0.3738,
     0.2771, 0.5505, 0.2634, 0.4021, 0.3228, 0.4254, 0.4314, 0.4977, 0.2631, 0.2483,
     0.2407, 0.2046, 0.2152, 0.1043, 0.146, 0.1127, 0.12, 0.0325, 0.0768), V41 = c(0.4647,
     0.5017, 0.2558, 0.062, 0.1043, 0.6096, 0.2003, 0.3051, 0.3318, 0.379, 0.2044,
     0.1638, 0.1679, 0.0683, 0.3267, 0.1306, 0.3019, 0.2917, 0.522, 0.4375, 0.4107,
     0.3422, 0.4196, 0.1513, 0.1552, 0.1659, 0.1836, 0.1679, 0.1716, 0.2502, 0.545,
     0.6035, 0.1713, 0.2984, 0.315, 0.2682, 0.0694, 0.1264, 0.1677, 0.161, 0.1581,
     0.2649, 0.42, 0.4482, 0.4925, 0.2933, 0.2769, 0.2344, 0.1901, 0.6508, 0.2912,
     0.1313, 0.0745, 0.37, 0.3741, 0.4344, 0.4059, 0.438, 0.3258, 0.2144, 0.1773,
     0.2409, 0.1103, 0.2087, 0.2964, 0.2837, 0.4627, 0.5869, 0.5331, 0.5657, 0.3924,
     0.1046, 0.2045, 0.1644, 0.5841, 0.434, 0.4325, 0.5597, 0.4467, 0.2575, 0.5177,
     0.2355, 0.2674, 0.3508, 0.2605, 0.3648, 0.3231, 0.0541, 0.3009, 0.3454, 0.5046,
     0.3051, 0.2613, 0.3103, 0.2437, 0.2518, 0.197, 0.2438, 0.1066, 0.0846, 0.0873,
     0.1201, 0.149, 0.0847), V42 = c(0.2587, 0.2377, 0.174, 0.1421, 0.0839, 0.6378,
     0.2031, 0.0836, 0.3821, 0.4105, 0.1986, 0.1583, 0.2328, 0.0334, 0.22, 0.1757,
     0.2331, 0.1769, 0.4824, 0.3178, 0.5067, 0.5147, 0.2873, 0.1745, 0.2522, 0.2633,
     0.0652, 0.0674, 0.1013, 0.1641, 0.4813, 0.5155, 0.1654, 0.3041, 0.292, 0.2112,
     0.03, 0.0799, 0.0644, 0.0056, 0.2191, 0.2714, 0.3874, 0.4208, 0.4041, 0.1155,
     0.2841, 0.3599, 0.2941, 0.4797, 0.301, 0.1775, 0.1395, 0.3324, 0.4443, 0.4008,
     0.3661, 0.4265, 0.3654, 0.2033, 0.2293, 0.1901, 0.2831, 0.2016, 0.4312, 0.4463,
     0.1614, 0.6431, 0.6952, 0.6443, 0.4808, 0.2339, 0.1937, 0.1902, 0.4527, 0.3954,
     0.4031, 0.4965, 0.4133, 0.2423, 0.3702, 0.1704, 0.2241, 0.3181, 0.1591, 0.3834,
     0.0448, 0.1874, 0.2075, 0.3882, 0.7179, 0.4393, 0.4697, 0.4512, 0.2715, 0.3184,
     0.2778, 0.3154, 0.211, 0.1055, 0.102, 0.1036, 0.0328, 0.2076), V43 = c(0.2129,
     0.1957, 0.2121, 0.1597, 0.1391, 0.2709, 0.2207, 0.1266, 0.1575, 0.3355, 0.0835,
     0.183, 0.1015, 0.0716, 0.2996, 0.2648, 0.2931, 0.1136, 0.4004, 0.2377, 0.4216,
     0.4372, 0.2296, 0.1756, 0.2121, 0.2552, 0.0758, 0.0609, 0.0766, 0.1605, 0.3406,
     0.3802, 0.26, 0.2275, 0.1902, 0.1513, 0.0893, 0.0378, 0.0159, 0.0351, 0.1701,
     0.1713, 0.244, 0.3054, 0.2402, 0.1705, 0.1733, 0.2785, 0.2211, 0.3736, 0.2563,
     0.1549, 0.1552, 0.2564, 0.3261, 0.337, 0.232, 0.2854, 0.2983, 0.1887, 0.2521,
     0.2077, 0.2385, 0.1669, 0.4155, 0.3178, 0.2494, 0.5826, 0.4288, 0.4241, 0.4602,
     0.1991, 0.0834, 0.1313, 0.4911, 0.4837, 0.4201, 0.5027, 0.3743, 0.2706, 0.224,
     0.2728, 0.3141, 0.3524, 0.1875, 0.3453, 0.3131, 0.3459, 0.1206, 0.324, 0.6163,
     0.4302, 0.4806, 0.3785, 0.1184, 0.1685, 0.1377, 0.2112, 0.2417, 0.1639, 0.1964,
     0.1977, 0.0537, 0.2505), V44 = c(0.2222, 0.1749, 0.1099, 0.1384, 0.0819,
     0.1419, 0.1778, 0.1381, 0.2228, 0.2998, 0.0908, 0.1886, 0.0713, 0.0976, 0.2205,
     0.1955, 0.2298, 0.0701, 0.3877, 0.2808, 0.2479, 0.247, 0.0949, 0.1424, 0.1801,
     0.1696, 0.0486, 0.0375, 0.0845, 0.1491, 0.1916, 0.2278, 0.3846, 0.148, 0.0696,
     0.1789, 0.1459, 0.1268, 0.0778, 0.1148, 0.0971, 0.0584, 0.2, 0.2235, 0.1392,
     0.1294, 0.0815, 0.1807, 0.1524, 0.2804, 0.1927, 0.1626, 0.0377, 0.2527, 0.1963,
     0.2518, 0.145, 0.2808, 0.1779, 0.137, 0.1464, 0.1767, 0.0255, 0.2872, 0.1824,
     0.0807, 0.3202, 0.4286, 0.3063, 0.4567, 0.4164, 0.11, 0.1502, 0.1776, 0.5762,
     0.5379, 0.4557, 0.5772, 0.3021, 0.2323, 0.0816, 0.4016, 0.3693, 0.3659, 0.2267,
     0.2096, 0.3387, 0.4646, 0.0255, 0.0926, 0.5663, 0.4831, 0.4921, 0.1269, 0.1157,
     0.0675, 0.0685, 0.0991, 0.1631, 0.1916, 0.2256, 0.1339, 0.1309, 0.1862),
     V45 = c(0.2111, 0.1304, 0.0985, 0.0372, 0.0678, 0.126, 0.1353, 0.1136, 0.1582,
     0.2748, 0.138, 0.1008, 0.0615, 0.0787, 0.1163, 0.0656, 0.2391, 0.1578, 0.1651,
     0.1374, 0.1586, 0.1708, 0.0095, 0.0908, 0.1473, 0.1467, 0.0353, 0.0533, 0.026,
     0.1326, 0.1134, 0.1522, 0.3754, 0.1102, 0.0758, 0.185, 0.1348, 0.1125, 0.0653,
     0.1331, 0.2217, 0.123, 0.2307, 0.2611, 0.1779, 0.0909, 0.0335, 0.0352, 0.0746,
     0.1982, 0.2062, 0.0708, 0.0636, 0.2137, 0.0864, 0.2101, 0.1017, 0.2395, 0.1535,
     0.1376, 0.0673, 0.1119, 0.1967, 0.4374, 0.1487, 0.1192, 0.2265, 0.4894, 0.5835,
     0.576, 0.5438, 0.0684, 0.1675, 0.2, 0.5013, 0.4485, 0.3955, 0.5907, 0.2069,
     0.1724, 0.0395, 0.4125, 0.2986, 0.2846, 0.1577, 0.1031, 0.413, 0.4366, 0.0298,
     0.1173, 0.5749, 0.5084, 0.5294, 0.1459, 0.1449, 0.1186, 0.0664, 0.0594, 0.0769,
     0.2085, 0.1814, 0.0902, 0.091, 0.1439), V46 = c(0.0176, 0.0597, 0.1271, 0.0688,
     0.0663, 0.1288, 0.1373, 0.0516, 0.1433, 0.2024, 0.1948, 0.0663, 0.0779, 0.0522,
     0.0635, 0.058, 0.191, 0.1938, 0.0442, 0.1136, 0.1124, 0.1343, 0.0527, 0.0138,
     0.0681, 0.1286, 0.0297, 0.0278, 0.0333, 0.0687, 0.064, 0.0801, 0.2414, 0.1178,
     0.091, 0.1717, 0.0391, 0.0505, 0.021, 0.0276, 0.2732, 0.22, 0.1886, 0.2798,
     0.1946, 0.08, 0.0933, 0.0473, 0.0606, 0.2438, 0.1751, 0.0129, 0.0443, 0.1789,
     0.1688, 0.1181, 0.1111, 0.0369, 0.1199, 0.0307, 0.0965, 0.0779, 0.1483, 0.3097,
     0.0138, 0.2134, 0.1146, 0.5777, 0.5692, 0.5293, 0.5649, 0.0303, 0.1058, 0.0765,
     0.4042, 0.2674, 0.2966, 0.4803, 0.179, 0.1457, 0.0785, 0.347, 0.2226, 0.1714,
     0.1211, 0.0798, 0.3639, 0.2581, 0.0691, 0.0566, 0.3593, 0.1952, 0.2216, 0.1092,
     0.1883, 0.1833, 0.1665, 0.194, 0.0723, 0.2335, 0.2012, 0.1085, 0.0757, 0.147
     ), V47 = c(0.1348, 0.1124, 0.1459, 0.0867, 0.1202, 0.079, 0.0749, 0.0073,
     0.1634, 0.1043, 0.1211, 0.0183, 0.0761, 0.05, 0.0465, 0.0319, 0.1096, 0.1106,
     0.0663, 0.1034, 0.0651, 0.0838, 0.0383, 0.0469, 0.1091, 0.0926, 0.0241, 0.0179,
     0.0205, 0.0602, 0.0911, 0.0804, 0.1077, 0.0608, 0.0441, 0.0898, 0.0546, 0.0949,
     0.0509, 0.0763, 0.1874, 0.2198, 0.196, 0.2392, 0.1723, 0.0567, 0.1018, 0.0322,
     0.0692, 0.1789, 0.0841, 0.0795, 0.0264, 0.101, 0.1991, 0.115, 0.0655, 0.0805,
     0.0959, 0.0373, 0.1492, 0.1344, 0.0434, 0.1578, 0.1164, 0.3241, 0.0476, 0.4315,
     0.263, 0.3287, 0.3195, 0.0674, 0.1111, 0.0727, 0.3123, 0.1541, 0.2095, 0.3877,
     0.1689, 0.1175, 0.1052, 0.2739, 0.0849, 0.0694, 0.0883, 0.0701, 0.2069, 0.1319,
     0.0781, 0.0766, 0.2526, 0.1539, 0.1401, 0.1485, 0.1954, 0.1878, 0.1807, 0.1937,
     0.0912, 0.1964, 0.1688, 0.1521, 0.1059, 0.0991), V48 = c(0.0744, 0.1047,
     0.1164, 0.0513, 0.0692, 0.0829, 0.0472, 0.0278, 0.1133, 0.0453, 0.0843, 0.0404,
     0.0845, 0.0231, 0.0422, 0.0301, 0.03, 0.0693, 0.0418, 0.0688, 0.0789, 0.0755,
     0.0107, 0.048, 0.0919, 0.0716, 0.0379, 0.0114, 0.0309, 0.0561, 0.098, 0.0752,
     0.0224, 0.0333, 0.0244, 0.0656, 0.0469, 0.0677, 0.0387, 0.0631, 0.1062, 0.1074,
     0.1701, 0.2021, 0.1522, 0.0198, 0.0309, 0.0408, 0.0446, 0.1706, 0.1035, 0.0762,
     0.0223, 0.0528, 0.1217, 0.055, 0.0271, 0.0541, 0.0765, 0.0606, 0.1128, 0.096,
     0.0627, 0.0553, 0.2052, 0.2945, 0.0943, 0.264, 0.1196, 0.1283, 0.2484, 0.0785,
     0.0849, 0.0749, 0.2232, 0.1359, 0.1558, 0.2779, 0.1341, 0.0868, 0.1034, 0.179,
     0.0359, 0.0303, 0.085, 0.0526, 0.0859, 0.0505, 0.0777, 0.0969, 0.2299, 0.2037,
     0.1888, 0.1385, 0.1492, 0.1114, 0.1245, 0.1082, 0.0812, 0.13, 0.1037, 0.1363,
     0.1005, 0.0041), V49 = c(0.013, 0.0507, 0.0777, 0.0092, 0.0152, 0.052, 0.0325,
     0.0372, 0.0567, 0.0337, 0.0589, 0.0108, 0.0592, 0.0221, 0.0174, 0.0272, 0.0171,
     0.0176, 0.0475, 0.0422, 0.0325, 0.0304, 0.0108, 0.0159, 0.0397, 0.0325, 0.0119,
     0.0073, 0.0101, 0.0306, 0.0563, 0.0566, 0.0155, 0.0276, 0.0265, 0.0445, 0.0201,
     0.0259, 0.0262, 0.0309, 0.0665, 0.0423, 0.1366, 0.1326, 0.0929, 0.0114, 0.0208,
     0.0163, 0.0344, 0.0762, 0.0641, 0.0117, 0.0187, 0.0453, 0.0628, 0.0293, 0.0244,
     0.0177, 0.0649, 0.0399, 0.0463, 0.0598, 0.0513, 0.0334, 0.1069, 0.1474, 0.0824,
     0.1794, 0.0983, 0.0698, 0.1299, 0.0455, 0.0596, 0.0449, 0.1085, 0.0941, 0.0884,
     0.1427, 0.0769, 0.0392, 0.0764, 0.0922, 0.0289, 0.0292, 0.0355, 0.0241, 0.06,
     0.0112, 0.0369, 0.0588, 0.1271, 0.1054, 0.0947, 0.0716, 0.0511, 0.031, 0.0516,
     0.0336, 0.0496, 0.0633, 0.0501, 0.0858, 0.0535, 0.0154), V50 = c(0.0106,
     0.0159, 0.0439, 0.0198, 0.0266, 0.0216, 0.0179, 0.0121, 0.0133, 0.0122, 0.0247,
     0.0143, 0.0068, 0.0144, 0.0172, 0.0074, 0.0383, 0.0205, 0.0235, 0.0117, 0.007,
     0.0074, 0.0077, 0.0045, 0.0093, 0.0258, 0.0073, 0.0116, 0.0095, 0.0154, 0.0187,
     0.0175, 0.0187, 0.01, 0.0095, 0.011, 0.0095, 0.017, 0.0101, 0.024, 0.0405,
     0.0162, 0.0398, 0.0358, 0.0179, 0.0151, 0.0318, 0.0088, 0.0082, 0.0238, 0.0153,
     0.0061, 0.0077, 0.0118, 0.0323, 0.0183, 0.0179, 0.0065, 0.0313, 0.0169, 0.0193,
     0.033, 0.0473, 0.0209, 0.0199, 0.0211, 0.0171, 0.0772, 0.0374, 0.0334, 0.0825,
     0.0246, 0.0201, 0.0134, 0.0414, 0.0261, 0.0265, 0.0424, 0.0222, 0.0131, 0.0216,
     0.0276, 0.0122, 0.0116, 0.0219, 0.0117, 0.0267, 0.0059, 0.0057, 0.005, 0.0356,
     0.0251, 0.0134, 0.0176, 0.0155, 0.0143, 0.0044, 0.0177, 0.0101, 0.0183, 0.0136,
     0.029, 0.0235, 0.0116), V51 = c(0.0033, 0.0195, 0.0061, 0.0118, 0.0174, 0.036,
     0.0045, 0.0153, 0.017, 0.0072, 0.0118, 0.0091, 0.0089, 0.0307, 0.0134, 0.0149,
     0.0053, 0.0309, 0.0066, 0.007, 0.0026, 0.0069, 0.0109, 0.0015, 0.0076, 0.0136,
     0.0051, 0.0092, 0.0047, 0.0029, 0.0088, 0.0058, 0.0125, 0.0023, 0.014, 0.0024,
     0.0155, 0.0033, 0.0161, 0.0115, 0.0113, 0.0093, 0.0143, 0.0128, 0.0242, 0.0085,
     0.0132, 0.0121, 0.0108, 0.0268, 0.0081, 0.0257, 0.0137, 9e-04, 0.0253, 0.0104,
     0.0109, 0.0222, 0.0185, 0.0135, 0.014, 0.0197, 0.0248, 0.0172, 0.0208, 0.0361,
     0.0244, 0.0798, 0.0291, 0.0342, 0.0243, 0.0151, 0.0071, 0.0174, 0.0253, 0.0079,
     0.0121, 0.0271, 0.0205, 0.0092, 0.0167, 0.0169, 0.0045, 0.0024, 0.0086, 0.0122,
     0.0125, 0.0041, 0.0091, 0.0118, 0.0367, 0.0357, 0.031, 0.0199, 0.0189, 0.0138,
     0.0185, 0.0209, 0.0089, 0.0137, 0.013, 0.0203, 0.0155, 0.0181), V52 = c(0.0232,
     0.0201, 0.0145, 0.009, 0.0176, 0.0331, 0.0084, 0.0092, 0.0035, 0.0108, 0.0088,
     0.0038, 0.0087, 0.0386, 0.0141, 0.0125, 0.009, 0.0212, 0.0062, 0.0167, 0.0093,
     0.0025, 0.0062, 0.0052, 0.0065, 0.0044, 0.0034, 0.0078, 0.0072, 0.0048, 0.0042,
     0.0091, 0.0028, 0.0069, 0.0074, 0.0062, 0.0091, 0.015, 0.0029, 0.0064, 0.0028,
     0.0046, 0.0093, 0.0172, 0.0083, 0.0178, 0.0118, 0.0067, 0.0149, 0.0081, 0.0191,
     0.0089, 0.0071, 0.0142, 0.0214, 0.0117, 0.0147, 0.0045, 0.0098, 0.0222, 0.0027,
     0.0189, 0.0274, 0.018, 0.0176, 0.0444, 0.0258, 0.0376, 0.0156, 0.0459, 0.021,
     0.0125, 0.0104, 0.0117, 0.0131, 0.0164, 0.0091, 0.02, 0.0123, 0.0078, 0.0089,
     0.0081, 0.0108, 0.0084, 0.0123, 0.0122, 0.004, 0.0056, 0.0134, 0.0146, 0.0176,
     0.0181, 0.0237, 0.0096, 0.015, 0.0108, 0.0072, 0.0134, 0.0083, 0.015, 0.012,
     0.0116, 0.016, 0.0146), V53 = c(0.0166, 0.0248, 0.0128, 0.0223, 0.0127, 0.0131,
     0.001, 0.0035, 0.0052, 0.007, 0.0104, 0.0096, 0.0032, 0.0147, 0.0191, 0.0134,
     0.0042, 0.0091, 0.0129, 0.0127, 0.0118, 0.0103, 0.0028, 0.0038, 0.0072, 0.0028,
     0.0129, 0.0041, 0.0054, 0.0023, 0.0175, 0.016, 0.0067, 0.0025, 0.0063, 0.0072,
     0.0151, 0.0111, 0.0078, 0.0022, 0.0036, 0.0044, 0.0033, 0.0138, 0.0037, 0.0073,
     0.012, 0.0032, 0.0077, 0.0129, 0.0182, 0.0262, 0.0082, 0.0179, 0.0262, 0.0101,
     0.017, 0.0136, 0.0178, 0.0175, 0.0068, 0.0204, 0.0205, 0.011, 0.0197, 0.023,
     0.0143, 0.0143, 0.0197, 0.0277, 0.0361, 0.0036, 0.0062, 0.0023, 0.0049, 0.012,
     0.0062, 0.007, 0.0067, 0.0071, 0.0051, 0.004, 0.0075, 0.01, 0.006, 0.0114,
     0.0136, 0.0104, 0.0097, 0.004, 0.0035, 0.0019, 0.0078, 0.0103, 0.006, 0.0062,
     0.0055, 0.0094, 0.008, 0.0076, 0.0039, 0.0098, 0.0029, 0.0129), V54 = c(0.0095,
     0.0131, 0.0145, 0.0179, 0.0088, 0.012, 0.0018, 0.0098, 0.0083, 0.0063, 0.0036,
     0.0142, 0.013, 0.0018, 0.0145, 0.0026, 0.0153, 0.0056, 0.0184, 0.0138, 0.0112,
     0.0074, 0.004, 0.0079, 0.0108, 0.0021, 0.01, 0.0013, 0.0022, 0.002, 0.0171,
     0.016, 0.012, 0.0027, 0.0081, 0.0113, 0.008, 0.0032, 0.0114, 0.0122, 0.0105,
     0.0078, 0.0113, 0.0079, 0.0095, 0.0079, 0.0051, 0.0109, 0.0036, 0.0161, 0.016,
     0.0108, 0.0232, 0.0079, 0.0177, 0.0061, 0.0158, 0.0113, 0.0077, 0.0127, 0.015,
     0.0085, 0.0141, 0.0234, 0.021, 0.029, 0.0226, 0.0272, 0.0135, 0.0172, 0.0239,
     0.0123, 0.0026, 0.0047, 0.0104, 0.0113, 0.0019, 0.007, 0.0011, 0.0081, 0.0015,
     0.0025, 0.0089, 0.0018, 0.0187, 0.0098, 0.0137, 0.0079, 0.0042, 0.0114, 0.0093,
     0.0102, 0.0144, 0.0093, 0.0082, 0.0044, 0.0074, 0.0047, 0.0026, 0.0032, 0.0053,
     0.0199, 0.0051, 0.0047), V55 = c(0.018, 0.007, 0.0058, 0.0084, 0.0098, 0.0108,
     0.0068, 0.0121, 0.0078, 0.003, 0.0088, 0.019, 0.0188, 0.01, 0.0065, 0.0038,
     0.0106, 0.0086, 0.0069, 0.009, 0.0094, 0.0123, 0.0075, 0.0114, 0.0051, 0.0022,
     0.0044, 0.0011, 0.0016, 0.004, 0.0079, 0.0081, 0.0012, 0.0052, 0.0087, 0.0012,
     0.0018, 0.0035, 0.0083, 0.0151, 0.012, 0.0102, 0.003, 0.0037, 0.0105, 0.0038,
     0.007, 0.0164, 0.0114, 0.0063, 0.029, 0.0138, 0.0198, 0.006, 0.0037, 0.0031,
     0.0046, 0.0053, 0.0074, 0.0022, 0.0012, 0.0043, 0.0185, 0.0276, 0.0141, 0.0141,
     0.0187, 0.0127, 0.0127, 0.0087, 0.0447, 0.0043, 0.0025, 0.0049, 0.0102, 0.0021,
     0.0045, 0.0086, 0.0026, 0.0034, 0.0075, 0.0036, 0.0036, 0.0035, 0.0111, 0.0027,
     0.0172, 0.0014, 0.0058, 0.0032, 0.0121, 0.0133, 0.017, 0.0025, 0.0091, 0.0072,
     0.0068, 0.0045, 0.0079, 0.0037, 0.0062, 0.0033, 0.0062, 0.0039), V56 = c(0.0244,
     0.0138, 0.0049, 0.0068, 0.0019, 0.0024, 0.0039, 6e-04, 0.0075, 0.0011, 0.0047,
     0.014, 0.0101, 0.0096, 0.0129, 0.0018, 0.002, 0.0092, 0.0198, 0.0051, 0.014,
     0.0069, 0.0039, 0.005, 0.0102, 0.0048, 0.0057, 0.0045, 0.0029, 0.0019, 0.005,
     0.007, 0.0022, 0.0036, 0.0044, 0.0022, 0.0078, 0.0169, 0.0058, 0.0056, 0.0087,
     0.0065, 0.0057, 0.0051, 0.003, 0.0116, 0.0015, 0.0151, 0.0085, 0.0119, 0.009,
     0.0187, 0.0074, 0.0131, 0.0068, 0.0099, 0.0073, 0.0165, 0.0095, 0.0124, 0.0133,
     0.0092, 0.0055, 0.0032, 0.0049, 0.0161, 0.0185, 0.0166, 0.0138, 0.0046, 0.0394,
     0.0114, 0.0061, 0.0031, 0.0092, 0.0097, 0.0079, 0.0089, 0.0049, 0.0064, 0.0058,
     0.0058, 0.0029, 0.0058, 0.0126, 0.0025, 0.0132, 0.0054, 0.0072, 0.0062, 0.0075,
     0.004, 0.0012, 0.0044, 0.0038, 7e-04, 0.0084, 0.0042, 0.0042, 0.0071, 0.0046,
     0.0101, 0.0089, 0.0061), V57 = c(0.0316, 0.0092, 0.0065, 0.0032, 0.0059,
     0.0045, 0.012, 0.0181, 0.0105, 7e-04, 0.0117, 0.0099, 0.0229, 0.0077, 0.0217,
     0.0113, 0.0105, 0.007, 0.0199, 0.0029, 0.0072, 0.0076, 0.0053, 0.003, 0.0041,
     0.0138, 0.003, 0.0039, 0.0058, 0.0034, 0.0112, 0.0135, 0.0058, 0.0026, 0.0028,
     0.0025, 0.0045, 0.0137, 3e-04, 0.0026, 0.0061, 0.0061, 0.009, 0.0258, 0.0132,
     0.0033, 0.0035, 0.007, 0.0101, 0.0194, 0.0242, 0.023, 0.0035, 0.0089, 0.0121,
     0.008, 0.0054, 0.0141, 0.0055, 0.0054, 0.0048, 0.0138, 0.0045, 0.0084, 0.0027,
     0.0177, 0.011, 0.0095, 0.0133, 0.0203, 0.0355, 0.0052, 0.0038, 0.0024, 0.0083,
     0.0072, 0.0031, 0.0074, 0.0029, 0.0037, 0.0016, 0.0067, 0.0013, 0.0011, 0.0081,
     0.0026, 0.011, 0.0015, 0.0041, 0.0101, 0.0056, 0.0042, 0.0109, 0.0021, 0.0056,
     0.0054, 0.0037, 0.0028, 0.0071, 0.004, 0.0045, 0.0065, 0.014, 0.004), V58 = c(0.0164,
     0.0143, 0.0093, 0.0035, 0.0058, 0.0037, 0.0132, 0.0094, 0.016, 0.0024, 0.002,
     0.0092, 0.0182, 0.018, 0.0087, 0.0058, 0.0049, 0.0116, 0.0102, 0.0122, 0.0022,
     0.0073, 0.0013, 0.0064, 0.0055, 0.014, 0.0035, 0.0022, 0.005, 0.0034, 0.0179,
     0.0067, 0.0042, 0.0036, 0.0019, 0.0059, 0.0026, 0.0015, 0.0023, 0.0029, 0.0061,
     0.0062, 0.0057, 0.0102, 0.0068, 0.0039, 8e-04, 0.0085, 0.0016, 0.014, 0.0224,
     0.0057, 0.01, 0.0084, 0.0077, 0.0107, 0.0033, 0.0077, 0.0045, 0.0021, 0.0244,
     0.0094, 0.0115, 0.0122, 0.0162, 0.0194, 0.0094, 0.0225, 0.0131, 0.013, 0.044,
     0.0091, 0.0101, 0.0039, 0.002, 0.006, 0.0063, 0.0042, 0.0022, 0.0036, 0.007,
     0.0035, 0.001, 9e-04, 0.0155, 0.005, 0.0122, 6e-04, 0.0045, 0.0068, 0.0021,
     0.003, 0.0036, 0.0069, 0.0056, 0.0035, 0.0024, 0.0036, 0.0044, 9e-04, 0.0022,
     0.0115, 0.0138, 0.0036), V59 = c(0.0095, 0.0036, 0.0059, 0.0056, 0.0059,
     0.0112, 0.007, 0.0116, 0.0095, 0.0057, 0.0091, 0.0052, 0.0046, 0.0109, 0.0077,
     0.0047, 0.007, 0.006, 0.007, 0.0056, 0.0055, 0.003, 0.0052, 0.0058, 0.005,
     0.0028, 0.0021, 0.0023, 0.0024, 0.0051, 0.0294, 0.0078, 0.0067, 6e-04, 0.0049,
     0.0039, 0.0036, 0.0069, 0.0026, 0.0104, 0.003, 0.0043, 0.0068, 0.0037, 0.0108,
     0.0081, 0.0044, 0.0117, 0.0028, 0.0332, 0.019, 0.0113, 0.0048, 0.0113, 0.0078,
     0.0161, 0.0045, 0.0246, 0.0063, 0.0028, 0.0077, 0.0105, 0.0152, 0.0082, 0.0059,
     0.0207, 0.0078, 0.0098, 0.0154, 0.0115, 0.0243, 8e-04, 0.0078, 0.0051, 0.0048,
     0.0017, 0.0048, 0.0055, 0.0022, 0.0012, 0.0074, 0.0043, 0.0032, 0.0033, 0.016,
     0.0073, 0.0114, 0.0081, 0.0047, 0.0053, 0.0043, 0.0031, 0.0043, 0.006, 0.0048,
     1e-04, 0.0034, 0.0013, 0.0022, 0.0015, 5e-04, 0.0193, 0.0077, 0.0061), V60 = c(0.0078,
     0.0103, 0.0022, 0.004, 0.0032, 0.0075, 0.0088, 0.0063, 0.0011, 0.0044, 0.0058,
     0.0075, 0.0038, 0.007, 0.0122, 0.0071, 0.008, 0.011, 0.0055, 0.002, 0.0122,
     0.0138, 0.0023, 0.003, 0.0087, 0.0064, 0.0027, 0.0016, 0.003, 0.0031, 0.0063,
     0.0068, 0.0012, 0.0035, 0.0023, 0.0048, 0.0024, 0.0051, 0.0027, 0.0163, 0.0078,
     0.0053, 0.0024, 0.0037, 0.009, 0.0053, 0.0077, 0.0056, 0.0014, 0.0439, 0.0096,
     0.0131, 0.0019, 0.0049, 0.0066, 0.0133, 0.0079, 0.0198, 0.0039, 0.0023, 0.0074,
     0.0093, 0.01, 0.0143, 0.0021, 0.0057, 0.0112, 0.0085, 0.0218, 0.0015, 0.0098,
     0.0092, 6e-04, 0.0015, 0.0036, 0.0036, 0.005, 0.0021, 0.0032, 0.0037, 0.0038,
     0.0033, 0.0047, 0.0026, 0.0085, 0.0022, 0.0068, 0.0043, 0.0054, 0.0087, 0.0017,
     0.0033, 0.0018, 0.0018, 0.0024, 0.0055, 7e-04, 0.0016, 0.0014, 0.0085, 0.0031,
     0.0157, 0.0031, 0.0115)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6",
     "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18",
     "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29",
     "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51",
     "V52", "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), class = "data.frame", row.names = c("3",
     "7", "9", "10", "13", "18", "19", "20", "25", "26", "29", "30", "35", "36", "37",
     "39", "43", "44", "46", "47", "49", "50", "52", "53", "54", "55", "59", "61",
     "63", "64", "66", "68", "69", "71", "73", "74", "77", "78", "80", "81", "83",
     "85", "87", "88", "90", "92", "93", "94", "95", "98", "100", "101", "104", "108",
     "110", "111", "114", "116", "118", "120", "123", "124", "131", "135", "138",
     "139", "140", "141", "142", "145", "148", "152", "154", "156", "158", "159",
     "161", "162", "163", "164", "166", "168", "169", "170", "172", "173", "175",
     "176", "179", "180", "182", "183", "184", "189", "191", "192", "193", "194",
     "195", "201", "202", "204", "206", "208")))
     21: xgboost::predict
     22: getExportedValue(pkg, name)
     23: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     2. Failure: generateCalibrationData (@test_base_generateCalibration.R#55) ------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     3. Failure: generateCalibrationData (@test_base_generateCalibration.R#57) ------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     4. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#72) --------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     5. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#74) --------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     6. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#46) ------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     7. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#48) ------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     8. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#216)
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     9. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#219)
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     10. Failure: generateThreshVsPerfData (@test_base_generateThreshVsPerf.R#119) --
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     11. Failure: generateThreshVsPerfData (@test_base_generateThreshVsPerf.R#121) --
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     12. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#18) -----------------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     13. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#20) -----------------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     14. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#22) -----------------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     15. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#22) -----------------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     testthat results ================================================================
     OK: 2349 SKIPPED: 1 FAILED: 15
     1. Error: downsample wrapper works with xgboost, we had issue #492 (@test_base_downsample.R#38)
     2. Failure: generateCalibrationData (@test_base_generateCalibration.R#55)
     3. Failure: generateCalibrationData (@test_base_generateCalibration.R#57)
     4. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#72)
     5. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#74)
     6. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#46)
     7. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#48)
     8. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#216)
     9. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#219)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 2.9
Flags: --no-stop-on-test-error
Check: compiled code
Result: NOTE
    File ‘mlr/libs/mlr.so’:
     Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’
    
    It is good practice to use registered native symbols and disable symbol
    search.
    
    See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual.
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 2.9
Flags: --no-stop-on-test-error
Check: tests
Result: ERROR
     Running ‘run-base.R’ [9m/10m]
     Running ‘run-classif.R’
     Running ‘run-cluster.R’
     Running ‘run-featsel.R’
     Running ‘run-learners-classif.R’
     Running ‘run-learners-classiflabelswitch.R’
     Running ‘run-learners-cluster.R’
     Running ‘run-learners-general.R’
     Running ‘run-learners-multilabel.R’
     Running ‘run-learners-regr.R’
     Running ‘run-learners-surv.R’
     Running ‘run-parallel.R’
     Running ‘run-regr.R’
     Running ‘run-stack.R’
     Running ‘run-surv.R’
     Running ‘run-tune.R’
    Running the tests in ‘tests/run-base.R’ failed.
    Complete output:
     > library(testthat)
     > test_check("mlr", filter = "base")
     Loading required package: mlr
     Loading required package: BBmisc
     Loading required package: ggplot2
     Loading required package: ParamHelpers
     Loading required package: stringi
     1. Error: downsample wrapper works with xgboost, we had issue #492 (@test_base_downsample.R#38)
     'predict' is not an exported object from 'namespace:xgboost'
     1: resample(lrn, binaryclass.task, rdesc) at testthat/test_base_downsample.R:38
     2: parallelMap(doResampleIteration, seq_len(rin$desc$iters), level = "mlr.resample",
     more.args = more.args)
     3: mapply(fun2, ..., MoreArgs = more.args, SIMPLIFY = FALSE, USE.NAMES = FALSE)
     4: (function (learner, task, rin, i, measures, weights, model, extract, show.info)
     {
     setSlaveOptions()
     if (show.info)
     messagef("[Resample] %s iter: %i", rin$desc$id, i)
     train.i = rin$train.inds[[i]]
     test.i = rin$test.inds[[i]]
     err.msgs = c(NA_character_, NA_character_)
     m = train(learner, task, subset = train.i, weights = weights[train.i])
     if (isFailureModel(m))
     err.msgs[1L] = getFailureModelMsg(m)
     ms.train = rep(NA, length(measures))
     ms.test = rep(NA, length(measures))
     pred.train = NULL
     pred.test = NULL
     pp = rin$desc$predict
     if (pp == "train") {
     pred.train = predict(m, task, subset = train.i)
     if (!is.na(pred.train$error))
     err.msgs[2L] = pred.train$error
     ms.train = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.train, measures = pm))
     }
     else if (pp == "test") {
     pred.test = predict(m, task, subset = test.i)
     if (!is.na(pred.test$error))
     err.msgs[2L] = pred.test$error
     ms.test = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.test, measures = pm))
     }
     else {
     pred.train = predict(m, task, subset = train.i)
     if (!is.na(pred.train$error))
     err.msgs[2L] = pred.train$error
     ms.train = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.train, measures = pm))
     pred.test = predict(m, task, subset = test.i)
     if (!is.na(pred.test$error))
     err.msgs[2L] = paste(err.msgs[2L], pred.test$error)
     ms.test = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.test, measures = pm))
     }
     ex = extract(m)
     list(measures.test = ms.test, measures.train = ms.train, model = if (model) m else NULL,
     pred.test = pred.test, pred.train = pred.train, err.msgs = err.msgs, extract = ex)
     })(dots[[1L]][[1L]], learner = structure(list(id = "classif.xgboost.downsampled",
     type = "classif", package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(
     pars = structure(list(dw.perc = structure(list(id = "dw.perc", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), dw.stratify = structure(list(id = "dw.stratify", type = "logical",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE,
     `FALSE` = FALSE), .Names = c("TRUE", "FALSE")), cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = FALSE, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("dw.perc", "dw.stratify")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     dw.perc = 0.5), .Names = "dw.perc"), predict.type = "response", fix.factors.prediction = FALSE,
     next.learner = structure(list(id = "classif.xgboost", type = "classif", package = "xgboost",
     properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"
     ), par.set = structure(list(pars = structure(list(booster = structure(list(
     id = "booster", type = "discrete", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type",
     "package", "properties", "par.set", "par.vals", "predict.type", "name", "short.name",
     "note", "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), model.subclass = "DownsampleModel"), .Names = c("id",
     "type", "package", "properties", "par.set", "par.vals", "predict.type", "fix.factors.prediction",
     "next.learner", "model.subclass"), class = c("DownsampleWrapper", "BaseWrapper",
     "Learner")), task = structure(list(type = "classif", env = <environment>, weights = NULL,
     blocking = NULL, task.desc = structure(list(id = "binary", type = "classif",
     target = "Class", size = 208L, n.feat = structure(c(60L, 0L, 0L), .Names = c("numerics",
     "factors", "ordered")), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE,
     class.levels = c("M", "R"), positive = "M", negative = "R"), .Names = c("id",
     "type", "target", "size", "n.feat", "has.missings", "has.weights", "has.blocking",
     "class.levels", "positive", "negative"), class = c("TaskDescClassif", "TaskDescSupervised",
     "TaskDesc"))), .Names = c("type", "env", "weights", "blocking", "task.desc"), class = c("ClassifTask",
     "SupervisedTask", "Task")), rin = structure(list(desc = structure(list(id = "cross-validation",
     iters = 2L, predict = "test", stratify = FALSE), .Names = c("id", "iters", "predict",
     "stratify"), class = c("CVDesc", "ResampleDesc")), size = 208L, train.inds = list(
     c(45L, 137L, 82L, 56L, 188L, 133L, 11L, 76L, 91L, 106L, 132L, 48L, 72L, 207L,
     149L, 28L, 143L, 97L, 186L, 198L, 122L, 127L, 27L, 65L, 75L, 203L, 157L, 146L,
     79L, 51L, 205L, 128L, 1L, 24L, 155L, 144L, 89L, 187L, 174L, 8L, 86L, 38L, 130L,
     109L, 99L, 125L, 12L, 2L, 200L, 134L, 42L, 6L, 165L, 199L, 84L, 177L, 14L, 4L,
     190L, 129L, 185L, 62L, 70L, 40L, 196L, 150L, 32L, 171L, 17L, 160L, 112L, 16L,
     33L, 147L, 41L, 197L, 136L, 105L, 58L, 167L, 23L, 57L, 31L, 181L, 22L, 113L,
     119L, 96L, 103L, 151L, 178L, 21L, 115L, 102L, 107L, 121L, 34L, 5L, 126L, 117L,
     15L, 67L, 153L, 60L), c(36L, 208L, 100L, 52L, 10L, 141L, 71L, 163L, 182L, 142L,
     172L, 116L, 80L, 206L, 192L, 30L, 110L, 54L, 124L, 68L, 164L, 43L, 37L, 98L,
     44L, 87L, 145L, 104L, 88L, 3L, 74L, 183L, 173L, 154L, 159L, 201L, 19L, 179L,
     9L, 193L, 7L, 13L, 93L, 118L, 94L, 92L, 140L, 83L, 18L, 156L, 49L, 53L, 108L,
     158L, 35L, 184L, 101L, 29L, 66L, 202L, 90L, 111L, 25L, 26L, 152L, 191L, 39L,
     180L, 69L, 189L, 175L, 63L, 138L, 61L, 85L, 135L, 139L, 73L, 81L, 123L, 20L,
     170L, 176L, 46L, 47L, 78L, 162L, 120L, 194L, 95L, 168L, 148L, 64L, 195L, 77L,
     131L, 169L, 204L, 59L, 161L, 55L, 166L, 114L, 50L)), test.inds = list(c(3L, 7L,
     9L, 10L, 13L, 18L, 19L, 20L, 25L, 26L, 29L, 30L, 35L, 36L, 37L, 39L, 43L, 44L, 46L,
     47L, 49L, 50L, 52L, 53L, 54L, 55L, 59L, 61L, 63L, 64L, 66L, 68L, 69L, 71L, 73L, 74L,
     77L, 78L, 80L, 81L, 83L, 85L, 87L, 88L, 90L, 92L, 93L, 94L, 95L, 98L, 100L, 101L,
     104L, 108L, 110L, 111L, 114L, 116L, 118L, 120L, 123L, 124L, 131L, 135L, 138L, 139L,
     140L, 141L, 142L, 145L, 148L, 152L, 154L, 156L, 158L, 159L, 161L, 162L, 163L, 164L,
     166L, 168L, 169L, 170L, 172L, 173L, 175L, 176L, 179L, 180L, 182L, 183L, 184L, 189L,
     191L, 192L, 193L, 194L, 195L, 201L, 202L, 204L, 206L, 208L), c(1L, 2L, 4L, 5L, 6L,
     8L, 11L, 12L, 14L, 15L, 16L, 17L, 21L, 22L, 23L, 24L, 27L, 28L, 31L, 32L, 33L, 34L,
     38L, 40L, 41L, 42L, 45L, 48L, 51L, 56L, 57L, 58L, 60L, 62L, 65L, 67L, 70L, 72L, 75L,
     76L, 79L, 82L, 84L, 86L, 89L, 91L, 96L, 97L, 99L, 102L, 103L, 105L, 106L, 107L, 109L,
     112L, 113L, 115L, 117L, 119L, 121L, 122L, 125L, 126L, 127L, 128L, 129L, 130L, 132L,
     133L, 134L, 136L, 137L, 143L, 144L, 146L, 147L, 149L, 150L, 151L, 153L, 155L, 157L,
     160L, 165L, 167L, 171L, 174L, 177L, 178L, 181L, 185L, 186L, 187L, 188L, 190L, 196L,
     197L, 198L, 199L, 200L, 203L, 205L, 207L)), group = structure(integer(0), .Label = character(0), class = "factor")), .Names = c("desc",
     "size", "train.inds", "test.inds", "group"), class = "ResampleInstance"), weights = NULL,
     measures = list(structure(list(id = "mmce", minimize = TRUE, properties = c("classif",
     "classif.multi", "req.pred", "req.truth"), fun = function (task, model, pred,
     feats, extra.args)
     {
     measureMMCE(pred$data$truth, pred$data$response)
     }, extra.args = list(), best = 0, worst = 1, name = "Mean misclassification error",
     note = "", aggr = structure(list(id = "test.mean", name = "Test mean", fun = function (task,
     perf.test, perf.train, measure, group, pred)
     mean(perf.test)), .Names = c("id", "name", "fun"), class = "Aggregation")), .Names = c("id",
     "minimize", "properties", "fun", "extra.args", "best", "worst", "name", "note",
     "aggr"), class = "Measure")), model = FALSE, extract = function (model)
     {
     }, show.info = FALSE)
     5: predict(m, task, subset = test.i)
     6: predict.WrappedModel(m, task, subset = test.i)
     7: system.time(fun1(p <- fun2(do.call(predictLearner2, pars))), gcFirst = FALSE)
     8: fun1(p <- fun2(do.call(predictLearner2, pars)))
     9: evalVis(expr)
     10: withVisible(eval(expr, pf))
     11: eval(expr, pf)
     12: eval(expr, pf)
     13: fun2(do.call(predictLearner2, pars))
     14: do.call(predictLearner2, pars)
     15: (function (.learner, .model, .newdata, ...)
     {
     if (.learner$fix.factors.prediction) {
     fls = .model$factor.levels
     ns = names(fls)
     ns = intersect(colnames(.newdata), ns)
     fls = fls[ns]
     if (length(ns) > 0L)
     .newdata[ns] = mapply(factor, x = .newdata[ns], levels = fls, SIMPLIFY = FALSE)
     }
     p = predictLearner(.learner, .model, .newdata, ...)
     p = checkPredictLearnerOutput(.learner, .model, p)
     return(p)
     })(.learner = structure(list(id = "classif.xgboost.downsampled", type = "classif",
     package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(pars = structure(list(
     dw.perc = structure(list(id = "dw.perc", type = "numeric", len = 1L, lower = 0,
     upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 1, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), dw.stratify = structure(list(
     id = "dw.stratify", type = "logical", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = FALSE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("dw.perc",
     "dw.stratify")), forbidden = NULL), .Names = c("pars", "forbidden"), class = c("LearnerParamSet",
     "ParamSet")), par.vals = structure(list(dw.perc = 0.5), .Names = "dw.perc"),
     predict.type = "response", fix.factors.prediction = FALSE, next.learner = structure(list(
     id = "classif.xgboost", type = "classif", package = "xgboost", properties = c("twoclass",
     "multiclass", "numerics", "factors", "prob", "weights"), par.set = structure(list(
     pars = structure(list(booster = structure(list(id = "booster", type = "discrete",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(gbtree = "gbtree",
     gblinear = "gblinear"), .Names = c("gbtree", "gblinear")), cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "gbtree", trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), silent = structure(list(id = "silent", type = "integer", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eta = structure(list(id = "eta", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.3, trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), gamma = structure(list(id = "gamma", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight",
     type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), subsample = structure(list(id = "subsample", type = "numeric", len = 1L,
     lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree",
     type = "numeric", len = 1L, lower = 0, upper = 1, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "binary:logistic",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "error", trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = 0.5, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
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     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type", "package",
     "properties", "par.set", "par.vals", "predict.type", "name", "short.name", "note",
     "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), learner.model = structure(list(handle = <pointer: 0x133f69f0>,
     raw = as.raw(c(0x00, 0x00, 0x00, 0x80, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
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     0x00, 0x00, 0x00, 0x62, 0x69, 0x6e, 0x61, 0x72, 0x79, 0x3a, 0x6c, 0x6f, 0x67,
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     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
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     0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
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     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00,
     0x00, 0x14, 0x00, 0x00, 0x80, 0x6e, 0xc5, 0x2e, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x80, 0xdf,
     0x4f, 0x2d, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00,
     0x00, 0x00, 0x3b, 0x00, 0x00, 0x80, 0x82, 0xe2, 0x47, 0x3b, 0x01, 0x00, 0x00,
     0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,
     0x9a, 0x99, 0x99, 0xbe, 0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x32, 0xa4, 0xf3, 0x3e, 0x02, 0x00,
     0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x8c, 0xaf, 0xf8, 0xbe, 0xc7,
     0x92, 0xac, 0x41, 0x00, 0x00, 0x50, 0x41, 0x25, 0x49, 0x92, 0x3d, 0x00, 0x00,
     0x00, 0x00, 0xef, 0xd4, 0x14, 0x41, 0x00, 0x00, 0xe8, 0x40, 0xd9, 0x64, 0x93,
     0x3f, 0x02, 0x00, 0x00, 0x00, 0x90, 0xb9, 0x43, 0x40, 0x00, 0x00, 0xb8, 0x40,
     0x68, 0x2f, 0xa1, 0xbf, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x80, 0x3f, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0xc8, 0x40, 0xd4, 0x08, 0xcb, 0x3f, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0x40, 0xf4,
     0x3c, 0xcf, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x6e, 0x69, 0x74, 0x65, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x30)), niter = 1, evaluation_log = structure(list(iter = 1, train_error = 0.076923), .Names = c("iter",
     "train_error"), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x1c2d4c8>),
     call = xgb.train(params = params, data = dtrain, nrounds = nrounds, watchlist = watchlist,
     verbose = verbose, print_every_n = print_every_n, early_stopping_rounds = early_stopping_rounds,
     maximize = maximize, save_period = save_period, save_name = save_name, xgb_model = xgb_model,
     callbacks = callbacks, objective = ..1), params = structure(list(objective = "binary:logistic",
     silent = 1), .Names = c("objective", "silent")), callbacks = structure(list(
     cb.print.evaluation = structure(function (env = parent.frame())
     {
     if (length(env$bst_evaluation) == 0 || period == 0 || NVL(env$rank, 0) !=
     0)
     return()
     i <- env$iteration
     if ((i - 1)%%period == 0 || i == env$begin_iteration || i == env$end_iteration) {
     msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
     cat(msg, "\n")
     }
     }, call = cb.print.evaluation(period = print_every_n), name = "cb.print.evaluation"),
     cb.evaluation.log = structure(function (env = parent.frame(), finalize = FALSE)
     {
     if (is.null(mnames))
     init(env)
     if (finalize)
     return(finalizer(env))
     ev <- env$bst_evaluation
     if (!is.null(env$bst_evaluation_err))
     ev <- c(ev, env$bst_evaluation_err)
     env$evaluation_log <- c(env$evaluation_log, list(c(iter = env$iteration,
     ev)))
     }, call = cb.evaluation.log(), name = "cb.evaluation.log"), cb.save.model = structure(function (env = parent.frame())
     {
     if (is.null(env$bst))
     stop("'save_model' callback requires the 'bst' booster object in its calling frame")
     if ((save_period > 0 && (env$iteration - env$begin_iteration)%%save_period ==
     0) || (save_period == 0 && env$iteration == env$end_iteration))
     xgb.save(env$bst, sprintf(save_name, env$iteration))
     }, call = cb.save.model(save_period = save_period, save_name = save_name), name = "cb.save.model")), .Names = c("cb.print.evaluation",
     "cb.evaluation.log", "cb.save.model"))), .Names = c("handle", "raw", "niter",
     "evaluation_log", "call", "params", "callbacks"), class = "xgb.Booster"), task.desc = structure(list(
     id = "binary", type = "classif", target = "Class", size = 52L, n.feat = structure(c(60L,
     0L, 0L), .Names = c("numerics", "factors", "ordered")), has.missings = FALSE,
     has.weights = FALSE, has.blocking = FALSE, class.levels = c("M", "R"), positive = "M",
     negative = "R"), .Names = c("id", "type", "target", "size", "n.feat", "has.missings",
     "has.weights", "has.blocking", "class.levels", "positive", "negative"), class = c("TaskDescClassif",
     "TaskDescSupervised", "TaskDesc")), subset = 1:52, features = c("V1", "V2", "V3",
     "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
     "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28",
     "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52",
     "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), factor.levels = structure(list(
     Class = c("M", "R")), .Names = "Class"), time = 0.111999999999995), .Names = c("learner",
     "learner.model", "task.desc", "subset", "features", "factor.levels", "time"), class = "WrappedModel")), .Names = "next.model", class = c("DownsampleModel",
     "ChainModel", "WrappedModel")), task.desc = structure(list(id = "binary", type = "classif",
     target = "Class", size = 208L, n.feat = structure(c(60L, 0L, 0L), .Names = c("numerics",
     "factors", "ordered")), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE,
     class.levels = c("M", "R"), positive = "M", negative = "R"), .Names = c("id",
     "type", "target", "size", "n.feat", "has.missings", "has.weights", "has.blocking",
     "class.levels", "positive", "negative"), class = c("TaskDescClassif", "TaskDescSupervised",
     "TaskDesc")), subset = c(45L, 137L, 82L, 56L, 188L, 133L, 11L, 76L, 91L, 106L, 132L,
     48L, 72L, 207L, 149L, 28L, 143L, 97L, 186L, 198L, 122L, 127L, 27L, 65L, 75L, 203L,
     157L, 146L, 79L, 51L, 205L, 128L, 1L, 24L, 155L, 144L, 89L, 187L, 174L, 8L, 86L,
     38L, 130L, 109L, 99L, 125L, 12L, 2L, 200L, 134L, 42L, 6L, 165L, 199L, 84L, 177L,
     14L, 4L, 190L, 129L, 185L, 62L, 70L, 40L, 196L, 150L, 32L, 171L, 17L, 160L, 112L,
     16L, 33L, 147L, 41L, 197L, 136L, 105L, 58L, 167L, 23L, 57L, 31L, 181L, 22L, 113L,
     119L, 96L, 103L, 151L, 178L, 21L, 115L, 102L, 107L, 121L, 34L, 5L, 126L, 117L, 15L,
     67L, 153L, 60L), features = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9",
     "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21",
     "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33",
     "V34", "V35", "V36", "V37", "V38", "V39", "V40", "V41", "V42", "V43", "V44", "V45",
     "V46", "V47", "V48", "V49", "V50", "V51", "V52", "V53", "V54", "V55", "V56", "V57",
     "V58", "V59", "V60"), factor.levels = structure(list(Class = c("M", "R")), .Names = "Class"),
     time = 0.141999999999996), .Names = c("learner", "learner.model", "task.desc",
     "subset", "features", "factor.levels", "time"), class = c("DownsampleModel", "BaseWrapperModel",
     "WrappedModel")), .newdata = structure(list(V1 = c(0.0262, 0.0317, 0.0223, 0.0164,
     0.0079, 0.0192, 0.027, 0.0126, 0.0293, 0.0201, 0.01, 0.0189, 0.0311, 0.0206, 0.0094,
     0.0123, 0.0211, 0.0093, 0.0408, 0.0308, 0.019, 0.0119, 0.0131, 0.0087, 0.0293, 0.0132,
     0.0225, 0.013, 0.0086, 0.0067, 0.0176, 0.0368, 0.0195, 0.0065, 0.0208, 0.0139, 0.0239,
     0.0336, 0.0108, 0.0229, 0.0409, 0.0378, 0.0188, 0.0856, 0.0235, 0.0253, 0.026, 0.0459,
     0.0025, 0.0491, 0.0201, 0.0629, 0.0162, 0.0428, 0.0264, 0.021, 0.0283, 0.0414, 0.0228,
     0.0261, 0.0249, 0.027, 0.0443, 0.1083, 0.043, 0.0731, 0.0164, 0.0412, 0.0707, 0.0299,
     0.0654, 0.0231, 0.0233, 0.0211, 0.0201, 0.0107, 0.0258, 0.0305, 0.0217, 0.0072, 0.0221,
     0.0137, 0.0015, 0.013, 0.0179, 0.018, 0.0191, 0.0294, 0.0197, 0.0394, 0.0423, 0.0095,
     0.0096, 0.0089, 0.0156, 0.0315, 0.0056, 0.0203, 0.0392, 0.0131, 0.0335, 0.0187, 0.0522,
     0.026), V2 = c(0.0582, 0.0956, 0.0375, 0.0173, 0.0086, 0.0607, 0.0092, 0.0149, 0.0644,
     0.0026, 0.0275, 0.0308, 0.0491, 0.0132, 0.0166, 0.0022, 0.0319, 0.0269, 0.0653, 0.0339,
     0.0038, 0.0582, 0.0068, 0.0046, 0.0378, 0.008, 0.0019, 6e-04, 0.0215, 0.0096, 0.0172,
     0.0403, 0.0142, 0.0122, 0.0186, 0.0222, 0.0189, 0.0294, 0.0086, 0.0369, 0.0421, 0.0318,
     0.037, 0.0454, 0.0291, 0.0808, 0.0192, 0.0437, 0.0309, 0.0279, 0.0423, 0.1065, 0.0253,
     0.0555, 0.0071, 0.0121, 0.0599, 0.0436, 0.0106, 0.0266, 0.0119, 0.0163, 0.0446, 0.107,
     0.0902, 0.1249, 0.0627, 0.1135, 0.1252, 0.0688, 0.0649, 0.0315, 0.0394, 0.0128, 0.0178,
     0.0453, 0.0433, 0.0363, 0.0152, 0.0027, 0.0065, 0.0297, 0.0186, 0.012, 0.0136, 0.0444,
     0.0173, 0.0123, 0.0394, 0.042, 0.0321, 0.0308, 0.0404, 0.0274, 0.021, 0.0252, 0.0267,
     0.0121, 0.0108, 0.0387, 0.0258, 0.0346, 0.0437, 0.0363), V3 = c(0.1099, 0.1321, 0.0484,
     0.0347, 0.0055, 0.0378, 0.0145, 0.0641, 0.039, 0.0138, 0.019, 0.0197, 0.0692, 0.0533,
     0.0398, 0.0196, 0.0415, 0.0217, 0.0397, 0.0202, 0.0642, 0.0623, 0.0308, 0.0081, 0.0257,
     0.0188, 0.0075, 0.0088, 0.0242, 0.0024, 0.0501, 0.0317, 0.0181, 0.0068, 0.0131, 0.0089,
     0.0466, 0.0476, 0.0058, 0.004, 0.0573, 0.0423, 0.0953, 0.0382, 0.0749, 0.0507, 0.0254,
     0.0347, 0.0171, 0.0592, 0.0554, 0.1526, 0.0262, 0.0708, 0.0342, 0.0203, 0.0656, 0.0447,
     0.013, 0.0223, 0.0277, 0.0341, 0.0235, 0.0257, 0.0833, 0.1665, 0.0738, 0.0518, 0.1447,
     0.0992, 0.0737, 0.017, 0.0416, 0.0015, 0.0274, 0.0289, 0.0547, 0.0214, 0.0346, 0.0089,
     0.0164, 0.0116, 0.0289, 0.0436, 0.0408, 0.0476, 0.0291, 0.0117, 0.0384, 0.0446, 0.0709,
     0.0539, 0.0682, 0.0248, 0.0282, 0.0167, 0.0221, 0.038, 0.0267, 0.0329, 0.0398, 0.0168,
     0.018, 0.0136), V4 = c(0.1083, 0.1408, 0.0475, 0.007, 0.025, 0.0774, 0.0278, 0.1732,
     0.0173, 0.0062, 0.0371, 0.0622, 0.0831, 0.0569, 0.0359, 0.0206, 0.0286, 0.0339, 0.0604,
     0.0889, 0.0452, 0.06, 0.0311, 0.023, 0.0062, 0.0141, 0.0097, 0.0456, 0.0445, 0.0058,
     0.0285, 0.0293, 0.0406, 0.0108, 0.0211, 0.0108, 0.044, 0.0539, 0.046, 0.0375, 0.013,
     0.035, 0.0824, 0.0203, 0.0519, 0.0244, 0.0061, 0.0456, 0.0228, 0.127, 0.0783, 0.1229,
     0.0386, 0.0618, 0.0793, 0.1036, 0.0229, 0.0844, 0.0842, 0.0749, 0.076, 0.0247, 0.1008,
     0.0837, 0.0813, 0.1496, 0.0608, 0.0232, 0.1644, 0.1021, 0.1132, 0.0226, 0.0547, 0.045,
     0.0232, 0.0713, 0.0681, 0.0227, 0.0346, 0.0061, 0.0487, 0.0082, 0.0195, 0.0624, 0.0633,
     0.0698, 0.0301, 0.0113, 0.0076, 0.0551, 0.0108, 0.0411, 0.0688, 0.0237, 0.0596, 0.0479,
     0.0561, 0.0128, 0.0257, 0.0078, 0.057, 0.0177, 0.0292, 0.0272), V5 = c(0.0974, 0.1674,
     0.0647, 0.0187, 0.0344, 0.1388, 0.0412, 0.2565, 0.0476, 0.0133, 0.0416, 0.008, 0.0079,
     0.0647, 0.0681, 0.018, 0.0121, 0.0305, 0.0496, 0.157, 0.0333, 0.1397, 0.0085, 0.0586,
     0.013, 0.0436, 0.0445, 0.0525, 0.0667, 0.0197, 0.0262, 0.082, 0.0391, 0.0217, 0.061,
     0.0215, 0.0657, 0.0794, 0.0752, 0.0455, 0.0183, 0.1787, 0.0249, 0.0385, 0.0227, 0.1724,
     0.0352, 0.0067, 0.0434, 0.1772, 0.062, 0.1437, 0.0645, 0.1215, 0.1043, 0.1675, 0.0839,
     0.0419, 0.1117, 0.1364, 0.1218, 0.0822, 0.2252, 0.0748, 0.0165, 0.1443, 0.0233, 0.0646,
     0.1693, 0.08, 0.2482, 0.041, 0.0993, 0.0711, 0.0724, 0.1075, 0.0784, 0.0456, 0.0484,
     0.042, 0.0519, 0.0241, 0.0515, 0.0428, 0.0596, 0.1615, 0.0463, 0.0497, 0.0251, 0.0597,
     0.107, 0.0613, 0.0887, 0.0224, 0.0462, 0.0902, 0.0936, 0.0537, 0.041, 0.0721, 0.0529,
     0.0393, 0.0351, 0.0214), V6 = c(0.228, 0.171, 0.0591, 0.0671, 0.0546, 0.0809, 0.0757,
     0.2559, 0.0816, 0.0151, 0.0201, 0.0789, 0.02, 0.1432, 0.0706, 0.0492, 0.0438, 0.1172,
     0.1817, 0.175, 0.069, 0.1883, 0.0767, 0.0682, 0.0612, 0.0668, 0.0906, 0.0778, 0.0771,
     0.0618, 0.0351, 0.1342, 0.0249, 0.0284, 0.0613, 0.0136, 0.0742, 0.0804, 0.0887, 0.1452,
     0.1019, 0.1635, 0.0488, 0.0534, 0.0834, 0.3823, 0.0701, 0.089, 0.1224, 0.1908, 0.0871,
     0.119, 0.0472, 0.1524, 0.0783, 0.0418, 0.1673, 0.1215, 0.1506, 0.1513, 0.1538, 0.1256,
     0.2611, 0.1125, 0.0277, 0.277, 0.1048, 0.1124, 0.0844, 0.0629, 0.1257, 0.0116, 0.1515,
     0.1563, 0.0833, 0.1019, 0.125, 0.0665, 0.0526, 0.0865, 0.0849, 0.0253, 0.0817, 0.0349,
     0.0808, 0.0887, 0.069, 0.0998, 0.0629, 0.1416, 0.0973, 0.1039, 0.0932, 0.0845, 0.0779,
     0.1057, 0.1146, 0.0874, 0.0491, 0.1341, 0.1091, 0.163, 0.1171, 0.0338), V7 = c(0.2431,
     0.0731, 0.0753, 0.1056, 0.0528, 0.0568, 0.1026, 0.2947, 0.0993, 0.0541, 0.0314, 0.144,
     0.0981, 0.1344, 0.102, 0.0033, 0.1299, 0.145, 0.1178, 0.092, 0.0901, 0.1422, 0.0771,
     0.0993, 0.0895, 0.0609, 0.0889, 0.0931, 0.0499, 0.0432, 0.0362, 0.1161, 0.0892, 0.0527,
     0.0612, 0.0659, 0.138, 0.1136, 0.1015, 0.2211, 0.1054, 0.0887, 0.1424, 0.214, 0.0677,
     0.3729, 0.1263, 0.1798, 0.1947, 0.2217, 0.1201, 0.0884, 0.1056, 0.1543, 0.1417, 0.0723,
     0.1154, 0.2002, 0.1776, 0.1316, 0.1192, 0.1323, 0.2061, 0.3322, 0.0569, 0.2555, 0.1338,
     0.1787, 0.0715, 0.013, 0.1797, 0.0223, 0.1674, 0.1518, 0.1232, 0.1606, 0.1296, 0.0939,
     0.0773, 0.1182, 0.0812, 0.0279, 0.1005, 0.0384, 0.209, 0.0596, 0.0576, 0.1326, 0.0747,
     0.0956, 0.0961, 0.1016, 0.0955, 0.1488, 0.1365, 0.1024, 0.0706, 0.1021, 0.1053, 0.1626,
     0.1709, 0.2028, 0.1257, 0.0655), V8 = c(0.3771, 0.1401, 0.0098, 0.0697, 0.0958, 0.0219,
     0.1138, 0.411, 0.0315, 0.021, 0.0651, 0.1451, 0.1016, 0.2041, 0.0893, 0.0398, 0.139,
     0.0638, 0.1024, 0.1353, 0.1454, 0.1447, 0.064, 0.0717, 0.1107, 0.0131, 0.0655, 0.0941,
     0.0906, 0.0951, 0.0535, 0.0663, 0.0973, 0.0575, 0.0506, 0.0954, 0.1099, 0.1228, 0.0494,
     0.1188, 0.107, 0.0817, 0.1972, 0.311, 0.2002, 0.3583, 0.108, 0.1741, 0.1661, 0.0768,
     0.2707, 0.0907, 0.1388, 0.0391, 0.1176, 0.0828, 0.1098, 0.1516, 0.0997, 0.1654, 0.1229,
     0.1584, 0.1668, 0.459, 0.2057, 0.1712, 0.0644, 0.2407, 0.0947, 0.0813, 0.0989, 0.0805,
     0.1513, 0.1206, 0.1298, 0.2119, 0.1729, 0.0972, 0.0862, 0.0999, 0.1833, 0.013, 0.0124,
     0.0446, 0.3465, 0.1071, 0.1103, 0.1117, 0.0578, 0.0802, 0.1323, 0.1394, 0.214, 0.1224,
     0.078, 0.1209, 0.0996, 0.0852, 0.169, 0.1902, 0.1684, 0.1694, 0.1178, 0.14), V9 = c(0.5598,
     0.2083, 0.0684, 0.0962, 0.1009, 0.1037, 0.0794, 0.4983, 0.0736, 0.0505, 0.1896, 0.1789,
     0.2025, 0.1571, 0.0381, 0.0791, 0.0695, 0.074, 0.0583, 0.1593, 0.074, 0.0487, 0.0726,
     0.0576, 0.0973, 0.0899, 0.1624, 0.1711, 0.1229, 0.0836, 0.0258, 0.0155, 0.084, 0.1054,
     0.0989, 0.0786, 0.1384, 0.1235, 0.0472, 0.075, 0.2302, 0.1779, 0.1873, 0.2837, 0.2876,
     0.3429, 0.1523, 0.1598, 0.1368, 0.1246, 0.1206, 0.2107, 0.0598, 0.061, 0.0453, 0.0494,
     0.137, 0.0818, 0.1428, 0.1864, 0.2119, 0.2017, 0.1801, 0.5526, 0.3887, 0.0466, 0.1522,
     0.2682, 0.1583, 0.1761, 0.246, 0.2365, 0.1723, 0.1666, 0.2085, 0.3061, 0.2794, 0.2535,
     0.1451, 0.1976, 0.2228, 0.0489, 0.1168, 0.1318, 0.5276, 0.3175, 0.2423, 0.2984, 0.1357,
     0.1618, 0.2462, 0.2592, 0.2546, 0.1569, 0.1038, 0.1241, 0.1673, 0.1136, 0.2105, 0.261,
     0.1865, 0.2328, 0.1258, 0.1843), V10 = c(0.6194, 0.3513, 0.1487, 0.0251, 0.124, 0.1186,
     0.152, 0.592, 0.086, 0.1097, 0.2668, 0.2522, 0.0767, 0.1573, 0.1328, 0.0475, 0.0568,
     0.136, 0.2176, 0.2795, 0.0349, 0.0864, 0.0901, 0.0818, 0.0751, 0.0922, 0.1452, 0.1483,
     0.1185, 0.118, 0.0474, 0.0506, 0.1191, 0.1109, 0.1093, 0.1015, 0.1376, 0.0842, 0.0393,
     0.1631, 0.2259, 0.2053, 0.1806, 0.2751, 0.3674, 0.2197, 0.163, 0.1408, 0.143, 0.2028,
     0.0279, 0.3597, 0.1334, 0.0113, 0.0945, 0.0686, 0.1767, 0.1975, 0.2227, 0.2013, 0.2531,
     0.2122, 0.3083, 0.5966, 0.7106, 0.1114, 0.078, 0.2058, 0.1247, 0.0998, 0.3422, 0.2461,
     0.2078, 0.1345, 0.272, 0.2936, 0.2954, 0.3127, 0.211, 0.2318, 0.181, 0.0874, 0.1476,
     0.1375, 0.5965, 0.2918, 0.3134, 0.3473, 0.1695, 0.2558, 0.2696, 0.3745, 0.2952, 0.2119,
     0.1567, 0.1533, 0.1859, 0.1747, 0.2471, 0.3193, 0.266, 0.2684, 0.2529, 0.2354), V11 = c(0.6333,
     0.1786, 0.1156, 0.0801, 0.1097, 0.1237, 0.1675, 0.5832, 0.0414, 0.0841, 0.3376, 0.2607,
     0.1767, 0.2327, 0.1303, 0.1152, 0.0869, 0.2132, 0.2459, 0.3336, 0.1459, 0.2143, 0.075,
     0.1315, 0.0528, 0.1445, 0.1442, 0.1532, 0.0775, 0.0978, 0.0526, 0.0906, 0.1522, 0.0937,
     0.1063, 0.1261, 0.0938, 0.0357, 0.1106, 0.2709, 0.2373, 0.3135, 0.2139, 0.2707, 0.2974,
     0.2653, 0.103, 0.2693, 0.0994, 0.0947, 0.2251, 0.5466, 0.2969, 0.1255, 0.1132, 0.1125,
     0.1995, 0.2309, 0.2621, 0.289, 0.2855, 0.221, 0.3794, 0.5304, 0.7342, 0.1739, 0.1791,
     0.1546, 0.234, 0.0523, 0.2128, 0.2245, 0.1239, 0.0785, 0.2188, 0.3104, 0.2506, 0.2192,
     0.2343, 0.2472, 0.2549, 0.11, 0.2118, 0.2026, 0.6254, 0.3273, 0.4786, 0.4231, 0.1734,
     0.3078, 0.3412, 0.4229, 0.4025, 0.3003, 0.2476, 0.2128, 0.2481, 0.2198, 0.268, 0.3468,
     0.3188, 0.3108, 0.2716, 0.272), V12 = c(0.706, 0.0658, 0.1654, 0.1056, 0.1215, 0.1601,
     0.137, 0.5419, 0.0472, 0.0942, 0.3282, 0.371, 0.2555, 0.1785, 0.0273, 0.052, 0.1935,
     0.3738, 0.3332, 0.294, 0.3473, 0.372, 0.0844, 0.1862, 0.1209, 0.1475, 0.0948, 0.11,
     0.1101, 0.0909, 0.1854, 0.2545, 0.1322, 0.0827, 0.1179, 0.0828, 0.0259, 0.0689, 0.1412,
     0.3358, 0.3323, 0.3118, 0.1523, 0.0946, 0.0837, 0.3223, 0.2187, 0.3259, 0.225, 0.2497,
     0.2615, 0.5205, 0.4754, 0.2473, 0.084, 0.1741, 0.2869, 0.3025, 0.3109, 0.365, 0.2961,
     0.2399, 0.5364, 0.2251, 0.5033, 0.316, 0.2681, 0.2671, 0.1764, 0.0904, 0.1377, 0.152,
     0.0236, 0.0367, 0.3037, 0.3431, 0.2601, 0.2621, 0.2087, 0.288, 0.2984, 0.1084, 0.2575,
     0.2389, 0.4507, 0.3035, 0.5239, 0.5044, 0.247, 0.3404, 0.4292, 0.4499, 0.5148, 0.3094,
     0.2783, 0.2536, 0.2712, 0.2721, 0.3049, 0.3738, 0.3553, 0.2933, 0.2374, 0.2442),
     V13 = c(0.5544, 0.0513, 0.3833, 0.1266, 0.1874, 0.352, 0.1361, 0.5472, 0.0835,
     0.1204, 0.2432, 0.3906, 0.2812, 0.1507, 0.0644, 0.1192, 0.1478, 0.3738, 0.3087,
     0.1608, 0.3197, 0.2665, 0.1226, 0.2789, 0.1763, 0.2087, 0.0618, 0.089, 0.1042,
     0.0656, 0.104, 0.1464, 0.1434, 0.092, 0.1291, 0.0493, 0.1499, 0.1705, 0.2202,
     0.4091, 0.3827, 0.3686, 0.1975, 0.102, 0.1912, 0.5582, 0.1542, 0.4545, 0.2444,
     0.2209, 0.177, 0.5127, 0.5677, 0.3011, 0.0717, 0.271, 0.3275, 0.3938, 0.2859,
     0.351, 0.3341, 0.2964, 0.6173, 0.2402, 0.3, 0.3249, 0.1788, 0.3141, 0.2284, 0.2655,
     0.4032, 0.1732, 0.1771, 0.1227, 0.2959, 0.2456, 0.2249, 0.2419, 0.1645, 0.2126,
     0.2624, 0.1094, 0.2354, 0.2112, 0.3693, 0.3033, 0.4393, 0.5237, 0.3141, 0.34,
     0.3682, 0.5404, 0.4901, 0.2743, 0.2896, 0.2686, 0.2934, 0.2105, 0.2863, 0.3055,
     0.3116, 0.2275, 0.1878, 0.1665), V14 = c(0.532, 0.3752, 0.3598, 0.089, 0.3383,
     0.4479, 0.1345, 0.5314, 0.0938, 0.042, 0.1268, 0.2672, 0.2722, 0.1916, 0.0712,
     0.1943, 0.1871, 0.2673, 0.2613, 0.3335, 0.2823, 0.2113, 0.1619, 0.2579, 0.2039,
     0.2558, 0.1641, 0.1236, 0.0853, 0.0593, 0.0948, 0.1272, 0.1244, 0.0911, 0.1591,
     0.0848, 0.2851, 0.3257, 0.2976, 0.44, 0.484, 0.3885, 0.4844, 0.4519, 0.504, 0.6916,
     0.263, 0.5785, 0.3239, 0.3195, 0.3709, 0.5395, 0.569, 0.3747, 0.1968, 0.3087,
     0.3769, 0.505, 0.3316, 0.3495, 0.4287, 0.4061, 0.7842, 0.2689, 0.1951, 0.2164,
     0.1039, 0.2904, 0.3115, 0.3099, 0.5684, 0.3099, 0.3115, 0.2614, 0.2059, 0.1887,
     0.2115, 0.2179, 0.1689, 0.0708, 0.1893, 0.1023, 0.1334, 0.1444, 0.2864, 0.2587,
     0.344, 0.4398, 0.3297, 0.3951, 0.394, 0.4303, 0.4127, 0.2547, 0.2956, 0.2803,
     0.2637, 0.1727, 0.2294, 0.1926, 0.1965, 0.0994, 0.0983, 0.0336), V15 = c(0.6479,
     0.5419, 0.1713, 0.0198, 0.3227, 0.3769, 0.2144, 0.4981, 0.1466, 0.0031, 0.1278,
     0.2716, 0.3227, 0.2061, 0.1204, 0.184, 0.1994, 0.2333, 0.3232, 0.4985, 0.0166,
     0.1103, 0.2317, 0.224, 0.2727, 0.2603, 0.0708, 0.1197, 0.0456, 0.0832, 0.0912,
     0.1223, 0.0653, 0.1487, 0.168, 0.1514, 0.5743, 0.4602, 0.4116, 0.5485, 0.6812,
     0.585, 0.7298, 0.6737, 0.6352, 0.7943, 0.294, 0.4471, 0.3039, 0.334, 0.4533,
     0.6558, 0.6421, 0.452, 0.2633, 0.3575, 0.4169, 0.5872, 0.3755, 0.4325, 0.5205,
     0.5095, 0.8392, 0.6646, 0.2767, 0.2031, 0.198, 0.3531, 0.4725, 0.352, 0.2398,
     0.438, 0.499, 0.428, 0.0906, 0.1184, 0.127, 0.1159, 0.165, 0.1194, 0.0668, 0.0601,
     0.0092, 0.0742, 0.1635, 0.1682, 0.2869, 0.3236, 0.2759, 0.3352, 0.2965, 0.3333,
     0.3575, 0.187, 0.3189, 0.1886, 0.188, 0.204, 0.1165, 0.1385, 0.178, 0.1801, 0.0683,
     0.1302), V16 = c(0.6931, 0.544, 0.1136, 0.1133, 0.2723, 0.5761, 0.5354, 0.6985,
     0.0809, 0.0162, 0.4441, 0.4183, 0.3463, 0.2307, 0.0717, 0.2077, 0.3283, 0.5367,
     0.3731, 0.7295, 0.0572, 0.1136, 0.2934, 0.2568, 0.2321, 0.1985, 0.0844, 0.1145,
     0.1304, 0.1297, 0.1688, 0.1669, 0.089, 0.1666, 0.1918, 0.1396, 0.8278, 0.6225,
     0.4754, 0.7213, 0.7555, 0.7868, 0.7807, 0.6699, 0.6804, 0.7152, 0.2978, 0.2231,
     0.241, 0.3323, 0.5553, 0.8705, 0.7487, 0.5392, 0.4191, 0.4998, 0.5036, 0.661,
     0.4499, 0.5398, 0.6087, 0.5512, 0.9016, 0.6632, 0.3737, 0.258, 0.3234, 0.5079,
     0.5543, 0.3892, 0.4331, 0.5595, 0.6707, 0.6122, 0.161, 0.208, 0.1193, 0.1237,
     0.1967, 0.2808, 0.2666, 0.0906, 0.1951, 0.1533, 0.0422, 0.1308, 0.3889, 0.2956,
     0.2056, 0.2252, 0.3172, 0.3496, 0.3447, 0.1452, 0.1892, 0.1485, 0.1405, 0.1786,
     0.2127, 0.2122, 0.2794, 0.22, 0.1503, 0.1708), V17 = c(0.6759, 0.515, 0.0349,
     0.2826, 0.3943, 0.6426, 0.683, 0.8292, 0.1179, 0.0624, 0.6795, 0.6988, 0.5395,
     0.236, 0.1224, 0.1956, 0.6861, 0.7312, 0.4203, 0.735, 0.2164, 0.1934, 0.3526,
     0.2933, 0.2676, 0.2394, 0.259, 0.2137, 0.269, 0.2038, 0.1568, 0.1424, 0.1226,
     0.1268, 0.1615, 0.1066, 0.8669, 0.7327, 0.539, 0.8137, 0.9522, 0.9739, 0.7906,
     0.7066, 0.7505, 0.3512, 0.0699, 0.2164, 0.0367, 0.278, 0.4616, 0.9786, 0.8999,
     0.6588, 0.505, 0.6011, 0.618, 0.7417, 0.4765, 0.6237, 0.7236, 0.6613, 1, 0.1674,
     0.2507, 0.1796, 0.3748, 0.4639, 0.5386, 0.3962, 0.5954, 0.682, 0.7655, 0.7435,
     0.18, 0.2736, 0.1794, 0.0886, 0.2934, 0.4221, 0.4274, 0.1313, 0.3685, 0.3052,
     0.1785, 0.2803, 0.442, 0.3286, 0.1162, 0.2086, 0.2825, 0.3426, 0.3068, 0.1457,
     0.173, 0.216, 0.2028, 0.1318, 0.2062, 0.2758, 0.287, 0.2732, 0.1723, 0.2177),
     V18 = c(0.7551, 0.4262, 0.3796, 0.3234, 0.6432, 0.679, 0.56, 0.7839, 0.2179,
     0.2127, 0.7051, 0.5733, 0.7911, 0.1299, 0.2349, 0.163, 0.5814, 0.7659, 0.5364,
     0.8253, 0.4563, 0.4142, 0.3657, 0.2991, 0.2934, 0.3134, 0.2679, 0.2838, 0.2947,
     0.3811, 0.0375, 0.1285, 0.1846, 0.1374, 0.1647, 0.1923, 0.8131, 0.7843, 0.6279,
     0.9185, 0.9826, 1, 0.6122, 0.5632, 0.6595, 0.2008, 0.1401, 0.3201, 0.1672, 0.2975,
     0.3797, 0.9335, 1, 0.7113, 0.6711, 0.647, 0.8025, 0.8006, 0.6254, 0.6876, 0.7577,
     0.6804, 0.8911, 0.0837, 0.2507, 0.2422, 0.2586, 0.1859, 0.3746, 0.2449, 0.5772,
     0.6164, 0.8485, 0.813, 0.218, 0.3274, 0.2185, 0.1755, 0.3709, 0.5279, 0.6291,
     0.2758, 0.4646, 0.4116, 0.4394, 0.4519, 0.3892, 0.3231, 0.1884, 0.2248, 0.305,
     0.2851, 0.2945, 0.2429, 0.2226, 0.2417, 0.2613, 0.226, 0.2222, 0.4576, 0.3969,
     0.2862, 0.2339, 0.3175), V19 = c(0.8929, 0.2024, 0.7401, 0.3238, 0.7271, 0.7157,
     0.3093, 0.8215, 0.3326, 0.3436, 0.7966, 0.2226, 0.9064, 0.3812, 0.3684, 0.1218,
     0.25, 0.6271, 0.7062, 0.8793, 0.3819, 0.3279, 0.3221, 0.3924, 0.3295, 0.4077,
     0.3094, 0.364, 0.3669, 0.4451, 0.1316, 0.1857, 0.388, 0.1095, 0.1397, 0.2991,
     0.9045, 0.7988, 0.706, 1, 0.8871, 0.9843, 0.42, 0.3785, 0.4509, 0.2676, 0.299,
     0.2915, 0.3038, 0.2948, 0.345, 0.7917, 0.969, 0.7602, 0.7922, 0.8067, 0.9333,
     0.8456, 0.7304, 0.7329, 0.7726, 0.652, 0.8753, 0.4331, 0.3292, 0.3609, 0.368,
     0.4474, 0.4583, 0.2355, 0.8176, 0.6803, 0.9805, 0.9006, 0.2026, 0.2344, 0.1646,
     0.1758, 0.4309, 0.5857, 0.7782, 0.366, 0.5418, 0.5466, 0.695, 0.6641, 0.4088,
     0.4528, 0.339, 0.3382, 0.2408, 0.4062, 0.4351, 0.3259, 0.2427, 0.2989, 0.2778,
     0.2358, 0.3241, 0.6487, 0.5599, 0.2034, 0.1962, 0.3714), V20 = c(0.8619, 0.4233,
     0.9925, 0.4333, 0.8673, 0.5466, 0.3226, 0.9363, 0.3258, 0.3813, 0.9401, 0.2631,
     0.8701, 0.5858, 0.3918, 0.1017, 0.1734, 0.4395, 0.8196, 0.9657, 0.5627, 0.6222,
     0.3093, 0.4691, 0.491, 0.4529, 0.4678, 0.543, 0.4948, 0.5224, 0.2086, 0.1136,
     0.3658, 0.1286, 0.1426, 0.3247, 0.9046, 0.8261, 0.7918, 0.9418, 0.8268, 0.861,
     0.2807, 0.2721, 0.2964, 0.4299, 0.3915, 0.4235, 0.4069, 0.1729, 0.2665, 0.7383,
     0.9032, 0.8672, 0.8381, 0.9008, 0.9399, 0.7939, 0.8702, 0.8107, 0.8098, 0.6788,
     0.7886, 0.8718, 0.4871, 0.181, 0.3508, 0.4079, 0.5961, 0.3045, 0.8835, 0.8435,
     1, 0.9603, 0.1506, 0.126, 0.074, 0.154, 0.4161, 0.6153, 0.7686, 0.5269, 0.626,
     0.5933, 0.8097, 0.7683, 0.5006, 0.6339, 0.3926, 0.4578, 0.542, 0.6833, 0.7264,
     0.3679, 0.3149, 0.3341, 0.3346, 0.3107, 0.433, 0.7154, 0.6936, 0.174, 0.1395,
     0.4552), V21 = c(0.7974, 0.7723, 0.9802, 0.6068, 0.9674, 0.5399, 0.443, 1, 0.2111,
     0.3825, 0.9857, 0.7473, 0.7672, 0.4497, 0.4925, 0.1354, 0.3363, 0.433, 0.8835,
     1, 0.6484, 0.7468, 0.4084, 0.5665, 0.5402, 0.4893, 0.5958, 0.6673, 0.6275, 0.5911,
     0.1976, 0.2069, 0.2297, 0.2146, 0.2429, 0.3797, 1, 1, 0.9493, 0.9116, 0.7561,
     0.8443, 0.5148, 0.5297, 0.4019, 0.528, 0.3598, 0.446, 0.3613, 0.3264, 0.2395,
     0.6908, 0.7685, 0.8416, 0.8759, 0.8906, 0.9275, 0.8804, 0.9349, 0.8396, 0.8995,
     0.7811, 0.7156, 0.7992, 0.6527, 0.2604, 0.5606, 0.54, 0.7464, 0.3112, 0.5248,
     0.9921, 1, 0.9162, 0.0521, 0.0576, 0.0625, 0.0512, 0.5116, 0.6753, 0.8099, 0.581,
     0.742, 0.6663, 0.855, 0.696, 0.7271, 0.7044, 0.4282, 0.6474, 0.6802, 0.765, 0.8147,
     0.3355, 0.4102, 0.3786, 0.383, 0.3906, 0.5071, 0.801, 0.7969, 0.413, 0.3164,
     0.57), V22 = c(0.6737, 0.9735, 0.889, 0.7652, 0.9847, 0.6362, 0.5573, 0.9224,
     0.2302, 0.4764, 0.8193, 0.7263, 0.2957, 0.4876, 0.8793, 0.3157, 0.5588, 0.4326,
     0.8299, 0.8707, 0.7235, 0.7676, 0.4285, 0.6464, 0.6257, 0.5666, 0.7245, 0.7979,
     0.8162, 0.6566, 0.0946, 0.0219, 0.261, 0.2889, 0.2816, 0.5658, 0.9976, 0.9814,
     1, 0.9349, 0.8217, 0.9061, 0.7569, 0.7697, 0.6794, 0.3489, 0.2403, 0.238, 0.1994,
     0.3834, 0.1127, 0.385, 0.6998, 0.7974, 0.9422, 0.9338, 0.945, 0.8384, 0.9614,
     0.8632, 0.9247, 0.8369, 0.7581, 0.3712, 0.8454, 0.6572, 0.5231, 0.4786, 0.7644,
     0.4698, 0.6373, 1, 0.9992, 0.914, 0.2143, 0.1241, 0.2381, 0.1805, 0.6501, 0.7873,
     0.8493, 0.6181, 0.8257, 0.7333, 0.8717, 0.4393, 0.9385, 0.8314, 0.5418, 0.6708,
     0.632, 0.667, 0.8103, 0.31, 0.3808, 0.3956, 0.4003, 0.3631, 0.5944, 0.7924, 0.7452,
     0.6879, 0.5888, 0.7397), V23 = c(0.4293, 0.939, 0.6712, 0.9203, 0.948, 0.7849,
     0.5782, 0.7839, 0.3361, 0.6313, 0.5789, 0.3393, 0.4148, 1, 0.9606, 0.4645, 0.6592,
     0.5544, 0.7609, 0.6471, 0.8242, 0.7867, 0.4663, 0.6774, 0.6826, 0.6234, 0.8773,
     0.9273, 0.9237, 0.6308, 0.1965, 0.24, 0.4193, 0.4238, 0.429, 0.7483, 0.9872,
     0.962, 0.9645, 0.7484, 0.6967, 0.5847, 0.8596, 0.8643, 0.8297, 0.143, 0.4208,
     0.6415, 0.4611, 0.3523, 0.2556, 0.0671, 0.6644, 0.8385, 1, 1, 0.8328, 0.7852,
     0.9126, 0.8747, 0.9365, 0.8969, 0.6372, 0.1703, 0.9739, 0.9734, 0.5469, 0.4332,
     0.5711, 0.5534, 0.8375, 0.7983, 0.9067, 0.7851, 0.4333, 0.3239, 0.4824, 0.4039,
     0.7717, 0.8974, 0.944, 0.5875, 0.8609, 0.7136, 0.8601, 0.2432, 1, 0.8449, 0.6448,
     0.7007, 0.5824, 0.5703, 0.6665, 0.3914, 0.4896, 0.5232, 0.5114, 0.4809, 0.7078,
     0.8793, 0.8203, 0.812, 0.7631, 0.8062), V24 = c(0.3648, 0.5559, 0.4286, 0.9719,
     0.8036, 0.7756, 0.6173, 0.547, 0.4259, 0.7523, 0.6394, 0.2824, 0.6043, 0.8675,
     0.8786, 0.5906, 0.7012, 0.736, 0.7605, 0.5973, 0.8766, 0.8253, 0.5956, 0.7577,
     0.7527, 0.6741, 0.9214, 0.9027, 0.871, 0.5998, 0.1242, 0.2547, 0.5848, 0.6168,
     0.6443, 0.8757, 0.9761, 0.9601, 0.9432, 0.5146, 0.6444, 0.4033, 1, 0.9304, 1,
     0.5453, 0.5675, 0.8966, 0.6849, 0.541, 0.5169, 0.0502, 0.5964, 0.9317, 0.9931,
     0.9102, 0.7773, 0.8479, 0.9443, 0.9607, 0.9853, 0.9856, 0.321, 0.1611, 1, 0.9757,
     0.6954, 0.6113, 0.6257, 0.4532, 0.6699, 0.5426, 0.6803, 0.5134, 0.5943, 0.4357,
     0.6372, 0.5697, 0.8491, 0.9828, 0.945, 0.4639, 0.84, 0.7014, 0.9201, 0.2886,
     0.9831, 0.8512, 0.7223, 0.7619, 0.6805, 0.5995, 0.6958, 0.528, 0.6292, 0.6913,
     0.686, 0.6531, 0.7641, 1, 0.9261, 0.8453, 0.8473, 0.8837), V25 = c(0.5331, 0.5268,
     0.3374, 0.9207, 0.6833, 0.578, 0.8132, 0.4562, 0.4609, 0.8675, 0.7043, 0.6053,
     0.3178, 0.4718, 0.6905, 0.6776, 0.8099, 0.8589, 0.8367, 0.8218, 1, 1, 0.6948,
     0.8856, 0.8504, 0.8282, 0.9282, 0.9192, 0.8052, 0.4958, 0.0616, 0.024, 0.5643,
     0.8167, 0.9061, 0.9048, 0.9009, 0.9118, 0.8658, 0.4106, 0.6948, 0.5946, 0.8457,
     0.9372, 0.824, 0.6338, 0.6094, 0.8918, 0.7272, 0.5228, 0.3779, 0.2717, 0.3711,
     0.8555, 0.9575, 0.8496, 0.7007, 0.7434, 1, 0.9716, 0.9776, 1, 0.2076, 0.2086,
     0.6665, 0.8079, 0.6352, 0.5091, 0.6695, 0.4464, 0.7756, 0.3952, 0.5103, 0.3439,
     0.6926, 0.5734, 0.7531, 0.6577, 0.9104, 1, 0.9655, 0.5424, 0.8949, 0.7758, 0.8729,
     0.4974, 0.9932, 0.9138, 0.7853, 0.7745, 0.5984, 0.6484, 0.7748, 0.6409, 0.7519,
     0.7868, 0.749, 0.7812, 0.8878, 0.9865, 0.881, 0.8919, 0.9424, 0.9432), V26 = c(0.2413,
     0.6826, 0.7366, 0.7545, 0.5136, 0.4862, 0.9819, 0.5922, 0.2606, 0.8788, 0.6875,
     0.5897, 0.3482, 0.5341, 0.6937, 0.8119, 0.8901, 0.8989, 0.8905, 0.7755, 0.8582,
     0.9481, 0.8386, 0.9419, 0.8938, 0.8823, 0.9942, 1, 0.8756, 0.5647, 0.2141, 0.1923,
     0.5448, 0.9622, 1, 0.7511, 0.9724, 0.9086, 0.7895, 0.3443, 0.8014, 0.6793, 0.6797,
     0.6247, 0.7115, 0.7712, 0.6323, 0.7529, 0.7152, 0.4475, 0.4082, 0.2839, 0.0921,
     0.6162, 0.8647, 0.7867, 0.6154, 0.6433, 0.9455, 0.9121, 1, 0.9395, 0.2279, 0.2847,
     0.5323, 0.6521, 0.6757, 0.4606, 0.7131, 0.467, 0.875, 0.5179, 0.4716, 0.329,
     0.7576, 0.7825, 0.8959, 0.7474, 0.8912, 0.846, 0.8045, 0.7367, 0.9945, 0.9137,
     0.8084, 0.8172, 0.9161, 0.9985, 0.7984, 0.6767, 0.8412, 0.8614, 0.8688, 0.7707,
     0.7985, 0.8337, 0.7843, 0.8395, 0.9711, 0.9474, 0.8814, 0.93, 0.9986, 1), V27 = c(0.507,
     0.5713, 0.9611, 0.8289, 0.309, 0.4181, 0.9823, 0.5448, 0.0874, 0.7901, 0.4081,
     0.4967, 0.6158, 0.6197, 0.5674, 0.8594, 0.8745, 0.942, 0.7652, 0.6111, 0.6563,
     0.7539, 0.8875, 1, 0.9928, 0.9196, 1, 0.9821, 1, 0.6906, 0.4642, 0.4753, 0.4772,
     0.828, 0.8087, 0.6858, 0.9675, 0.7931, 0.6501, 0.6981, 0.6053, 0.6389, 0.6971,
     0.6024, 0.7726, 0.6838, 0.6549, 0.6838, 0.7102, 0.534, 0.5353, 0.2234, 0.0481,
     0.4139, 0.7215, 0.7688, 0.581, 0.5514, 0.8815, 0.8576, 0.9896, 0.8917, 0.3309,
     0.2211, 0.4024, 0.4915, 0.8499, 0.7243, 0.7567, 0.4621, 0.83, 0.565, 0.498, 0.2571,
     0.8787, 0.9252, 0.9941, 0.8543, 0.8189, 0.6055, 0.4969, 0.9089, 1, 0.9964, 0.8694,
     1, 0.8237, 1, 0.8847, 0.7373, 0.9911, 0.9819, 1, 0.8754, 0.883, 0.9199, 0.9021,
     0.918, 0.988, 0.9474, 0.9301, 0.9987, 0.9699, 0.9375), V28 = c(0.8533, 0.5429,
     0.7353, 0.8907, 0.0832, 0.2457, 0.9166, 0.3971, 0.2862, 0.8357, 0.1811, 0.8616,
     0.8049, 0.7143, 0.654, 0.9228, 0.7887, 0.9401, 0.5897, 0.4195, 0.5087, 0.6008,
     0.6404, 0.8564, 0.9134, 0.8965, 0.9071, 0.9092, 0.9858, 0.8513, 0.6471, 0.7003,
     0.6897, 0.5816, 0.6119, 0.7043, 0.7633, 0.5877, 0.4492, 0.8713, 0.6084, 0.5002,
     0.5843, 0.681, 0.6124, 0.8015, 0.7673, 0.839, 0.8516, 0.5323, 0.5116, 0.1911,
     0.0876, 0.3269, 0.5801, 0.7718, 0.4454, 0.3519, 0.752, 0.8798, 0.9076, 0.8105,
     0.2847, 0.6134, 0.3444, 0.5363, 0.8025, 0.8987, 0.8077, 0.6988, 0.6896, 0.3042,
     0.6196, 0.3685, 0.906, 0.9349, 0.9957, 0.9085, 0.6779, 0.3036, 0.396, 1, 0.9649,
     1, 0.8411, 0.9238, 0.6957, 0.7544, 0.9582, 0.7834, 0.9187, 0.938, 0.9941, 1,
     0.9915, 1, 1, 0.9769, 0.9812, 0.9315, 0.9955, 1, 1, 0.7603), V29 = c(0.6036,
     0.2177, 0.4856, 0.7309, 0.4019, 0.0716, 0.7423, 0.0882, 0.5606, 0.9631, 0.2064,
     0.8339, 0.6289, 0.5605, 0.7802, 0.8387, 0.8725, 0.9379, 0.3037, 0.299, 0.4817,
     0.5437, 0.3308, 0.679, 0.708, 0.7549, 0.8545, 0.8184, 0.9427, 1, 0.634, 0.6825,
     0.9797, 0.4667, 0.526, 0.5864, 0.4434, 0.3474, 0.4739, 0.9013, 0.8877, 0.5578,
     0.4772, 0.5047, 0.4936, 0.8073, 1, 1, 1, 0.3907, 0.4544, 0.0408, 0.104, 0.3108,
     0.4964, 0.6268, 0.3707, 0.3168, 0.7068, 0.772, 0.7306, 0.6828, 0.1949, 0.5807,
     0.4239, 0.7649, 0.6563, 0.8826, 0.8477, 0.7626, 0.3372, 0.1881, 0.7171, 0.5765,
     0.8528, 0.9348, 0.9328, 0.8668, 0.5368, 0.0144, 0.3856, 0.8247, 0.8747, 0.8881,
     0.5793, 0.8519, 0.4536, 0.4661, 0.899, 0.9619, 0.8005, 0.8435, 0.8793, 0.9806,
     0.9223, 0.899, 0.8888, 0.8937, 0.9464, 0.8326, 0.8576, 0.8104, 0.863, 0.7123),
     V30 = c(0.8514, 0.2149, 0.1594, 0.6896, 0.2344, 0.0613, 0.7736, 0.2385, 0.8344,
     0.9619, 0.3917, 0.4084, 0.4999, 0.3728, 0.7575, 0.7238, 0.9376, 0.8575, 0.0823,
     0.1354, 0.453, 0.5387, 0.3425, 0.5587, 0.6318, 0.6736, 0.7293, 0.6962, 0.8114,
     0.9166, 0.6107, 0.6443, 1, 0.3539, 0.3677, 0.3773, 0.3822, 0.4235, 0.6153, 0.8014,
     0.8557, 0.4831, 0.5201, 0.5775, 0.5648, 0.831, 0.8463, 0.8362, 0.769, 0.3456,
     0.4258, 0.2531, 0.1714, 0.2554, 0.4886, 0.4301, 0.2891, 0.3346, 0.5986, 0.5711,
     0.5758, 0.5572, 0.1671, 0.6925, 0.4182, 0.525, 0.8591, 0.9201, 0.9289, 0.7025,
     0.6405, 0.396, 0.6316, 0.619, 0.9087, 1, 0.9344, 0.8892, 0.5207, 0.2526, 0.5574,
     0.5441, 0.6257, 0.6585, 0.3754, 0.7722, 0.3281, 0.3924, 0.6831, 1, 0.6713, 0.6074,
     0.6482, 0.6969, 0.6981, 0.6456, 0.6511, 0.7022, 0.8542, 0.6213, 0.6069, 0.6199,
     0.6979, 0.8358), V31 = c(0.8512, 0.5811, 0.3007, 0.5829, 0.1905, 0.1816, 0.8473,
     0.2005, 0.8096, 0.9236, 0.3791, 0.2268, 0.583, 0.2481, 0.5836, 0.6292, 0.892,
     0.7284, 0.2787, 0.2438, 0.4521, 0.5619, 0.492, 0.4147, 0.6126, 0.6463, 0.6499,
     0.59, 0.6987, 0.7676, 0.7046, 0.7063, 0.9546, 0.2727, 0.2746, 0.2206, 0.4727,
     0.4633, 0.4929, 0.438, 0.5563, 0.4729, 0.4241, 0.4754, 0.4906, 0.7792, 0.5509,
     0.5427, 0.4841, 0.4091, 0.3869, 0.1979, 0.3264, 0.3367, 0.4079, 0.2077, 0.2185,
     0.2056, 0.3857, 0.4264, 0.4469, 0.4301, 0.1025, 0.3825, 0.4393, 0.5101, 0.6655,
     0.8005, 0.9513, 0.7382, 0.7138, 0.2286, 0.3554, 0.4613, 0.9657, 0.9308, 0.8854,
     0.9065, 0.5651, 0.4335, 0.7309, 0.3349, 0.2184, 0.2707, 0.3485, 0.5772, 0.2522,
     0.3849, 0.6108, 0.8086, 0.5632, 0.5403, 0.5876, 0.4973, 0.6167, 0.5967, 0.6083,
     0.65, 0.6457, 0.3772, 0.3934, 0.6041, 0.7717, 0.7622), V32 = c(0.5045, 0.6323,
     0.4096, 0.4935, 0.1235, 0.4493, 0.7352, 0.0587, 0.725, 0.8903, 0.2042, 0.1745,
     0.666, 0.1921, 0.6316, 0.5181, 0.7508, 0.67, 0.7241, 0.5624, 0.4532, 0.5141,
     0.4592, 0.2946, 0.4638, 0.5007, 0.6071, 0.5447, 0.681, 0.6177, 0.5376, 0.5373,
     0.8835, 0.141, 0.102, 0.2628, 0.4007, 0.341, 0.3195, 0.1319, 0.2897, 0.3318,
     0.1592, 0.24, 0.182, 0.5049, 0.4444, 0.4577, 0.3717, 0.4639, 0.3939, 0.1891,
     0.4612, 0.4465, 0.2443, 0.1198, 0.1711, 0.1032, 0.251, 0.286, 0.3719, 0.3339,
     0.1362, 0.4303, 0.1162, 0.4219, 0.5369, 0.6033, 0.7995, 0.7446, 0.8202, 0.3544,
     0.2897, 0.3615, 0.9306, 0.8478, 0.769, 0.8522, 0.5749, 0.4918, 0.8549, 0.0877,
     0.2945, 0.1746, 0.4639, 0.519, 0.3964, 0.4674, 0.548, 0.5558, 0.7332, 0.689,
     0.6408, 0.502, 0.5069, 0.4355, 0.4463, 0.5069, 0.3397, 0.2822, 0.2464, 0.5547,
     0.7305, 0.4567), V33 = c(0.1862, 0.2965, 0.317, 0.3101, 0.1717, 0.5976, 0.6671,
     0.2544, 0.8048, 0.9708, 0.2227, 0.0507, 0.4124, 0.1386, 0.8108, 0.4629, 0.6832,
     0.7547, 0.8032, 0.5555, 0.5385, 0.6084, 0.3034, 0.2025, 0.2797, 0.3663, 0.5588,
     0.5142, 0.6591, 0.5468, 0.5934, 0.6601, 0.7662, 0.1863, 0.1339, 0.2672, 0.3381,
     0.2849, 0.3735, 0.1709, 0.3638, 0.3969, 0.1668, 0.2779, 0.1811, 0.1413, 0.5169,
     0.8067, 0.6096, 0.558, 0.4661, 0.2433, 0.3939, 0.5, 0.1768, 0.166, 0.3578, 0.3168,
     0.2162, 0.3114, 0.2079, 0.2035, 0.2212, 0.7791, 0.4336, 0.416, 0.3118, 0.212,
     0.4362, 0.7927, 0.6657, 0.4187, 0.4316, 0.4434, 0.7774, 0.7605, 0.6865, 0.7204,
     0.525, 0.5409, 0.9425, 0.16, 0.3645, 0.2709, 0.6495, 0.6824, 0.4154, 0.4245,
     0.5058, 0.5409, 0.6038, 0.5977, 0.4972, 0.5359, 0.3921, 0.2997, 0.2948, 0.3903,
     0.3828, 0.2042, 0.1645, 0.416, 0.5197, 0.1715), V34 = c(0.2709, 0.1873, 0.3305,
     0.0306, 0.2351, 0.3785, 0.6083, 0.2009, 0.9435, 0.9647, 0.3341, 0.1588, 0.126,
     0.3325, 0.9039, 0.5255, 0.761, 0.8773, 0.805, 0.6963, 0.5308, 0.5621, 0.4366,
     0.0688, 0.1721, 0.2298, 0.5967, 0.5389, 0.6954, 0.5516, 0.8443, 0.8708, 0.6547,
     0.2176, 0.1582, 0.2907, 0.3172, 0.2847, 0.3336, 0.2484, 0.4786, 0.3894, 0.0588,
     0.1997, 0.1107, 0.2767, 0.4268, 0.6973, 0.511, 0.5727, 0.3974, 0.1956, 0.505,
     0.5111, 0.2472, 0.2618, 0.3947, 0.404, 0.0968, 0.2066, 0.0955, 0.0798, 0.1124,
     0.8703, 0.6553, 0.1906, 0.3763, 0.2866, 0.4048, 0.5227, 0.5254, 0.2398, 0.3791,
     0.3864, 0.6643, 0.704, 0.639, 0.62, 0.4255, 0.5961, 0.8726, 0.4169, 0.5012, 0.4853,
     0.6901, 0.622, 0.3308, 0.3095, 0.4476, 0.4988, 0.2575, 0.3244, 0.2755, 0.3842,
     0.3524, 0.2294, 0.1729, 0.3009, 0.3204, 0.219, 0.114, 0.1472, 0.1786, 0.1549),
     V35 = c(0.4232, 0.2969, 0.3408, 0.0244, 0.2489, 0.2495, 0.6239, 0.0329, 1, 0.7892,
     0.3984, 0.304, 0.2487, 0.2883, 0.8647, 0.5147, 0.9017, 0.9919, 0.7676, 0.7298,
     0.5356, 0.5956, 0.5175, 0.1171, 0.1665, 0.1362, 0.6275, 0.5531, 0.729, 0.5463,
     0.9481, 0.9518, 0.5447, 0.236, 0.1952, 0.1982, 0.2222, 0.1742, 0.1052, 0.3044,
     0.2908, 0.2314, 0.3967, 0.5305, 0.4603, 0.5084, 0.1802, 0.3915, 0.2586, 0.6355,
     0.2194, 0.2667, 0.4833, 0.5194, 0.3518, 0.3862, 0.2867, 0.4282, 0.1323, 0.1165,
     0.0488, 0.0809, 0.1677, 1, 0.6172, 0.0223, 0.2801, 0.4033, 0.4952, 0.3967, 0.296,
     0.1847, 0.2421, 0.3093, 0.6604, 0.7539, 0.6378, 0.6253, 0.333, 0.5248, 0.6673,
     0.6576, 0.7843, 0.7184, 0.5666, 0.5054, 0.1445, 0.0752, 0.2401, 0.3108, 0.0349,
     0.0516, 0.03, 0.1848, 0.2183, 0.1866, 0.1488, 0.1565, 0.1331, 0.2223, 0.0956,
     0.0849, 0.1098, 0.1641), V36 = c(0.3043, 0.5163, 0.2186, 0.1108, 0.3649, 0.5771,
     0.5972, 0.1547, 0.896, 0.5307, 0.5077, 0.1369, 0.4676, 0.3228, 0.6695, 0.3929,
     1, 0.9922, 0.7468, 0.7022, 0.5271, 0.6078, 0.5122, 0.2157, 0.2561, 0.2123, 0.5459,
     0.5318, 0.668, 0.5515, 0.9705, 0.9605, 0.4593, 0.1725, 0.1787, 0.2288, 0.0733,
     0.0549, 0.0671, 0.2312, 0.0899, 0.1036, 0.7147, 0.7409, 0.665, 0.4787, 0.0791,
     0.1558, 0.0916, 0.7563, 0.1816, 0.134, 0.3511, 0.4619, 0.3762, 0.3958, 0.2401,
     0.4538, 0.1344, 0.0185, 0.1406, 0.1525, 0.1039, 0.9212, 0.4373, 0.4219, 0.0875,
     0.2803, 0.1712, 0.3042, 0.0704, 0.376, 0.0944, 0.2138, 0.6884, 0.799, 0.6629,
     0.6848, 0.2331, 0.3777, 0.4694, 0.739, 0.9361, 0.8209, 0.5188, 0.3578, 0.1923,
     0.2885, 0.1405, 0.2897, 0.1799, 0.3157, 0.3356, 0.1149, 0.1245, 0.0922, 0.0801,
     0.0985, 0.044, 0.1327, 0.008, 0.0608, 0.1446, 0.1869), V37 = c(0.6116, 0.6153,
     0.2463, 0.1594, 0.3382, 0.8852, 0.5715, 0.1212, 0.5516, 0.2718, 0.5534, 0.1605,
     0.5382, 0.2607, 0.4027, 0.1279, 0.9123, 0.9419, 0.6253, 0.5468, 0.426, 0.5025,
     0.4746, 0.2216, 0.2735, 0.2395, 0.4786, 0.4826, 0.5917, 0.4561, 0.7766, 0.7712,
     0.4679, 0.0589, 0.0429, 0.3186, 0.2692, 0.1192, 0.0379, 0.1338, 0.2043, 0.1312,
     0.7319, 0.7775, 0.6423, 0.1356, 0.0535, 0.1598, 0.0947, 0.6903, 0.1023, 0.1073,
     0.2319, 0.4234, 0.2909, 0.3248, 0.3619, 0.3704, 0.225, 0.1302, 0.2554, 0.2626,
     0.2562, 0.9386, 0.4118, 0.5496, 0.3319, 0.3087, 0.3652, 0.1309, 0.097, 0.4331,
     0.0351, 0.1112, 0.6938, 0.7673, 0.5983, 0.7337, 0.1451, 0.2369, 0.1546, 0.7963,
     0.8195, 0.7536, 0.506, 0.3809, 0.3208, 0.4072, 0.1772, 0.2244, 0.3039, 0.359,
     0.3167, 0.157, 0.1592, 0.1829, 0.177, 0.22, 0.1234, 0.0521, 0.0702, 0.0969, 0.1066,
     0.2655), V38 = c(0.6756, 0.4283, 0.2726, 0.1371, 0.1589, 0.8409, 0.5242, 0.2446,
     0.3037, 0.1953, 0.3352, 0.2061, 0.315, 0.204, 0.237, 0.0411, 0.7388, 0.8388,
     0.173, 0.1421, 0.2436, 0.2829, 0.4902, 0.2776, 0.3209, 0.2673, 0.3965, 0.379,
     0.4899, 0.3466, 0.6313, 0.6772, 0.1987, 0.0621, 0.1096, 0.2871, 0.1888, 0.1154,
     0.0461, 0.2056, 0.1707, 0.0864, 0.3509, 0.4424, 0.2166, 0.2299, 0.1906, 0.2161,
     0.2287, 0.6176, 0.2108, 0.2023, 0.4029, 0.4372, 0.2311, 0.2302, 0.3314, 0.3741,
     0.3244, 0.248, 0.2054, 0.2456, 0.2624, 0.9303, 0.3641, 0.2483, 0.4237, 0.355,
     0.3763, 0.2408, 0.3941, 0.3626, 0.0844, 0.1386, 0.5932, 0.5955, 0.4565, 0.6281,
     0.1648, 0.172, 0.1748, 0.7493, 0.6207, 0.6496, 0.3885, 0.3813, 0.3367, 0.317,
     0.1742, 0.096, 0.476, 0.3881, 0.4133, 0.1311, 0.1626, 0.1743, 0.1382, 0.2243,
     0.203, 0.0618, 0.0936, 0.1411, 0.144, 0.1713), V39 = c(0.5375, 0.5479, 0.168,
     0.0696, 0.0989, 0.357, 0.2924, 0.3171, 0.2338, 0.1374, 0.2723, 0.0734, 0.2139,
     0.2396, 0.2685, 0.0859, 0.5915, 0.6605, 0.2916, 0.4738, 0.1205, 0.0477, 0.4603,
     0.2309, 0.2724, 0.2865, 0.2087, 0.1831, 0.3439, 0.3384, 0.576, 0.6431, 0.0699,
     0.1847, 0.1762, 0.2921, 0.0712, 0.0855, 0.1694, 0.2474, 0.0407, 0.2569, 0.0589,
     0.1416, 0.1951, 0.2789, 0.2561, 0.5178, 0.348, 0.5379, 0.3253, 0.1794, 0.3676,
     0.4277, 0.3168, 0.325, 0.3763, 0.3839, 0.3939, 0.1637, 0.1614, 0.198, 0.2236,
     0.7314, 0.4572, 0.2034, 0.1801, 0.2545, 0.2841, 0.178, 0.6028, 0.2519, 0.0436,
     0.1523, 0.5774, 0.4731, 0.3129, 0.5725, 0.2694, 0.1878, 0.3607, 0.6795, 0.4513,
     0.4708, 0.3762, 0.3359, 0.5683, 0.2863, 0.3326, 0.2287, 0.5756, 0.5716, 0.6281,
     0.1583, 0.2356, 0.2452, 0.2404, 0.2736, 0.1652, 0.1416, 0.0894, 0.1676, 0.1929,
     0.0959), V40 = c(0.4719, 0.6133, 0.2792, 0.0452, 0.1089, 0.3133, 0.1536, 0.3195,
     0.2382, 0.3105, 0.2278, 0.0202, 0.1848, 0.1319, 0.3662, 0.1131, 0.4057, 0.4816,
     0.5003, 0.641, 0.3845, 0.2811, 0.446, 0.1444, 0.188, 0.206, 0.1651, 0.175, 0.2366,
     0.2853, 0.6148, 0.672, 0.1493, 0.2452, 0.2481, 0.2806, 0.1062, 0.1811, 0.2169,
     0.279, 0.1286, 0.3179, 0.269, 0.3508, 0.4947, 0.3833, 0.2153, 0.4782, 0.2095,
     0.5622, 0.3697, 0.0227, 0.151, 0.4433, 0.3554, 0.4022, 0.4767, 0.3494, 0.3806,
     0.1103, 0.2232, 0.2412, 0.118, 0.4791, 0.4367, 0.2729, 0.3743, 0.1432, 0.0427,
     0.1598, 0.3521, 0.187, 0.113, 0.0996, 0.6223, 0.484, 0.4158, 0.6119, 0.373, 0.325,
     0.5208, 0.4713, 0.3004, 0.3482, 0.3738, 0.2771, 0.5505, 0.2634, 0.4021, 0.3228,
     0.4254, 0.4314, 0.4977, 0.2631, 0.2483, 0.2407, 0.2046, 0.2152, 0.1043, 0.146,
     0.1127, 0.12, 0.0325, 0.0768), V41 = c(0.4647, 0.5017, 0.2558, 0.062, 0.1043,
     0.6096, 0.2003, 0.3051, 0.3318, 0.379, 0.2044, 0.1638, 0.1679, 0.0683, 0.3267,
     0.1306, 0.3019, 0.2917, 0.522, 0.4375, 0.4107, 0.3422, 0.4196, 0.1513, 0.1552,
     0.1659, 0.1836, 0.1679, 0.1716, 0.2502, 0.545, 0.6035, 0.1713, 0.2984, 0.315,
     0.2682, 0.0694, 0.1264, 0.1677, 0.161, 0.1581, 0.2649, 0.42, 0.4482, 0.4925,
     0.2933, 0.2769, 0.2344, 0.1901, 0.6508, 0.2912, 0.1313, 0.0745, 0.37, 0.3741,
     0.4344, 0.4059, 0.438, 0.3258, 0.2144, 0.1773, 0.2409, 0.1103, 0.2087, 0.2964,
     0.2837, 0.4627, 0.5869, 0.5331, 0.5657, 0.3924, 0.1046, 0.2045, 0.1644, 0.5841,
     0.434, 0.4325, 0.5597, 0.4467, 0.2575, 0.5177, 0.2355, 0.2674, 0.3508, 0.2605,
     0.3648, 0.3231, 0.0541, 0.3009, 0.3454, 0.5046, 0.3051, 0.2613, 0.3103, 0.2437,
     0.2518, 0.197, 0.2438, 0.1066, 0.0846, 0.0873, 0.1201, 0.149, 0.0847), V42 = c(0.2587,
     0.2377, 0.174, 0.1421, 0.0839, 0.6378, 0.2031, 0.0836, 0.3821, 0.4105, 0.1986,
     0.1583, 0.2328, 0.0334, 0.22, 0.1757, 0.2331, 0.1769, 0.4824, 0.3178, 0.5067,
     0.5147, 0.2873, 0.1745, 0.2522, 0.2633, 0.0652, 0.0674, 0.1013, 0.1641, 0.4813,
     0.5155, 0.1654, 0.3041, 0.292, 0.2112, 0.03, 0.0799, 0.0644, 0.0056, 0.2191,
     0.2714, 0.3874, 0.4208, 0.4041, 0.1155, 0.2841, 0.3599, 0.2941, 0.4797, 0.301,
     0.1775, 0.1395, 0.3324, 0.4443, 0.4008, 0.3661, 0.4265, 0.3654, 0.2033, 0.2293,
     0.1901, 0.2831, 0.2016, 0.4312, 0.4463, 0.1614, 0.6431, 0.6952, 0.6443, 0.4808,
     0.2339, 0.1937, 0.1902, 0.4527, 0.3954, 0.4031, 0.4965, 0.4133, 0.2423, 0.3702,
     0.1704, 0.2241, 0.3181, 0.1591, 0.3834, 0.0448, 0.1874, 0.2075, 0.3882, 0.7179,
     0.4393, 0.4697, 0.4512, 0.2715, 0.3184, 0.2778, 0.3154, 0.211, 0.1055, 0.102,
     0.1036, 0.0328, 0.2076), V43 = c(0.2129, 0.1957, 0.2121, 0.1597, 0.1391, 0.2709,
     0.2207, 0.1266, 0.1575, 0.3355, 0.0835, 0.183, 0.1015, 0.0716, 0.2996, 0.2648,
     0.2931, 0.1136, 0.4004, 0.2377, 0.4216, 0.4372, 0.2296, 0.1756, 0.2121, 0.2552,
     0.0758, 0.0609, 0.0766, 0.1605, 0.3406, 0.3802, 0.26, 0.2275, 0.1902, 0.1513,
     0.0893, 0.0378, 0.0159, 0.0351, 0.1701, 0.1713, 0.244, 0.3054, 0.2402, 0.1705,
     0.1733, 0.2785, 0.2211, 0.3736, 0.2563, 0.1549, 0.1552, 0.2564, 0.3261, 0.337,
     0.232, 0.2854, 0.2983, 0.1887, 0.2521, 0.2077, 0.2385, 0.1669, 0.4155, 0.3178,
     0.2494, 0.5826, 0.4288, 0.4241, 0.4602, 0.1991, 0.0834, 0.1313, 0.4911, 0.4837,
     0.4201, 0.5027, 0.3743, 0.2706, 0.224, 0.2728, 0.3141, 0.3524, 0.1875, 0.3453,
     0.3131, 0.3459, 0.1206, 0.324, 0.6163, 0.4302, 0.4806, 0.3785, 0.1184, 0.1685,
     0.1377, 0.2112, 0.2417, 0.1639, 0.1964, 0.1977, 0.0537, 0.2505), V44 = c(0.2222,
     0.1749, 0.1099, 0.1384, 0.0819, 0.1419, 0.1778, 0.1381, 0.2228, 0.2998, 0.0908,
     0.1886, 0.0713, 0.0976, 0.2205, 0.1955, 0.2298, 0.0701, 0.3877, 0.2808, 0.2479,
     0.247, 0.0949, 0.1424, 0.1801, 0.1696, 0.0486, 0.0375, 0.0845, 0.1491, 0.1916,
     0.2278, 0.3846, 0.148, 0.0696, 0.1789, 0.1459, 0.1268, 0.0778, 0.1148, 0.0971,
     0.0584, 0.2, 0.2235, 0.1392, 0.1294, 0.0815, 0.1807, 0.1524, 0.2804, 0.1927,
     0.1626, 0.0377, 0.2527, 0.1963, 0.2518, 0.145, 0.2808, 0.1779, 0.137, 0.1464,
     0.1767, 0.0255, 0.2872, 0.1824, 0.0807, 0.3202, 0.4286, 0.3063, 0.4567, 0.4164,
     0.11, 0.1502, 0.1776, 0.5762, 0.5379, 0.4557, 0.5772, 0.3021, 0.2323, 0.0816,
     0.4016, 0.3693, 0.3659, 0.2267, 0.2096, 0.3387, 0.4646, 0.0255, 0.0926, 0.5663,
     0.4831, 0.4921, 0.1269, 0.1157, 0.0675, 0.0685, 0.0991, 0.1631, 0.1916, 0.2256,
     0.1339, 0.1309, 0.1862), V45 = c(0.2111, 0.1304, 0.0985, 0.0372, 0.0678, 0.126,
     0.1353, 0.1136, 0.1582, 0.2748, 0.138, 0.1008, 0.0615, 0.0787, 0.1163, 0.0656,
     0.2391, 0.1578, 0.1651, 0.1374, 0.1586, 0.1708, 0.0095, 0.0908, 0.1473, 0.1467,
     0.0353, 0.0533, 0.026, 0.1326, 0.1134, 0.1522, 0.3754, 0.1102, 0.0758, 0.185,
     0.1348, 0.1125, 0.0653, 0.1331, 0.2217, 0.123, 0.2307, 0.2611, 0.1779, 0.0909,
     0.0335, 0.0352, 0.0746, 0.1982, 0.2062, 0.0708, 0.0636, 0.2137, 0.0864, 0.2101,
     0.1017, 0.2395, 0.1535, 0.1376, 0.0673, 0.1119, 0.1967, 0.4374, 0.1487, 0.1192,
     0.2265, 0.4894, 0.5835, 0.576, 0.5438, 0.0684, 0.1675, 0.2, 0.5013, 0.4485, 0.3955,
     0.5907, 0.2069, 0.1724, 0.0395, 0.4125, 0.2986, 0.2846, 0.1577, 0.1031, 0.413,
     0.4366, 0.0298, 0.1173, 0.5749, 0.5084, 0.5294, 0.1459, 0.1449, 0.1186, 0.0664,
     0.0594, 0.0769, 0.2085, 0.1814, 0.0902, 0.091, 0.1439), V46 = c(0.0176, 0.0597,
     0.1271, 0.0688, 0.0663, 0.1288, 0.1373, 0.0516, 0.1433, 0.2024, 0.1948, 0.0663,
     0.0779, 0.0522, 0.0635, 0.058, 0.191, 0.1938, 0.0442, 0.1136, 0.1124, 0.1343,
     0.0527, 0.0138, 0.0681, 0.1286, 0.0297, 0.0278, 0.0333, 0.0687, 0.064, 0.0801,
     0.2414, 0.1178, 0.091, 0.1717, 0.0391, 0.0505, 0.021, 0.0276, 0.2732, 0.22, 0.1886,
     0.2798, 0.1946, 0.08, 0.0933, 0.0473, 0.0606, 0.2438, 0.1751, 0.0129, 0.0443,
     0.1789, 0.1688, 0.1181, 0.1111, 0.0369, 0.1199, 0.0307, 0.0965, 0.0779, 0.1483,
     0.3097, 0.0138, 0.2134, 0.1146, 0.5777, 0.5692, 0.5293, 0.5649, 0.0303, 0.1058,
     0.0765, 0.4042, 0.2674, 0.2966, 0.4803, 0.179, 0.1457, 0.0785, 0.347, 0.2226,
     0.1714, 0.1211, 0.0798, 0.3639, 0.2581, 0.0691, 0.0566, 0.3593, 0.1952, 0.2216,
     0.1092, 0.1883, 0.1833, 0.1665, 0.194, 0.0723, 0.2335, 0.2012, 0.1085, 0.0757,
     0.147), V47 = c(0.1348, 0.1124, 0.1459, 0.0867, 0.1202, 0.079, 0.0749, 0.0073,
     0.1634, 0.1043, 0.1211, 0.0183, 0.0761, 0.05, 0.0465, 0.0319, 0.1096, 0.1106,
     0.0663, 0.1034, 0.0651, 0.0838, 0.0383, 0.0469, 0.1091, 0.0926, 0.0241, 0.0179,
     0.0205, 0.0602, 0.0911, 0.0804, 0.1077, 0.0608, 0.0441, 0.0898, 0.0546, 0.0949,
     0.0509, 0.0763, 0.1874, 0.2198, 0.196, 0.2392, 0.1723, 0.0567, 0.1018, 0.0322,
     0.0692, 0.1789, 0.0841, 0.0795, 0.0264, 0.101, 0.1991, 0.115, 0.0655, 0.0805,
     0.0959, 0.0373, 0.1492, 0.1344, 0.0434, 0.1578, 0.1164, 0.3241, 0.0476, 0.4315,
     0.263, 0.3287, 0.3195, 0.0674, 0.1111, 0.0727, 0.3123, 0.1541, 0.2095, 0.3877,
     0.1689, 0.1175, 0.1052, 0.2739, 0.0849, 0.0694, 0.0883, 0.0701, 0.2069, 0.1319,
     0.0781, 0.0766, 0.2526, 0.1539, 0.1401, 0.1485, 0.1954, 0.1878, 0.1807, 0.1937,
     0.0912, 0.1964, 0.1688, 0.1521, 0.1059, 0.0991), V48 = c(0.0744, 0.1047, 0.1164,
     0.0513, 0.0692, 0.0829, 0.0472, 0.0278, 0.1133, 0.0453, 0.0843, 0.0404, 0.0845,
     0.0231, 0.0422, 0.0301, 0.03, 0.0693, 0.0418, 0.0688, 0.0789, 0.0755, 0.0107,
     0.048, 0.0919, 0.0716, 0.0379, 0.0114, 0.0309, 0.0561, 0.098, 0.0752, 0.0224,
     0.0333, 0.0244, 0.0656, 0.0469, 0.0677, 0.0387, 0.0631, 0.1062, 0.1074, 0.1701,
     0.2021, 0.1522, 0.0198, 0.0309, 0.0408, 0.0446, 0.1706, 0.1035, 0.0762, 0.0223,
     0.0528, 0.1217, 0.055, 0.0271, 0.0541, 0.0765, 0.0606, 0.1128, 0.096, 0.0627,
     0.0553, 0.2052, 0.2945, 0.0943, 0.264, 0.1196, 0.1283, 0.2484, 0.0785, 0.0849,
     0.0749, 0.2232, 0.1359, 0.1558, 0.2779, 0.1341, 0.0868, 0.1034, 0.179, 0.0359,
     0.0303, 0.085, 0.0526, 0.0859, 0.0505, 0.0777, 0.0969, 0.2299, 0.2037, 0.1888,
     0.1385, 0.1492, 0.1114, 0.1245, 0.1082, 0.0812, 0.13, 0.1037, 0.1363, 0.1005,
     0.0041), V49 = c(0.013, 0.0507, 0.0777, 0.0092, 0.0152, 0.052, 0.0325, 0.0372,
     0.0567, 0.0337, 0.0589, 0.0108, 0.0592, 0.0221, 0.0174, 0.0272, 0.0171, 0.0176,
     0.0475, 0.0422, 0.0325, 0.0304, 0.0108, 0.0159, 0.0397, 0.0325, 0.0119, 0.0073,
     0.0101, 0.0306, 0.0563, 0.0566, 0.0155, 0.0276, 0.0265, 0.0445, 0.0201, 0.0259,
     0.0262, 0.0309, 0.0665, 0.0423, 0.1366, 0.1326, 0.0929, 0.0114, 0.0208, 0.0163,
     0.0344, 0.0762, 0.0641, 0.0117, 0.0187, 0.0453, 0.0628, 0.0293, 0.0244, 0.0177,
     0.0649, 0.0399, 0.0463, 0.0598, 0.0513, 0.0334, 0.1069, 0.1474, 0.0824, 0.1794,
     0.0983, 0.0698, 0.1299, 0.0455, 0.0596, 0.0449, 0.1085, 0.0941, 0.0884, 0.1427,
     0.0769, 0.0392, 0.0764, 0.0922, 0.0289, 0.0292, 0.0355, 0.0241, 0.06, 0.0112,
     0.0369, 0.0588, 0.1271, 0.1054, 0.0947, 0.0716, 0.0511, 0.031, 0.0516, 0.0336,
     0.0496, 0.0633, 0.0501, 0.0858, 0.0535, 0.0154), V50 = c(0.0106, 0.0159, 0.0439,
     0.0198, 0.0266, 0.0216, 0.0179, 0.0121, 0.0133, 0.0122, 0.0247, 0.0143, 0.0068,
     0.0144, 0.0172, 0.0074, 0.0383, 0.0205, 0.0235, 0.0117, 0.007, 0.0074, 0.0077,
     0.0045, 0.0093, 0.0258, 0.0073, 0.0116, 0.0095, 0.0154, 0.0187, 0.0175, 0.0187,
     0.01, 0.0095, 0.011, 0.0095, 0.017, 0.0101, 0.024, 0.0405, 0.0162, 0.0398, 0.0358,
     0.0179, 0.0151, 0.0318, 0.0088, 0.0082, 0.0238, 0.0153, 0.0061, 0.0077, 0.0118,
     0.0323, 0.0183, 0.0179, 0.0065, 0.0313, 0.0169, 0.0193, 0.033, 0.0473, 0.0209,
     0.0199, 0.0211, 0.0171, 0.0772, 0.0374, 0.0334, 0.0825, 0.0246, 0.0201, 0.0134,
     0.0414, 0.0261, 0.0265, 0.0424, 0.0222, 0.0131, 0.0216, 0.0276, 0.0122, 0.0116,
     0.0219, 0.0117, 0.0267, 0.0059, 0.0057, 0.005, 0.0356, 0.0251, 0.0134, 0.0176,
     0.0155, 0.0143, 0.0044, 0.0177, 0.0101, 0.0183, 0.0136, 0.029, 0.0235, 0.0116
     ), V51 = c(0.0033, 0.0195, 0.0061, 0.0118, 0.0174, 0.036, 0.0045, 0.0153, 0.017,
     0.0072, 0.0118, 0.0091, 0.0089, 0.0307, 0.0134, 0.0149, 0.0053, 0.0309, 0.0066,
     0.007, 0.0026, 0.0069, 0.0109, 0.0015, 0.0076, 0.0136, 0.0051, 0.0092, 0.0047,
     0.0029, 0.0088, 0.0058, 0.0125, 0.0023, 0.014, 0.0024, 0.0155, 0.0033, 0.0161,
     0.0115, 0.0113, 0.0093, 0.0143, 0.0128, 0.0242, 0.0085, 0.0132, 0.0121, 0.0108,
     0.0268, 0.0081, 0.0257, 0.0137, 9e-04, 0.0253, 0.0104, 0.0109, 0.0222, 0.0185,
     0.0135, 0.014, 0.0197, 0.0248, 0.0172, 0.0208, 0.0361, 0.0244, 0.0798, 0.0291,
     0.0342, 0.0243, 0.0151, 0.0071, 0.0174, 0.0253, 0.0079, 0.0121, 0.0271, 0.0205,
     0.0092, 0.0167, 0.0169, 0.0045, 0.0024, 0.0086, 0.0122, 0.0125, 0.0041, 0.0091,
     0.0118, 0.0367, 0.0357, 0.031, 0.0199, 0.0189, 0.0138, 0.0185, 0.0209, 0.0089,
     0.0137, 0.013, 0.0203, 0.0155, 0.0181), V52 = c(0.0232, 0.0201, 0.0145, 0.009,
     0.0176, 0.0331, 0.0084, 0.0092, 0.0035, 0.0108, 0.0088, 0.0038, 0.0087, 0.0386,
     0.0141, 0.0125, 0.009, 0.0212, 0.0062, 0.0167, 0.0093, 0.0025, 0.0062, 0.0052,
     0.0065, 0.0044, 0.0034, 0.0078, 0.0072, 0.0048, 0.0042, 0.0091, 0.0028, 0.0069,
     0.0074, 0.0062, 0.0091, 0.015, 0.0029, 0.0064, 0.0028, 0.0046, 0.0093, 0.0172,
     0.0083, 0.0178, 0.0118, 0.0067, 0.0149, 0.0081, 0.0191, 0.0089, 0.0071, 0.0142,
     0.0214, 0.0117, 0.0147, 0.0045, 0.0098, 0.0222, 0.0027, 0.0189, 0.0274, 0.018,
     0.0176, 0.0444, 0.0258, 0.0376, 0.0156, 0.0459, 0.021, 0.0125, 0.0104, 0.0117,
     0.0131, 0.0164, 0.0091, 0.02, 0.0123, 0.0078, 0.0089, 0.0081, 0.0108, 0.0084,
     0.0123, 0.0122, 0.004, 0.0056, 0.0134, 0.0146, 0.0176, 0.0181, 0.0237, 0.0096,
     0.015, 0.0108, 0.0072, 0.0134, 0.0083, 0.015, 0.012, 0.0116, 0.016, 0.0146),
     V53 = c(0.0166, 0.0248, 0.0128, 0.0223, 0.0127, 0.0131, 0.001, 0.0035, 0.0052,
     0.007, 0.0104, 0.0096, 0.0032, 0.0147, 0.0191, 0.0134, 0.0042, 0.0091, 0.0129,
     0.0127, 0.0118, 0.0103, 0.0028, 0.0038, 0.0072, 0.0028, 0.0129, 0.0041, 0.0054,
     0.0023, 0.0175, 0.016, 0.0067, 0.0025, 0.0063, 0.0072, 0.0151, 0.0111, 0.0078,
     0.0022, 0.0036, 0.0044, 0.0033, 0.0138, 0.0037, 0.0073, 0.012, 0.0032, 0.0077,
     0.0129, 0.0182, 0.0262, 0.0082, 0.0179, 0.0262, 0.0101, 0.017, 0.0136, 0.0178,
     0.0175, 0.0068, 0.0204, 0.0205, 0.011, 0.0197, 0.023, 0.0143, 0.0143, 0.0197,
     0.0277, 0.0361, 0.0036, 0.0062, 0.0023, 0.0049, 0.012, 0.0062, 0.007, 0.0067,
     0.0071, 0.0051, 0.004, 0.0075, 0.01, 0.006, 0.0114, 0.0136, 0.0104, 0.0097, 0.004,
     0.0035, 0.0019, 0.0078, 0.0103, 0.006, 0.0062, 0.0055, 0.0094, 0.008, 0.0076,
     0.0039, 0.0098, 0.0029, 0.0129), V54 = c(0.0095, 0.0131, 0.0145, 0.0179, 0.0088,
     0.012, 0.0018, 0.0098, 0.0083, 0.0063, 0.0036, 0.0142, 0.013, 0.0018, 0.0145,
     0.0026, 0.0153, 0.0056, 0.0184, 0.0138, 0.0112, 0.0074, 0.004, 0.0079, 0.0108,
     0.0021, 0.01, 0.0013, 0.0022, 0.002, 0.0171, 0.016, 0.012, 0.0027, 0.0081, 0.0113,
     0.008, 0.0032, 0.0114, 0.0122, 0.0105, 0.0078, 0.0113, 0.0079, 0.0095, 0.0079,
     0.0051, 0.0109, 0.0036, 0.0161, 0.016, 0.0108, 0.0232, 0.0079, 0.0177, 0.0061,
     0.0158, 0.0113, 0.0077, 0.0127, 0.015, 0.0085, 0.0141, 0.0234, 0.021, 0.029,
     0.0226, 0.0272, 0.0135, 0.0172, 0.0239, 0.0123, 0.0026, 0.0047, 0.0104, 0.0113,
     0.0019, 0.007, 0.0011, 0.0081, 0.0015, 0.0025, 0.0089, 0.0018, 0.0187, 0.0098,
     0.0137, 0.0079, 0.0042, 0.0114, 0.0093, 0.0102, 0.0144, 0.0093, 0.0082, 0.0044,
     0.0074, 0.0047, 0.0026, 0.0032, 0.0053, 0.0199, 0.0051, 0.0047), V55 = c(0.018,
     0.007, 0.0058, 0.0084, 0.0098, 0.0108, 0.0068, 0.0121, 0.0078, 0.003, 0.0088,
     0.019, 0.0188, 0.01, 0.0065, 0.0038, 0.0106, 0.0086, 0.0069, 0.009, 0.0094, 0.0123,
     0.0075, 0.0114, 0.0051, 0.0022, 0.0044, 0.0011, 0.0016, 0.004, 0.0079, 0.0081,
     0.0012, 0.0052, 0.0087, 0.0012, 0.0018, 0.0035, 0.0083, 0.0151, 0.012, 0.0102,
     0.003, 0.0037, 0.0105, 0.0038, 0.007, 0.0164, 0.0114, 0.0063, 0.029, 0.0138,
     0.0198, 0.006, 0.0037, 0.0031, 0.0046, 0.0053, 0.0074, 0.0022, 0.0012, 0.0043,
     0.0185, 0.0276, 0.0141, 0.0141, 0.0187, 0.0127, 0.0127, 0.0087, 0.0447, 0.0043,
     0.0025, 0.0049, 0.0102, 0.0021, 0.0045, 0.0086, 0.0026, 0.0034, 0.0075, 0.0036,
     0.0036, 0.0035, 0.0111, 0.0027, 0.0172, 0.0014, 0.0058, 0.0032, 0.0121, 0.0133,
     0.017, 0.0025, 0.0091, 0.0072, 0.0068, 0.0045, 0.0079, 0.0037, 0.0062, 0.0033,
     0.0062, 0.0039), V56 = c(0.0244, 0.0138, 0.0049, 0.0068, 0.0019, 0.0024, 0.0039,
     6e-04, 0.0075, 0.0011, 0.0047, 0.014, 0.0101, 0.0096, 0.0129, 0.0018, 0.002,
     0.0092, 0.0198, 0.0051, 0.014, 0.0069, 0.0039, 0.005, 0.0102, 0.0048, 0.0057,
     0.0045, 0.0029, 0.0019, 0.005, 0.007, 0.0022, 0.0036, 0.0044, 0.0022, 0.0078,
     0.0169, 0.0058, 0.0056, 0.0087, 0.0065, 0.0057, 0.0051, 0.003, 0.0116, 0.0015,
     0.0151, 0.0085, 0.0119, 0.009, 0.0187, 0.0074, 0.0131, 0.0068, 0.0099, 0.0073,
     0.0165, 0.0095, 0.0124, 0.0133, 0.0092, 0.0055, 0.0032, 0.0049, 0.0161, 0.0185,
     0.0166, 0.0138, 0.0046, 0.0394, 0.0114, 0.0061, 0.0031, 0.0092, 0.0097, 0.0079,
     0.0089, 0.0049, 0.0064, 0.0058, 0.0058, 0.0029, 0.0058, 0.0126, 0.0025, 0.0132,
     0.0054, 0.0072, 0.0062, 0.0075, 0.004, 0.0012, 0.0044, 0.0038, 7e-04, 0.0084,
     0.0042, 0.0042, 0.0071, 0.0046, 0.0101, 0.0089, 0.0061), V57 = c(0.0316, 0.0092,
     0.0065, 0.0032, 0.0059, 0.0045, 0.012, 0.0181, 0.0105, 7e-04, 0.0117, 0.0099,
     0.0229, 0.0077, 0.0217, 0.0113, 0.0105, 0.007, 0.0199, 0.0029, 0.0072, 0.0076,
     0.0053, 0.003, 0.0041, 0.0138, 0.003, 0.0039, 0.0058, 0.0034, 0.0112, 0.0135,
     0.0058, 0.0026, 0.0028, 0.0025, 0.0045, 0.0137, 3e-04, 0.0026, 0.0061, 0.0061,
     0.009, 0.0258, 0.0132, 0.0033, 0.0035, 0.007, 0.0101, 0.0194, 0.0242, 0.023,
     0.0035, 0.0089, 0.0121, 0.008, 0.0054, 0.0141, 0.0055, 0.0054, 0.0048, 0.0138,
     0.0045, 0.0084, 0.0027, 0.0177, 0.011, 0.0095, 0.0133, 0.0203, 0.0355, 0.0052,
     0.0038, 0.0024, 0.0083, 0.0072, 0.0031, 0.0074, 0.0029, 0.0037, 0.0016, 0.0067,
     0.0013, 0.0011, 0.0081, 0.0026, 0.011, 0.0015, 0.0041, 0.0101, 0.0056, 0.0042,
     0.0109, 0.0021, 0.0056, 0.0054, 0.0037, 0.0028, 0.0071, 0.004, 0.0045, 0.0065,
     0.014, 0.004), V58 = c(0.0164, 0.0143, 0.0093, 0.0035, 0.0058, 0.0037, 0.0132,
     0.0094, 0.016, 0.0024, 0.002, 0.0092, 0.0182, 0.018, 0.0087, 0.0058, 0.0049,
     0.0116, 0.0102, 0.0122, 0.0022, 0.0073, 0.0013, 0.0064, 0.0055, 0.014, 0.0035,
     0.0022, 0.005, 0.0034, 0.0179, 0.0067, 0.0042, 0.0036, 0.0019, 0.0059, 0.0026,
     0.0015, 0.0023, 0.0029, 0.0061, 0.0062, 0.0057, 0.0102, 0.0068, 0.0039, 8e-04,
     0.0085, 0.0016, 0.014, 0.0224, 0.0057, 0.01, 0.0084, 0.0077, 0.0107, 0.0033,
     0.0077, 0.0045, 0.0021, 0.0244, 0.0094, 0.0115, 0.0122, 0.0162, 0.0194, 0.0094,
     0.0225, 0.0131, 0.013, 0.044, 0.0091, 0.0101, 0.0039, 0.002, 0.006, 0.0063, 0.0042,
     0.0022, 0.0036, 0.007, 0.0035, 0.001, 9e-04, 0.0155, 0.005, 0.0122, 6e-04, 0.0045,
     0.0068, 0.0021, 0.003, 0.0036, 0.0069, 0.0056, 0.0035, 0.0024, 0.0036, 0.0044,
     9e-04, 0.0022, 0.0115, 0.0138, 0.0036), V59 = c(0.0095, 0.0036, 0.0059, 0.0056,
     0.0059, 0.0112, 0.007, 0.0116, 0.0095, 0.0057, 0.0091, 0.0052, 0.0046, 0.0109,
     0.0077, 0.0047, 0.007, 0.006, 0.007, 0.0056, 0.0055, 0.003, 0.0052, 0.0058, 0.005,
     0.0028, 0.0021, 0.0023, 0.0024, 0.0051, 0.0294, 0.0078, 0.0067, 6e-04, 0.0049,
     0.0039, 0.0036, 0.0069, 0.0026, 0.0104, 0.003, 0.0043, 0.0068, 0.0037, 0.0108,
     0.0081, 0.0044, 0.0117, 0.0028, 0.0332, 0.019, 0.0113, 0.0048, 0.0113, 0.0078,
     0.0161, 0.0045, 0.0246, 0.0063, 0.0028, 0.0077, 0.0105, 0.0152, 0.0082, 0.0059,
     0.0207, 0.0078, 0.0098, 0.0154, 0.0115, 0.0243, 8e-04, 0.0078, 0.0051, 0.0048,
     0.0017, 0.0048, 0.0055, 0.0022, 0.0012, 0.0074, 0.0043, 0.0032, 0.0033, 0.016,
     0.0073, 0.0114, 0.0081, 0.0047, 0.0053, 0.0043, 0.0031, 0.0043, 0.006, 0.0048,
     1e-04, 0.0034, 0.0013, 0.0022, 0.0015, 5e-04, 0.0193, 0.0077, 0.0061), V60 = c(0.0078,
     0.0103, 0.0022, 0.004, 0.0032, 0.0075, 0.0088, 0.0063, 0.0011, 0.0044, 0.0058,
     0.0075, 0.0038, 0.007, 0.0122, 0.0071, 0.008, 0.011, 0.0055, 0.002, 0.0122, 0.0138,
     0.0023, 0.003, 0.0087, 0.0064, 0.0027, 0.0016, 0.003, 0.0031, 0.0063, 0.0068,
     0.0012, 0.0035, 0.0023, 0.0048, 0.0024, 0.0051, 0.0027, 0.0163, 0.0078, 0.0053,
     0.0024, 0.0037, 0.009, 0.0053, 0.0077, 0.0056, 0.0014, 0.0439, 0.0096, 0.0131,
     0.0019, 0.0049, 0.0066, 0.0133, 0.0079, 0.0198, 0.0039, 0.0023, 0.0074, 0.0093,
     0.01, 0.0143, 0.0021, 0.0057, 0.0112, 0.0085, 0.0218, 0.0015, 0.0098, 0.0092,
     6e-04, 0.0015, 0.0036, 0.0036, 0.005, 0.0021, 0.0032, 0.0037, 0.0038, 0.0033,
     0.0047, 0.0026, 0.0085, 0.0022, 0.0068, 0.0043, 0.0054, 0.0087, 0.0017, 0.0033,
     0.0018, 0.0018, 0.0024, 0.0055, 7e-04, 0.0016, 0.0014, 0.0085, 0.0031, 0.0157,
     0.0031, 0.0115)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8",
     "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20",
     "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32",
     "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40", "V41", "V42", "V43", "V44",
     "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52", "V53", "V54", "V55", "V56",
     "V57", "V58", "V59", "V60"), class = "data.frame", row.names = c("3", "7", "9", "10",
     "13", "18", "19", "20", "25", "26", "29", "30", "35", "36", "37", "39", "43", "44",
     "46", "47", "49", "50", "52", "53", "54", "55", "59", "61", "63", "64", "66", "68",
     "69", "71", "73", "74", "77", "78", "80", "81", "83", "85", "87", "88", "90", "92",
     "93", "94", "95", "98", "100", "101", "104", "108", "110", "111", "114", "116", "118",
     "120", "123", "124", "131", "135", "138", "139", "140", "141", "142", "145", "148",
     "152", "154", "156", "158", "159", "161", "162", "163", "164", "166", "168", "169",
     "170", "172", "173", "175", "176", "179", "180", "182", "183", "184", "189", "191",
     "192", "193", "194", "195", "201", "202", "204", "206", "208")))
     16: predictLearner(.learner, .model, .newdata, ...)
     17: predictLearner.BaseWrapper(.learner, .model, .newdata, ...)
     18: do.call(predictLearner, c(list(.learner = .learner$next.learner, .model = .model$learner.model$next.model,
     .newdata = .newdata), args))
     19: (function (.learner, .model, .newdata, ...)
     {
     lmod = getLearnerModel(.model)
     if (inherits(lmod, "NoFeaturesModel")) {
     predict_nofeatures(.model, .newdata)
     }
     else {
     assertDataFrame(.newdata, min.rows = 1L, min.cols = 1L)
     UseMethod("predictLearner")
     }
     })(.learner = structure(list(id = "classif.xgboost", type = "classif", package = "xgboost",
     properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"
     ), par.set = structure(list(pars = structure(list(booster = structure(list(id = "booster",
     type = "discrete", len = 1L, lower = NULL, upper = NULL, values = structure(list(
     gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree", "gblinear"
     )), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), silent = structure(list(id = "silent", type = "integer", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), eta = structure(list(id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), gamma = structure(list(id = "gamma", type = "numeric", len = 1L, lower = 0,
     upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), max_depth = structure(list(
     id = "max_depth", type = "integer", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 6, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), subsample = structure(list(id = "subsample", type = "numeric", len = 1L,
     lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 1, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), colsample_bytree = structure(list(
     id = "colsample_bytree", type = "numeric", len = 1L, lower = 0, upper = 1,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), lambda = structure(list(id = "lambda", type = "numeric", len = 1L, lower = 0,
     upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), lambda_bias = structure(list(
     id = "lambda_bias", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L, lower = 0,
     upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), objective = structure(list(
     id = "objective", type = "untyped", len = 1L, lower = NULL, upper = NULL,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "binary:logistic",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), base_score = structure(list(id = "base_score", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), missing = structure(list(id = "missing", type = "numeric", len = 1L, lower = -Inf,
     upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), nthread = structure(list(
     id = "nthread", type = "integer", len = 1L, lower = 1, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 16, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), feval = structure(list(id = "feval", type = "untyped", len = 1L, lower = NULL,
     upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), verbose = structure(list(
     id = "verbose", type = "integer", len = 1L, lower = 0, upper = 2, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 2, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len", "lower",
     "upper", "values", "cnames", "allow.inf", "has.default", "default", "trafo",
     "requires", "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), maximize = structure(list(id = "maximize", type = "logical", len = 1L, lower = NULL,
     upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("booster", "silent", "eta", "gamma", "max_depth", "min_child_weight",
     "subsample", "colsample_bytree", "num_parallel_tree", "lambda", "lambda_bias",
     "alpha", "objective", "eval_metric", "base_score", "missing", "nthread", "nrounds",
     "feval", "verbose", "print.every.n", "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     nrounds = 1), .Names = "nrounds"), predict.type = "response", name = "eXtreme Gradient Boosting",
     short.name = "xgboost", note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type", "package",
     "properties", "par.set", "par.vals", "predict.type", "name", "short.name", "note",
     "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), .model = structure(list(learner = structure(list(id = "classif.xgboost",
     type = "classif", package = "xgboost", properties = c("twoclass", "multiclass",
     "numerics", "factors", "prob", "weights"), par.set = structure(list(pars = structure(list(
     booster = structure(list(id = "booster", type = "discrete", len = 1L, lower = NULL,
     upper = NULL, values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type", "package",
     "properties", "par.set", "par.vals", "predict.type", "name", "short.name", "note",
     "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), learner.model = structure(list(handle = <pointer: 0x133f69f0>,
     raw = as.raw(c(0x00, 0x00, 0x00, 0x80, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x62, 0x69, 0x6e, 0x61, 0x72, 0x79, 0x3a, 0x6c, 0x6f, 0x67,
     0x69, 0x73, 0x74, 0x69, 0x63, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x67, 0x62, 0x74, 0x72, 0x65, 0x65, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00,
     0x00, 0x14, 0x00, 0x00, 0x80, 0x6e, 0xc5, 0x2e, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x80, 0xdf,
     0x4f, 0x2d, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00,
     0x00, 0x00, 0x3b, 0x00, 0x00, 0x80, 0x82, 0xe2, 0x47, 0x3b, 0x01, 0x00, 0x00,
     0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,
     0x9a, 0x99, 0x99, 0xbe, 0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x32, 0xa4, 0xf3, 0x3e, 0x02, 0x00,
     0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x8c, 0xaf, 0xf8, 0xbe, 0xc7,
     0x92, 0xac, 0x41, 0x00, 0x00, 0x50, 0x41, 0x25, 0x49, 0x92, 0x3d, 0x00, 0x00,
     0x00, 0x00, 0xef, 0xd4, 0x14, 0x41, 0x00, 0x00, 0xe8, 0x40, 0xd9, 0x64, 0x93,
     0x3f, 0x02, 0x00, 0x00, 0x00, 0x90, 0xb9, 0x43, 0x40, 0x00, 0x00, 0xb8, 0x40,
     0x68, 0x2f, 0xa1, 0xbf, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x80, 0x3f, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0xc8, 0x40, 0xd4, 0x08, 0xcb, 0x3f, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0x40, 0xf4,
     0x3c, 0xcf, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x6e, 0x69, 0x74, 0x65, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x30)), niter = 1, evaluation_log = structure(list(iter = 1, train_error = 0.076923), .Names = c("iter",
     "train_error"), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x1c2d4c8>),
     call = xgb.train(params = params, data = dtrain, nrounds = nrounds, watchlist = watchlist,
     verbose = verbose, print_every_n = print_every_n, early_stopping_rounds = early_stopping_rounds,
     maximize = maximize, save_period = save_period, save_name = save_name, xgb_model = xgb_model,
     callbacks = callbacks, objective = ..1), params = structure(list(objective = "binary:logistic",
     silent = 1), .Names = c("objective", "silent")), callbacks = structure(list(
     cb.print.evaluation = structure(function (env = parent.frame())
     {
     if (length(env$bst_evaluation) == 0 || period == 0 || NVL(env$rank, 0) !=
     0)
     return()
     i <- env$iteration
     if ((i - 1)%%period == 0 || i == env$begin_iteration || i == env$end_iteration) {
     msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
     cat(msg, "\n")
     }
     }, call = cb.print.evaluation(period = print_every_n), name = "cb.print.evaluation"),
     cb.evaluation.log = structure(function (env = parent.frame(), finalize = FALSE)
     {
     if (is.null(mnames))
     init(env)
     if (finalize)
     return(finalizer(env))
     ev <- env$bst_evaluation
     if (!is.null(env$bst_evaluation_err))
     ev <- c(ev, env$bst_evaluation_err)
     env$evaluation_log <- c(env$evaluation_log, list(c(iter = env$iteration,
     ev)))
     }, call = cb.evaluation.log(), name = "cb.evaluation.log"), cb.save.model = structure(function (env = parent.frame())
     {
     if (is.null(env$bst))
     stop("'save_model' callback requires the 'bst' booster object in its calling frame")
     if ((save_period > 0 && (env$iteration - env$begin_iteration)%%save_period ==
     0) || (save_period == 0 && env$iteration == env$end_iteration))
     xgb.save(env$bst, sprintf(save_name, env$iteration))
     }, call = cb.save.model(save_period = save_period, save_name = save_name), name = "cb.save.model")), .Names = c("cb.print.evaluation",
     "cb.evaluation.log", "cb.save.model"))), .Names = c("handle", "raw", "niter",
     "evaluation_log", "call", "params", "callbacks"), class = "xgb.Booster"), task.desc = structure(list(
     id = "binary", type = "classif", target = "Class", size = 52L, n.feat = structure(c(60L,
     0L, 0L), .Names = c("numerics", "factors", "ordered")), has.missings = FALSE,
     has.weights = FALSE, has.blocking = FALSE, class.levels = c("M", "R"), positive = "M",
     negative = "R"), .Names = c("id", "type", "target", "size", "n.feat", "has.missings",
     "has.weights", "has.blocking", "class.levels", "positive", "negative"), class = c("TaskDescClassif",
     "TaskDescSupervised", "TaskDesc")), subset = 1:52, features = c("V1", "V2", "V3",
     "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
     "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28",
     "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52",
     "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), factor.levels = structure(list(
     Class = c("M", "R")), .Names = "Class"), time = 0.111999999999995), .Names = c("learner",
     "learner.model", "task.desc", "subset", "features", "factor.levels", "time"), class = "WrappedModel"),
     .newdata = structure(list(V1 = c(0.0262, 0.0317, 0.0223, 0.0164, 0.0079, 0.0192,
     0.027, 0.0126, 0.0293, 0.0201, 0.01, 0.0189, 0.0311, 0.0206, 0.0094, 0.0123,
     0.0211, 0.0093, 0.0408, 0.0308, 0.019, 0.0119, 0.0131, 0.0087, 0.0293, 0.0132,
     0.0225, 0.013, 0.0086, 0.0067, 0.0176, 0.0368, 0.0195, 0.0065, 0.0208, 0.0139,
     0.0239, 0.0336, 0.0108, 0.0229, 0.0409, 0.0378, 0.0188, 0.0856, 0.0235, 0.0253,
     0.026, 0.0459, 0.0025, 0.0491, 0.0201, 0.0629, 0.0162, 0.0428, 0.0264, 0.021,
     0.0283, 0.0414, 0.0228, 0.0261, 0.0249, 0.027, 0.0443, 0.1083, 0.043, 0.0731,
     0.0164, 0.0412, 0.0707, 0.0299, 0.0654, 0.0231, 0.0233, 0.0211, 0.0201, 0.0107,
     0.0258, 0.0305, 0.0217, 0.0072, 0.0221, 0.0137, 0.0015, 0.013, 0.0179, 0.018,
     0.0191, 0.0294, 0.0197, 0.0394, 0.0423, 0.0095, 0.0096, 0.0089, 0.0156, 0.0315,
     0.0056, 0.0203, 0.0392, 0.0131, 0.0335, 0.0187, 0.0522, 0.026), V2 = c(0.0582,
     0.0956, 0.0375, 0.0173, 0.0086, 0.0607, 0.0092, 0.0149, 0.0644, 0.0026, 0.0275,
     0.0308, 0.0491, 0.0132, 0.0166, 0.0022, 0.0319, 0.0269, 0.0653, 0.0339, 0.0038,
     0.0582, 0.0068, 0.0046, 0.0378, 0.008, 0.0019, 6e-04, 0.0215, 0.0096, 0.0172,
     0.0403, 0.0142, 0.0122, 0.0186, 0.0222, 0.0189, 0.0294, 0.0086, 0.0369, 0.0421,
     0.0318, 0.037, 0.0454, 0.0291, 0.0808, 0.0192, 0.0437, 0.0309, 0.0279, 0.0423,
     0.1065, 0.0253, 0.0555, 0.0071, 0.0121, 0.0599, 0.0436, 0.0106, 0.0266, 0.0119,
     0.0163, 0.0446, 0.107, 0.0902, 0.1249, 0.0627, 0.1135, 0.1252, 0.0688, 0.0649,
     0.0315, 0.0394, 0.0128, 0.0178, 0.0453, 0.0433, 0.0363, 0.0152, 0.0027, 0.0065,
     0.0297, 0.0186, 0.012, 0.0136, 0.0444, 0.0173, 0.0123, 0.0394, 0.042, 0.0321,
     0.0308, 0.0404, 0.0274, 0.021, 0.0252, 0.0267, 0.0121, 0.0108, 0.0387, 0.0258,
     0.0346, 0.0437, 0.0363), V3 = c(0.1099, 0.1321, 0.0484, 0.0347, 0.0055, 0.0378,
     0.0145, 0.0641, 0.039, 0.0138, 0.019, 0.0197, 0.0692, 0.0533, 0.0398, 0.0196,
     0.0415, 0.0217, 0.0397, 0.0202, 0.0642, 0.0623, 0.0308, 0.0081, 0.0257, 0.0188,
     0.0075, 0.0088, 0.0242, 0.0024, 0.0501, 0.0317, 0.0181, 0.0068, 0.0131, 0.0089,
     0.0466, 0.0476, 0.0058, 0.004, 0.0573, 0.0423, 0.0953, 0.0382, 0.0749, 0.0507,
     0.0254, 0.0347, 0.0171, 0.0592, 0.0554, 0.1526, 0.0262, 0.0708, 0.0342, 0.0203,
     0.0656, 0.0447, 0.013, 0.0223, 0.0277, 0.0341, 0.0235, 0.0257, 0.0833, 0.1665,
     0.0738, 0.0518, 0.1447, 0.0992, 0.0737, 0.017, 0.0416, 0.0015, 0.0274, 0.0289,
     0.0547, 0.0214, 0.0346, 0.0089, 0.0164, 0.0116, 0.0289, 0.0436, 0.0408, 0.0476,
     0.0291, 0.0117, 0.0384, 0.0446, 0.0709, 0.0539, 0.0682, 0.0248, 0.0282, 0.0167,
     0.0221, 0.038, 0.0267, 0.0329, 0.0398, 0.0168, 0.018, 0.0136), V4 = c(0.1083,
     0.1408, 0.0475, 0.007, 0.025, 0.0774, 0.0278, 0.1732, 0.0173, 0.0062, 0.0371,
     0.0622, 0.0831, 0.0569, 0.0359, 0.0206, 0.0286, 0.0339, 0.0604, 0.0889, 0.0452,
     0.06, 0.0311, 0.023, 0.0062, 0.0141, 0.0097, 0.0456, 0.0445, 0.0058, 0.0285,
     0.0293, 0.0406, 0.0108, 0.0211, 0.0108, 0.044, 0.0539, 0.046, 0.0375, 0.013,
     0.035, 0.0824, 0.0203, 0.0519, 0.0244, 0.0061, 0.0456, 0.0228, 0.127, 0.0783,
     0.1229, 0.0386, 0.0618, 0.0793, 0.1036, 0.0229, 0.0844, 0.0842, 0.0749, 0.076,
     0.0247, 0.1008, 0.0837, 0.0813, 0.1496, 0.0608, 0.0232, 0.1644, 0.1021, 0.1132,
     0.0226, 0.0547, 0.045, 0.0232, 0.0713, 0.0681, 0.0227, 0.0346, 0.0061, 0.0487,
     0.0082, 0.0195, 0.0624, 0.0633, 0.0698, 0.0301, 0.0113, 0.0076, 0.0551, 0.0108,
     0.0411, 0.0688, 0.0237, 0.0596, 0.0479, 0.0561, 0.0128, 0.0257, 0.0078, 0.057,
     0.0177, 0.0292, 0.0272), V5 = c(0.0974, 0.1674, 0.0647, 0.0187, 0.0344, 0.1388,
     0.0412, 0.2565, 0.0476, 0.0133, 0.0416, 0.008, 0.0079, 0.0647, 0.0681, 0.018,
     0.0121, 0.0305, 0.0496, 0.157, 0.0333, 0.1397, 0.0085, 0.0586, 0.013, 0.0436,
     0.0445, 0.0525, 0.0667, 0.0197, 0.0262, 0.082, 0.0391, 0.0217, 0.061, 0.0215,
     0.0657, 0.0794, 0.0752, 0.0455, 0.0183, 0.1787, 0.0249, 0.0385, 0.0227, 0.1724,
     0.0352, 0.0067, 0.0434, 0.1772, 0.062, 0.1437, 0.0645, 0.1215, 0.1043, 0.1675,
     0.0839, 0.0419, 0.1117, 0.1364, 0.1218, 0.0822, 0.2252, 0.0748, 0.0165, 0.1443,
     0.0233, 0.0646, 0.1693, 0.08, 0.2482, 0.041, 0.0993, 0.0711, 0.0724, 0.1075,
     0.0784, 0.0456, 0.0484, 0.042, 0.0519, 0.0241, 0.0515, 0.0428, 0.0596, 0.1615,
     0.0463, 0.0497, 0.0251, 0.0597, 0.107, 0.0613, 0.0887, 0.0224, 0.0462, 0.0902,
     0.0936, 0.0537, 0.041, 0.0721, 0.0529, 0.0393, 0.0351, 0.0214), V6 = c(0.228,
     0.171, 0.0591, 0.0671, 0.0546, 0.0809, 0.0757, 0.2559, 0.0816, 0.0151, 0.0201,
     0.0789, 0.02, 0.1432, 0.0706, 0.0492, 0.0438, 0.1172, 0.1817, 0.175, 0.069, 0.1883,
     0.0767, 0.0682, 0.0612, 0.0668, 0.0906, 0.0778, 0.0771, 0.0618, 0.0351, 0.1342,
     0.0249, 0.0284, 0.0613, 0.0136, 0.0742, 0.0804, 0.0887, 0.1452, 0.1019, 0.1635,
     0.0488, 0.0534, 0.0834, 0.3823, 0.0701, 0.089, 0.1224, 0.1908, 0.0871, 0.119,
     0.0472, 0.1524, 0.0783, 0.0418, 0.1673, 0.1215, 0.1506, 0.1513, 0.1538, 0.1256,
     0.2611, 0.1125, 0.0277, 0.277, 0.1048, 0.1124, 0.0844, 0.0629, 0.1257, 0.0116,
     0.1515, 0.1563, 0.0833, 0.1019, 0.125, 0.0665, 0.0526, 0.0865, 0.0849, 0.0253,
     0.0817, 0.0349, 0.0808, 0.0887, 0.069, 0.0998, 0.0629, 0.1416, 0.0973, 0.1039,
     0.0932, 0.0845, 0.0779, 0.1057, 0.1146, 0.0874, 0.0491, 0.1341, 0.1091, 0.163,
     0.1171, 0.0338), V7 = c(0.2431, 0.0731, 0.0753, 0.1056, 0.0528, 0.0568, 0.1026,
     0.2947, 0.0993, 0.0541, 0.0314, 0.144, 0.0981, 0.1344, 0.102, 0.0033, 0.1299,
     0.145, 0.1178, 0.092, 0.0901, 0.1422, 0.0771, 0.0993, 0.0895, 0.0609, 0.0889,
     0.0931, 0.0499, 0.0432, 0.0362, 0.1161, 0.0892, 0.0527, 0.0612, 0.0659, 0.138,
     0.1136, 0.1015, 0.2211, 0.1054, 0.0887, 0.1424, 0.214, 0.0677, 0.3729, 0.1263,
     0.1798, 0.1947, 0.2217, 0.1201, 0.0884, 0.1056, 0.1543, 0.1417, 0.0723, 0.1154,
     0.2002, 0.1776, 0.1316, 0.1192, 0.1323, 0.2061, 0.3322, 0.0569, 0.2555, 0.1338,
     0.1787, 0.0715, 0.013, 0.1797, 0.0223, 0.1674, 0.1518, 0.1232, 0.1606, 0.1296,
     0.0939, 0.0773, 0.1182, 0.0812, 0.0279, 0.1005, 0.0384, 0.209, 0.0596, 0.0576,
     0.1326, 0.0747, 0.0956, 0.0961, 0.1016, 0.0955, 0.1488, 0.1365, 0.1024, 0.0706,
     0.1021, 0.1053, 0.1626, 0.1709, 0.2028, 0.1257, 0.0655), V8 = c(0.3771, 0.1401,
     0.0098, 0.0697, 0.0958, 0.0219, 0.1138, 0.411, 0.0315, 0.021, 0.0651, 0.1451,
     0.1016, 0.2041, 0.0893, 0.0398, 0.139, 0.0638, 0.1024, 0.1353, 0.1454, 0.1447,
     0.064, 0.0717, 0.1107, 0.0131, 0.0655, 0.0941, 0.0906, 0.0951, 0.0535, 0.0663,
     0.0973, 0.0575, 0.0506, 0.0954, 0.1099, 0.1228, 0.0494, 0.1188, 0.107, 0.0817,
     0.1972, 0.311, 0.2002, 0.3583, 0.108, 0.1741, 0.1661, 0.0768, 0.2707, 0.0907,
     0.1388, 0.0391, 0.1176, 0.0828, 0.1098, 0.1516, 0.0997, 0.1654, 0.1229, 0.1584,
     0.1668, 0.459, 0.2057, 0.1712, 0.0644, 0.2407, 0.0947, 0.0813, 0.0989, 0.0805,
     0.1513, 0.1206, 0.1298, 0.2119, 0.1729, 0.0972, 0.0862, 0.0999, 0.1833, 0.013,
     0.0124, 0.0446, 0.3465, 0.1071, 0.1103, 0.1117, 0.0578, 0.0802, 0.1323, 0.1394,
     0.214, 0.1224, 0.078, 0.1209, 0.0996, 0.0852, 0.169, 0.1902, 0.1684, 0.1694,
     0.1178, 0.14), V9 = c(0.5598, 0.2083, 0.0684, 0.0962, 0.1009, 0.1037, 0.0794,
     0.4983, 0.0736, 0.0505, 0.1896, 0.1789, 0.2025, 0.1571, 0.0381, 0.0791, 0.0695,
     0.074, 0.0583, 0.1593, 0.074, 0.0487, 0.0726, 0.0576, 0.0973, 0.0899, 0.1624,
     0.1711, 0.1229, 0.0836, 0.0258, 0.0155, 0.084, 0.1054, 0.0989, 0.0786, 0.1384,
     0.1235, 0.0472, 0.075, 0.2302, 0.1779, 0.1873, 0.2837, 0.2876, 0.3429, 0.1523,
     0.1598, 0.1368, 0.1246, 0.1206, 0.2107, 0.0598, 0.061, 0.0453, 0.0494, 0.137,
     0.0818, 0.1428, 0.1864, 0.2119, 0.2017, 0.1801, 0.5526, 0.3887, 0.0466, 0.1522,
     0.2682, 0.1583, 0.1761, 0.246, 0.2365, 0.1723, 0.1666, 0.2085, 0.3061, 0.2794,
     0.2535, 0.1451, 0.1976, 0.2228, 0.0489, 0.1168, 0.1318, 0.5276, 0.3175, 0.2423,
     0.2984, 0.1357, 0.1618, 0.2462, 0.2592, 0.2546, 0.1569, 0.1038, 0.1241, 0.1673,
     0.1136, 0.2105, 0.261, 0.1865, 0.2328, 0.1258, 0.1843), V10 = c(0.6194, 0.3513,
     0.1487, 0.0251, 0.124, 0.1186, 0.152, 0.592, 0.086, 0.1097, 0.2668, 0.2522, 0.0767,
     0.1573, 0.1328, 0.0475, 0.0568, 0.136, 0.2176, 0.2795, 0.0349, 0.0864, 0.0901,
     0.0818, 0.0751, 0.0922, 0.1452, 0.1483, 0.1185, 0.118, 0.0474, 0.0506, 0.1191,
     0.1109, 0.1093, 0.1015, 0.1376, 0.0842, 0.0393, 0.1631, 0.2259, 0.2053, 0.1806,
     0.2751, 0.3674, 0.2197, 0.163, 0.1408, 0.143, 0.2028, 0.0279, 0.3597, 0.1334,
     0.0113, 0.0945, 0.0686, 0.1767, 0.1975, 0.2227, 0.2013, 0.2531, 0.2122, 0.3083,
     0.5966, 0.7106, 0.1114, 0.078, 0.2058, 0.1247, 0.0998, 0.3422, 0.2461, 0.2078,
     0.1345, 0.272, 0.2936, 0.2954, 0.3127, 0.211, 0.2318, 0.181, 0.0874, 0.1476,
     0.1375, 0.5965, 0.2918, 0.3134, 0.3473, 0.1695, 0.2558, 0.2696, 0.3745, 0.2952,
     0.2119, 0.1567, 0.1533, 0.1859, 0.1747, 0.2471, 0.3193, 0.266, 0.2684, 0.2529,
     0.2354), V11 = c(0.6333, 0.1786, 0.1156, 0.0801, 0.1097, 0.1237, 0.1675, 0.5832,
     0.0414, 0.0841, 0.3376, 0.2607, 0.1767, 0.2327, 0.1303, 0.1152, 0.0869, 0.2132,
     0.2459, 0.3336, 0.1459, 0.2143, 0.075, 0.1315, 0.0528, 0.1445, 0.1442, 0.1532,
     0.0775, 0.0978, 0.0526, 0.0906, 0.1522, 0.0937, 0.1063, 0.1261, 0.0938, 0.0357,
     0.1106, 0.2709, 0.2373, 0.3135, 0.2139, 0.2707, 0.2974, 0.2653, 0.103, 0.2693,
     0.0994, 0.0947, 0.2251, 0.5466, 0.2969, 0.1255, 0.1132, 0.1125, 0.1995, 0.2309,
     0.2621, 0.289, 0.2855, 0.221, 0.3794, 0.5304, 0.7342, 0.1739, 0.1791, 0.1546,
     0.234, 0.0523, 0.2128, 0.2245, 0.1239, 0.0785, 0.2188, 0.3104, 0.2506, 0.2192,
     0.2343, 0.2472, 0.2549, 0.11, 0.2118, 0.2026, 0.6254, 0.3273, 0.4786, 0.4231,
     0.1734, 0.3078, 0.3412, 0.4229, 0.4025, 0.3003, 0.2476, 0.2128, 0.2481, 0.2198,
     0.268, 0.3468, 0.3188, 0.3108, 0.2716, 0.272), V12 = c(0.706, 0.0658, 0.1654,
     0.1056, 0.1215, 0.1601, 0.137, 0.5419, 0.0472, 0.0942, 0.3282, 0.371, 0.2555,
     0.1785, 0.0273, 0.052, 0.1935, 0.3738, 0.3332, 0.294, 0.3473, 0.372, 0.0844,
     0.1862, 0.1209, 0.1475, 0.0948, 0.11, 0.1101, 0.0909, 0.1854, 0.2545, 0.1322,
     0.0827, 0.1179, 0.0828, 0.0259, 0.0689, 0.1412, 0.3358, 0.3323, 0.3118, 0.1523,
     0.0946, 0.0837, 0.3223, 0.2187, 0.3259, 0.225, 0.2497, 0.2615, 0.5205, 0.4754,
     0.2473, 0.084, 0.1741, 0.2869, 0.3025, 0.3109, 0.365, 0.2961, 0.2399, 0.5364,
     0.2251, 0.5033, 0.316, 0.2681, 0.2671, 0.1764, 0.0904, 0.1377, 0.152, 0.0236,
     0.0367, 0.3037, 0.3431, 0.2601, 0.2621, 0.2087, 0.288, 0.2984, 0.1084, 0.2575,
     0.2389, 0.4507, 0.3035, 0.5239, 0.5044, 0.247, 0.3404, 0.4292, 0.4499, 0.5148,
     0.3094, 0.2783, 0.2536, 0.2712, 0.2721, 0.3049, 0.3738, 0.3553, 0.2933, 0.2374,
     0.2442), V13 = c(0.5544, 0.0513, 0.3833, 0.1266, 0.1874, 0.352, 0.1361, 0.5472,
     0.0835, 0.1204, 0.2432, 0.3906, 0.2812, 0.1507, 0.0644, 0.1192, 0.1478, 0.3738,
     0.3087, 0.1608, 0.3197, 0.2665, 0.1226, 0.2789, 0.1763, 0.2087, 0.0618, 0.089,
     0.1042, 0.0656, 0.104, 0.1464, 0.1434, 0.092, 0.1291, 0.0493, 0.1499, 0.1705,
     0.2202, 0.4091, 0.3827, 0.3686, 0.1975, 0.102, 0.1912, 0.5582, 0.1542, 0.4545,
     0.2444, 0.2209, 0.177, 0.5127, 0.5677, 0.3011, 0.0717, 0.271, 0.3275, 0.3938,
     0.2859, 0.351, 0.3341, 0.2964, 0.6173, 0.2402, 0.3, 0.3249, 0.1788, 0.3141, 0.2284,
     0.2655, 0.4032, 0.1732, 0.1771, 0.1227, 0.2959, 0.2456, 0.2249, 0.2419, 0.1645,
     0.2126, 0.2624, 0.1094, 0.2354, 0.2112, 0.3693, 0.3033, 0.4393, 0.5237, 0.3141,
     0.34, 0.3682, 0.5404, 0.4901, 0.2743, 0.2896, 0.2686, 0.2934, 0.2105, 0.2863,
     0.3055, 0.3116, 0.2275, 0.1878, 0.1665), V14 = c(0.532, 0.3752, 0.3598, 0.089,
     0.3383, 0.4479, 0.1345, 0.5314, 0.0938, 0.042, 0.1268, 0.2672, 0.2722, 0.1916,
     0.0712, 0.1943, 0.1871, 0.2673, 0.2613, 0.3335, 0.2823, 0.2113, 0.1619, 0.2579,
     0.2039, 0.2558, 0.1641, 0.1236, 0.0853, 0.0593, 0.0948, 0.1272, 0.1244, 0.0911,
     0.1591, 0.0848, 0.2851, 0.3257, 0.2976, 0.44, 0.484, 0.3885, 0.4844, 0.4519,
     0.504, 0.6916, 0.263, 0.5785, 0.3239, 0.3195, 0.3709, 0.5395, 0.569, 0.3747,
     0.1968, 0.3087, 0.3769, 0.505, 0.3316, 0.3495, 0.4287, 0.4061, 0.7842, 0.2689,
     0.1951, 0.2164, 0.1039, 0.2904, 0.3115, 0.3099, 0.5684, 0.3099, 0.3115, 0.2614,
     0.2059, 0.1887, 0.2115, 0.2179, 0.1689, 0.0708, 0.1893, 0.1023, 0.1334, 0.1444,
     0.2864, 0.2587, 0.344, 0.4398, 0.3297, 0.3951, 0.394, 0.4303, 0.4127, 0.2547,
     0.2956, 0.2803, 0.2637, 0.1727, 0.2294, 0.1926, 0.1965, 0.0994, 0.0983, 0.0336
     ), V15 = c(0.6479, 0.5419, 0.1713, 0.0198, 0.3227, 0.3769, 0.2144, 0.4981, 0.1466,
     0.0031, 0.1278, 0.2716, 0.3227, 0.2061, 0.1204, 0.184, 0.1994, 0.2333, 0.3232,
     0.4985, 0.0166, 0.1103, 0.2317, 0.224, 0.2727, 0.2603, 0.0708, 0.1197, 0.0456,
     0.0832, 0.0912, 0.1223, 0.0653, 0.1487, 0.168, 0.1514, 0.5743, 0.4602, 0.4116,
     0.5485, 0.6812, 0.585, 0.7298, 0.6737, 0.6352, 0.7943, 0.294, 0.4471, 0.3039,
     0.334, 0.4533, 0.6558, 0.6421, 0.452, 0.2633, 0.3575, 0.4169, 0.5872, 0.3755,
     0.4325, 0.5205, 0.5095, 0.8392, 0.6646, 0.2767, 0.2031, 0.198, 0.3531, 0.4725,
     0.352, 0.2398, 0.438, 0.499, 0.428, 0.0906, 0.1184, 0.127, 0.1159, 0.165, 0.1194,
     0.0668, 0.0601, 0.0092, 0.0742, 0.1635, 0.1682, 0.2869, 0.3236, 0.2759, 0.3352,
     0.2965, 0.3333, 0.3575, 0.187, 0.3189, 0.1886, 0.188, 0.204, 0.1165, 0.1385,
     0.178, 0.1801, 0.0683, 0.1302), V16 = c(0.6931, 0.544, 0.1136, 0.1133, 0.2723,
     0.5761, 0.5354, 0.6985, 0.0809, 0.0162, 0.4441, 0.4183, 0.3463, 0.2307, 0.0717,
     0.2077, 0.3283, 0.5367, 0.3731, 0.7295, 0.0572, 0.1136, 0.2934, 0.2568, 0.2321,
     0.1985, 0.0844, 0.1145, 0.1304, 0.1297, 0.1688, 0.1669, 0.089, 0.1666, 0.1918,
     0.1396, 0.8278, 0.6225, 0.4754, 0.7213, 0.7555, 0.7868, 0.7807, 0.6699, 0.6804,
     0.7152, 0.2978, 0.2231, 0.241, 0.3323, 0.5553, 0.8705, 0.7487, 0.5392, 0.4191,
     0.4998, 0.5036, 0.661, 0.4499, 0.5398, 0.6087, 0.5512, 0.9016, 0.6632, 0.3737,
     0.258, 0.3234, 0.5079, 0.5543, 0.3892, 0.4331, 0.5595, 0.6707, 0.6122, 0.161,
     0.208, 0.1193, 0.1237, 0.1967, 0.2808, 0.2666, 0.0906, 0.1951, 0.1533, 0.0422,
     0.1308, 0.3889, 0.2956, 0.2056, 0.2252, 0.3172, 0.3496, 0.3447, 0.1452, 0.1892,
     0.1485, 0.1405, 0.1786, 0.2127, 0.2122, 0.2794, 0.22, 0.1503, 0.1708), V17 = c(0.6759,
     0.515, 0.0349, 0.2826, 0.3943, 0.6426, 0.683, 0.8292, 0.1179, 0.0624, 0.6795,
     0.6988, 0.5395, 0.236, 0.1224, 0.1956, 0.6861, 0.7312, 0.4203, 0.735, 0.2164,
     0.1934, 0.3526, 0.2933, 0.2676, 0.2394, 0.259, 0.2137, 0.269, 0.2038, 0.1568,
     0.1424, 0.1226, 0.1268, 0.1615, 0.1066, 0.8669, 0.7327, 0.539, 0.8137, 0.9522,
     0.9739, 0.7906, 0.7066, 0.7505, 0.3512, 0.0699, 0.2164, 0.0367, 0.278, 0.4616,
     0.9786, 0.8999, 0.6588, 0.505, 0.6011, 0.618, 0.7417, 0.4765, 0.6237, 0.7236,
     0.6613, 1, 0.1674, 0.2507, 0.1796, 0.3748, 0.4639, 0.5386, 0.3962, 0.5954, 0.682,
     0.7655, 0.7435, 0.18, 0.2736, 0.1794, 0.0886, 0.2934, 0.4221, 0.4274, 0.1313,
     0.3685, 0.3052, 0.1785, 0.2803, 0.442, 0.3286, 0.1162, 0.2086, 0.2825, 0.3426,
     0.3068, 0.1457, 0.173, 0.216, 0.2028, 0.1318, 0.2062, 0.2758, 0.287, 0.2732,
     0.1723, 0.2177), V18 = c(0.7551, 0.4262, 0.3796, 0.3234, 0.6432, 0.679, 0.56,
     0.7839, 0.2179, 0.2127, 0.7051, 0.5733, 0.7911, 0.1299, 0.2349, 0.163, 0.5814,
     0.7659, 0.5364, 0.8253, 0.4563, 0.4142, 0.3657, 0.2991, 0.2934, 0.3134, 0.2679,
     0.2838, 0.2947, 0.3811, 0.0375, 0.1285, 0.1846, 0.1374, 0.1647, 0.1923, 0.8131,
     0.7843, 0.6279, 0.9185, 0.9826, 1, 0.6122, 0.5632, 0.6595, 0.2008, 0.1401, 0.3201,
     0.1672, 0.2975, 0.3797, 0.9335, 1, 0.7113, 0.6711, 0.647, 0.8025, 0.8006, 0.6254,
     0.6876, 0.7577, 0.6804, 0.8911, 0.0837, 0.2507, 0.2422, 0.2586, 0.1859, 0.3746,
     0.2449, 0.5772, 0.6164, 0.8485, 0.813, 0.218, 0.3274, 0.2185, 0.1755, 0.3709,
     0.5279, 0.6291, 0.2758, 0.4646, 0.4116, 0.4394, 0.4519, 0.3892, 0.3231, 0.1884,
     0.2248, 0.305, 0.2851, 0.2945, 0.2429, 0.2226, 0.2417, 0.2613, 0.226, 0.2222,
     0.4576, 0.3969, 0.2862, 0.2339, 0.3175), V19 = c(0.8929, 0.2024, 0.7401, 0.3238,
     0.7271, 0.7157, 0.3093, 0.8215, 0.3326, 0.3436, 0.7966, 0.2226, 0.9064, 0.3812,
     0.3684, 0.1218, 0.25, 0.6271, 0.7062, 0.8793, 0.3819, 0.3279, 0.3221, 0.3924,
     0.3295, 0.4077, 0.3094, 0.364, 0.3669, 0.4451, 0.1316, 0.1857, 0.388, 0.1095,
     0.1397, 0.2991, 0.9045, 0.7988, 0.706, 1, 0.8871, 0.9843, 0.42, 0.3785, 0.4509,
     0.2676, 0.299, 0.2915, 0.3038, 0.2948, 0.345, 0.7917, 0.969, 0.7602, 0.7922,
     0.8067, 0.9333, 0.8456, 0.7304, 0.7329, 0.7726, 0.652, 0.8753, 0.4331, 0.3292,
     0.3609, 0.368, 0.4474, 0.4583, 0.2355, 0.8176, 0.6803, 0.9805, 0.9006, 0.2026,
     0.2344, 0.1646, 0.1758, 0.4309, 0.5857, 0.7782, 0.366, 0.5418, 0.5466, 0.695,
     0.6641, 0.4088, 0.4528, 0.339, 0.3382, 0.2408, 0.4062, 0.4351, 0.3259, 0.2427,
     0.2989, 0.2778, 0.2358, 0.3241, 0.6487, 0.5599, 0.2034, 0.1962, 0.3714), V20 = c(0.8619,
     0.4233, 0.9925, 0.4333, 0.8673, 0.5466, 0.3226, 0.9363, 0.3258, 0.3813, 0.9401,
     0.2631, 0.8701, 0.5858, 0.3918, 0.1017, 0.1734, 0.4395, 0.8196, 0.9657, 0.5627,
     0.6222, 0.3093, 0.4691, 0.491, 0.4529, 0.4678, 0.543, 0.4948, 0.5224, 0.2086,
     0.1136, 0.3658, 0.1286, 0.1426, 0.3247, 0.9046, 0.8261, 0.7918, 0.9418, 0.8268,
     0.861, 0.2807, 0.2721, 0.2964, 0.4299, 0.3915, 0.4235, 0.4069, 0.1729, 0.2665,
     0.7383, 0.9032, 0.8672, 0.8381, 0.9008, 0.9399, 0.7939, 0.8702, 0.8107, 0.8098,
     0.6788, 0.7886, 0.8718, 0.4871, 0.181, 0.3508, 0.4079, 0.5961, 0.3045, 0.8835,
     0.8435, 1, 0.9603, 0.1506, 0.126, 0.074, 0.154, 0.4161, 0.6153, 0.7686, 0.5269,
     0.626, 0.5933, 0.8097, 0.7683, 0.5006, 0.6339, 0.3926, 0.4578, 0.542, 0.6833,
     0.7264, 0.3679, 0.3149, 0.3341, 0.3346, 0.3107, 0.433, 0.7154, 0.6936, 0.174,
     0.1395, 0.4552), V21 = c(0.7974, 0.7723, 0.9802, 0.6068, 0.9674, 0.5399, 0.443,
     1, 0.2111, 0.3825, 0.9857, 0.7473, 0.7672, 0.4497, 0.4925, 0.1354, 0.3363, 0.433,
     0.8835, 1, 0.6484, 0.7468, 0.4084, 0.5665, 0.5402, 0.4893, 0.5958, 0.6673, 0.6275,
     0.5911, 0.1976, 0.2069, 0.2297, 0.2146, 0.2429, 0.3797, 1, 1, 0.9493, 0.9116,
     0.7561, 0.8443, 0.5148, 0.5297, 0.4019, 0.528, 0.3598, 0.446, 0.3613, 0.3264,
     0.2395, 0.6908, 0.7685, 0.8416, 0.8759, 0.8906, 0.9275, 0.8804, 0.9349, 0.8396,
     0.8995, 0.7811, 0.7156, 0.7992, 0.6527, 0.2604, 0.5606, 0.54, 0.7464, 0.3112,
     0.5248, 0.9921, 1, 0.9162, 0.0521, 0.0576, 0.0625, 0.0512, 0.5116, 0.6753, 0.8099,
     0.581, 0.742, 0.6663, 0.855, 0.696, 0.7271, 0.7044, 0.4282, 0.6474, 0.6802, 0.765,
     0.8147, 0.3355, 0.4102, 0.3786, 0.383, 0.3906, 0.5071, 0.801, 0.7969, 0.413,
     0.3164, 0.57), V22 = c(0.6737, 0.9735, 0.889, 0.7652, 0.9847, 0.6362, 0.5573,
     0.9224, 0.2302, 0.4764, 0.8193, 0.7263, 0.2957, 0.4876, 0.8793, 0.3157, 0.5588,
     0.4326, 0.8299, 0.8707, 0.7235, 0.7676, 0.4285, 0.6464, 0.6257, 0.5666, 0.7245,
     0.7979, 0.8162, 0.6566, 0.0946, 0.0219, 0.261, 0.2889, 0.2816, 0.5658, 0.9976,
     0.9814, 1, 0.9349, 0.8217, 0.9061, 0.7569, 0.7697, 0.6794, 0.3489, 0.2403, 0.238,
     0.1994, 0.3834, 0.1127, 0.385, 0.6998, 0.7974, 0.9422, 0.9338, 0.945, 0.8384,
     0.9614, 0.8632, 0.9247, 0.8369, 0.7581, 0.3712, 0.8454, 0.6572, 0.5231, 0.4786,
     0.7644, 0.4698, 0.6373, 1, 0.9992, 0.914, 0.2143, 0.1241, 0.2381, 0.1805, 0.6501,
     0.7873, 0.8493, 0.6181, 0.8257, 0.7333, 0.8717, 0.4393, 0.9385, 0.8314, 0.5418,
     0.6708, 0.632, 0.667, 0.8103, 0.31, 0.3808, 0.3956, 0.4003, 0.3631, 0.5944, 0.7924,
     0.7452, 0.6879, 0.5888, 0.7397), V23 = c(0.4293, 0.939, 0.6712, 0.9203, 0.948,
     0.7849, 0.5782, 0.7839, 0.3361, 0.6313, 0.5789, 0.3393, 0.4148, 1, 0.9606, 0.4645,
     0.6592, 0.5544, 0.7609, 0.6471, 0.8242, 0.7867, 0.4663, 0.6774, 0.6826, 0.6234,
     0.8773, 0.9273, 0.9237, 0.6308, 0.1965, 0.24, 0.4193, 0.4238, 0.429, 0.7483,
     0.9872, 0.962, 0.9645, 0.7484, 0.6967, 0.5847, 0.8596, 0.8643, 0.8297, 0.143,
     0.4208, 0.6415, 0.4611, 0.3523, 0.2556, 0.0671, 0.6644, 0.8385, 1, 1, 0.8328,
     0.7852, 0.9126, 0.8747, 0.9365, 0.8969, 0.6372, 0.1703, 0.9739, 0.9734, 0.5469,
     0.4332, 0.5711, 0.5534, 0.8375, 0.7983, 0.9067, 0.7851, 0.4333, 0.3239, 0.4824,
     0.4039, 0.7717, 0.8974, 0.944, 0.5875, 0.8609, 0.7136, 0.8601, 0.2432, 1, 0.8449,
     0.6448, 0.7007, 0.5824, 0.5703, 0.6665, 0.3914, 0.4896, 0.5232, 0.5114, 0.4809,
     0.7078, 0.8793, 0.8203, 0.812, 0.7631, 0.8062), V24 = c(0.3648, 0.5559, 0.4286,
     0.9719, 0.8036, 0.7756, 0.6173, 0.547, 0.4259, 0.7523, 0.6394, 0.2824, 0.6043,
     0.8675, 0.8786, 0.5906, 0.7012, 0.736, 0.7605, 0.5973, 0.8766, 0.8253, 0.5956,
     0.7577, 0.7527, 0.6741, 0.9214, 0.9027, 0.871, 0.5998, 0.1242, 0.2547, 0.5848,
     0.6168, 0.6443, 0.8757, 0.9761, 0.9601, 0.9432, 0.5146, 0.6444, 0.4033, 1, 0.9304,
     1, 0.5453, 0.5675, 0.8966, 0.6849, 0.541, 0.5169, 0.0502, 0.5964, 0.9317, 0.9931,
     0.9102, 0.7773, 0.8479, 0.9443, 0.9607, 0.9853, 0.9856, 0.321, 0.1611, 1, 0.9757,
     0.6954, 0.6113, 0.6257, 0.4532, 0.6699, 0.5426, 0.6803, 0.5134, 0.5943, 0.4357,
     0.6372, 0.5697, 0.8491, 0.9828, 0.945, 0.4639, 0.84, 0.7014, 0.9201, 0.2886,
     0.9831, 0.8512, 0.7223, 0.7619, 0.6805, 0.5995, 0.6958, 0.528, 0.6292, 0.6913,
     0.686, 0.6531, 0.7641, 1, 0.9261, 0.8453, 0.8473, 0.8837), V25 = c(0.5331, 0.5268,
     0.3374, 0.9207, 0.6833, 0.578, 0.8132, 0.4562, 0.4609, 0.8675, 0.7043, 0.6053,
     0.3178, 0.4718, 0.6905, 0.6776, 0.8099, 0.8589, 0.8367, 0.8218, 1, 1, 0.6948,
     0.8856, 0.8504, 0.8282, 0.9282, 0.9192, 0.8052, 0.4958, 0.0616, 0.024, 0.5643,
     0.8167, 0.9061, 0.9048, 0.9009, 0.9118, 0.8658, 0.4106, 0.6948, 0.5946, 0.8457,
     0.9372, 0.824, 0.6338, 0.6094, 0.8918, 0.7272, 0.5228, 0.3779, 0.2717, 0.3711,
     0.8555, 0.9575, 0.8496, 0.7007, 0.7434, 1, 0.9716, 0.9776, 1, 0.2076, 0.2086,
     0.6665, 0.8079, 0.6352, 0.5091, 0.6695, 0.4464, 0.7756, 0.3952, 0.5103, 0.3439,
     0.6926, 0.5734, 0.7531, 0.6577, 0.9104, 1, 0.9655, 0.5424, 0.8949, 0.7758, 0.8729,
     0.4974, 0.9932, 0.9138, 0.7853, 0.7745, 0.5984, 0.6484, 0.7748, 0.6409, 0.7519,
     0.7868, 0.749, 0.7812, 0.8878, 0.9865, 0.881, 0.8919, 0.9424, 0.9432), V26 = c(0.2413,
     0.6826, 0.7366, 0.7545, 0.5136, 0.4862, 0.9819, 0.5922, 0.2606, 0.8788, 0.6875,
     0.5897, 0.3482, 0.5341, 0.6937, 0.8119, 0.8901, 0.8989, 0.8905, 0.7755, 0.8582,
     0.9481, 0.8386, 0.9419, 0.8938, 0.8823, 0.9942, 1, 0.8756, 0.5647, 0.2141, 0.1923,
     0.5448, 0.9622, 1, 0.7511, 0.9724, 0.9086, 0.7895, 0.3443, 0.8014, 0.6793, 0.6797,
     0.6247, 0.7115, 0.7712, 0.6323, 0.7529, 0.7152, 0.4475, 0.4082, 0.2839, 0.0921,
     0.6162, 0.8647, 0.7867, 0.6154, 0.6433, 0.9455, 0.9121, 1, 0.9395, 0.2279, 0.2847,
     0.5323, 0.6521, 0.6757, 0.4606, 0.7131, 0.467, 0.875, 0.5179, 0.4716, 0.329,
     0.7576, 0.7825, 0.8959, 0.7474, 0.8912, 0.846, 0.8045, 0.7367, 0.9945, 0.9137,
     0.8084, 0.8172, 0.9161, 0.9985, 0.7984, 0.6767, 0.8412, 0.8614, 0.8688, 0.7707,
     0.7985, 0.8337, 0.7843, 0.8395, 0.9711, 0.9474, 0.8814, 0.93, 0.9986, 1), V27 = c(0.507,
     0.5713, 0.9611, 0.8289, 0.309, 0.4181, 0.9823, 0.5448, 0.0874, 0.7901, 0.4081,
     0.4967, 0.6158, 0.6197, 0.5674, 0.8594, 0.8745, 0.942, 0.7652, 0.6111, 0.6563,
     0.7539, 0.8875, 1, 0.9928, 0.9196, 1, 0.9821, 1, 0.6906, 0.4642, 0.4753, 0.4772,
     0.828, 0.8087, 0.6858, 0.9675, 0.7931, 0.6501, 0.6981, 0.6053, 0.6389, 0.6971,
     0.6024, 0.7726, 0.6838, 0.6549, 0.6838, 0.7102, 0.534, 0.5353, 0.2234, 0.0481,
     0.4139, 0.7215, 0.7688, 0.581, 0.5514, 0.8815, 0.8576, 0.9896, 0.8917, 0.3309,
     0.2211, 0.4024, 0.4915, 0.8499, 0.7243, 0.7567, 0.4621, 0.83, 0.565, 0.498, 0.2571,
     0.8787, 0.9252, 0.9941, 0.8543, 0.8189, 0.6055, 0.4969, 0.9089, 1, 0.9964, 0.8694,
     1, 0.8237, 1, 0.8847, 0.7373, 0.9911, 0.9819, 1, 0.8754, 0.883, 0.9199, 0.9021,
     0.918, 0.988, 0.9474, 0.9301, 0.9987, 0.9699, 0.9375), V28 = c(0.8533, 0.5429,
     0.7353, 0.8907, 0.0832, 0.2457, 0.9166, 0.3971, 0.2862, 0.8357, 0.1811, 0.8616,
     0.8049, 0.7143, 0.654, 0.9228, 0.7887, 0.9401, 0.5897, 0.4195, 0.5087, 0.6008,
     0.6404, 0.8564, 0.9134, 0.8965, 0.9071, 0.9092, 0.9858, 0.8513, 0.6471, 0.7003,
     0.6897, 0.5816, 0.6119, 0.7043, 0.7633, 0.5877, 0.4492, 0.8713, 0.6084, 0.5002,
     0.5843, 0.681, 0.6124, 0.8015, 0.7673, 0.839, 0.8516, 0.5323, 0.5116, 0.1911,
     0.0876, 0.3269, 0.5801, 0.7718, 0.4454, 0.3519, 0.752, 0.8798, 0.9076, 0.8105,
     0.2847, 0.6134, 0.3444, 0.5363, 0.8025, 0.8987, 0.8077, 0.6988, 0.6896, 0.3042,
     0.6196, 0.3685, 0.906, 0.9349, 0.9957, 0.9085, 0.6779, 0.3036, 0.396, 1, 0.9649,
     1, 0.8411, 0.9238, 0.6957, 0.7544, 0.9582, 0.7834, 0.9187, 0.938, 0.9941, 1,
     0.9915, 1, 1, 0.9769, 0.9812, 0.9315, 0.9955, 1, 1, 0.7603), V29 = c(0.6036,
     0.2177, 0.4856, 0.7309, 0.4019, 0.0716, 0.7423, 0.0882, 0.5606, 0.9631, 0.2064,
     0.8339, 0.6289, 0.5605, 0.7802, 0.8387, 0.8725, 0.9379, 0.3037, 0.299, 0.4817,
     0.5437, 0.3308, 0.679, 0.708, 0.7549, 0.8545, 0.8184, 0.9427, 1, 0.634, 0.6825,
     0.9797, 0.4667, 0.526, 0.5864, 0.4434, 0.3474, 0.4739, 0.9013, 0.8877, 0.5578,
     0.4772, 0.5047, 0.4936, 0.8073, 1, 1, 1, 0.3907, 0.4544, 0.0408, 0.104, 0.3108,
     0.4964, 0.6268, 0.3707, 0.3168, 0.7068, 0.772, 0.7306, 0.6828, 0.1949, 0.5807,
     0.4239, 0.7649, 0.6563, 0.8826, 0.8477, 0.7626, 0.3372, 0.1881, 0.7171, 0.5765,
     0.8528, 0.9348, 0.9328, 0.8668, 0.5368, 0.0144, 0.3856, 0.8247, 0.8747, 0.8881,
     0.5793, 0.8519, 0.4536, 0.4661, 0.899, 0.9619, 0.8005, 0.8435, 0.8793, 0.9806,
     0.9223, 0.899, 0.8888, 0.8937, 0.9464, 0.8326, 0.8576, 0.8104, 0.863, 0.7123),
     V30 = c(0.8514, 0.2149, 0.1594, 0.6896, 0.2344, 0.0613, 0.7736, 0.2385, 0.8344,
     0.9619, 0.3917, 0.4084, 0.4999, 0.3728, 0.7575, 0.7238, 0.9376, 0.8575, 0.0823,
     0.1354, 0.453, 0.5387, 0.3425, 0.5587, 0.6318, 0.6736, 0.7293, 0.6962, 0.8114,
     0.9166, 0.6107, 0.6443, 1, 0.3539, 0.3677, 0.3773, 0.3822, 0.4235, 0.6153,
     0.8014, 0.8557, 0.4831, 0.5201, 0.5775, 0.5648, 0.831, 0.8463, 0.8362, 0.769,
     0.3456, 0.4258, 0.2531, 0.1714, 0.2554, 0.4886, 0.4301, 0.2891, 0.3346, 0.5986,
     0.5711, 0.5758, 0.5572, 0.1671, 0.6925, 0.4182, 0.525, 0.8591, 0.9201, 0.9289,
     0.7025, 0.6405, 0.396, 0.6316, 0.619, 0.9087, 1, 0.9344, 0.8892, 0.5207,
     0.2526, 0.5574, 0.5441, 0.6257, 0.6585, 0.3754, 0.7722, 0.3281, 0.3924, 0.6831,
     1, 0.6713, 0.6074, 0.6482, 0.6969, 0.6981, 0.6456, 0.6511, 0.7022, 0.8542,
     0.6213, 0.6069, 0.6199, 0.6979, 0.8358), V31 = c(0.8512, 0.5811, 0.3007,
     0.5829, 0.1905, 0.1816, 0.8473, 0.2005, 0.8096, 0.9236, 0.3791, 0.2268, 0.583,
     0.2481, 0.5836, 0.6292, 0.892, 0.7284, 0.2787, 0.2438, 0.4521, 0.5619, 0.492,
     0.4147, 0.6126, 0.6463, 0.6499, 0.59, 0.6987, 0.7676, 0.7046, 0.7063, 0.9546,
     0.2727, 0.2746, 0.2206, 0.4727, 0.4633, 0.4929, 0.438, 0.5563, 0.4729, 0.4241,
     0.4754, 0.4906, 0.7792, 0.5509, 0.5427, 0.4841, 0.4091, 0.3869, 0.1979, 0.3264,
     0.3367, 0.4079, 0.2077, 0.2185, 0.2056, 0.3857, 0.4264, 0.4469, 0.4301, 0.1025,
     0.3825, 0.4393, 0.5101, 0.6655, 0.8005, 0.9513, 0.7382, 0.7138, 0.2286, 0.3554,
     0.4613, 0.9657, 0.9308, 0.8854, 0.9065, 0.5651, 0.4335, 0.7309, 0.3349, 0.2184,
     0.2707, 0.3485, 0.5772, 0.2522, 0.3849, 0.6108, 0.8086, 0.5632, 0.5403, 0.5876,
     0.4973, 0.6167, 0.5967, 0.6083, 0.65, 0.6457, 0.3772, 0.3934, 0.6041, 0.7717,
     0.7622), V32 = c(0.5045, 0.6323, 0.4096, 0.4935, 0.1235, 0.4493, 0.7352,
     0.0587, 0.725, 0.8903, 0.2042, 0.1745, 0.666, 0.1921, 0.6316, 0.5181, 0.7508,
     0.67, 0.7241, 0.5624, 0.4532, 0.5141, 0.4592, 0.2946, 0.4638, 0.5007, 0.6071,
     0.5447, 0.681, 0.6177, 0.5376, 0.5373, 0.8835, 0.141, 0.102, 0.2628, 0.4007,
     0.341, 0.3195, 0.1319, 0.2897, 0.3318, 0.1592, 0.24, 0.182, 0.5049, 0.4444,
     0.4577, 0.3717, 0.4639, 0.3939, 0.1891, 0.4612, 0.4465, 0.2443, 0.1198, 0.1711,
     0.1032, 0.251, 0.286, 0.3719, 0.3339, 0.1362, 0.4303, 0.1162, 0.4219, 0.5369,
     0.6033, 0.7995, 0.7446, 0.8202, 0.3544, 0.2897, 0.3615, 0.9306, 0.8478, 0.769,
     0.8522, 0.5749, 0.4918, 0.8549, 0.0877, 0.2945, 0.1746, 0.4639, 0.519, 0.3964,
     0.4674, 0.548, 0.5558, 0.7332, 0.689, 0.6408, 0.502, 0.5069, 0.4355, 0.4463,
     0.5069, 0.3397, 0.2822, 0.2464, 0.5547, 0.7305, 0.4567), V33 = c(0.1862,
     0.2965, 0.317, 0.3101, 0.1717, 0.5976, 0.6671, 0.2544, 0.8048, 0.9708, 0.2227,
     0.0507, 0.4124, 0.1386, 0.8108, 0.4629, 0.6832, 0.7547, 0.8032, 0.5555, 0.5385,
     0.6084, 0.3034, 0.2025, 0.2797, 0.3663, 0.5588, 0.5142, 0.6591, 0.5468, 0.5934,
     0.6601, 0.7662, 0.1863, 0.1339, 0.2672, 0.3381, 0.2849, 0.3735, 0.1709, 0.3638,
     0.3969, 0.1668, 0.2779, 0.1811, 0.1413, 0.5169, 0.8067, 0.6096, 0.558, 0.4661,
     0.2433, 0.3939, 0.5, 0.1768, 0.166, 0.3578, 0.3168, 0.2162, 0.3114, 0.2079,
     0.2035, 0.2212, 0.7791, 0.4336, 0.416, 0.3118, 0.212, 0.4362, 0.7927, 0.6657,
     0.4187, 0.4316, 0.4434, 0.7774, 0.7605, 0.6865, 0.7204, 0.525, 0.5409, 0.9425,
     0.16, 0.3645, 0.2709, 0.6495, 0.6824, 0.4154, 0.4245, 0.5058, 0.5409, 0.6038,
     0.5977, 0.4972, 0.5359, 0.3921, 0.2997, 0.2948, 0.3903, 0.3828, 0.2042, 0.1645,
     0.416, 0.5197, 0.1715), V34 = c(0.2709, 0.1873, 0.3305, 0.0306, 0.2351, 0.3785,
     0.6083, 0.2009, 0.9435, 0.9647, 0.3341, 0.1588, 0.126, 0.3325, 0.9039, 0.5255,
     0.761, 0.8773, 0.805, 0.6963, 0.5308, 0.5621, 0.4366, 0.0688, 0.1721, 0.2298,
     0.5967, 0.5389, 0.6954, 0.5516, 0.8443, 0.8708, 0.6547, 0.2176, 0.1582, 0.2907,
     0.3172, 0.2847, 0.3336, 0.2484, 0.4786, 0.3894, 0.0588, 0.1997, 0.1107, 0.2767,
     0.4268, 0.6973, 0.511, 0.5727, 0.3974, 0.1956, 0.505, 0.5111, 0.2472, 0.2618,
     0.3947, 0.404, 0.0968, 0.2066, 0.0955, 0.0798, 0.1124, 0.8703, 0.6553, 0.1906,
     0.3763, 0.2866, 0.4048, 0.5227, 0.5254, 0.2398, 0.3791, 0.3864, 0.6643, 0.704,
     0.639, 0.62, 0.4255, 0.5961, 0.8726, 0.4169, 0.5012, 0.4853, 0.6901, 0.622,
     0.3308, 0.3095, 0.4476, 0.4988, 0.2575, 0.3244, 0.2755, 0.3842, 0.3524, 0.2294,
     0.1729, 0.3009, 0.3204, 0.219, 0.114, 0.1472, 0.1786, 0.1549), V35 = c(0.4232,
     0.2969, 0.3408, 0.0244, 0.2489, 0.2495, 0.6239, 0.0329, 1, 0.7892, 0.3984,
     0.304, 0.2487, 0.2883, 0.8647, 0.5147, 0.9017, 0.9919, 0.7676, 0.7298, 0.5356,
     0.5956, 0.5175, 0.1171, 0.1665, 0.1362, 0.6275, 0.5531, 0.729, 0.5463, 0.9481,
     0.9518, 0.5447, 0.236, 0.1952, 0.1982, 0.2222, 0.1742, 0.1052, 0.3044, 0.2908,
     0.2314, 0.3967, 0.5305, 0.4603, 0.5084, 0.1802, 0.3915, 0.2586, 0.6355, 0.2194,
     0.2667, 0.4833, 0.5194, 0.3518, 0.3862, 0.2867, 0.4282, 0.1323, 0.1165, 0.0488,
     0.0809, 0.1677, 1, 0.6172, 0.0223, 0.2801, 0.4033, 0.4952, 0.3967, 0.296,
     0.1847, 0.2421, 0.3093, 0.6604, 0.7539, 0.6378, 0.6253, 0.333, 0.5248, 0.6673,
     0.6576, 0.7843, 0.7184, 0.5666, 0.5054, 0.1445, 0.0752, 0.2401, 0.3108, 0.0349,
     0.0516, 0.03, 0.1848, 0.2183, 0.1866, 0.1488, 0.1565, 0.1331, 0.2223, 0.0956,
     0.0849, 0.1098, 0.1641), V36 = c(0.3043, 0.5163, 0.2186, 0.1108, 0.3649,
     0.5771, 0.5972, 0.1547, 0.896, 0.5307, 0.5077, 0.1369, 0.4676, 0.3228, 0.6695,
     0.3929, 1, 0.9922, 0.7468, 0.7022, 0.5271, 0.6078, 0.5122, 0.2157, 0.2561,
     0.2123, 0.5459, 0.5318, 0.668, 0.5515, 0.9705, 0.9605, 0.4593, 0.1725, 0.1787,
     0.2288, 0.0733, 0.0549, 0.0671, 0.2312, 0.0899, 0.1036, 0.7147, 0.7409, 0.665,
     0.4787, 0.0791, 0.1558, 0.0916, 0.7563, 0.1816, 0.134, 0.3511, 0.4619, 0.3762,
     0.3958, 0.2401, 0.4538, 0.1344, 0.0185, 0.1406, 0.1525, 0.1039, 0.9212, 0.4373,
     0.4219, 0.0875, 0.2803, 0.1712, 0.3042, 0.0704, 0.376, 0.0944, 0.2138, 0.6884,
     0.799, 0.6629, 0.6848, 0.2331, 0.3777, 0.4694, 0.739, 0.9361, 0.8209, 0.5188,
     0.3578, 0.1923, 0.2885, 0.1405, 0.2897, 0.1799, 0.3157, 0.3356, 0.1149, 0.1245,
     0.0922, 0.0801, 0.0985, 0.044, 0.1327, 0.008, 0.0608, 0.1446, 0.1869), V37 = c(0.6116,
     0.6153, 0.2463, 0.1594, 0.3382, 0.8852, 0.5715, 0.1212, 0.5516, 0.2718, 0.5534,
     0.1605, 0.5382, 0.2607, 0.4027, 0.1279, 0.9123, 0.9419, 0.6253, 0.5468, 0.426,
     0.5025, 0.4746, 0.2216, 0.2735, 0.2395, 0.4786, 0.4826, 0.5917, 0.4561, 0.7766,
     0.7712, 0.4679, 0.0589, 0.0429, 0.3186, 0.2692, 0.1192, 0.0379, 0.1338, 0.2043,
     0.1312, 0.7319, 0.7775, 0.6423, 0.1356, 0.0535, 0.1598, 0.0947, 0.6903, 0.1023,
     0.1073, 0.2319, 0.4234, 0.2909, 0.3248, 0.3619, 0.3704, 0.225, 0.1302, 0.2554,
     0.2626, 0.2562, 0.9386, 0.4118, 0.5496, 0.3319, 0.3087, 0.3652, 0.1309, 0.097,
     0.4331, 0.0351, 0.1112, 0.6938, 0.7673, 0.5983, 0.7337, 0.1451, 0.2369, 0.1546,
     0.7963, 0.8195, 0.7536, 0.506, 0.3809, 0.3208, 0.4072, 0.1772, 0.2244, 0.3039,
     0.359, 0.3167, 0.157, 0.1592, 0.1829, 0.177, 0.22, 0.1234, 0.0521, 0.0702,
     0.0969, 0.1066, 0.2655), V38 = c(0.6756, 0.4283, 0.2726, 0.1371, 0.1589,
     0.8409, 0.5242, 0.2446, 0.3037, 0.1953, 0.3352, 0.2061, 0.315, 0.204, 0.237,
     0.0411, 0.7388, 0.8388, 0.173, 0.1421, 0.2436, 0.2829, 0.4902, 0.2776, 0.3209,
     0.2673, 0.3965, 0.379, 0.4899, 0.3466, 0.6313, 0.6772, 0.1987, 0.0621, 0.1096,
     0.2871, 0.1888, 0.1154, 0.0461, 0.2056, 0.1707, 0.0864, 0.3509, 0.4424, 0.2166,
     0.2299, 0.1906, 0.2161, 0.2287, 0.6176, 0.2108, 0.2023, 0.4029, 0.4372, 0.2311,
     0.2302, 0.3314, 0.3741, 0.3244, 0.248, 0.2054, 0.2456, 0.2624, 0.9303, 0.3641,
     0.2483, 0.4237, 0.355, 0.3763, 0.2408, 0.3941, 0.3626, 0.0844, 0.1386, 0.5932,
     0.5955, 0.4565, 0.6281, 0.1648, 0.172, 0.1748, 0.7493, 0.6207, 0.6496, 0.3885,
     0.3813, 0.3367, 0.317, 0.1742, 0.096, 0.476, 0.3881, 0.4133, 0.1311, 0.1626,
     0.1743, 0.1382, 0.2243, 0.203, 0.0618, 0.0936, 0.1411, 0.144, 0.1713), V39 = c(0.5375,
     0.5479, 0.168, 0.0696, 0.0989, 0.357, 0.2924, 0.3171, 0.2338, 0.1374, 0.2723,
     0.0734, 0.2139, 0.2396, 0.2685, 0.0859, 0.5915, 0.6605, 0.2916, 0.4738, 0.1205,
     0.0477, 0.4603, 0.2309, 0.2724, 0.2865, 0.2087, 0.1831, 0.3439, 0.3384, 0.576,
     0.6431, 0.0699, 0.1847, 0.1762, 0.2921, 0.0712, 0.0855, 0.1694, 0.2474, 0.0407,
     0.2569, 0.0589, 0.1416, 0.1951, 0.2789, 0.2561, 0.5178, 0.348, 0.5379, 0.3253,
     0.1794, 0.3676, 0.4277, 0.3168, 0.325, 0.3763, 0.3839, 0.3939, 0.1637, 0.1614,
     0.198, 0.2236, 0.7314, 0.4572, 0.2034, 0.1801, 0.2545, 0.2841, 0.178, 0.6028,
     0.2519, 0.0436, 0.1523, 0.5774, 0.4731, 0.3129, 0.5725, 0.2694, 0.1878, 0.3607,
     0.6795, 0.4513, 0.4708, 0.3762, 0.3359, 0.5683, 0.2863, 0.3326, 0.2287, 0.5756,
     0.5716, 0.6281, 0.1583, 0.2356, 0.2452, 0.2404, 0.2736, 0.1652, 0.1416, 0.0894,
     0.1676, 0.1929, 0.0959), V40 = c(0.4719, 0.6133, 0.2792, 0.0452, 0.1089,
     0.3133, 0.1536, 0.3195, 0.2382, 0.3105, 0.2278, 0.0202, 0.1848, 0.1319, 0.3662,
     0.1131, 0.4057, 0.4816, 0.5003, 0.641, 0.3845, 0.2811, 0.446, 0.1444, 0.188,
     0.206, 0.1651, 0.175, 0.2366, 0.2853, 0.6148, 0.672, 0.1493, 0.2452, 0.2481,
     0.2806, 0.1062, 0.1811, 0.2169, 0.279, 0.1286, 0.3179, 0.269, 0.3508, 0.4947,
     0.3833, 0.2153, 0.4782, 0.2095, 0.5622, 0.3697, 0.0227, 0.151, 0.4433, 0.3554,
     0.4022, 0.4767, 0.3494, 0.3806, 0.1103, 0.2232, 0.2412, 0.118, 0.4791, 0.4367,
     0.2729, 0.3743, 0.1432, 0.0427, 0.1598, 0.3521, 0.187, 0.113, 0.0996, 0.6223,
     0.484, 0.4158, 0.6119, 0.373, 0.325, 0.5208, 0.4713, 0.3004, 0.3482, 0.3738,
     0.2771, 0.5505, 0.2634, 0.4021, 0.3228, 0.4254, 0.4314, 0.4977, 0.2631, 0.2483,
     0.2407, 0.2046, 0.2152, 0.1043, 0.146, 0.1127, 0.12, 0.0325, 0.0768), V41 = c(0.4647,
     0.5017, 0.2558, 0.062, 0.1043, 0.6096, 0.2003, 0.3051, 0.3318, 0.379, 0.2044,
     0.1638, 0.1679, 0.0683, 0.3267, 0.1306, 0.3019, 0.2917, 0.522, 0.4375, 0.4107,
     0.3422, 0.4196, 0.1513, 0.1552, 0.1659, 0.1836, 0.1679, 0.1716, 0.2502, 0.545,
     0.6035, 0.1713, 0.2984, 0.315, 0.2682, 0.0694, 0.1264, 0.1677, 0.161, 0.1581,
     0.2649, 0.42, 0.4482, 0.4925, 0.2933, 0.2769, 0.2344, 0.1901, 0.6508, 0.2912,
     0.1313, 0.0745, 0.37, 0.3741, 0.4344, 0.4059, 0.438, 0.3258, 0.2144, 0.1773,
     0.2409, 0.1103, 0.2087, 0.2964, 0.2837, 0.4627, 0.5869, 0.5331, 0.5657, 0.3924,
     0.1046, 0.2045, 0.1644, 0.5841, 0.434, 0.4325, 0.5597, 0.4467, 0.2575, 0.5177,
     0.2355, 0.2674, 0.3508, 0.2605, 0.3648, 0.3231, 0.0541, 0.3009, 0.3454, 0.5046,
     0.3051, 0.2613, 0.3103, 0.2437, 0.2518, 0.197, 0.2438, 0.1066, 0.0846, 0.0873,
     0.1201, 0.149, 0.0847), V42 = c(0.2587, 0.2377, 0.174, 0.1421, 0.0839, 0.6378,
     0.2031, 0.0836, 0.3821, 0.4105, 0.1986, 0.1583, 0.2328, 0.0334, 0.22, 0.1757,
     0.2331, 0.1769, 0.4824, 0.3178, 0.5067, 0.5147, 0.2873, 0.1745, 0.2522, 0.2633,
     0.0652, 0.0674, 0.1013, 0.1641, 0.4813, 0.5155, 0.1654, 0.3041, 0.292, 0.2112,
     0.03, 0.0799, 0.0644, 0.0056, 0.2191, 0.2714, 0.3874, 0.4208, 0.4041, 0.1155,
     0.2841, 0.3599, 0.2941, 0.4797, 0.301, 0.1775, 0.1395, 0.3324, 0.4443, 0.4008,
     0.3661, 0.4265, 0.3654, 0.2033, 0.2293, 0.1901, 0.2831, 0.2016, 0.4312, 0.4463,
     0.1614, 0.6431, 0.6952, 0.6443, 0.4808, 0.2339, 0.1937, 0.1902, 0.4527, 0.3954,
     0.4031, 0.4965, 0.4133, 0.2423, 0.3702, 0.1704, 0.2241, 0.3181, 0.1591, 0.3834,
     0.0448, 0.1874, 0.2075, 0.3882, 0.7179, 0.4393, 0.4697, 0.4512, 0.2715, 0.3184,
     0.2778, 0.3154, 0.211, 0.1055, 0.102, 0.1036, 0.0328, 0.2076), V43 = c(0.2129,
     0.1957, 0.2121, 0.1597, 0.1391, 0.2709, 0.2207, 0.1266, 0.1575, 0.3355, 0.0835,
     0.183, 0.1015, 0.0716, 0.2996, 0.2648, 0.2931, 0.1136, 0.4004, 0.2377, 0.4216,
     0.4372, 0.2296, 0.1756, 0.2121, 0.2552, 0.0758, 0.0609, 0.0766, 0.1605, 0.3406,
     0.3802, 0.26, 0.2275, 0.1902, 0.1513, 0.0893, 0.0378, 0.0159, 0.0351, 0.1701,
     0.1713, 0.244, 0.3054, 0.2402, 0.1705, 0.1733, 0.2785, 0.2211, 0.3736, 0.2563,
     0.1549, 0.1552, 0.2564, 0.3261, 0.337, 0.232, 0.2854, 0.2983, 0.1887, 0.2521,
     0.2077, 0.2385, 0.1669, 0.4155, 0.3178, 0.2494, 0.5826, 0.4288, 0.4241, 0.4602,
     0.1991, 0.0834, 0.1313, 0.4911, 0.4837, 0.4201, 0.5027, 0.3743, 0.2706, 0.224,
     0.2728, 0.3141, 0.3524, 0.1875, 0.3453, 0.3131, 0.3459, 0.1206, 0.324, 0.6163,
     0.4302, 0.4806, 0.3785, 0.1184, 0.1685, 0.1377, 0.2112, 0.2417, 0.1639, 0.1964,
     0.1977, 0.0537, 0.2505), V44 = c(0.2222, 0.1749, 0.1099, 0.1384, 0.0819,
     0.1419, 0.1778, 0.1381, 0.2228, 0.2998, 0.0908, 0.1886, 0.0713, 0.0976, 0.2205,
     0.1955, 0.2298, 0.0701, 0.3877, 0.2808, 0.2479, 0.247, 0.0949, 0.1424, 0.1801,
     0.1696, 0.0486, 0.0375, 0.0845, 0.1491, 0.1916, 0.2278, 0.3846, 0.148, 0.0696,
     0.1789, 0.1459, 0.1268, 0.0778, 0.1148, 0.0971, 0.0584, 0.2, 0.2235, 0.1392,
     0.1294, 0.0815, 0.1807, 0.1524, 0.2804, 0.1927, 0.1626, 0.0377, 0.2527, 0.1963,
     0.2518, 0.145, 0.2808, 0.1779, 0.137, 0.1464, 0.1767, 0.0255, 0.2872, 0.1824,
     0.0807, 0.3202, 0.4286, 0.3063, 0.4567, 0.4164, 0.11, 0.1502, 0.1776, 0.5762,
     0.5379, 0.4557, 0.5772, 0.3021, 0.2323, 0.0816, 0.4016, 0.3693, 0.3659, 0.2267,
     0.2096, 0.3387, 0.4646, 0.0255, 0.0926, 0.5663, 0.4831, 0.4921, 0.1269, 0.1157,
     0.0675, 0.0685, 0.0991, 0.1631, 0.1916, 0.2256, 0.1339, 0.1309, 0.1862),
     V45 = c(0.2111, 0.1304, 0.0985, 0.0372, 0.0678, 0.126, 0.1353, 0.1136, 0.1582,
     0.2748, 0.138, 0.1008, 0.0615, 0.0787, 0.1163, 0.0656, 0.2391, 0.1578, 0.1651,
     0.1374, 0.1586, 0.1708, 0.0095, 0.0908, 0.1473, 0.1467, 0.0353, 0.0533, 0.026,
     0.1326, 0.1134, 0.1522, 0.3754, 0.1102, 0.0758, 0.185, 0.1348, 0.1125, 0.0653,
     0.1331, 0.2217, 0.123, 0.2307, 0.2611, 0.1779, 0.0909, 0.0335, 0.0352, 0.0746,
     0.1982, 0.2062, 0.0708, 0.0636, 0.2137, 0.0864, 0.2101, 0.1017, 0.2395, 0.1535,
     0.1376, 0.0673, 0.1119, 0.1967, 0.4374, 0.1487, 0.1192, 0.2265, 0.4894, 0.5835,
     0.576, 0.5438, 0.0684, 0.1675, 0.2, 0.5013, 0.4485, 0.3955, 0.5907, 0.2069,
     0.1724, 0.0395, 0.4125, 0.2986, 0.2846, 0.1577, 0.1031, 0.413, 0.4366, 0.0298,
     0.1173, 0.5749, 0.5084, 0.5294, 0.1459, 0.1449, 0.1186, 0.0664, 0.0594, 0.0769,
     0.2085, 0.1814, 0.0902, 0.091, 0.1439), V46 = c(0.0176, 0.0597, 0.1271, 0.0688,
     0.0663, 0.1288, 0.1373, 0.0516, 0.1433, 0.2024, 0.1948, 0.0663, 0.0779, 0.0522,
     0.0635, 0.058, 0.191, 0.1938, 0.0442, 0.1136, 0.1124, 0.1343, 0.0527, 0.0138,
     0.0681, 0.1286, 0.0297, 0.0278, 0.0333, 0.0687, 0.064, 0.0801, 0.2414, 0.1178,
     0.091, 0.1717, 0.0391, 0.0505, 0.021, 0.0276, 0.2732, 0.22, 0.1886, 0.2798,
     0.1946, 0.08, 0.0933, 0.0473, 0.0606, 0.2438, 0.1751, 0.0129, 0.0443, 0.1789,
     0.1688, 0.1181, 0.1111, 0.0369, 0.1199, 0.0307, 0.0965, 0.0779, 0.1483, 0.3097,
     0.0138, 0.2134, 0.1146, 0.5777, 0.5692, 0.5293, 0.5649, 0.0303, 0.1058, 0.0765,
     0.4042, 0.2674, 0.2966, 0.4803, 0.179, 0.1457, 0.0785, 0.347, 0.2226, 0.1714,
     0.1211, 0.0798, 0.3639, 0.2581, 0.0691, 0.0566, 0.3593, 0.1952, 0.2216, 0.1092,
     0.1883, 0.1833, 0.1665, 0.194, 0.0723, 0.2335, 0.2012, 0.1085, 0.0757, 0.147
     ), V47 = c(0.1348, 0.1124, 0.1459, 0.0867, 0.1202, 0.079, 0.0749, 0.0073,
     0.1634, 0.1043, 0.1211, 0.0183, 0.0761, 0.05, 0.0465, 0.0319, 0.1096, 0.1106,
     0.0663, 0.1034, 0.0651, 0.0838, 0.0383, 0.0469, 0.1091, 0.0926, 0.0241, 0.0179,
     0.0205, 0.0602, 0.0911, 0.0804, 0.1077, 0.0608, 0.0441, 0.0898, 0.0546, 0.0949,
     0.0509, 0.0763, 0.1874, 0.2198, 0.196, 0.2392, 0.1723, 0.0567, 0.1018, 0.0322,
     0.0692, 0.1789, 0.0841, 0.0795, 0.0264, 0.101, 0.1991, 0.115, 0.0655, 0.0805,
     0.0959, 0.0373, 0.1492, 0.1344, 0.0434, 0.1578, 0.1164, 0.3241, 0.0476, 0.4315,
     0.263, 0.3287, 0.3195, 0.0674, 0.1111, 0.0727, 0.3123, 0.1541, 0.2095, 0.3877,
     0.1689, 0.1175, 0.1052, 0.2739, 0.0849, 0.0694, 0.0883, 0.0701, 0.2069, 0.1319,
     0.0781, 0.0766, 0.2526, 0.1539, 0.1401, 0.1485, 0.1954, 0.1878, 0.1807, 0.1937,
     0.0912, 0.1964, 0.1688, 0.1521, 0.1059, 0.0991), V48 = c(0.0744, 0.1047,
     0.1164, 0.0513, 0.0692, 0.0829, 0.0472, 0.0278, 0.1133, 0.0453, 0.0843, 0.0404,
     0.0845, 0.0231, 0.0422, 0.0301, 0.03, 0.0693, 0.0418, 0.0688, 0.0789, 0.0755,
     0.0107, 0.048, 0.0919, 0.0716, 0.0379, 0.0114, 0.0309, 0.0561, 0.098, 0.0752,
     0.0224, 0.0333, 0.0244, 0.0656, 0.0469, 0.0677, 0.0387, 0.0631, 0.1062, 0.1074,
     0.1701, 0.2021, 0.1522, 0.0198, 0.0309, 0.0408, 0.0446, 0.1706, 0.1035, 0.0762,
     0.0223, 0.0528, 0.1217, 0.055, 0.0271, 0.0541, 0.0765, 0.0606, 0.1128, 0.096,
     0.0627, 0.0553, 0.2052, 0.2945, 0.0943, 0.264, 0.1196, 0.1283, 0.2484, 0.0785,
     0.0849, 0.0749, 0.2232, 0.1359, 0.1558, 0.2779, 0.1341, 0.0868, 0.1034, 0.179,
     0.0359, 0.0303, 0.085, 0.0526, 0.0859, 0.0505, 0.0777, 0.0969, 0.2299, 0.2037,
     0.1888, 0.1385, 0.1492, 0.1114, 0.1245, 0.1082, 0.0812, 0.13, 0.1037, 0.1363,
     0.1005, 0.0041), V49 = c(0.013, 0.0507, 0.0777, 0.0092, 0.0152, 0.052, 0.0325,
     0.0372, 0.0567, 0.0337, 0.0589, 0.0108, 0.0592, 0.0221, 0.0174, 0.0272, 0.0171,
     0.0176, 0.0475, 0.0422, 0.0325, 0.0304, 0.0108, 0.0159, 0.0397, 0.0325, 0.0119,
     0.0073, 0.0101, 0.0306, 0.0563, 0.0566, 0.0155, 0.0276, 0.0265, 0.0445, 0.0201,
     0.0259, 0.0262, 0.0309, 0.0665, 0.0423, 0.1366, 0.1326, 0.0929, 0.0114, 0.0208,
     0.0163, 0.0344, 0.0762, 0.0641, 0.0117, 0.0187, 0.0453, 0.0628, 0.0293, 0.0244,
     0.0177, 0.0649, 0.0399, 0.0463, 0.0598, 0.0513, 0.0334, 0.1069, 0.1474, 0.0824,
     0.1794, 0.0983, 0.0698, 0.1299, 0.0455, 0.0596, 0.0449, 0.1085, 0.0941, 0.0884,
     0.1427, 0.0769, 0.0392, 0.0764, 0.0922, 0.0289, 0.0292, 0.0355, 0.0241, 0.06,
     0.0112, 0.0369, 0.0588, 0.1271, 0.1054, 0.0947, 0.0716, 0.0511, 0.031, 0.0516,
     0.0336, 0.0496, 0.0633, 0.0501, 0.0858, 0.0535, 0.0154), V50 = c(0.0106,
     0.0159, 0.0439, 0.0198, 0.0266, 0.0216, 0.0179, 0.0121, 0.0133, 0.0122, 0.0247,
     0.0143, 0.0068, 0.0144, 0.0172, 0.0074, 0.0383, 0.0205, 0.0235, 0.0117, 0.007,
     0.0074, 0.0077, 0.0045, 0.0093, 0.0258, 0.0073, 0.0116, 0.0095, 0.0154, 0.0187,
     0.0175, 0.0187, 0.01, 0.0095, 0.011, 0.0095, 0.017, 0.0101, 0.024, 0.0405,
     0.0162, 0.0398, 0.0358, 0.0179, 0.0151, 0.0318, 0.0088, 0.0082, 0.0238, 0.0153,
     0.0061, 0.0077, 0.0118, 0.0323, 0.0183, 0.0179, 0.0065, 0.0313, 0.0169, 0.0193,
     0.033, 0.0473, 0.0209, 0.0199, 0.0211, 0.0171, 0.0772, 0.0374, 0.0334, 0.0825,
     0.0246, 0.0201, 0.0134, 0.0414, 0.0261, 0.0265, 0.0424, 0.0222, 0.0131, 0.0216,
     0.0276, 0.0122, 0.0116, 0.0219, 0.0117, 0.0267, 0.0059, 0.0057, 0.005, 0.0356,
     0.0251, 0.0134, 0.0176, 0.0155, 0.0143, 0.0044, 0.0177, 0.0101, 0.0183, 0.0136,
     0.029, 0.0235, 0.0116), V51 = c(0.0033, 0.0195, 0.0061, 0.0118, 0.0174, 0.036,
     0.0045, 0.0153, 0.017, 0.0072, 0.0118, 0.0091, 0.0089, 0.0307, 0.0134, 0.0149,
     0.0053, 0.0309, 0.0066, 0.007, 0.0026, 0.0069, 0.0109, 0.0015, 0.0076, 0.0136,
     0.0051, 0.0092, 0.0047, 0.0029, 0.0088, 0.0058, 0.0125, 0.0023, 0.014, 0.0024,
     0.0155, 0.0033, 0.0161, 0.0115, 0.0113, 0.0093, 0.0143, 0.0128, 0.0242, 0.0085,
     0.0132, 0.0121, 0.0108, 0.0268, 0.0081, 0.0257, 0.0137, 9e-04, 0.0253, 0.0104,
     0.0109, 0.0222, 0.0185, 0.0135, 0.014, 0.0197, 0.0248, 0.0172, 0.0208, 0.0361,
     0.0244, 0.0798, 0.0291, 0.0342, 0.0243, 0.0151, 0.0071, 0.0174, 0.0253, 0.0079,
     0.0121, 0.0271, 0.0205, 0.0092, 0.0167, 0.0169, 0.0045, 0.0024, 0.0086, 0.0122,
     0.0125, 0.0041, 0.0091, 0.0118, 0.0367, 0.0357, 0.031, 0.0199, 0.0189, 0.0138,
     0.0185, 0.0209, 0.0089, 0.0137, 0.013, 0.0203, 0.0155, 0.0181), V52 = c(0.0232,
     0.0201, 0.0145, 0.009, 0.0176, 0.0331, 0.0084, 0.0092, 0.0035, 0.0108, 0.0088,
     0.0038, 0.0087, 0.0386, 0.0141, 0.0125, 0.009, 0.0212, 0.0062, 0.0167, 0.0093,
     0.0025, 0.0062, 0.0052, 0.0065, 0.0044, 0.0034, 0.0078, 0.0072, 0.0048, 0.0042,
     0.0091, 0.0028, 0.0069, 0.0074, 0.0062, 0.0091, 0.015, 0.0029, 0.0064, 0.0028,
     0.0046, 0.0093, 0.0172, 0.0083, 0.0178, 0.0118, 0.0067, 0.0149, 0.0081, 0.0191,
     0.0089, 0.0071, 0.0142, 0.0214, 0.0117, 0.0147, 0.0045, 0.0098, 0.0222, 0.0027,
     0.0189, 0.0274, 0.018, 0.0176, 0.0444, 0.0258, 0.0376, 0.0156, 0.0459, 0.021,
     0.0125, 0.0104, 0.0117, 0.0131, 0.0164, 0.0091, 0.02, 0.0123, 0.0078, 0.0089,
     0.0081, 0.0108, 0.0084, 0.0123, 0.0122, 0.004, 0.0056, 0.0134, 0.0146, 0.0176,
     0.0181, 0.0237, 0.0096, 0.015, 0.0108, 0.0072, 0.0134, 0.0083, 0.015, 0.012,
     0.0116, 0.016, 0.0146), V53 = c(0.0166, 0.0248, 0.0128, 0.0223, 0.0127, 0.0131,
     0.001, 0.0035, 0.0052, 0.007, 0.0104, 0.0096, 0.0032, 0.0147, 0.0191, 0.0134,
     0.0042, 0.0091, 0.0129, 0.0127, 0.0118, 0.0103, 0.0028, 0.0038, 0.0072, 0.0028,
     0.0129, 0.0041, 0.0054, 0.0023, 0.0175, 0.016, 0.0067, 0.0025, 0.0063, 0.0072,
     0.0151, 0.0111, 0.0078, 0.0022, 0.0036, 0.0044, 0.0033, 0.0138, 0.0037, 0.0073,
     0.012, 0.0032, 0.0077, 0.0129, 0.0182, 0.0262, 0.0082, 0.0179, 0.0262, 0.0101,
     0.017, 0.0136, 0.0178, 0.0175, 0.0068, 0.0204, 0.0205, 0.011, 0.0197, 0.023,
     0.0143, 0.0143, 0.0197, 0.0277, 0.0361, 0.0036, 0.0062, 0.0023, 0.0049, 0.012,
     0.0062, 0.007, 0.0067, 0.0071, 0.0051, 0.004, 0.0075, 0.01, 0.006, 0.0114,
     0.0136, 0.0104, 0.0097, 0.004, 0.0035, 0.0019, 0.0078, 0.0103, 0.006, 0.0062,
     0.0055, 0.0094, 0.008, 0.0076, 0.0039, 0.0098, 0.0029, 0.0129), V54 = c(0.0095,
     0.0131, 0.0145, 0.0179, 0.0088, 0.012, 0.0018, 0.0098, 0.0083, 0.0063, 0.0036,
     0.0142, 0.013, 0.0018, 0.0145, 0.0026, 0.0153, 0.0056, 0.0184, 0.0138, 0.0112,
     0.0074, 0.004, 0.0079, 0.0108, 0.0021, 0.01, 0.0013, 0.0022, 0.002, 0.0171,
     0.016, 0.012, 0.0027, 0.0081, 0.0113, 0.008, 0.0032, 0.0114, 0.0122, 0.0105,
     0.0078, 0.0113, 0.0079, 0.0095, 0.0079, 0.0051, 0.0109, 0.0036, 0.0161, 0.016,
     0.0108, 0.0232, 0.0079, 0.0177, 0.0061, 0.0158, 0.0113, 0.0077, 0.0127, 0.015,
     0.0085, 0.0141, 0.0234, 0.021, 0.029, 0.0226, 0.0272, 0.0135, 0.0172, 0.0239,
     0.0123, 0.0026, 0.0047, 0.0104, 0.0113, 0.0019, 0.007, 0.0011, 0.0081, 0.0015,
     0.0025, 0.0089, 0.0018, 0.0187, 0.0098, 0.0137, 0.0079, 0.0042, 0.0114, 0.0093,
     0.0102, 0.0144, 0.0093, 0.0082, 0.0044, 0.0074, 0.0047, 0.0026, 0.0032, 0.0053,
     0.0199, 0.0051, 0.0047), V55 = c(0.018, 0.007, 0.0058, 0.0084, 0.0098, 0.0108,
     0.0068, 0.0121, 0.0078, 0.003, 0.0088, 0.019, 0.0188, 0.01, 0.0065, 0.0038,
     0.0106, 0.0086, 0.0069, 0.009, 0.0094, 0.0123, 0.0075, 0.0114, 0.0051, 0.0022,
     0.0044, 0.0011, 0.0016, 0.004, 0.0079, 0.0081, 0.0012, 0.0052, 0.0087, 0.0012,
     0.0018, 0.0035, 0.0083, 0.0151, 0.012, 0.0102, 0.003, 0.0037, 0.0105, 0.0038,
     0.007, 0.0164, 0.0114, 0.0063, 0.029, 0.0138, 0.0198, 0.006, 0.0037, 0.0031,
     0.0046, 0.0053, 0.0074, 0.0022, 0.0012, 0.0043, 0.0185, 0.0276, 0.0141, 0.0141,
     0.0187, 0.0127, 0.0127, 0.0087, 0.0447, 0.0043, 0.0025, 0.0049, 0.0102, 0.0021,
     0.0045, 0.0086, 0.0026, 0.0034, 0.0075, 0.0036, 0.0036, 0.0035, 0.0111, 0.0027,
     0.0172, 0.0014, 0.0058, 0.0032, 0.0121, 0.0133, 0.017, 0.0025, 0.0091, 0.0072,
     0.0068, 0.0045, 0.0079, 0.0037, 0.0062, 0.0033, 0.0062, 0.0039), V56 = c(0.0244,
     0.0138, 0.0049, 0.0068, 0.0019, 0.0024, 0.0039, 6e-04, 0.0075, 0.0011, 0.0047,
     0.014, 0.0101, 0.0096, 0.0129, 0.0018, 0.002, 0.0092, 0.0198, 0.0051, 0.014,
     0.0069, 0.0039, 0.005, 0.0102, 0.0048, 0.0057, 0.0045, 0.0029, 0.0019, 0.005,
     0.007, 0.0022, 0.0036, 0.0044, 0.0022, 0.0078, 0.0169, 0.0058, 0.0056, 0.0087,
     0.0065, 0.0057, 0.0051, 0.003, 0.0116, 0.0015, 0.0151, 0.0085, 0.0119, 0.009,
     0.0187, 0.0074, 0.0131, 0.0068, 0.0099, 0.0073, 0.0165, 0.0095, 0.0124, 0.0133,
     0.0092, 0.0055, 0.0032, 0.0049, 0.0161, 0.0185, 0.0166, 0.0138, 0.0046, 0.0394,
     0.0114, 0.0061, 0.0031, 0.0092, 0.0097, 0.0079, 0.0089, 0.0049, 0.0064, 0.0058,
     0.0058, 0.0029, 0.0058, 0.0126, 0.0025, 0.0132, 0.0054, 0.0072, 0.0062, 0.0075,
     0.004, 0.0012, 0.0044, 0.0038, 7e-04, 0.0084, 0.0042, 0.0042, 0.0071, 0.0046,
     0.0101, 0.0089, 0.0061), V57 = c(0.0316, 0.0092, 0.0065, 0.0032, 0.0059,
     0.0045, 0.012, 0.0181, 0.0105, 7e-04, 0.0117, 0.0099, 0.0229, 0.0077, 0.0217,
     0.0113, 0.0105, 0.007, 0.0199, 0.0029, 0.0072, 0.0076, 0.0053, 0.003, 0.0041,
     0.0138, 0.003, 0.0039, 0.0058, 0.0034, 0.0112, 0.0135, 0.0058, 0.0026, 0.0028,
     0.0025, 0.0045, 0.0137, 3e-04, 0.0026, 0.0061, 0.0061, 0.009, 0.0258, 0.0132,
     0.0033, 0.0035, 0.007, 0.0101, 0.0194, 0.0242, 0.023, 0.0035, 0.0089, 0.0121,
     0.008, 0.0054, 0.0141, 0.0055, 0.0054, 0.0048, 0.0138, 0.0045, 0.0084, 0.0027,
     0.0177, 0.011, 0.0095, 0.0133, 0.0203, 0.0355, 0.0052, 0.0038, 0.0024, 0.0083,
     0.0072, 0.0031, 0.0074, 0.0029, 0.0037, 0.0016, 0.0067, 0.0013, 0.0011, 0.0081,
     0.0026, 0.011, 0.0015, 0.0041, 0.0101, 0.0056, 0.0042, 0.0109, 0.0021, 0.0056,
     0.0054, 0.0037, 0.0028, 0.0071, 0.004, 0.0045, 0.0065, 0.014, 0.004), V58 = c(0.0164,
     0.0143, 0.0093, 0.0035, 0.0058, 0.0037, 0.0132, 0.0094, 0.016, 0.0024, 0.002,
     0.0092, 0.0182, 0.018, 0.0087, 0.0058, 0.0049, 0.0116, 0.0102, 0.0122, 0.0022,
     0.0073, 0.0013, 0.0064, 0.0055, 0.014, 0.0035, 0.0022, 0.005, 0.0034, 0.0179,
     0.0067, 0.0042, 0.0036, 0.0019, 0.0059, 0.0026, 0.0015, 0.0023, 0.0029, 0.0061,
     0.0062, 0.0057, 0.0102, 0.0068, 0.0039, 8e-04, 0.0085, 0.0016, 0.014, 0.0224,
     0.0057, 0.01, 0.0084, 0.0077, 0.0107, 0.0033, 0.0077, 0.0045, 0.0021, 0.0244,
     0.0094, 0.0115, 0.0122, 0.0162, 0.0194, 0.0094, 0.0225, 0.0131, 0.013, 0.044,
     0.0091, 0.0101, 0.0039, 0.002, 0.006, 0.0063, 0.0042, 0.0022, 0.0036, 0.007,
     0.0035, 0.001, 9e-04, 0.0155, 0.005, 0.0122, 6e-04, 0.0045, 0.0068, 0.0021,
     0.003, 0.0036, 0.0069, 0.0056, 0.0035, 0.0024, 0.0036, 0.0044, 9e-04, 0.0022,
     0.0115, 0.0138, 0.0036), V59 = c(0.0095, 0.0036, 0.0059, 0.0056, 0.0059,
     0.0112, 0.007, 0.0116, 0.0095, 0.0057, 0.0091, 0.0052, 0.0046, 0.0109, 0.0077,
     0.0047, 0.007, 0.006, 0.007, 0.0056, 0.0055, 0.003, 0.0052, 0.0058, 0.005,
     0.0028, 0.0021, 0.0023, 0.0024, 0.0051, 0.0294, 0.0078, 0.0067, 6e-04, 0.0049,
     0.0039, 0.0036, 0.0069, 0.0026, 0.0104, 0.003, 0.0043, 0.0068, 0.0037, 0.0108,
     0.0081, 0.0044, 0.0117, 0.0028, 0.0332, 0.019, 0.0113, 0.0048, 0.0113, 0.0078,
     0.0161, 0.0045, 0.0246, 0.0063, 0.0028, 0.0077, 0.0105, 0.0152, 0.0082, 0.0059,
     0.0207, 0.0078, 0.0098, 0.0154, 0.0115, 0.0243, 8e-04, 0.0078, 0.0051, 0.0048,
     0.0017, 0.0048, 0.0055, 0.0022, 0.0012, 0.0074, 0.0043, 0.0032, 0.0033, 0.016,
     0.0073, 0.0114, 0.0081, 0.0047, 0.0053, 0.0043, 0.0031, 0.0043, 0.006, 0.0048,
     1e-04, 0.0034, 0.0013, 0.0022, 0.0015, 5e-04, 0.0193, 0.0077, 0.0061), V60 = c(0.0078,
     0.0103, 0.0022, 0.004, 0.0032, 0.0075, 0.0088, 0.0063, 0.0011, 0.0044, 0.0058,
     0.0075, 0.0038, 0.007, 0.0122, 0.0071, 0.008, 0.011, 0.0055, 0.002, 0.0122,
     0.0138, 0.0023, 0.003, 0.0087, 0.0064, 0.0027, 0.0016, 0.003, 0.0031, 0.0063,
     0.0068, 0.0012, 0.0035, 0.0023, 0.0048, 0.0024, 0.0051, 0.0027, 0.0163, 0.0078,
     0.0053, 0.0024, 0.0037, 0.009, 0.0053, 0.0077, 0.0056, 0.0014, 0.0439, 0.0096,
     0.0131, 0.0019, 0.0049, 0.0066, 0.0133, 0.0079, 0.0198, 0.0039, 0.0023, 0.0074,
     0.0093, 0.01, 0.0143, 0.0021, 0.0057, 0.0112, 0.0085, 0.0218, 0.0015, 0.0098,
     0.0092, 6e-04, 0.0015, 0.0036, 0.0036, 0.005, 0.0021, 0.0032, 0.0037, 0.0038,
     0.0033, 0.0047, 0.0026, 0.0085, 0.0022, 0.0068, 0.0043, 0.0054, 0.0087, 0.0017,
     0.0033, 0.0018, 0.0018, 0.0024, 0.0055, 7e-04, 0.0016, 0.0014, 0.0085, 0.0031,
     0.0157, 0.0031, 0.0115)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6",
     "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18",
     "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29",
     "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51",
     "V52", "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), class = "data.frame", row.names = c("3",
     "7", "9", "10", "13", "18", "19", "20", "25", "26", "29", "30", "35", "36", "37",
     "39", "43", "44", "46", "47", "49", "50", "52", "53", "54", "55", "59", "61",
     "63", "64", "66", "68", "69", "71", "73", "74", "77", "78", "80", "81", "83",
     "85", "87", "88", "90", "92", "93", "94", "95", "98", "100", "101", "104", "108",
     "110", "111", "114", "116", "118", "120", "123", "124", "131", "135", "138",
     "139", "140", "141", "142", "145", "148", "152", "154", "156", "158", "159",
     "161", "162", "163", "164", "166", "168", "169", "170", "172", "173", "175",
     "176", "179", "180", "182", "183", "184", "189", "191", "192", "193", "194",
     "195", "201", "202", "204", "206", "208")))
     20: predictLearner.classif.xgboost(.learner = structure(list(id = "classif.xgboost",
     type = "classif", package = "xgboost", properties = c("twoclass", "multiclass",
     "numerics", "factors", "prob", "weights"), par.set = structure(list(pars = structure(list(
     booster = structure(list(id = "booster", type = "discrete", len = 1L, lower = NULL,
     upper = NULL, values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type", "package",
     "properties", "par.set", "par.vals", "predict.type", "name", "short.name", "note",
     "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), .model = structure(list(learner = structure(list(id = "classif.xgboost",
     type = "classif", package = "xgboost", properties = c("twoclass", "multiclass",
     "numerics", "factors", "prob", "weights"), par.set = structure(list(pars = structure(list(
     booster = structure(list(id = "booster", type = "discrete", len = 1L, lower = NULL,
     upper = NULL, values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type", "package",
     "properties", "par.set", "par.vals", "predict.type", "name", "short.name", "note",
     "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), learner.model = structure(list(handle = <pointer: 0x133f69f0>,
     raw = as.raw(c(0x00, 0x00, 0x00, 0x80, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x62, 0x69, 0x6e, 0x61, 0x72, 0x79, 0x3a, 0x6c, 0x6f, 0x67,
     0x69, 0x73, 0x74, 0x69, 0x63, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x67, 0x62, 0x74, 0x72, 0x65, 0x65, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00,
     0x00, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00,
     0x00, 0x14, 0x00, 0x00, 0x80, 0x6e, 0xc5, 0x2e, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x80, 0xdf,
     0x4f, 0x2d, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00,
     0x00, 0x00, 0x3b, 0x00, 0x00, 0x80, 0x82, 0xe2, 0x47, 0x3b, 0x01, 0x00, 0x00,
     0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,
     0x9a, 0x99, 0x99, 0xbe, 0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x32, 0xa4, 0xf3, 0x3e, 0x02, 0x00,
     0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x8c, 0xaf, 0xf8, 0xbe, 0xc7,
     0x92, 0xac, 0x41, 0x00, 0x00, 0x50, 0x41, 0x25, 0x49, 0x92, 0x3d, 0x00, 0x00,
     0x00, 0x00, 0xef, 0xd4, 0x14, 0x41, 0x00, 0x00, 0xe8, 0x40, 0xd9, 0x64, 0x93,
     0x3f, 0x02, 0x00, 0x00, 0x00, 0x90, 0xb9, 0x43, 0x40, 0x00, 0x00, 0xb8, 0x40,
     0x68, 0x2f, 0xa1, 0xbf, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x80, 0x3f, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0xc8, 0x40, 0xd4, 0x08, 0xcb, 0x3f, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0x40, 0xf4,
     0x3c, 0xcf, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x6e, 0x69, 0x74, 0x65, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x30)), niter = 1, evaluation_log = structure(list(iter = 1, train_error = 0.076923), .Names = c("iter",
     "train_error"), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x1c2d4c8>),
     call = xgb.train(params = params, data = dtrain, nrounds = nrounds, watchlist = watchlist,
     verbose = verbose, print_every_n = print_every_n, early_stopping_rounds = early_stopping_rounds,
     maximize = maximize, save_period = save_period, save_name = save_name, xgb_model = xgb_model,
     callbacks = callbacks, objective = ..1), params = structure(list(objective = "binary:logistic",
     silent = 1), .Names = c("objective", "silent")), callbacks = structure(list(
     cb.print.evaluation = structure(function (env = parent.frame())
     {
     if (length(env$bst_evaluation) == 0 || period == 0 || NVL(env$rank, 0) !=
     0)
     return()
     i <- env$iteration
     if ((i - 1)%%period == 0 || i == env$begin_iteration || i == env$end_iteration) {
     msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
     cat(msg, "\n")
     }
     }, call = cb.print.evaluation(period = print_every_n), name = "cb.print.evaluation"),
     cb.evaluation.log = structure(function (env = parent.frame(), finalize = FALSE)
     {
     if (is.null(mnames))
     init(env)
     if (finalize)
     return(finalizer(env))
     ev <- env$bst_evaluation
     if (!is.null(env$bst_evaluation_err))
     ev <- c(ev, env$bst_evaluation_err)
     env$evaluation_log <- c(env$evaluation_log, list(c(iter = env$iteration,
     ev)))
     }, call = cb.evaluation.log(), name = "cb.evaluation.log"), cb.save.model = structure(function (env = parent.frame())
     {
     if (is.null(env$bst))
     stop("'save_model' callback requires the 'bst' booster object in its calling frame")
     if ((save_period > 0 && (env$iteration - env$begin_iteration)%%save_period ==
     0) || (save_period == 0 && env$iteration == env$end_iteration))
     xgb.save(env$bst, sprintf(save_name, env$iteration))
     }, call = cb.save.model(save_period = save_period, save_name = save_name), name = "cb.save.model")), .Names = c("cb.print.evaluation",
     "cb.evaluation.log", "cb.save.model"))), .Names = c("handle", "raw", "niter",
     "evaluation_log", "call", "params", "callbacks"), class = "xgb.Booster"), task.desc = structure(list(
     id = "binary", type = "classif", target = "Class", size = 52L, n.feat = structure(c(60L,
     0L, 0L), .Names = c("numerics", "factors", "ordered")), has.missings = FALSE,
     has.weights = FALSE, has.blocking = FALSE, class.levels = c("M", "R"), positive = "M",
     negative = "R"), .Names = c("id", "type", "target", "size", "n.feat", "has.missings",
     "has.weights", "has.blocking", "class.levels", "positive", "negative"), class = c("TaskDescClassif",
     "TaskDescSupervised", "TaskDesc")), subset = 1:52, features = c("V1", "V2", "V3",
     "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
     "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28",
     "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52",
     "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), factor.levels = structure(list(
     Class = c("M", "R")), .Names = "Class"), time = 0.111999999999995), .Names = c("learner",
     "learner.model", "task.desc", "subset", "features", "factor.levels", "time"), class = "WrappedModel"),
     .newdata = structure(list(V1 = c(0.0262, 0.0317, 0.0223, 0.0164, 0.0079, 0.0192,
     0.027, 0.0126, 0.0293, 0.0201, 0.01, 0.0189, 0.0311, 0.0206, 0.0094, 0.0123,
     0.0211, 0.0093, 0.0408, 0.0308, 0.019, 0.0119, 0.0131, 0.0087, 0.0293, 0.0132,
     0.0225, 0.013, 0.0086, 0.0067, 0.0176, 0.0368, 0.0195, 0.0065, 0.0208, 0.0139,
     0.0239, 0.0336, 0.0108, 0.0229, 0.0409, 0.0378, 0.0188, 0.0856, 0.0235, 0.0253,
     0.026, 0.0459, 0.0025, 0.0491, 0.0201, 0.0629, 0.0162, 0.0428, 0.0264, 0.021,
     0.0283, 0.0414, 0.0228, 0.0261, 0.0249, 0.027, 0.0443, 0.1083, 0.043, 0.0731,
     0.0164, 0.0412, 0.0707, 0.0299, 0.0654, 0.0231, 0.0233, 0.0211, 0.0201, 0.0107,
     0.0258, 0.0305, 0.0217, 0.0072, 0.0221, 0.0137, 0.0015, 0.013, 0.0179, 0.018,
     0.0191, 0.0294, 0.0197, 0.0394, 0.0423, 0.0095, 0.0096, 0.0089, 0.0156, 0.0315,
     0.0056, 0.0203, 0.0392, 0.0131, 0.0335, 0.0187, 0.0522, 0.026), V2 = c(0.0582,
     0.0956, 0.0375, 0.0173, 0.0086, 0.0607, 0.0092, 0.0149, 0.0644, 0.0026, 0.0275,
     0.0308, 0.0491, 0.0132, 0.0166, 0.0022, 0.0319, 0.0269, 0.0653, 0.0339, 0.0038,
     0.0582, 0.0068, 0.0046, 0.0378, 0.008, 0.0019, 6e-04, 0.0215, 0.0096, 0.0172,
     0.0403, 0.0142, 0.0122, 0.0186, 0.0222, 0.0189, 0.0294, 0.0086, 0.0369, 0.0421,
     0.0318, 0.037, 0.0454, 0.0291, 0.0808, 0.0192, 0.0437, 0.0309, 0.0279, 0.0423,
     0.1065, 0.0253, 0.0555, 0.0071, 0.0121, 0.0599, 0.0436, 0.0106, 0.0266, 0.0119,
     0.0163, 0.0446, 0.107, 0.0902, 0.1249, 0.0627, 0.1135, 0.1252, 0.0688, 0.0649,
     0.0315, 0.0394, 0.0128, 0.0178, 0.0453, 0.0433, 0.0363, 0.0152, 0.0027, 0.0065,
     0.0297, 0.0186, 0.012, 0.0136, 0.0444, 0.0173, 0.0123, 0.0394, 0.042, 0.0321,
     0.0308, 0.0404, 0.0274, 0.021, 0.0252, 0.0267, 0.0121, 0.0108, 0.0387, 0.0258,
     0.0346, 0.0437, 0.0363), V3 = c(0.1099, 0.1321, 0.0484, 0.0347, 0.0055, 0.0378,
     0.0145, 0.0641, 0.039, 0.0138, 0.019, 0.0197, 0.0692, 0.0533, 0.0398, 0.0196,
     0.0415, 0.0217, 0.0397, 0.0202, 0.0642, 0.0623, 0.0308, 0.0081, 0.0257, 0.0188,
     0.0075, 0.0088, 0.0242, 0.0024, 0.0501, 0.0317, 0.0181, 0.0068, 0.0131, 0.0089,
     0.0466, 0.0476, 0.0058, 0.004, 0.0573, 0.0423, 0.0953, 0.0382, 0.0749, 0.0507,
     0.0254, 0.0347, 0.0171, 0.0592, 0.0554, 0.1526, 0.0262, 0.0708, 0.0342, 0.0203,
     0.0656, 0.0447, 0.013, 0.0223, 0.0277, 0.0341, 0.0235, 0.0257, 0.0833, 0.1665,
     0.0738, 0.0518, 0.1447, 0.0992, 0.0737, 0.017, 0.0416, 0.0015, 0.0274, 0.0289,
     0.0547, 0.0214, 0.0346, 0.0089, 0.0164, 0.0116, 0.0289, 0.0436, 0.0408, 0.0476,
     0.0291, 0.0117, 0.0384, 0.0446, 0.0709, 0.0539, 0.0682, 0.0248, 0.0282, 0.0167,
     0.0221, 0.038, 0.0267, 0.0329, 0.0398, 0.0168, 0.018, 0.0136), V4 = c(0.1083,
     0.1408, 0.0475, 0.007, 0.025, 0.0774, 0.0278, 0.1732, 0.0173, 0.0062, 0.0371,
     0.0622, 0.0831, 0.0569, 0.0359, 0.0206, 0.0286, 0.0339, 0.0604, 0.0889, 0.0452,
     0.06, 0.0311, 0.023, 0.0062, 0.0141, 0.0097, 0.0456, 0.0445, 0.0058, 0.0285,
     0.0293, 0.0406, 0.0108, 0.0211, 0.0108, 0.044, 0.0539, 0.046, 0.0375, 0.013,
     0.035, 0.0824, 0.0203, 0.0519, 0.0244, 0.0061, 0.0456, 0.0228, 0.127, 0.0783,
     0.1229, 0.0386, 0.0618, 0.0793, 0.1036, 0.0229, 0.0844, 0.0842, 0.0749, 0.076,
     0.0247, 0.1008, 0.0837, 0.0813, 0.1496, 0.0608, 0.0232, 0.1644, 0.1021, 0.1132,
     0.0226, 0.0547, 0.045, 0.0232, 0.0713, 0.0681, 0.0227, 0.0346, 0.0061, 0.0487,
     0.0082, 0.0195, 0.0624, 0.0633, 0.0698, 0.0301, 0.0113, 0.0076, 0.0551, 0.0108,
     0.0411, 0.0688, 0.0237, 0.0596, 0.0479, 0.0561, 0.0128, 0.0257, 0.0078, 0.057,
     0.0177, 0.0292, 0.0272), V5 = c(0.0974, 0.1674, 0.0647, 0.0187, 0.0344, 0.1388,
     0.0412, 0.2565, 0.0476, 0.0133, 0.0416, 0.008, 0.0079, 0.0647, 0.0681, 0.018,
     0.0121, 0.0305, 0.0496, 0.157, 0.0333, 0.1397, 0.0085, 0.0586, 0.013, 0.0436,
     0.0445, 0.0525, 0.0667, 0.0197, 0.0262, 0.082, 0.0391, 0.0217, 0.061, 0.0215,
     0.0657, 0.0794, 0.0752, 0.0455, 0.0183, 0.1787, 0.0249, 0.0385, 0.0227, 0.1724,
     0.0352, 0.0067, 0.0434, 0.1772, 0.062, 0.1437, 0.0645, 0.1215, 0.1043, 0.1675,
     0.0839, 0.0419, 0.1117, 0.1364, 0.1218, 0.0822, 0.2252, 0.0748, 0.0165, 0.1443,
     0.0233, 0.0646, 0.1693, 0.08, 0.2482, 0.041, 0.0993, 0.0711, 0.0724, 0.1075,
     0.0784, 0.0456, 0.0484, 0.042, 0.0519, 0.0241, 0.0515, 0.0428, 0.0596, 0.1615,
     0.0463, 0.0497, 0.0251, 0.0597, 0.107, 0.0613, 0.0887, 0.0224, 0.0462, 0.0902,
     0.0936, 0.0537, 0.041, 0.0721, 0.0529, 0.0393, 0.0351, 0.0214), V6 = c(0.228,
     0.171, 0.0591, 0.0671, 0.0546, 0.0809, 0.0757, 0.2559, 0.0816, 0.0151, 0.0201,
     0.0789, 0.02, 0.1432, 0.0706, 0.0492, 0.0438, 0.1172, 0.1817, 0.175, 0.069, 0.1883,
     0.0767, 0.0682, 0.0612, 0.0668, 0.0906, 0.0778, 0.0771, 0.0618, 0.0351, 0.1342,
     0.0249, 0.0284, 0.0613, 0.0136, 0.0742, 0.0804, 0.0887, 0.1452, 0.1019, 0.1635,
     0.0488, 0.0534, 0.0834, 0.3823, 0.0701, 0.089, 0.1224, 0.1908, 0.0871, 0.119,
     0.0472, 0.1524, 0.0783, 0.0418, 0.1673, 0.1215, 0.1506, 0.1513, 0.1538, 0.1256,
     0.2611, 0.1125, 0.0277, 0.277, 0.1048, 0.1124, 0.0844, 0.0629, 0.1257, 0.0116,
     0.1515, 0.1563, 0.0833, 0.1019, 0.125, 0.0665, 0.0526, 0.0865, 0.0849, 0.0253,
     0.0817, 0.0349, 0.0808, 0.0887, 0.069, 0.0998, 0.0629, 0.1416, 0.0973, 0.1039,
     0.0932, 0.0845, 0.0779, 0.1057, 0.1146, 0.0874, 0.0491, 0.1341, 0.1091, 0.163,
     0.1171, 0.0338), V7 = c(0.2431, 0.0731, 0.0753, 0.1056, 0.0528, 0.0568, 0.1026,
     0.2947, 0.0993, 0.0541, 0.0314, 0.144, 0.0981, 0.1344, 0.102, 0.0033, 0.1299,
     0.145, 0.1178, 0.092, 0.0901, 0.1422, 0.0771, 0.0993, 0.0895, 0.0609, 0.0889,
     0.0931, 0.0499, 0.0432, 0.0362, 0.1161, 0.0892, 0.0527, 0.0612, 0.0659, 0.138,
     0.1136, 0.1015, 0.2211, 0.1054, 0.0887, 0.1424, 0.214, 0.0677, 0.3729, 0.1263,
     0.1798, 0.1947, 0.2217, 0.1201, 0.0884, 0.1056, 0.1543, 0.1417, 0.0723, 0.1154,
     0.2002, 0.1776, 0.1316, 0.1192, 0.1323, 0.2061, 0.3322, 0.0569, 0.2555, 0.1338,
     0.1787, 0.0715, 0.013, 0.1797, 0.0223, 0.1674, 0.1518, 0.1232, 0.1606, 0.1296,
     0.0939, 0.0773, 0.1182, 0.0812, 0.0279, 0.1005, 0.0384, 0.209, 0.0596, 0.0576,
     0.1326, 0.0747, 0.0956, 0.0961, 0.1016, 0.0955, 0.1488, 0.1365, 0.1024, 0.0706,
     0.1021, 0.1053, 0.1626, 0.1709, 0.2028, 0.1257, 0.0655), V8 = c(0.3771, 0.1401,
     0.0098, 0.0697, 0.0958, 0.0219, 0.1138, 0.411, 0.0315, 0.021, 0.0651, 0.1451,
     0.1016, 0.2041, 0.0893, 0.0398, 0.139, 0.0638, 0.1024, 0.1353, 0.1454, 0.1447,
     0.064, 0.0717, 0.1107, 0.0131, 0.0655, 0.0941, 0.0906, 0.0951, 0.0535, 0.0663,
     0.0973, 0.0575, 0.0506, 0.0954, 0.1099, 0.1228, 0.0494, 0.1188, 0.107, 0.0817,
     0.1972, 0.311, 0.2002, 0.3583, 0.108, 0.1741, 0.1661, 0.0768, 0.2707, 0.0907,
     0.1388, 0.0391, 0.1176, 0.0828, 0.1098, 0.1516, 0.0997, 0.1654, 0.1229, 0.1584,
     0.1668, 0.459, 0.2057, 0.1712, 0.0644, 0.2407, 0.0947, 0.0813, 0.0989, 0.0805,
     0.1513, 0.1206, 0.1298, 0.2119, 0.1729, 0.0972, 0.0862, 0.0999, 0.1833, 0.013,
     0.0124, 0.0446, 0.3465, 0.1071, 0.1103, 0.1117, 0.0578, 0.0802, 0.1323, 0.1394,
     0.214, 0.1224, 0.078, 0.1209, 0.0996, 0.0852, 0.169, 0.1902, 0.1684, 0.1694,
     0.1178, 0.14), V9 = c(0.5598, 0.2083, 0.0684, 0.0962, 0.1009, 0.1037, 0.0794,
     0.4983, 0.0736, 0.0505, 0.1896, 0.1789, 0.2025, 0.1571, 0.0381, 0.0791, 0.0695,
     0.074, 0.0583, 0.1593, 0.074, 0.0487, 0.0726, 0.0576, 0.0973, 0.0899, 0.1624,
     0.1711, 0.1229, 0.0836, 0.0258, 0.0155, 0.084, 0.1054, 0.0989, 0.0786, 0.1384,
     0.1235, 0.0472, 0.075, 0.2302, 0.1779, 0.1873, 0.2837, 0.2876, 0.3429, 0.1523,
     0.1598, 0.1368, 0.1246, 0.1206, 0.2107, 0.0598, 0.061, 0.0453, 0.0494, 0.137,
     0.0818, 0.1428, 0.1864, 0.2119, 0.2017, 0.1801, 0.5526, 0.3887, 0.0466, 0.1522,
     0.2682, 0.1583, 0.1761, 0.246, 0.2365, 0.1723, 0.1666, 0.2085, 0.3061, 0.2794,
     0.2535, 0.1451, 0.1976, 0.2228, 0.0489, 0.1168, 0.1318, 0.5276, 0.3175, 0.2423,
     0.2984, 0.1357, 0.1618, 0.2462, 0.2592, 0.2546, 0.1569, 0.1038, 0.1241, 0.1673,
     0.1136, 0.2105, 0.261, 0.1865, 0.2328, 0.1258, 0.1843), V10 = c(0.6194, 0.3513,
     0.1487, 0.0251, 0.124, 0.1186, 0.152, 0.592, 0.086, 0.1097, 0.2668, 0.2522, 0.0767,
     0.1573, 0.1328, 0.0475, 0.0568, 0.136, 0.2176, 0.2795, 0.0349, 0.0864, 0.0901,
     0.0818, 0.0751, 0.0922, 0.1452, 0.1483, 0.1185, 0.118, 0.0474, 0.0506, 0.1191,
     0.1109, 0.1093, 0.1015, 0.1376, 0.0842, 0.0393, 0.1631, 0.2259, 0.2053, 0.1806,
     0.2751, 0.3674, 0.2197, 0.163, 0.1408, 0.143, 0.2028, 0.0279, 0.3597, 0.1334,
     0.0113, 0.0945, 0.0686, 0.1767, 0.1975, 0.2227, 0.2013, 0.2531, 0.2122, 0.3083,
     0.5966, 0.7106, 0.1114, 0.078, 0.2058, 0.1247, 0.0998, 0.3422, 0.2461, 0.2078,
     0.1345, 0.272, 0.2936, 0.2954, 0.3127, 0.211, 0.2318, 0.181, 0.0874, 0.1476,
     0.1375, 0.5965, 0.2918, 0.3134, 0.3473, 0.1695, 0.2558, 0.2696, 0.3745, 0.2952,
     0.2119, 0.1567, 0.1533, 0.1859, 0.1747, 0.2471, 0.3193, 0.266, 0.2684, 0.2529,
     0.2354), V11 = c(0.6333, 0.1786, 0.1156, 0.0801, 0.1097, 0.1237, 0.1675, 0.5832,
     0.0414, 0.0841, 0.3376, 0.2607, 0.1767, 0.2327, 0.1303, 0.1152, 0.0869, 0.2132,
     0.2459, 0.3336, 0.1459, 0.2143, 0.075, 0.1315, 0.0528, 0.1445, 0.1442, 0.1532,
     0.0775, 0.0978, 0.0526, 0.0906, 0.1522, 0.0937, 0.1063, 0.1261, 0.0938, 0.0357,
     0.1106, 0.2709, 0.2373, 0.3135, 0.2139, 0.2707, 0.2974, 0.2653, 0.103, 0.2693,
     0.0994, 0.0947, 0.2251, 0.5466, 0.2969, 0.1255, 0.1132, 0.1125, 0.1995, 0.2309,
     0.2621, 0.289, 0.2855, 0.221, 0.3794, 0.5304, 0.7342, 0.1739, 0.1791, 0.1546,
     0.234, 0.0523, 0.2128, 0.2245, 0.1239, 0.0785, 0.2188, 0.3104, 0.2506, 0.2192,
     0.2343, 0.2472, 0.2549, 0.11, 0.2118, 0.2026, 0.6254, 0.3273, 0.4786, 0.4231,
     0.1734, 0.3078, 0.3412, 0.4229, 0.4025, 0.3003, 0.2476, 0.2128, 0.2481, 0.2198,
     0.268, 0.3468, 0.3188, 0.3108, 0.2716, 0.272), V12 = c(0.706, 0.0658, 0.1654,
     0.1056, 0.1215, 0.1601, 0.137, 0.5419, 0.0472, 0.0942, 0.3282, 0.371, 0.2555,
     0.1785, 0.0273, 0.052, 0.1935, 0.3738, 0.3332, 0.294, 0.3473, 0.372, 0.0844,
     0.1862, 0.1209, 0.1475, 0.0948, 0.11, 0.1101, 0.0909, 0.1854, 0.2545, 0.1322,
     0.0827, 0.1179, 0.0828, 0.0259, 0.0689, 0.1412, 0.3358, 0.3323, 0.3118, 0.1523,
     0.0946, 0.0837, 0.3223, 0.2187, 0.3259, 0.225, 0.2497, 0.2615, 0.5205, 0.4754,
     0.2473, 0.084, 0.1741, 0.2869, 0.3025, 0.3109, 0.365, 0.2961, 0.2399, 0.5364,
     0.2251, 0.5033, 0.316, 0.2681, 0.2671, 0.1764, 0.0904, 0.1377, 0.152, 0.0236,
     0.0367, 0.3037, 0.3431, 0.2601, 0.2621, 0.2087, 0.288, 0.2984, 0.1084, 0.2575,
     0.2389, 0.4507, 0.3035, 0.5239, 0.5044, 0.247, 0.3404, 0.4292, 0.4499, 0.5148,
     0.3094, 0.2783, 0.2536, 0.2712, 0.2721, 0.3049, 0.3738, 0.3553, 0.2933, 0.2374,
     0.2442), V13 = c(0.5544, 0.0513, 0.3833, 0.1266, 0.1874, 0.352, 0.1361, 0.5472,
     0.0835, 0.1204, 0.2432, 0.3906, 0.2812, 0.1507, 0.0644, 0.1192, 0.1478, 0.3738,
     0.3087, 0.1608, 0.3197, 0.2665, 0.1226, 0.2789, 0.1763, 0.2087, 0.0618, 0.089,
     0.1042, 0.0656, 0.104, 0.1464, 0.1434, 0.092, 0.1291, 0.0493, 0.1499, 0.1705,
     0.2202, 0.4091, 0.3827, 0.3686, 0.1975, 0.102, 0.1912, 0.5582, 0.1542, 0.4545,
     0.2444, 0.2209, 0.177, 0.5127, 0.5677, 0.3011, 0.0717, 0.271, 0.3275, 0.3938,
     0.2859, 0.351, 0.3341, 0.2964, 0.6173, 0.2402, 0.3, 0.3249, 0.1788, 0.3141, 0.2284,
     0.2655, 0.4032, 0.1732, 0.1771, 0.1227, 0.2959, 0.2456, 0.2249, 0.2419, 0.1645,
     0.2126, 0.2624, 0.1094, 0.2354, 0.2112, 0.3693, 0.3033, 0.4393, 0.5237, 0.3141,
     0.34, 0.3682, 0.5404, 0.4901, 0.2743, 0.2896, 0.2686, 0.2934, 0.2105, 0.2863,
     0.3055, 0.3116, 0.2275, 0.1878, 0.1665), V14 = c(0.532, 0.3752, 0.3598, 0.089,
     0.3383, 0.4479, 0.1345, 0.5314, 0.0938, 0.042, 0.1268, 0.2672, 0.2722, 0.1916,
     0.0712, 0.1943, 0.1871, 0.2673, 0.2613, 0.3335, 0.2823, 0.2113, 0.1619, 0.2579,
     0.2039, 0.2558, 0.1641, 0.1236, 0.0853, 0.0593, 0.0948, 0.1272, 0.1244, 0.0911,
     0.1591, 0.0848, 0.2851, 0.3257, 0.2976, 0.44, 0.484, 0.3885, 0.4844, 0.4519,
     0.504, 0.6916, 0.263, 0.5785, 0.3239, 0.3195, 0.3709, 0.5395, 0.569, 0.3747,
     0.1968, 0.3087, 0.3769, 0.505, 0.3316, 0.3495, 0.4287, 0.4061, 0.7842, 0.2689,
     0.1951, 0.2164, 0.1039, 0.2904, 0.3115, 0.3099, 0.5684, 0.3099, 0.3115, 0.2614,
     0.2059, 0.1887, 0.2115, 0.2179, 0.1689, 0.0708, 0.1893, 0.1023, 0.1334, 0.1444,
     0.2864, 0.2587, 0.344, 0.4398, 0.3297, 0.3951, 0.394, 0.4303, 0.4127, 0.2547,
     0.2956, 0.2803, 0.2637, 0.1727, 0.2294, 0.1926, 0.1965, 0.0994, 0.0983, 0.0336
     ), V15 = c(0.6479, 0.5419, 0.1713, 0.0198, 0.3227, 0.3769, 0.2144, 0.4981, 0.1466,
     0.0031, 0.1278, 0.2716, 0.3227, 0.2061, 0.1204, 0.184, 0.1994, 0.2333, 0.3232,
     0.4985, 0.0166, 0.1103, 0.2317, 0.224, 0.2727, 0.2603, 0.0708, 0.1197, 0.0456,
     0.0832, 0.0912, 0.1223, 0.0653, 0.1487, 0.168, 0.1514, 0.5743, 0.4602, 0.4116,
     0.5485, 0.6812, 0.585, 0.7298, 0.6737, 0.6352, 0.7943, 0.294, 0.4471, 0.3039,
     0.334, 0.4533, 0.6558, 0.6421, 0.452, 0.2633, 0.3575, 0.4169, 0.5872, 0.3755,
     0.4325, 0.5205, 0.5095, 0.8392, 0.6646, 0.2767, 0.2031, 0.198, 0.3531, 0.4725,
     0.352, 0.2398, 0.438, 0.499, 0.428, 0.0906, 0.1184, 0.127, 0.1159, 0.165, 0.1194,
     0.0668, 0.0601, 0.0092, 0.0742, 0.1635, 0.1682, 0.2869, 0.3236, 0.2759, 0.3352,
     0.2965, 0.3333, 0.3575, 0.187, 0.3189, 0.1886, 0.188, 0.204, 0.1165, 0.1385,
     0.178, 0.1801, 0.0683, 0.1302), V16 = c(0.6931, 0.544, 0.1136, 0.1133, 0.2723,
     0.5761, 0.5354, 0.6985, 0.0809, 0.0162, 0.4441, 0.4183, 0.3463, 0.2307, 0.0717,
     0.2077, 0.3283, 0.5367, 0.3731, 0.7295, 0.0572, 0.1136, 0.2934, 0.2568, 0.2321,
     0.1985, 0.0844, 0.1145, 0.1304, 0.1297, 0.1688, 0.1669, 0.089, 0.1666, 0.1918,
     0.1396, 0.8278, 0.6225, 0.4754, 0.7213, 0.7555, 0.7868, 0.7807, 0.6699, 0.6804,
     0.7152, 0.2978, 0.2231, 0.241, 0.3323, 0.5553, 0.8705, 0.7487, 0.5392, 0.4191,
     0.4998, 0.5036, 0.661, 0.4499, 0.5398, 0.6087, 0.5512, 0.9016, 0.6632, 0.3737,
     0.258, 0.3234, 0.5079, 0.5543, 0.3892, 0.4331, 0.5595, 0.6707, 0.6122, 0.161,
     0.208, 0.1193, 0.1237, 0.1967, 0.2808, 0.2666, 0.0906, 0.1951, 0.1533, 0.0422,
     0.1308, 0.3889, 0.2956, 0.2056, 0.2252, 0.3172, 0.3496, 0.3447, 0.1452, 0.1892,
     0.1485, 0.1405, 0.1786, 0.2127, 0.2122, 0.2794, 0.22, 0.1503, 0.1708), V17 = c(0.6759,
     0.515, 0.0349, 0.2826, 0.3943, 0.6426, 0.683, 0.8292, 0.1179, 0.0624, 0.6795,
     0.6988, 0.5395, 0.236, 0.1224, 0.1956, 0.6861, 0.7312, 0.4203, 0.735, 0.2164,
     0.1934, 0.3526, 0.2933, 0.2676, 0.2394, 0.259, 0.2137, 0.269, 0.2038, 0.1568,
     0.1424, 0.1226, 0.1268, 0.1615, 0.1066, 0.8669, 0.7327, 0.539, 0.8137, 0.9522,
     0.9739, 0.7906, 0.7066, 0.7505, 0.3512, 0.0699, 0.2164, 0.0367, 0.278, 0.4616,
     0.9786, 0.8999, 0.6588, 0.505, 0.6011, 0.618, 0.7417, 0.4765, 0.6237, 0.7236,
     0.6613, 1, 0.1674, 0.2507, 0.1796, 0.3748, 0.4639, 0.5386, 0.3962, 0.5954, 0.682,
     0.7655, 0.7435, 0.18, 0.2736, 0.1794, 0.0886, 0.2934, 0.4221, 0.4274, 0.1313,
     0.3685, 0.3052, 0.1785, 0.2803, 0.442, 0.3286, 0.1162, 0.2086, 0.2825, 0.3426,
     0.3068, 0.1457, 0.173, 0.216, 0.2028, 0.1318, 0.2062, 0.2758, 0.287, 0.2732,
     0.1723, 0.2177), V18 = c(0.7551, 0.4262, 0.3796, 0.3234, 0.6432, 0.679, 0.56,
     0.7839, 0.2179, 0.2127, 0.7051, 0.5733, 0.7911, 0.1299, 0.2349, 0.163, 0.5814,
     0.7659, 0.5364, 0.8253, 0.4563, 0.4142, 0.3657, 0.2991, 0.2934, 0.3134, 0.2679,
     0.2838, 0.2947, 0.3811, 0.0375, 0.1285, 0.1846, 0.1374, 0.1647, 0.1923, 0.8131,
     0.7843, 0.6279, 0.9185, 0.9826, 1, 0.6122, 0.5632, 0.6595, 0.2008, 0.1401, 0.3201,
     0.1672, 0.2975, 0.3797, 0.9335, 1, 0.7113, 0.6711, 0.647, 0.8025, 0.8006, 0.6254,
     0.6876, 0.7577, 0.6804, 0.8911, 0.0837, 0.2507, 0.2422, 0.2586, 0.1859, 0.3746,
     0.2449, 0.5772, 0.6164, 0.8485, 0.813, 0.218, 0.3274, 0.2185, 0.1755, 0.3709,
     0.5279, 0.6291, 0.2758, 0.4646, 0.4116, 0.4394, 0.4519, 0.3892, 0.3231, 0.1884,
     0.2248, 0.305, 0.2851, 0.2945, 0.2429, 0.2226, 0.2417, 0.2613, 0.226, 0.2222,
     0.4576, 0.3969, 0.2862, 0.2339, 0.3175), V19 = c(0.8929, 0.2024, 0.7401, 0.3238,
     0.7271, 0.7157, 0.3093, 0.8215, 0.3326, 0.3436, 0.7966, 0.2226, 0.9064, 0.3812,
     0.3684, 0.1218, 0.25, 0.6271, 0.7062, 0.8793, 0.3819, 0.3279, 0.3221, 0.3924,
     0.3295, 0.4077, 0.3094, 0.364, 0.3669, 0.4451, 0.1316, 0.1857, 0.388, 0.1095,
     0.1397, 0.2991, 0.9045, 0.7988, 0.706, 1, 0.8871, 0.9843, 0.42, 0.3785, 0.4509,
     0.2676, 0.299, 0.2915, 0.3038, 0.2948, 0.345, 0.7917, 0.969, 0.7602, 0.7922,
     0.8067, 0.9333, 0.8456, 0.7304, 0.7329, 0.7726, 0.652, 0.8753, 0.4331, 0.3292,
     0.3609, 0.368, 0.4474, 0.4583, 0.2355, 0.8176, 0.6803, 0.9805, 0.9006, 0.2026,
     0.2344, 0.1646, 0.1758, 0.4309, 0.5857, 0.7782, 0.366, 0.5418, 0.5466, 0.695,
     0.6641, 0.4088, 0.4528, 0.339, 0.3382, 0.2408, 0.4062, 0.4351, 0.3259, 0.2427,
     0.2989, 0.2778, 0.2358, 0.3241, 0.6487, 0.5599, 0.2034, 0.1962, 0.3714), V20 = c(0.8619,
     0.4233, 0.9925, 0.4333, 0.8673, 0.5466, 0.3226, 0.9363, 0.3258, 0.3813, 0.9401,
     0.2631, 0.8701, 0.5858, 0.3918, 0.1017, 0.1734, 0.4395, 0.8196, 0.9657, 0.5627,
     0.6222, 0.3093, 0.4691, 0.491, 0.4529, 0.4678, 0.543, 0.4948, 0.5224, 0.2086,
     0.1136, 0.3658, 0.1286, 0.1426, 0.3247, 0.9046, 0.8261, 0.7918, 0.9418, 0.8268,
     0.861, 0.2807, 0.2721, 0.2964, 0.4299, 0.3915, 0.4235, 0.4069, 0.1729, 0.2665,
     0.7383, 0.9032, 0.8672, 0.8381, 0.9008, 0.9399, 0.7939, 0.8702, 0.8107, 0.8098,
     0.6788, 0.7886, 0.8718, 0.4871, 0.181, 0.3508, 0.4079, 0.5961, 0.3045, 0.8835,
     0.8435, 1, 0.9603, 0.1506, 0.126, 0.074, 0.154, 0.4161, 0.6153, 0.7686, 0.5269,
     0.626, 0.5933, 0.8097, 0.7683, 0.5006, 0.6339, 0.3926, 0.4578, 0.542, 0.6833,
     0.7264, 0.3679, 0.3149, 0.3341, 0.3346, 0.3107, 0.433, 0.7154, 0.6936, 0.174,
     0.1395, 0.4552), V21 = c(0.7974, 0.7723, 0.9802, 0.6068, 0.9674, 0.5399, 0.443,
     1, 0.2111, 0.3825, 0.9857, 0.7473, 0.7672, 0.4497, 0.4925, 0.1354, 0.3363, 0.433,
     0.8835, 1, 0.6484, 0.7468, 0.4084, 0.5665, 0.5402, 0.4893, 0.5958, 0.6673, 0.6275,
     0.5911, 0.1976, 0.2069, 0.2297, 0.2146, 0.2429, 0.3797, 1, 1, 0.9493, 0.9116,
     0.7561, 0.8443, 0.5148, 0.5297, 0.4019, 0.528, 0.3598, 0.446, 0.3613, 0.3264,
     0.2395, 0.6908, 0.7685, 0.8416, 0.8759, 0.8906, 0.9275, 0.8804, 0.9349, 0.8396,
     0.8995, 0.7811, 0.7156, 0.7992, 0.6527, 0.2604, 0.5606, 0.54, 0.7464, 0.3112,
     0.5248, 0.9921, 1, 0.9162, 0.0521, 0.0576, 0.0625, 0.0512, 0.5116, 0.6753, 0.8099,
     0.581, 0.742, 0.6663, 0.855, 0.696, 0.7271, 0.7044, 0.4282, 0.6474, 0.6802, 0.765,
     0.8147, 0.3355, 0.4102, 0.3786, 0.383, 0.3906, 0.5071, 0.801, 0.7969, 0.413,
     0.3164, 0.57), V22 = c(0.6737, 0.9735, 0.889, 0.7652, 0.9847, 0.6362, 0.5573,
     0.9224, 0.2302, 0.4764, 0.8193, 0.7263, 0.2957, 0.4876, 0.8793, 0.3157, 0.5588,
     0.4326, 0.8299, 0.8707, 0.7235, 0.7676, 0.4285, 0.6464, 0.6257, 0.5666, 0.7245,
     0.7979, 0.8162, 0.6566, 0.0946, 0.0219, 0.261, 0.2889, 0.2816, 0.5658, 0.9976,
     0.9814, 1, 0.9349, 0.8217, 0.9061, 0.7569, 0.7697, 0.6794, 0.3489, 0.2403, 0.238,
     0.1994, 0.3834, 0.1127, 0.385, 0.6998, 0.7974, 0.9422, 0.9338, 0.945, 0.8384,
     0.9614, 0.8632, 0.9247, 0.8369, 0.7581, 0.3712, 0.8454, 0.6572, 0.5231, 0.4786,
     0.7644, 0.4698, 0.6373, 1, 0.9992, 0.914, 0.2143, 0.1241, 0.2381, 0.1805, 0.6501,
     0.7873, 0.8493, 0.6181, 0.8257, 0.7333, 0.8717, 0.4393, 0.9385, 0.8314, 0.5418,
     0.6708, 0.632, 0.667, 0.8103, 0.31, 0.3808, 0.3956, 0.4003, 0.3631, 0.5944, 0.7924,
     0.7452, 0.6879, 0.5888, 0.7397), V23 = c(0.4293, 0.939, 0.6712, 0.9203, 0.948,
     0.7849, 0.5782, 0.7839, 0.3361, 0.6313, 0.5789, 0.3393, 0.4148, 1, 0.9606, 0.4645,
     0.6592, 0.5544, 0.7609, 0.6471, 0.8242, 0.7867, 0.4663, 0.6774, 0.6826, 0.6234,
     0.8773, 0.9273, 0.9237, 0.6308, 0.1965, 0.24, 0.4193, 0.4238, 0.429, 0.7483,
     0.9872, 0.962, 0.9645, 0.7484, 0.6967, 0.5847, 0.8596, 0.8643, 0.8297, 0.143,
     0.4208, 0.6415, 0.4611, 0.3523, 0.2556, 0.0671, 0.6644, 0.8385, 1, 1, 0.8328,
     0.7852, 0.9126, 0.8747, 0.9365, 0.8969, 0.6372, 0.1703, 0.9739, 0.9734, 0.5469,
     0.4332, 0.5711, 0.5534, 0.8375, 0.7983, 0.9067, 0.7851, 0.4333, 0.3239, 0.4824,
     0.4039, 0.7717, 0.8974, 0.944, 0.5875, 0.8609, 0.7136, 0.8601, 0.2432, 1, 0.8449,
     0.6448, 0.7007, 0.5824, 0.5703, 0.6665, 0.3914, 0.4896, 0.5232, 0.5114, 0.4809,
     0.7078, 0.8793, 0.8203, 0.812, 0.7631, 0.8062), V24 = c(0.3648, 0.5559, 0.4286,
     0.9719, 0.8036, 0.7756, 0.6173, 0.547, 0.4259, 0.7523, 0.6394, 0.2824, 0.6043,
     0.8675, 0.8786, 0.5906, 0.7012, 0.736, 0.7605, 0.5973, 0.8766, 0.8253, 0.5956,
     0.7577, 0.7527, 0.6741, 0.9214, 0.9027, 0.871, 0.5998, 0.1242, 0.2547, 0.5848,
     0.6168, 0.6443, 0.8757, 0.9761, 0.9601, 0.9432, 0.5146, 0.6444, 0.4033, 1, 0.9304,
     1, 0.5453, 0.5675, 0.8966, 0.6849, 0.541, 0.5169, 0.0502, 0.5964, 0.9317, 0.9931,
     0.9102, 0.7773, 0.8479, 0.9443, 0.9607, 0.9853, 0.9856, 0.321, 0.1611, 1, 0.9757,
     0.6954, 0.6113, 0.6257, 0.4532, 0.6699, 0.5426, 0.6803, 0.5134, 0.5943, 0.4357,
     0.6372, 0.5697, 0.8491, 0.9828, 0.945, 0.4639, 0.84, 0.7014, 0.9201, 0.2886,
     0.9831, 0.8512, 0.7223, 0.7619, 0.6805, 0.5995, 0.6958, 0.528, 0.6292, 0.6913,
     0.686, 0.6531, 0.7641, 1, 0.9261, 0.8453, 0.8473, 0.8837), V25 = c(0.5331, 0.5268,
     0.3374, 0.9207, 0.6833, 0.578, 0.8132, 0.4562, 0.4609, 0.8675, 0.7043, 0.6053,
     0.3178, 0.4718, 0.6905, 0.6776, 0.8099, 0.8589, 0.8367, 0.8218, 1, 1, 0.6948,
     0.8856, 0.8504, 0.8282, 0.9282, 0.9192, 0.8052, 0.4958, 0.0616, 0.024, 0.5643,
     0.8167, 0.9061, 0.9048, 0.9009, 0.9118, 0.8658, 0.4106, 0.6948, 0.5946, 0.8457,
     0.9372, 0.824, 0.6338, 0.6094, 0.8918, 0.7272, 0.5228, 0.3779, 0.2717, 0.3711,
     0.8555, 0.9575, 0.8496, 0.7007, 0.7434, 1, 0.9716, 0.9776, 1, 0.2076, 0.2086,
     0.6665, 0.8079, 0.6352, 0.5091, 0.6695, 0.4464, 0.7756, 0.3952, 0.5103, 0.3439,
     0.6926, 0.5734, 0.7531, 0.6577, 0.9104, 1, 0.9655, 0.5424, 0.8949, 0.7758, 0.8729,
     0.4974, 0.9932, 0.9138, 0.7853, 0.7745, 0.5984, 0.6484, 0.7748, 0.6409, 0.7519,
     0.7868, 0.749, 0.7812, 0.8878, 0.9865, 0.881, 0.8919, 0.9424, 0.9432), V26 = c(0.2413,
     0.6826, 0.7366, 0.7545, 0.5136, 0.4862, 0.9819, 0.5922, 0.2606, 0.8788, 0.6875,
     0.5897, 0.3482, 0.5341, 0.6937, 0.8119, 0.8901, 0.8989, 0.8905, 0.7755, 0.8582,
     0.9481, 0.8386, 0.9419, 0.8938, 0.8823, 0.9942, 1, 0.8756, 0.5647, 0.2141, 0.1923,
     0.5448, 0.9622, 1, 0.7511, 0.9724, 0.9086, 0.7895, 0.3443, 0.8014, 0.6793, 0.6797,
     0.6247, 0.7115, 0.7712, 0.6323, 0.7529, 0.7152, 0.4475, 0.4082, 0.2839, 0.0921,
     0.6162, 0.8647, 0.7867, 0.6154, 0.6433, 0.9455, 0.9121, 1, 0.9395, 0.2279, 0.2847,
     0.5323, 0.6521, 0.6757, 0.4606, 0.7131, 0.467, 0.875, 0.5179, 0.4716, 0.329,
     0.7576, 0.7825, 0.8959, 0.7474, 0.8912, 0.846, 0.8045, 0.7367, 0.9945, 0.9137,
     0.8084, 0.8172, 0.9161, 0.9985, 0.7984, 0.6767, 0.8412, 0.8614, 0.8688, 0.7707,
     0.7985, 0.8337, 0.7843, 0.8395, 0.9711, 0.9474, 0.8814, 0.93, 0.9986, 1), V27 = c(0.507,
     0.5713, 0.9611, 0.8289, 0.309, 0.4181, 0.9823, 0.5448, 0.0874, 0.7901, 0.4081,
     0.4967, 0.6158, 0.6197, 0.5674, 0.8594, 0.8745, 0.942, 0.7652, 0.6111, 0.6563,
     0.7539, 0.8875, 1, 0.9928, 0.9196, 1, 0.9821, 1, 0.6906, 0.4642, 0.4753, 0.4772,
     0.828, 0.8087, 0.6858, 0.9675, 0.7931, 0.6501, 0.6981, 0.6053, 0.6389, 0.6971,
     0.6024, 0.7726, 0.6838, 0.6549, 0.6838, 0.7102, 0.534, 0.5353, 0.2234, 0.0481,
     0.4139, 0.7215, 0.7688, 0.581, 0.5514, 0.8815, 0.8576, 0.9896, 0.8917, 0.3309,
     0.2211, 0.4024, 0.4915, 0.8499, 0.7243, 0.7567, 0.4621, 0.83, 0.565, 0.498, 0.2571,
     0.8787, 0.9252, 0.9941, 0.8543, 0.8189, 0.6055, 0.4969, 0.9089, 1, 0.9964, 0.8694,
     1, 0.8237, 1, 0.8847, 0.7373, 0.9911, 0.9819, 1, 0.8754, 0.883, 0.9199, 0.9021,
     0.918, 0.988, 0.9474, 0.9301, 0.9987, 0.9699, 0.9375), V28 = c(0.8533, 0.5429,
     0.7353, 0.8907, 0.0832, 0.2457, 0.9166, 0.3971, 0.2862, 0.8357, 0.1811, 0.8616,
     0.8049, 0.7143, 0.654, 0.9228, 0.7887, 0.9401, 0.5897, 0.4195, 0.5087, 0.6008,
     0.6404, 0.8564, 0.9134, 0.8965, 0.9071, 0.9092, 0.9858, 0.8513, 0.6471, 0.7003,
     0.6897, 0.5816, 0.6119, 0.7043, 0.7633, 0.5877, 0.4492, 0.8713, 0.6084, 0.5002,
     0.5843, 0.681, 0.6124, 0.8015, 0.7673, 0.839, 0.8516, 0.5323, 0.5116, 0.1911,
     0.0876, 0.3269, 0.5801, 0.7718, 0.4454, 0.3519, 0.752, 0.8798, 0.9076, 0.8105,
     0.2847, 0.6134, 0.3444, 0.5363, 0.8025, 0.8987, 0.8077, 0.6988, 0.6896, 0.3042,
     0.6196, 0.3685, 0.906, 0.9349, 0.9957, 0.9085, 0.6779, 0.3036, 0.396, 1, 0.9649,
     1, 0.8411, 0.9238, 0.6957, 0.7544, 0.9582, 0.7834, 0.9187, 0.938, 0.9941, 1,
     0.9915, 1, 1, 0.9769, 0.9812, 0.9315, 0.9955, 1, 1, 0.7603), V29 = c(0.6036,
     0.2177, 0.4856, 0.7309, 0.4019, 0.0716, 0.7423, 0.0882, 0.5606, 0.9631, 0.2064,
     0.8339, 0.6289, 0.5605, 0.7802, 0.8387, 0.8725, 0.9379, 0.3037, 0.299, 0.4817,
     0.5437, 0.3308, 0.679, 0.708, 0.7549, 0.8545, 0.8184, 0.9427, 1, 0.634, 0.6825,
     0.9797, 0.4667, 0.526, 0.5864, 0.4434, 0.3474, 0.4739, 0.9013, 0.8877, 0.5578,
     0.4772, 0.5047, 0.4936, 0.8073, 1, 1, 1, 0.3907, 0.4544, 0.0408, 0.104, 0.3108,
     0.4964, 0.6268, 0.3707, 0.3168, 0.7068, 0.772, 0.7306, 0.6828, 0.1949, 0.5807,
     0.4239, 0.7649, 0.6563, 0.8826, 0.8477, 0.7626, 0.3372, 0.1881, 0.7171, 0.5765,
     0.8528, 0.9348, 0.9328, 0.8668, 0.5368, 0.0144, 0.3856, 0.8247, 0.8747, 0.8881,
     0.5793, 0.8519, 0.4536, 0.4661, 0.899, 0.9619, 0.8005, 0.8435, 0.8793, 0.9806,
     0.9223, 0.899, 0.8888, 0.8937, 0.9464, 0.8326, 0.8576, 0.8104, 0.863, 0.7123),
     V30 = c(0.8514, 0.2149, 0.1594, 0.6896, 0.2344, 0.0613, 0.7736, 0.2385, 0.8344,
     0.9619, 0.3917, 0.4084, 0.4999, 0.3728, 0.7575, 0.7238, 0.9376, 0.8575, 0.0823,
     0.1354, 0.453, 0.5387, 0.3425, 0.5587, 0.6318, 0.6736, 0.7293, 0.6962, 0.8114,
     0.9166, 0.6107, 0.6443, 1, 0.3539, 0.3677, 0.3773, 0.3822, 0.4235, 0.6153,
     0.8014, 0.8557, 0.4831, 0.5201, 0.5775, 0.5648, 0.831, 0.8463, 0.8362, 0.769,
     0.3456, 0.4258, 0.2531, 0.1714, 0.2554, 0.4886, 0.4301, 0.2891, 0.3346, 0.5986,
     0.5711, 0.5758, 0.5572, 0.1671, 0.6925, 0.4182, 0.525, 0.8591, 0.9201, 0.9289,
     0.7025, 0.6405, 0.396, 0.6316, 0.619, 0.9087, 1, 0.9344, 0.8892, 0.5207,
     0.2526, 0.5574, 0.5441, 0.6257, 0.6585, 0.3754, 0.7722, 0.3281, 0.3924, 0.6831,
     1, 0.6713, 0.6074, 0.6482, 0.6969, 0.6981, 0.6456, 0.6511, 0.7022, 0.8542,
     0.6213, 0.6069, 0.6199, 0.6979, 0.8358), V31 = c(0.8512, 0.5811, 0.3007,
     0.5829, 0.1905, 0.1816, 0.8473, 0.2005, 0.8096, 0.9236, 0.3791, 0.2268, 0.583,
     0.2481, 0.5836, 0.6292, 0.892, 0.7284, 0.2787, 0.2438, 0.4521, 0.5619, 0.492,
     0.4147, 0.6126, 0.6463, 0.6499, 0.59, 0.6987, 0.7676, 0.7046, 0.7063, 0.9546,
     0.2727, 0.2746, 0.2206, 0.4727, 0.4633, 0.4929, 0.438, 0.5563, 0.4729, 0.4241,
     0.4754, 0.4906, 0.7792, 0.5509, 0.5427, 0.4841, 0.4091, 0.3869, 0.1979, 0.3264,
     0.3367, 0.4079, 0.2077, 0.2185, 0.2056, 0.3857, 0.4264, 0.4469, 0.4301, 0.1025,
     0.3825, 0.4393, 0.5101, 0.6655, 0.8005, 0.9513, 0.7382, 0.7138, 0.2286, 0.3554,
     0.4613, 0.9657, 0.9308, 0.8854, 0.9065, 0.5651, 0.4335, 0.7309, 0.3349, 0.2184,
     0.2707, 0.3485, 0.5772, 0.2522, 0.3849, 0.6108, 0.8086, 0.5632, 0.5403, 0.5876,
     0.4973, 0.6167, 0.5967, 0.6083, 0.65, 0.6457, 0.3772, 0.3934, 0.6041, 0.7717,
     0.7622), V32 = c(0.5045, 0.6323, 0.4096, 0.4935, 0.1235, 0.4493, 0.7352,
     0.0587, 0.725, 0.8903, 0.2042, 0.1745, 0.666, 0.1921, 0.6316, 0.5181, 0.7508,
     0.67, 0.7241, 0.5624, 0.4532, 0.5141, 0.4592, 0.2946, 0.4638, 0.5007, 0.6071,
     0.5447, 0.681, 0.6177, 0.5376, 0.5373, 0.8835, 0.141, 0.102, 0.2628, 0.4007,
     0.341, 0.3195, 0.1319, 0.2897, 0.3318, 0.1592, 0.24, 0.182, 0.5049, 0.4444,
     0.4577, 0.3717, 0.4639, 0.3939, 0.1891, 0.4612, 0.4465, 0.2443, 0.1198, 0.1711,
     0.1032, 0.251, 0.286, 0.3719, 0.3339, 0.1362, 0.4303, 0.1162, 0.4219, 0.5369,
     0.6033, 0.7995, 0.7446, 0.8202, 0.3544, 0.2897, 0.3615, 0.9306, 0.8478, 0.769,
     0.8522, 0.5749, 0.4918, 0.8549, 0.0877, 0.2945, 0.1746, 0.4639, 0.519, 0.3964,
     0.4674, 0.548, 0.5558, 0.7332, 0.689, 0.6408, 0.502, 0.5069, 0.4355, 0.4463,
     0.5069, 0.3397, 0.2822, 0.2464, 0.5547, 0.7305, 0.4567), V33 = c(0.1862,
     0.2965, 0.317, 0.3101, 0.1717, 0.5976, 0.6671, 0.2544, 0.8048, 0.9708, 0.2227,
     0.0507, 0.4124, 0.1386, 0.8108, 0.4629, 0.6832, 0.7547, 0.8032, 0.5555, 0.5385,
     0.6084, 0.3034, 0.2025, 0.2797, 0.3663, 0.5588, 0.5142, 0.6591, 0.5468, 0.5934,
     0.6601, 0.7662, 0.1863, 0.1339, 0.2672, 0.3381, 0.2849, 0.3735, 0.1709, 0.3638,
     0.3969, 0.1668, 0.2779, 0.1811, 0.1413, 0.5169, 0.8067, 0.6096, 0.558, 0.4661,
     0.2433, 0.3939, 0.5, 0.1768, 0.166, 0.3578, 0.3168, 0.2162, 0.3114, 0.2079,
     0.2035, 0.2212, 0.7791, 0.4336, 0.416, 0.3118, 0.212, 0.4362, 0.7927, 0.6657,
     0.4187, 0.4316, 0.4434, 0.7774, 0.7605, 0.6865, 0.7204, 0.525, 0.5409, 0.9425,
     0.16, 0.3645, 0.2709, 0.6495, 0.6824, 0.4154, 0.4245, 0.5058, 0.5409, 0.6038,
     0.5977, 0.4972, 0.5359, 0.3921, 0.2997, 0.2948, 0.3903, 0.3828, 0.2042, 0.1645,
     0.416, 0.5197, 0.1715), V34 = c(0.2709, 0.1873, 0.3305, 0.0306, 0.2351, 0.3785,
     0.6083, 0.2009, 0.9435, 0.9647, 0.3341, 0.1588, 0.126, 0.3325, 0.9039, 0.5255,
     0.761, 0.8773, 0.805, 0.6963, 0.5308, 0.5621, 0.4366, 0.0688, 0.1721, 0.2298,
     0.5967, 0.5389, 0.6954, 0.5516, 0.8443, 0.8708, 0.6547, 0.2176, 0.1582, 0.2907,
     0.3172, 0.2847, 0.3336, 0.2484, 0.4786, 0.3894, 0.0588, 0.1997, 0.1107, 0.2767,
     0.4268, 0.6973, 0.511, 0.5727, 0.3974, 0.1956, 0.505, 0.5111, 0.2472, 0.2618,
     0.3947, 0.404, 0.0968, 0.2066, 0.0955, 0.0798, 0.1124, 0.8703, 0.6553, 0.1906,
     0.3763, 0.2866, 0.4048, 0.5227, 0.5254, 0.2398, 0.3791, 0.3864, 0.6643, 0.704,
     0.639, 0.62, 0.4255, 0.5961, 0.8726, 0.4169, 0.5012, 0.4853, 0.6901, 0.622,
     0.3308, 0.3095, 0.4476, 0.4988, 0.2575, 0.3244, 0.2755, 0.3842, 0.3524, 0.2294,
     0.1729, 0.3009, 0.3204, 0.219, 0.114, 0.1472, 0.1786, 0.1549), V35 = c(0.4232,
     0.2969, 0.3408, 0.0244, 0.2489, 0.2495, 0.6239, 0.0329, 1, 0.7892, 0.3984,
     0.304, 0.2487, 0.2883, 0.8647, 0.5147, 0.9017, 0.9919, 0.7676, 0.7298, 0.5356,
     0.5956, 0.5175, 0.1171, 0.1665, 0.1362, 0.6275, 0.5531, 0.729, 0.5463, 0.9481,
     0.9518, 0.5447, 0.236, 0.1952, 0.1982, 0.2222, 0.1742, 0.1052, 0.3044, 0.2908,
     0.2314, 0.3967, 0.5305, 0.4603, 0.5084, 0.1802, 0.3915, 0.2586, 0.6355, 0.2194,
     0.2667, 0.4833, 0.5194, 0.3518, 0.3862, 0.2867, 0.4282, 0.1323, 0.1165, 0.0488,
     0.0809, 0.1677, 1, 0.6172, 0.0223, 0.2801, 0.4033, 0.4952, 0.3967, 0.296,
     0.1847, 0.2421, 0.3093, 0.6604, 0.7539, 0.6378, 0.6253, 0.333, 0.5248, 0.6673,
     0.6576, 0.7843, 0.7184, 0.5666, 0.5054, 0.1445, 0.0752, 0.2401, 0.3108, 0.0349,
     0.0516, 0.03, 0.1848, 0.2183, 0.1866, 0.1488, 0.1565, 0.1331, 0.2223, 0.0956,
     0.0849, 0.1098, 0.1641), V36 = c(0.3043, 0.5163, 0.2186, 0.1108, 0.3649,
     0.5771, 0.5972, 0.1547, 0.896, 0.5307, 0.5077, 0.1369, 0.4676, 0.3228, 0.6695,
     0.3929, 1, 0.9922, 0.7468, 0.7022, 0.5271, 0.6078, 0.5122, 0.2157, 0.2561,
     0.2123, 0.5459, 0.5318, 0.668, 0.5515, 0.9705, 0.9605, 0.4593, 0.1725, 0.1787,
     0.2288, 0.0733, 0.0549, 0.0671, 0.2312, 0.0899, 0.1036, 0.7147, 0.7409, 0.665,
     0.4787, 0.0791, 0.1558, 0.0916, 0.7563, 0.1816, 0.134, 0.3511, 0.4619, 0.3762,
     0.3958, 0.2401, 0.4538, 0.1344, 0.0185, 0.1406, 0.1525, 0.1039, 0.9212, 0.4373,
     0.4219, 0.0875, 0.2803, 0.1712, 0.3042, 0.0704, 0.376, 0.0944, 0.2138, 0.6884,
     0.799, 0.6629, 0.6848, 0.2331, 0.3777, 0.4694, 0.739, 0.9361, 0.8209, 0.5188,
     0.3578, 0.1923, 0.2885, 0.1405, 0.2897, 0.1799, 0.3157, 0.3356, 0.1149, 0.1245,
     0.0922, 0.0801, 0.0985, 0.044, 0.1327, 0.008, 0.0608, 0.1446, 0.1869), V37 = c(0.6116,
     0.6153, 0.2463, 0.1594, 0.3382, 0.8852, 0.5715, 0.1212, 0.5516, 0.2718, 0.5534,
     0.1605, 0.5382, 0.2607, 0.4027, 0.1279, 0.9123, 0.9419, 0.6253, 0.5468, 0.426,
     0.5025, 0.4746, 0.2216, 0.2735, 0.2395, 0.4786, 0.4826, 0.5917, 0.4561, 0.7766,
     0.7712, 0.4679, 0.0589, 0.0429, 0.3186, 0.2692, 0.1192, 0.0379, 0.1338, 0.2043,
     0.1312, 0.7319, 0.7775, 0.6423, 0.1356, 0.0535, 0.1598, 0.0947, 0.6903, 0.1023,
     0.1073, 0.2319, 0.4234, 0.2909, 0.3248, 0.3619, 0.3704, 0.225, 0.1302, 0.2554,
     0.2626, 0.2562, 0.9386, 0.4118, 0.5496, 0.3319, 0.3087, 0.3652, 0.1309, 0.097,
     0.4331, 0.0351, 0.1112, 0.6938, 0.7673, 0.5983, 0.7337, 0.1451, 0.2369, 0.1546,
     0.7963, 0.8195, 0.7536, 0.506, 0.3809, 0.3208, 0.4072, 0.1772, 0.2244, 0.3039,
     0.359, 0.3167, 0.157, 0.1592, 0.1829, 0.177, 0.22, 0.1234, 0.0521, 0.0702,
     0.0969, 0.1066, 0.2655), V38 = c(0.6756, 0.4283, 0.2726, 0.1371, 0.1589,
     0.8409, 0.5242, 0.2446, 0.3037, 0.1953, 0.3352, 0.2061, 0.315, 0.204, 0.237,
     0.0411, 0.7388, 0.8388, 0.173, 0.1421, 0.2436, 0.2829, 0.4902, 0.2776, 0.3209,
     0.2673, 0.3965, 0.379, 0.4899, 0.3466, 0.6313, 0.6772, 0.1987, 0.0621, 0.1096,
     0.2871, 0.1888, 0.1154, 0.0461, 0.2056, 0.1707, 0.0864, 0.3509, 0.4424, 0.2166,
     0.2299, 0.1906, 0.2161, 0.2287, 0.6176, 0.2108, 0.2023, 0.4029, 0.4372, 0.2311,
     0.2302, 0.3314, 0.3741, 0.3244, 0.248, 0.2054, 0.2456, 0.2624, 0.9303, 0.3641,
     0.2483, 0.4237, 0.355, 0.3763, 0.2408, 0.3941, 0.3626, 0.0844, 0.1386, 0.5932,
     0.5955, 0.4565, 0.6281, 0.1648, 0.172, 0.1748, 0.7493, 0.6207, 0.6496, 0.3885,
     0.3813, 0.3367, 0.317, 0.1742, 0.096, 0.476, 0.3881, 0.4133, 0.1311, 0.1626,
     0.1743, 0.1382, 0.2243, 0.203, 0.0618, 0.0936, 0.1411, 0.144, 0.1713), V39 = c(0.5375,
     0.5479, 0.168, 0.0696, 0.0989, 0.357, 0.2924, 0.3171, 0.2338, 0.1374, 0.2723,
     0.0734, 0.2139, 0.2396, 0.2685, 0.0859, 0.5915, 0.6605, 0.2916, 0.4738, 0.1205,
     0.0477, 0.4603, 0.2309, 0.2724, 0.2865, 0.2087, 0.1831, 0.3439, 0.3384, 0.576,
     0.6431, 0.0699, 0.1847, 0.1762, 0.2921, 0.0712, 0.0855, 0.1694, 0.2474, 0.0407,
     0.2569, 0.0589, 0.1416, 0.1951, 0.2789, 0.2561, 0.5178, 0.348, 0.5379, 0.3253,
     0.1794, 0.3676, 0.4277, 0.3168, 0.325, 0.3763, 0.3839, 0.3939, 0.1637, 0.1614,
     0.198, 0.2236, 0.7314, 0.4572, 0.2034, 0.1801, 0.2545, 0.2841, 0.178, 0.6028,
     0.2519, 0.0436, 0.1523, 0.5774, 0.4731, 0.3129, 0.5725, 0.2694, 0.1878, 0.3607,
     0.6795, 0.4513, 0.4708, 0.3762, 0.3359, 0.5683, 0.2863, 0.3326, 0.2287, 0.5756,
     0.5716, 0.6281, 0.1583, 0.2356, 0.2452, 0.2404, 0.2736, 0.1652, 0.1416, 0.0894,
     0.1676, 0.1929, 0.0959), V40 = c(0.4719, 0.6133, 0.2792, 0.0452, 0.1089,
     0.3133, 0.1536, 0.3195, 0.2382, 0.3105, 0.2278, 0.0202, 0.1848, 0.1319, 0.3662,
     0.1131, 0.4057, 0.4816, 0.5003, 0.641, 0.3845, 0.2811, 0.446, 0.1444, 0.188,
     0.206, 0.1651, 0.175, 0.2366, 0.2853, 0.6148, 0.672, 0.1493, 0.2452, 0.2481,
     0.2806, 0.1062, 0.1811, 0.2169, 0.279, 0.1286, 0.3179, 0.269, 0.3508, 0.4947,
     0.3833, 0.2153, 0.4782, 0.2095, 0.5622, 0.3697, 0.0227, 0.151, 0.4433, 0.3554,
     0.4022, 0.4767, 0.3494, 0.3806, 0.1103, 0.2232, 0.2412, 0.118, 0.4791, 0.4367,
     0.2729, 0.3743, 0.1432, 0.0427, 0.1598, 0.3521, 0.187, 0.113, 0.0996, 0.6223,
     0.484, 0.4158, 0.6119, 0.373, 0.325, 0.5208, 0.4713, 0.3004, 0.3482, 0.3738,
     0.2771, 0.5505, 0.2634, 0.4021, 0.3228, 0.4254, 0.4314, 0.4977, 0.2631, 0.2483,
     0.2407, 0.2046, 0.2152, 0.1043, 0.146, 0.1127, 0.12, 0.0325, 0.0768), V41 = c(0.4647,
     0.5017, 0.2558, 0.062, 0.1043, 0.6096, 0.2003, 0.3051, 0.3318, 0.379, 0.2044,
     0.1638, 0.1679, 0.0683, 0.3267, 0.1306, 0.3019, 0.2917, 0.522, 0.4375, 0.4107,
     0.3422, 0.4196, 0.1513, 0.1552, 0.1659, 0.1836, 0.1679, 0.1716, 0.2502, 0.545,
     0.6035, 0.1713, 0.2984, 0.315, 0.2682, 0.0694, 0.1264, 0.1677, 0.161, 0.1581,
     0.2649, 0.42, 0.4482, 0.4925, 0.2933, 0.2769, 0.2344, 0.1901, 0.6508, 0.2912,
     0.1313, 0.0745, 0.37, 0.3741, 0.4344, 0.4059, 0.438, 0.3258, 0.2144, 0.1773,
     0.2409, 0.1103, 0.2087, 0.2964, 0.2837, 0.4627, 0.5869, 0.5331, 0.5657, 0.3924,
     0.1046, 0.2045, 0.1644, 0.5841, 0.434, 0.4325, 0.5597, 0.4467, 0.2575, 0.5177,
     0.2355, 0.2674, 0.3508, 0.2605, 0.3648, 0.3231, 0.0541, 0.3009, 0.3454, 0.5046,
     0.3051, 0.2613, 0.3103, 0.2437, 0.2518, 0.197, 0.2438, 0.1066, 0.0846, 0.0873,
     0.1201, 0.149, 0.0847), V42 = c(0.2587, 0.2377, 0.174, 0.1421, 0.0839, 0.6378,
     0.2031, 0.0836, 0.3821, 0.4105, 0.1986, 0.1583, 0.2328, 0.0334, 0.22, 0.1757,
     0.2331, 0.1769, 0.4824, 0.3178, 0.5067, 0.5147, 0.2873, 0.1745, 0.2522, 0.2633,
     0.0652, 0.0674, 0.1013, 0.1641, 0.4813, 0.5155, 0.1654, 0.3041, 0.292, 0.2112,
     0.03, 0.0799, 0.0644, 0.0056, 0.2191, 0.2714, 0.3874, 0.4208, 0.4041, 0.1155,
     0.2841, 0.3599, 0.2941, 0.4797, 0.301, 0.1775, 0.1395, 0.3324, 0.4443, 0.4008,
     0.3661, 0.4265, 0.3654, 0.2033, 0.2293, 0.1901, 0.2831, 0.2016, 0.4312, 0.4463,
     0.1614, 0.6431, 0.6952, 0.6443, 0.4808, 0.2339, 0.1937, 0.1902, 0.4527, 0.3954,
     0.4031, 0.4965, 0.4133, 0.2423, 0.3702, 0.1704, 0.2241, 0.3181, 0.1591, 0.3834,
     0.0448, 0.1874, 0.2075, 0.3882, 0.7179, 0.4393, 0.4697, 0.4512, 0.2715, 0.3184,
     0.2778, 0.3154, 0.211, 0.1055, 0.102, 0.1036, 0.0328, 0.2076), V43 = c(0.2129,
     0.1957, 0.2121, 0.1597, 0.1391, 0.2709, 0.2207, 0.1266, 0.1575, 0.3355, 0.0835,
     0.183, 0.1015, 0.0716, 0.2996, 0.2648, 0.2931, 0.1136, 0.4004, 0.2377, 0.4216,
     0.4372, 0.2296, 0.1756, 0.2121, 0.2552, 0.0758, 0.0609, 0.0766, 0.1605, 0.3406,
     0.3802, 0.26, 0.2275, 0.1902, 0.1513, 0.0893, 0.0378, 0.0159, 0.0351, 0.1701,
     0.1713, 0.244, 0.3054, 0.2402, 0.1705, 0.1733, 0.2785, 0.2211, 0.3736, 0.2563,
     0.1549, 0.1552, 0.2564, 0.3261, 0.337, 0.232, 0.2854, 0.2983, 0.1887, 0.2521,
     0.2077, 0.2385, 0.1669, 0.4155, 0.3178, 0.2494, 0.5826, 0.4288, 0.4241, 0.4602,
     0.1991, 0.0834, 0.1313, 0.4911, 0.4837, 0.4201, 0.5027, 0.3743, 0.2706, 0.224,
     0.2728, 0.3141, 0.3524, 0.1875, 0.3453, 0.3131, 0.3459, 0.1206, 0.324, 0.6163,
     0.4302, 0.4806, 0.3785, 0.1184, 0.1685, 0.1377, 0.2112, 0.2417, 0.1639, 0.1964,
     0.1977, 0.0537, 0.2505), V44 = c(0.2222, 0.1749, 0.1099, 0.1384, 0.0819,
     0.1419, 0.1778, 0.1381, 0.2228, 0.2998, 0.0908, 0.1886, 0.0713, 0.0976, 0.2205,
     0.1955, 0.2298, 0.0701, 0.3877, 0.2808, 0.2479, 0.247, 0.0949, 0.1424, 0.1801,
     0.1696, 0.0486, 0.0375, 0.0845, 0.1491, 0.1916, 0.2278, 0.3846, 0.148, 0.0696,
     0.1789, 0.1459, 0.1268, 0.0778, 0.1148, 0.0971, 0.0584, 0.2, 0.2235, 0.1392,
     0.1294, 0.0815, 0.1807, 0.1524, 0.2804, 0.1927, 0.1626, 0.0377, 0.2527, 0.1963,
     0.2518, 0.145, 0.2808, 0.1779, 0.137, 0.1464, 0.1767, 0.0255, 0.2872, 0.1824,
     0.0807, 0.3202, 0.4286, 0.3063, 0.4567, 0.4164, 0.11, 0.1502, 0.1776, 0.5762,
     0.5379, 0.4557, 0.5772, 0.3021, 0.2323, 0.0816, 0.4016, 0.3693, 0.3659, 0.2267,
     0.2096, 0.3387, 0.4646, 0.0255, 0.0926, 0.5663, 0.4831, 0.4921, 0.1269, 0.1157,
     0.0675, 0.0685, 0.0991, 0.1631, 0.1916, 0.2256, 0.1339, 0.1309, 0.1862),
     V45 = c(0.2111, 0.1304, 0.0985, 0.0372, 0.0678, 0.126, 0.1353, 0.1136, 0.1582,
     0.2748, 0.138, 0.1008, 0.0615, 0.0787, 0.1163, 0.0656, 0.2391, 0.1578, 0.1651,
     0.1374, 0.1586, 0.1708, 0.0095, 0.0908, 0.1473, 0.1467, 0.0353, 0.0533, 0.026,
     0.1326, 0.1134, 0.1522, 0.3754, 0.1102, 0.0758, 0.185, 0.1348, 0.1125, 0.0653,
     0.1331, 0.2217, 0.123, 0.2307, 0.2611, 0.1779, 0.0909, 0.0335, 0.0352, 0.0746,
     0.1982, 0.2062, 0.0708, 0.0636, 0.2137, 0.0864, 0.2101, 0.1017, 0.2395, 0.1535,
     0.1376, 0.0673, 0.1119, 0.1967, 0.4374, 0.1487, 0.1192, 0.2265, 0.4894, 0.5835,
     0.576, 0.5438, 0.0684, 0.1675, 0.2, 0.5013, 0.4485, 0.3955, 0.5907, 0.2069,
     0.1724, 0.0395, 0.4125, 0.2986, 0.2846, 0.1577, 0.1031, 0.413, 0.4366, 0.0298,
     0.1173, 0.5749, 0.5084, 0.5294, 0.1459, 0.1449, 0.1186, 0.0664, 0.0594, 0.0769,
     0.2085, 0.1814, 0.0902, 0.091, 0.1439), V46 = c(0.0176, 0.0597, 0.1271, 0.0688,
     0.0663, 0.1288, 0.1373, 0.0516, 0.1433, 0.2024, 0.1948, 0.0663, 0.0779, 0.0522,
     0.0635, 0.058, 0.191, 0.1938, 0.0442, 0.1136, 0.1124, 0.1343, 0.0527, 0.0138,
     0.0681, 0.1286, 0.0297, 0.0278, 0.0333, 0.0687, 0.064, 0.0801, 0.2414, 0.1178,
     0.091, 0.1717, 0.0391, 0.0505, 0.021, 0.0276, 0.2732, 0.22, 0.1886, 0.2798,
     0.1946, 0.08, 0.0933, 0.0473, 0.0606, 0.2438, 0.1751, 0.0129, 0.0443, 0.1789,
     0.1688, 0.1181, 0.1111, 0.0369, 0.1199, 0.0307, 0.0965, 0.0779, 0.1483, 0.3097,
     0.0138, 0.2134, 0.1146, 0.5777, 0.5692, 0.5293, 0.5649, 0.0303, 0.1058, 0.0765,
     0.4042, 0.2674, 0.2966, 0.4803, 0.179, 0.1457, 0.0785, 0.347, 0.2226, 0.1714,
     0.1211, 0.0798, 0.3639, 0.2581, 0.0691, 0.0566, 0.3593, 0.1952, 0.2216, 0.1092,
     0.1883, 0.1833, 0.1665, 0.194, 0.0723, 0.2335, 0.2012, 0.1085, 0.0757, 0.147
     ), V47 = c(0.1348, 0.1124, 0.1459, 0.0867, 0.1202, 0.079, 0.0749, 0.0073,
     0.1634, 0.1043, 0.1211, 0.0183, 0.0761, 0.05, 0.0465, 0.0319, 0.1096, 0.1106,
     0.0663, 0.1034, 0.0651, 0.0838, 0.0383, 0.0469, 0.1091, 0.0926, 0.0241, 0.0179,
     0.0205, 0.0602, 0.0911, 0.0804, 0.1077, 0.0608, 0.0441, 0.0898, 0.0546, 0.0949,
     0.0509, 0.0763, 0.1874, 0.2198, 0.196, 0.2392, 0.1723, 0.0567, 0.1018, 0.0322,
     0.0692, 0.1789, 0.0841, 0.0795, 0.0264, 0.101, 0.1991, 0.115, 0.0655, 0.0805,
     0.0959, 0.0373, 0.1492, 0.1344, 0.0434, 0.1578, 0.1164, 0.3241, 0.0476, 0.4315,
     0.263, 0.3287, 0.3195, 0.0674, 0.1111, 0.0727, 0.3123, 0.1541, 0.2095, 0.3877,
     0.1689, 0.1175, 0.1052, 0.2739, 0.0849, 0.0694, 0.0883, 0.0701, 0.2069, 0.1319,
     0.0781, 0.0766, 0.2526, 0.1539, 0.1401, 0.1485, 0.1954, 0.1878, 0.1807, 0.1937,
     0.0912, 0.1964, 0.1688, 0.1521, 0.1059, 0.0991), V48 = c(0.0744, 0.1047,
     0.1164, 0.0513, 0.0692, 0.0829, 0.0472, 0.0278, 0.1133, 0.0453, 0.0843, 0.0404,
     0.0845, 0.0231, 0.0422, 0.0301, 0.03, 0.0693, 0.0418, 0.0688, 0.0789, 0.0755,
     0.0107, 0.048, 0.0919, 0.0716, 0.0379, 0.0114, 0.0309, 0.0561, 0.098, 0.0752,
     0.0224, 0.0333, 0.0244, 0.0656, 0.0469, 0.0677, 0.0387, 0.0631, 0.1062, 0.1074,
     0.1701, 0.2021, 0.1522, 0.0198, 0.0309, 0.0408, 0.0446, 0.1706, 0.1035, 0.0762,
     0.0223, 0.0528, 0.1217, 0.055, 0.0271, 0.0541, 0.0765, 0.0606, 0.1128, 0.096,
     0.0627, 0.0553, 0.2052, 0.2945, 0.0943, 0.264, 0.1196, 0.1283, 0.2484, 0.0785,
     0.0849, 0.0749, 0.2232, 0.1359, 0.1558, 0.2779, 0.1341, 0.0868, 0.1034, 0.179,
     0.0359, 0.0303, 0.085, 0.0526, 0.0859, 0.0505, 0.0777, 0.0969, 0.2299, 0.2037,
     0.1888, 0.1385, 0.1492, 0.1114, 0.1245, 0.1082, 0.0812, 0.13, 0.1037, 0.1363,
     0.1005, 0.0041), V49 = c(0.013, 0.0507, 0.0777, 0.0092, 0.0152, 0.052, 0.0325,
     0.0372, 0.0567, 0.0337, 0.0589, 0.0108, 0.0592, 0.0221, 0.0174, 0.0272, 0.0171,
     0.0176, 0.0475, 0.0422, 0.0325, 0.0304, 0.0108, 0.0159, 0.0397, 0.0325, 0.0119,
     0.0073, 0.0101, 0.0306, 0.0563, 0.0566, 0.0155, 0.0276, 0.0265, 0.0445, 0.0201,
     0.0259, 0.0262, 0.0309, 0.0665, 0.0423, 0.1366, 0.1326, 0.0929, 0.0114, 0.0208,
     0.0163, 0.0344, 0.0762, 0.0641, 0.0117, 0.0187, 0.0453, 0.0628, 0.0293, 0.0244,
     0.0177, 0.0649, 0.0399, 0.0463, 0.0598, 0.0513, 0.0334, 0.1069, 0.1474, 0.0824,
     0.1794, 0.0983, 0.0698, 0.1299, 0.0455, 0.0596, 0.0449, 0.1085, 0.0941, 0.0884,
     0.1427, 0.0769, 0.0392, 0.0764, 0.0922, 0.0289, 0.0292, 0.0355, 0.0241, 0.06,
     0.0112, 0.0369, 0.0588, 0.1271, 0.1054, 0.0947, 0.0716, 0.0511, 0.031, 0.0516,
     0.0336, 0.0496, 0.0633, 0.0501, 0.0858, 0.0535, 0.0154), V50 = c(0.0106,
     0.0159, 0.0439, 0.0198, 0.0266, 0.0216, 0.0179, 0.0121, 0.0133, 0.0122, 0.0247,
     0.0143, 0.0068, 0.0144, 0.0172, 0.0074, 0.0383, 0.0205, 0.0235, 0.0117, 0.007,
     0.0074, 0.0077, 0.0045, 0.0093, 0.0258, 0.0073, 0.0116, 0.0095, 0.0154, 0.0187,
     0.0175, 0.0187, 0.01, 0.0095, 0.011, 0.0095, 0.017, 0.0101, 0.024, 0.0405,
     0.0162, 0.0398, 0.0358, 0.0179, 0.0151, 0.0318, 0.0088, 0.0082, 0.0238, 0.0153,
     0.0061, 0.0077, 0.0118, 0.0323, 0.0183, 0.0179, 0.0065, 0.0313, 0.0169, 0.0193,
     0.033, 0.0473, 0.0209, 0.0199, 0.0211, 0.0171, 0.0772, 0.0374, 0.0334, 0.0825,
     0.0246, 0.0201, 0.0134, 0.0414, 0.0261, 0.0265, 0.0424, 0.0222, 0.0131, 0.0216,
     0.0276, 0.0122, 0.0116, 0.0219, 0.0117, 0.0267, 0.0059, 0.0057, 0.005, 0.0356,
     0.0251, 0.0134, 0.0176, 0.0155, 0.0143, 0.0044, 0.0177, 0.0101, 0.0183, 0.0136,
     0.029, 0.0235, 0.0116), V51 = c(0.0033, 0.0195, 0.0061, 0.0118, 0.0174, 0.036,
     0.0045, 0.0153, 0.017, 0.0072, 0.0118, 0.0091, 0.0089, 0.0307, 0.0134, 0.0149,
     0.0053, 0.0309, 0.0066, 0.007, 0.0026, 0.0069, 0.0109, 0.0015, 0.0076, 0.0136,
     0.0051, 0.0092, 0.0047, 0.0029, 0.0088, 0.0058, 0.0125, 0.0023, 0.014, 0.0024,
     0.0155, 0.0033, 0.0161, 0.0115, 0.0113, 0.0093, 0.0143, 0.0128, 0.0242, 0.0085,
     0.0132, 0.0121, 0.0108, 0.0268, 0.0081, 0.0257, 0.0137, 9e-04, 0.0253, 0.0104,
     0.0109, 0.0222, 0.0185, 0.0135, 0.014, 0.0197, 0.0248, 0.0172, 0.0208, 0.0361,
     0.0244, 0.0798, 0.0291, 0.0342, 0.0243, 0.0151, 0.0071, 0.0174, 0.0253, 0.0079,
     0.0121, 0.0271, 0.0205, 0.0092, 0.0167, 0.0169, 0.0045, 0.0024, 0.0086, 0.0122,
     0.0125, 0.0041, 0.0091, 0.0118, 0.0367, 0.0357, 0.031, 0.0199, 0.0189, 0.0138,
     0.0185, 0.0209, 0.0089, 0.0137, 0.013, 0.0203, 0.0155, 0.0181), V52 = c(0.0232,
     0.0201, 0.0145, 0.009, 0.0176, 0.0331, 0.0084, 0.0092, 0.0035, 0.0108, 0.0088,
     0.0038, 0.0087, 0.0386, 0.0141, 0.0125, 0.009, 0.0212, 0.0062, 0.0167, 0.0093,
     0.0025, 0.0062, 0.0052, 0.0065, 0.0044, 0.0034, 0.0078, 0.0072, 0.0048, 0.0042,
     0.0091, 0.0028, 0.0069, 0.0074, 0.0062, 0.0091, 0.015, 0.0029, 0.0064, 0.0028,
     0.0046, 0.0093, 0.0172, 0.0083, 0.0178, 0.0118, 0.0067, 0.0149, 0.0081, 0.0191,
     0.0089, 0.0071, 0.0142, 0.0214, 0.0117, 0.0147, 0.0045, 0.0098, 0.0222, 0.0027,
     0.0189, 0.0274, 0.018, 0.0176, 0.0444, 0.0258, 0.0376, 0.0156, 0.0459, 0.021,
     0.0125, 0.0104, 0.0117, 0.0131, 0.0164, 0.0091, 0.02, 0.0123, 0.0078, 0.0089,
     0.0081, 0.0108, 0.0084, 0.0123, 0.0122, 0.004, 0.0056, 0.0134, 0.0146, 0.0176,
     0.0181, 0.0237, 0.0096, 0.015, 0.0108, 0.0072, 0.0134, 0.0083, 0.015, 0.012,
     0.0116, 0.016, 0.0146), V53 = c(0.0166, 0.0248, 0.0128, 0.0223, 0.0127, 0.0131,
     0.001, 0.0035, 0.0052, 0.007, 0.0104, 0.0096, 0.0032, 0.0147, 0.0191, 0.0134,
     0.0042, 0.0091, 0.0129, 0.0127, 0.0118, 0.0103, 0.0028, 0.0038, 0.0072, 0.0028,
     0.0129, 0.0041, 0.0054, 0.0023, 0.0175, 0.016, 0.0067, 0.0025, 0.0063, 0.0072,
     0.0151, 0.0111, 0.0078, 0.0022, 0.0036, 0.0044, 0.0033, 0.0138, 0.0037, 0.0073,
     0.012, 0.0032, 0.0077, 0.0129, 0.0182, 0.0262, 0.0082, 0.0179, 0.0262, 0.0101,
     0.017, 0.0136, 0.0178, 0.0175, 0.0068, 0.0204, 0.0205, 0.011, 0.0197, 0.023,
     0.0143, 0.0143, 0.0197, 0.0277, 0.0361, 0.0036, 0.0062, 0.0023, 0.0049, 0.012,
     0.0062, 0.007, 0.0067, 0.0071, 0.0051, 0.004, 0.0075, 0.01, 0.006, 0.0114,
     0.0136, 0.0104, 0.0097, 0.004, 0.0035, 0.0019, 0.0078, 0.0103, 0.006, 0.0062,
     0.0055, 0.0094, 0.008, 0.0076, 0.0039, 0.0098, 0.0029, 0.0129), V54 = c(0.0095,
     0.0131, 0.0145, 0.0179, 0.0088, 0.012, 0.0018, 0.0098, 0.0083, 0.0063, 0.0036,
     0.0142, 0.013, 0.0018, 0.0145, 0.0026, 0.0153, 0.0056, 0.0184, 0.0138, 0.0112,
     0.0074, 0.004, 0.0079, 0.0108, 0.0021, 0.01, 0.0013, 0.0022, 0.002, 0.0171,
     0.016, 0.012, 0.0027, 0.0081, 0.0113, 0.008, 0.0032, 0.0114, 0.0122, 0.0105,
     0.0078, 0.0113, 0.0079, 0.0095, 0.0079, 0.0051, 0.0109, 0.0036, 0.0161, 0.016,
     0.0108, 0.0232, 0.0079, 0.0177, 0.0061, 0.0158, 0.0113, 0.0077, 0.0127, 0.015,
     0.0085, 0.0141, 0.0234, 0.021, 0.029, 0.0226, 0.0272, 0.0135, 0.0172, 0.0239,
     0.0123, 0.0026, 0.0047, 0.0104, 0.0113, 0.0019, 0.007, 0.0011, 0.0081, 0.0015,
     0.0025, 0.0089, 0.0018, 0.0187, 0.0098, 0.0137, 0.0079, 0.0042, 0.0114, 0.0093,
     0.0102, 0.0144, 0.0093, 0.0082, 0.0044, 0.0074, 0.0047, 0.0026, 0.0032, 0.0053,
     0.0199, 0.0051, 0.0047), V55 = c(0.018, 0.007, 0.0058, 0.0084, 0.0098, 0.0108,
     0.0068, 0.0121, 0.0078, 0.003, 0.0088, 0.019, 0.0188, 0.01, 0.0065, 0.0038,
     0.0106, 0.0086, 0.0069, 0.009, 0.0094, 0.0123, 0.0075, 0.0114, 0.0051, 0.0022,
     0.0044, 0.0011, 0.0016, 0.004, 0.0079, 0.0081, 0.0012, 0.0052, 0.0087, 0.0012,
     0.0018, 0.0035, 0.0083, 0.0151, 0.012, 0.0102, 0.003, 0.0037, 0.0105, 0.0038,
     0.007, 0.0164, 0.0114, 0.0063, 0.029, 0.0138, 0.0198, 0.006, 0.0037, 0.0031,
     0.0046, 0.0053, 0.0074, 0.0022, 0.0012, 0.0043, 0.0185, 0.0276, 0.0141, 0.0141,
     0.0187, 0.0127, 0.0127, 0.0087, 0.0447, 0.0043, 0.0025, 0.0049, 0.0102, 0.0021,
     0.0045, 0.0086, 0.0026, 0.0034, 0.0075, 0.0036, 0.0036, 0.0035, 0.0111, 0.0027,
     0.0172, 0.0014, 0.0058, 0.0032, 0.0121, 0.0133, 0.017, 0.0025, 0.0091, 0.0072,
     0.0068, 0.0045, 0.0079, 0.0037, 0.0062, 0.0033, 0.0062, 0.0039), V56 = c(0.0244,
     0.0138, 0.0049, 0.0068, 0.0019, 0.0024, 0.0039, 6e-04, 0.0075, 0.0011, 0.0047,
     0.014, 0.0101, 0.0096, 0.0129, 0.0018, 0.002, 0.0092, 0.0198, 0.0051, 0.014,
     0.0069, 0.0039, 0.005, 0.0102, 0.0048, 0.0057, 0.0045, 0.0029, 0.0019, 0.005,
     0.007, 0.0022, 0.0036, 0.0044, 0.0022, 0.0078, 0.0169, 0.0058, 0.0056, 0.0087,
     0.0065, 0.0057, 0.0051, 0.003, 0.0116, 0.0015, 0.0151, 0.0085, 0.0119, 0.009,
     0.0187, 0.0074, 0.0131, 0.0068, 0.0099, 0.0073, 0.0165, 0.0095, 0.0124, 0.0133,
     0.0092, 0.0055, 0.0032, 0.0049, 0.0161, 0.0185, 0.0166, 0.0138, 0.0046, 0.0394,
     0.0114, 0.0061, 0.0031, 0.0092, 0.0097, 0.0079, 0.0089, 0.0049, 0.0064, 0.0058,
     0.0058, 0.0029, 0.0058, 0.0126, 0.0025, 0.0132, 0.0054, 0.0072, 0.0062, 0.0075,
     0.004, 0.0012, 0.0044, 0.0038, 7e-04, 0.0084, 0.0042, 0.0042, 0.0071, 0.0046,
     0.0101, 0.0089, 0.0061), V57 = c(0.0316, 0.0092, 0.0065, 0.0032, 0.0059,
     0.0045, 0.012, 0.0181, 0.0105, 7e-04, 0.0117, 0.0099, 0.0229, 0.0077, 0.0217,
     0.0113, 0.0105, 0.007, 0.0199, 0.0029, 0.0072, 0.0076, 0.0053, 0.003, 0.0041,
     0.0138, 0.003, 0.0039, 0.0058, 0.0034, 0.0112, 0.0135, 0.0058, 0.0026, 0.0028,
     0.0025, 0.0045, 0.0137, 3e-04, 0.0026, 0.0061, 0.0061, 0.009, 0.0258, 0.0132,
     0.0033, 0.0035, 0.007, 0.0101, 0.0194, 0.0242, 0.023, 0.0035, 0.0089, 0.0121,
     0.008, 0.0054, 0.0141, 0.0055, 0.0054, 0.0048, 0.0138, 0.0045, 0.0084, 0.0027,
     0.0177, 0.011, 0.0095, 0.0133, 0.0203, 0.0355, 0.0052, 0.0038, 0.0024, 0.0083,
     0.0072, 0.0031, 0.0074, 0.0029, 0.0037, 0.0016, 0.0067, 0.0013, 0.0011, 0.0081,
     0.0026, 0.011, 0.0015, 0.0041, 0.0101, 0.0056, 0.0042, 0.0109, 0.0021, 0.0056,
     0.0054, 0.0037, 0.0028, 0.0071, 0.004, 0.0045, 0.0065, 0.014, 0.004), V58 = c(0.0164,
     0.0143, 0.0093, 0.0035, 0.0058, 0.0037, 0.0132, 0.0094, 0.016, 0.0024, 0.002,
     0.0092, 0.0182, 0.018, 0.0087, 0.0058, 0.0049, 0.0116, 0.0102, 0.0122, 0.0022,
     0.0073, 0.0013, 0.0064, 0.0055, 0.014, 0.0035, 0.0022, 0.005, 0.0034, 0.0179,
     0.0067, 0.0042, 0.0036, 0.0019, 0.0059, 0.0026, 0.0015, 0.0023, 0.0029, 0.0061,
     0.0062, 0.0057, 0.0102, 0.0068, 0.0039, 8e-04, 0.0085, 0.0016, 0.014, 0.0224,
     0.0057, 0.01, 0.0084, 0.0077, 0.0107, 0.0033, 0.0077, 0.0045, 0.0021, 0.0244,
     0.0094, 0.0115, 0.0122, 0.0162, 0.0194, 0.0094, 0.0225, 0.0131, 0.013, 0.044,
     0.0091, 0.0101, 0.0039, 0.002, 0.006, 0.0063, 0.0042, 0.0022, 0.0036, 0.007,
     0.0035, 0.001, 9e-04, 0.0155, 0.005, 0.0122, 6e-04, 0.0045, 0.0068, 0.0021,
     0.003, 0.0036, 0.0069, 0.0056, 0.0035, 0.0024, 0.0036, 0.0044, 9e-04, 0.0022,
     0.0115, 0.0138, 0.0036), V59 = c(0.0095, 0.0036, 0.0059, 0.0056, 0.0059,
     0.0112, 0.007, 0.0116, 0.0095, 0.0057, 0.0091, 0.0052, 0.0046, 0.0109, 0.0077,
     0.0047, 0.007, 0.006, 0.007, 0.0056, 0.0055, 0.003, 0.0052, 0.0058, 0.005,
     0.0028, 0.0021, 0.0023, 0.0024, 0.0051, 0.0294, 0.0078, 0.0067, 6e-04, 0.0049,
     0.0039, 0.0036, 0.0069, 0.0026, 0.0104, 0.003, 0.0043, 0.0068, 0.0037, 0.0108,
     0.0081, 0.0044, 0.0117, 0.0028, 0.0332, 0.019, 0.0113, 0.0048, 0.0113, 0.0078,
     0.0161, 0.0045, 0.0246, 0.0063, 0.0028, 0.0077, 0.0105, 0.0152, 0.0082, 0.0059,
     0.0207, 0.0078, 0.0098, 0.0154, 0.0115, 0.0243, 8e-04, 0.0078, 0.0051, 0.0048,
     0.0017, 0.0048, 0.0055, 0.0022, 0.0012, 0.0074, 0.0043, 0.0032, 0.0033, 0.016,
     0.0073, 0.0114, 0.0081, 0.0047, 0.0053, 0.0043, 0.0031, 0.0043, 0.006, 0.0048,
     1e-04, 0.0034, 0.0013, 0.0022, 0.0015, 5e-04, 0.0193, 0.0077, 0.0061), V60 = c(0.0078,
     0.0103, 0.0022, 0.004, 0.0032, 0.0075, 0.0088, 0.0063, 0.0011, 0.0044, 0.0058,
     0.0075, 0.0038, 0.007, 0.0122, 0.0071, 0.008, 0.011, 0.0055, 0.002, 0.0122,
     0.0138, 0.0023, 0.003, 0.0087, 0.0064, 0.0027, 0.0016, 0.003, 0.0031, 0.0063,
     0.0068, 0.0012, 0.0035, 0.0023, 0.0048, 0.0024, 0.0051, 0.0027, 0.0163, 0.0078,
     0.0053, 0.0024, 0.0037, 0.009, 0.0053, 0.0077, 0.0056, 0.0014, 0.0439, 0.0096,
     0.0131, 0.0019, 0.0049, 0.0066, 0.0133, 0.0079, 0.0198, 0.0039, 0.0023, 0.0074,
     0.0093, 0.01, 0.0143, 0.0021, 0.0057, 0.0112, 0.0085, 0.0218, 0.0015, 0.0098,
     0.0092, 6e-04, 0.0015, 0.0036, 0.0036, 0.005, 0.0021, 0.0032, 0.0037, 0.0038,
     0.0033, 0.0047, 0.0026, 0.0085, 0.0022, 0.0068, 0.0043, 0.0054, 0.0087, 0.0017,
     0.0033, 0.0018, 0.0018, 0.0024, 0.0055, 7e-04, 0.0016, 0.0014, 0.0085, 0.0031,
     0.0157, 0.0031, 0.0115)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6",
     "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18",
     "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29",
     "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51",
     "V52", "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), class = "data.frame", row.names = c("3",
     "7", "9", "10", "13", "18", "19", "20", "25", "26", "29", "30", "35", "36", "37",
     "39", "43", "44", "46", "47", "49", "50", "52", "53", "54", "55", "59", "61",
     "63", "64", "66", "68", "69", "71", "73", "74", "77", "78", "80", "81", "83",
     "85", "87", "88", "90", "92", "93", "94", "95", "98", "100", "101", "104", "108",
     "110", "111", "114", "116", "118", "120", "123", "124", "131", "135", "138",
     "139", "140", "141", "142", "145", "148", "152", "154", "156", "158", "159",
     "161", "162", "163", "164", "166", "168", "169", "170", "172", "173", "175",
     "176", "179", "180", "182", "183", "184", "189", "191", "192", "193", "194",
     "195", "201", "202", "204", "206", "208")))
     21: xgboost::predict
     22: getExportedValue(pkg, name)
     23: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)),
     call. = FALSE, domain = NA)
    
     2. Failure: generateCalibrationData (@test_base_generateCalibration.R#55) ------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     3. Failure: generateCalibrationData (@test_base_generateCalibration.R#57) ------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     4. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#72) --------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     5. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#74) --------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     6. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#46) ------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     7. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#48) ------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     8. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#216)
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     9. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#219)
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     10. Failure: generateThreshVsPerfData (@test_base_generateThreshVsPerf.R#119) --
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     11. Failure: generateThreshVsPerfData (@test_base_generateThreshVsPerf.R#121) --
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     12. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#18) -----------------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     13. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#20) -----------------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     14. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#22) -----------------
     obj$facet$nrow not equal to 2.
     target is NULL, current is numeric
    
    
     15. Failure: BenchmarkResult (@test_base_plotBMRBoxplots.R#22) -----------------
     obj$facet$ncol not equal to 2.
     target is NULL, current is numeric
    
    
     testthat results ================================================================
     OK: 2349 SKIPPED: 1 FAILED: 15
     1. Error: downsample wrapper works with xgboost, we had issue #492 (@test_base_downsample.R#38)
     2. Failure: generateCalibrationData (@test_base_generateCalibration.R#55)
     3. Failure: generateCalibrationData (@test_base_generateCalibration.R#57)
     4. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#72)
     5. Failure: plotFilterValues (@test_base_generateFilterValuesData.R#74)
     6. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#46)
     7. Failure: generateLearningCurve (@test_base_generateLearningCurve.R#48)
     8. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#216)
     9. Failure: generatePartialDependenceData (@test_base_generatePartialDependence.R#219)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 2.9
Flags: --no-stop-on-test-error
Check: tests
Result: ERROR
     Running ‘run-base.R’ [384s/513s]
     Running ‘run-classif.R’
     Running ‘run-cluster.R’
     Running ‘run-featsel.R’
     Running ‘run-learners-classif.R’
     Running ‘run-learners-classiflabelswitch.R’
     Running ‘run-learners-cluster.R’
     Running ‘run-learners-general.R’
     Running ‘run-learners-multilabel.R’
     Running ‘run-learners-regr.R’
     Running ‘run-learners-surv.R’
     Running ‘run-parallel.R’
     Running ‘run-regr.R’
     Running ‘run-stack.R’
     Running ‘run-surv.R’
     Running ‘run-tune.R’
    Running the tests in ‘tests/run-base.R’ failed.
    Complete output:
     > library(testthat)
     > test_check("mlr", filter = "base")
     Loading required package: mlr
     Loading required package: BBmisc
     Loading required package: ggplot2
     Loading required package: ParamHelpers
     Loading required package: stringi
     1. Error: downsample wrapper works with xgboost, we had issue #492 (@test_base_downsample.R#38)
     'predict' is not an exported object from 'namespace:xgboost'
     1: resample(lrn, binaryclass.task, rdesc) at testthat/test_base_downsample.R:38
     2: parallelMap(doResampleIteration, seq_len(rin$desc$iters), level = "mlr.resample",
     more.args = more.args)
     3: mapply(fun2, ..., MoreArgs = more.args, SIMPLIFY = FALSE, USE.NAMES = FALSE)
     4: (function (learner, task, rin, i, measures, weights, model, extract, show.info)
     {
     setSlaveOptions()
     if (show.info)
     messagef("[Resample] %s iter: %i", rin$desc$id, i)
     train.i = rin$train.inds[[i]]
     test.i = rin$test.inds[[i]]
     err.msgs = c(NA_character_, NA_character_)
     m = train(learner, task, subset = train.i, weights = weights[train.i])
     if (isFailureModel(m))
     err.msgs[1L] = getFailureModelMsg(m)
     ms.train = rep(NA, length(measures))
     ms.test = rep(NA, length(measures))
     pred.train = NULL
     pred.test = NULL
     pp = rin$desc$predict
     if (pp == "train") {
     pred.train = predict(m, task, subset = train.i)
     if (!is.na(pred.train$error))
     err.msgs[2L] = pred.train$error
     ms.train = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.train, measures = pm))
     }
     else if (pp == "test") {
     pred.test = predict(m, task, subset = test.i)
     if (!is.na(pred.test$error))
     err.msgs[2L] = pred.test$error
     ms.test = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.test, measures = pm))
     }
     else {
     pred.train = predict(m, task, subset = train.i)
     if (!is.na(pred.train$error))
     err.msgs[2L] = pred.train$error
     ms.train = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.train, measures = pm))
     pred.test = predict(m, task, subset = test.i)
     if (!is.na(pred.test$error))
     err.msgs[2L] = paste(err.msgs[2L], pred.test$error)
     ms.test = vnapply(measures, function(pm) performance(task = task, model = m,
     pred = pred.test, measures = pm))
     }
     ex = extract(m)
     list(measures.test = ms.test, measures.train = ms.train, model = if (model) m else NULL,
     pred.test = pred.test, pred.train = pred.train, err.msgs = err.msgs, extract = ex)
     })(dots[[1L]][[1L]], learner = structure(list(id = "classif.xgboost.downsampled",
     type = "classif", package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(
     pars = structure(list(dw.perc = structure(list(id = "dw.perc", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), dw.stratify = structure(list(id = "dw.stratify", type = "logical",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE,
     `FALSE` = FALSE), .Names = c("TRUE", "FALSE")), cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = FALSE, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("dw.perc", "dw.stratify")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     dw.perc = 0.5), .Names = "dw.perc"), predict.type = "response", fix.factors.prediction = FALSE,
     next.learner = structure(list(id = "classif.xgboost", type = "classif", package = "xgboost",
     properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"
     ), par.set = structure(list(pars = structure(list(booster = structure(list(
     id = "booster", type = "discrete", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer", len = 1L,
     lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("booster",
     "silent", "eta", "gamma", "max_depth", "min_child_weight", "subsample", "colsample_bytree",
     "num_parallel_tree", "lambda", "lambda_bias", "alpha", "objective", "eval_metric",
     "base_score", "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars", "forbidden"
     ), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(nrounds = 1), .Names = "nrounds"),
     predict.type = "response", name = "eXtreme Gradient Boosting", short.name = "xgboost",
     note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type",
     "package", "properties", "par.set", "par.vals", "predict.type", "name", "short.name",
     "note", "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), model.subclass = "DownsampleModel"), .Names = c("id",
     "type", "package", "properties", "par.set", "par.vals", "predict.type", "fix.factors.prediction",
     "next.learner", "model.subclass"), class = c("DownsampleWrapper", "BaseWrapper",
     "Learner")), task = structure(list(type = "classif", env = <environment>, weights = NULL,
     blocking = NULL, task.desc = structure(list(id = "binary", type = "classif",
     target = "Class", size = 208L, n.feat = structure(c(60L, 0L, 0L), .Names = c("numerics",
     "factors", "ordered")), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE,
     class.levels = c("M", "R"), positive = "M", negative = "R"), .Names = c("id",
     "type", "target", "size", "n.feat", "has.missings", "has.weights", "has.blocking",
     "class.levels", "positive", "negative"), class = c("TaskDescClassif", "TaskDescSupervised",
     "TaskDesc"))), .Names = c("type", "env", "weights", "blocking", "task.desc"), class = c("ClassifTask",
     "SupervisedTask", "Task")), rin = structure(list(desc = structure(list(id = "cross-validation",
     iters = 2L, predict = "test", stratify = FALSE), .Names = c("id", "iters", "predict",
     "stratify"), class = c("CVDesc", "ResampleDesc")), size = 208L, train.inds = list(
     c(45L, 137L, 82L, 56L, 188L, 133L, 11L, 76L, 91L, 106L, 132L, 48L, 72L, 207L,
     149L, 28L, 143L, 97L, 186L, 198L, 122L, 127L, 27L, 65L, 75L, 203L, 157L, 146L,
     79L, 51L, 205L, 128L, 1L, 24L, 155L, 144L, 89L, 187L, 174L, 8L, 86L, 38L, 130L,
     109L, 99L, 125L, 12L, 2L, 200L, 134L, 42L, 6L, 165L, 199L, 84L, 177L, 14L, 4L,
     190L, 129L, 185L, 62L, 70L, 40L, 196L, 150L, 32L, 171L, 17L, 160L, 112L, 16L,
     33L, 147L, 41L, 197L, 136L, 105L, 58L, 167L, 23L, 57L, 31L, 181L, 22L, 113L,
     119L, 96L, 103L, 151L, 178L, 21L, 115L, 102L, 107L, 121L, 34L, 5L, 126L, 117L,
     15L, 67L, 153L, 60L), c(36L, 208L, 100L, 52L, 10L, 141L, 71L, 163L, 182L, 142L,
     172L, 116L, 80L, 206L, 192L, 30L, 110L, 54L, 124L, 68L, 164L, 43L, 37L, 98L,
     44L, 87L, 145L, 104L, 88L, 3L, 74L, 183L, 173L, 154L, 159L, 201L, 19L, 179L,
     9L, 193L, 7L, 13L, 93L, 118L, 94L, 92L, 140L, 83L, 18L, 156L, 49L, 53L, 108L,
     158L, 35L, 184L, 101L, 29L, 66L, 202L, 90L, 111L, 25L, 26L, 152L, 191L, 39L,
     180L, 69L, 189L, 175L, 63L, 138L, 61L, 85L, 135L, 139L, 73L, 81L, 123L, 20L,
     170L, 176L, 46L, 47L, 78L, 162L, 120L, 194L, 95L, 168L, 148L, 64L, 195L, 77L,
     131L, 169L, 204L, 59L, 161L, 55L, 166L, 114L, 50L)), test.inds = list(c(3L, 7L,
     9L, 10L, 13L, 18L, 19L, 20L, 25L, 26L, 29L, 30L, 35L, 36L, 37L, 39L, 43L, 44L, 46L,
     47L, 49L, 50L, 52L, 53L, 54L, 55L, 59L, 61L, 63L, 64L, 66L, 68L, 69L, 71L, 73L, 74L,
     77L, 78L, 80L, 81L, 83L, 85L, 87L, 88L, 90L, 92L, 93L, 94L, 95L, 98L, 100L, 101L,
     104L, 108L, 110L, 111L, 114L, 116L, 118L, 120L, 123L, 124L, 131L, 135L, 138L, 139L,
     140L, 141L, 142L, 145L, 148L, 152L, 154L, 156L, 158L, 159L, 161L, 162L, 163L, 164L,
     166L, 168L, 169L, 170L, 172L, 173L, 175L, 176L, 179L, 180L, 182L, 183L, 184L, 189L,
     191L, 192L, 193L, 194L, 195L, 201L, 202L, 204L, 206L, 208L), c(1L, 2L, 4L, 5L, 6L,
     8L, 11L, 12L, 14L, 15L, 16L, 17L, 21L, 22L, 23L, 24L, 27L, 28L, 31L, 32L, 33L, 34L,
     38L, 40L, 41L, 42L, 45L, 48L, 51L, 56L, 57L, 58L, 60L, 62L, 65L, 67L, 70L, 72L, 75L,
     76L, 79L, 82L, 84L, 86L, 89L, 91L, 96L, 97L, 99L, 102L, 103L, 105L, 106L, 107L, 109L,
     112L, 113L, 115L, 117L, 119L, 121L, 122L, 125L, 126L, 127L, 128L, 129L, 130L, 132L,
     133L, 134L, 136L, 137L, 143L, 144L, 146L, 147L, 149L, 150L, 151L, 153L, 155L, 157L,
     160L, 165L, 167L, 171L, 174L, 177L, 178L, 181L, 185L, 186L, 187L, 188L, 190L, 196L,
     197L, 198L, 199L, 200L, 203L, 205L, 207L)), group = structure(integer(0), .Label = character(0), class = "factor")), .Names = c("desc",
     "size", "train.inds", "test.inds", "group"), class = "ResampleInstance"), weights = NULL,
     measures = list(structure(list(id = "mmce", minimize = TRUE, properties = c("classif",
     "classif.multi", "req.pred", "req.truth"), fun = function (task, model, pred,
     feats, extra.args)
     {
     measureMMCE(pred$data$truth, pred$data$response)
     }, extra.args = list(), best = 0, worst = 1, name = "Mean misclassification error",
     note = "", aggr = structure(list(id = "test.mean", name = "Test mean", fun = function (task,
     perf.test, perf.train, measure, group, pred)
     mean(perf.test)), .Names = c("id", "name", "fun"), class = "Aggregation")), .Names = c("id",
     "minimize", "properties", "fun", "extra.args", "best", "worst", "name", "note",
     "aggr"), class = "Measure")), model = FALSE, extract = function (model)
     {
     }, show.info = FALSE)
     5: predict(m, task, subset = test.i)
     6: predict.WrappedModel(m, task, subset = test.i)
     7: system.time(fun1(p <- fun2(do.call(predictLearner2, pars))), gcFirst = FALSE)
     8: fun1(p <- fun2(do.call(predictLearner2, pars)))
     9: evalVis(expr)
     10: withVisible(eval(expr, pf))
     11: eval(expr, pf)
     12: eval(expr, pf)
     13: fun2(do.call(predictLearner2, pars))
     14: do.call(predictLearner2, pars)
     15: (function (.learner, .model, .newdata, ...)
     {
     if (.learner$fix.factors.prediction) {
     fls = .model$factor.levels
     ns = names(fls)
     ns = intersect(colnames(.newdata), ns)
     fls = fls[ns]
     if (length(ns) > 0L)
     .newdata[ns] = mapply(factor, x = .newdata[ns], levels = fls, SIMPLIFY = FALSE)
     }
     p = predictLearner(.learner, .model, .newdata, ...)
     p = checkPredictLearnerOutput(.learner, .model, p)
     return(p)
     })(.learner = structure(list(id = "classif.xgboost.downsampled", type = "classif",
     package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(pars = structure(list(
     dw.perc = structure(list(id = "dw.perc", type = "numeric", len = 1L, lower = 0,
     upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 1, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), dw.stratify = structure(list(
     id = "dw.stratify", type = "logical", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(`TRUE` = TRUE, `FALSE` = FALSE), .Names = c("TRUE",
     "FALSE")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = FALSE,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param"))), .Names = c("dw.perc",
     "dw.stratify")), forbidden = NULL), .Names = c("pars", "forbidden"), class = c("LearnerParamSet",
     "ParamSet")), par.vals = structure(list(dw.perc = 0.5), .Names = "dw.perc"),
     predict.type = "response", fix.factors.prediction = FALSE, next.learner = structure(list(
     id = "classif.xgboost", type = "classif", package = "xgboost", properties = c("twoclass",
     "multiclass", "numerics", "factors", "prob", "weights"), par.set = structure(list(
     pars = structure(list(booster = structure(list(id = "booster", type = "discrete",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(gbtree = "gbtree",
     gblinear = "gblinear"), .Names = c("gbtree", "gblinear")), cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "gbtree", trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), silent = structure(list(id = "silent", type = "integer", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eta = structure(list(id = "eta", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.3, trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), gamma = structure(list(id = "gamma", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight",
     type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), subsample = structure(list(id = "subsample", type = "numeric", len = 1L,
     lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree",
     type = "numeric", len = 1L, lower = 0, upper = 1, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "binary:logistic",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = "error", trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = 0.5, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), feval = structure(list(id = "feval", type = "untyped", len = 1L,
     lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = NULL, trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), verbose = structure(list(id = "verbose", type = "integer",
     len = 1L, lower = 0, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 2, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), print.every.n = structure(list(id = "print.every.n", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type",
     "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), early.stop.round = structure(list(id = "early.stop.round",
     type = "integer", len = 1L, lower = 1, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper",
     "values", "cnames", "allow.inf", "has.default", "default", "trafo", "requires",
     "tunable", "special.vals", "when"), class = c("LearnerParam", "Param"
     )), maximize = structure(list(id = "maximize", type = "logical", len = 1L,
     lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE,
     `FALSE` = FALSE), .Names = c("TRUE", "FALSE")), cnames = NULL,
     allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("booster", "silent", "eta", "gamma", "max_depth",
     "min_child_weight", "subsample", "colsample_bytree", "num_parallel_tree",
     "lambda", "lambda_bias", "alpha", "objective", "eval_metric", "base_score",
     "missing", "nthread", "nrounds", "feval", "verbose", "print.every.n",
     "early.stop.round", "maximize")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     nrounds = 1), .Names = "nrounds"), predict.type = "response", name = "eXtreme Gradient Boosting",
     short.name = "xgboost", note = "All settings are passed directly, rather than through `xgboost`'s `params` argument. `nrounds` has been set to `1` by default. `num_class` is set internally, so do not set this manually.",
     fix.factors.prediction = FALSE, config = list()), .Names = c("id", "type",
     "package", "properties", "par.set", "par.vals", "predict.type", "name", "short.name",
     "note", "fix.factors.prediction", "config"), class = c("classif.xgboost", "RLearnerClassif",
     "RLearner", "Learner")), model.subclass = "DownsampleModel"), .Names = c("id",
     "type", "package", "properties", "par.set", "par.vals", "predict.type", "fix.factors.prediction",
     "next.learner", "model.subclass"), class = c("DownsampleWrapper", "BaseWrapper",
     "Learner")), .model = structure(list(learner = structure(list(id = "classif.xgboost.downsampled",
     type = "classif", package = c("mlr", "xgboost"), properties = NULL, par.set = structure(list(
     pars = structure(list(dw.perc = structure(list(id = "dw.perc", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), dw.stratify = structure(list(id = "dw.stratify", type = "logical",
     len = 1L, lower = NULL, upper = NULL, values = structure(list(`TRUE` = TRUE,
     `FALSE` = FALSE), .Names = c("TRUE", "FALSE")), cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = FALSE, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param"))), .Names = c("dw.perc", "dw.stratify")), forbidden = NULL), .Names = c("pars",
     "forbidden"), class = c("LearnerParamSet", "ParamSet")), par.vals = structure(list(
     dw.perc = 0.5), .Names = "dw.perc"), predict.type = "response", fix.factors.prediction = FALSE,
     next.learner = structure(list(id = "classif.xgboost", type = "classif", package = "xgboost",
     properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "weights"
     ), par.set = structure(list(pars = structure(list(booster = structure(list(
     id = "booster", type = "discrete", len = 1L, lower = NULL, upper = NULL,
     values = structure(list(gbtree = "gbtree", gblinear = "gblinear"), .Names = c("gbtree",
     "gblinear")), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "gbtree",
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), silent = structure(list(
     id = "silent", type = "integer", len = 1L, lower = -Inf, upper = Inf,
     values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE,
     default = 0, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), eta = structure(list(
     id = "eta", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.3,
     trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(),
     when = "train"), .Names = c("id", "type", "len", "lower", "upper", "values",
     "cnames", "allow.inf", "has.default", "default", "trafo", "requires", "tunable",
     "special.vals", "when"), class = c("LearnerParam", "Param")), gamma = structure(list(
     id = "gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL,
     cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL,
     requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), max_depth = structure(list(id = "max_depth", type = "integer",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 6, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), min_child_weight = structure(list(id = "min_child_weight", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), subsample = structure(list(id = "subsample", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), colsample_bytree = structure(list(id = "colsample_bytree", type = "numeric",
     len = 1L, lower = 0, upper = 1, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), num_parallel_tree = structure(list(id = "num_parallel_tree", type = "integer",
     len = 1L, lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda = structure(list(id = "lambda", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), lambda_bias = structure(list(id = "lambda_bias", type = "numeric",
     len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), alpha = structure(list(id = "alpha", type = "numeric", len = 1L,
     lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), objective = structure(list(id = "objective", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "binary:logistic", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), eval_metric = structure(list(id = "eval_metric", type = "untyped",
     len = 1L, lower = NULL, upper = NULL, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = "error", trafo = NULL, requires = NULL,
     tunable = TRUE, special.vals = list(), when = "train"), .Names = c("id",
     "type", "len", "lower", "upper", "values", "cnames", "allow.inf", "has.default",
     "default", "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), base_score = structure(list(id = "base_score", type = "numeric",
     len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0.5, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), missing = structure(list(id = "missing", type = "numeric", len = 1L,
     lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nthread = structure(list(id = "nthread", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
     has.default = TRUE, default = 16, trafo = NULL, requires = NULL, tunable = TRUE,
     special.vals = list(), when = "train"), .Names = c("id", "type", "len",
     "lower", "upper", "values", "cnames", "allow.inf", "has.default", "default",
     "trafo", "requires", "tunable", "special.vals", "when"), class = c("LearnerParam",
     "Param")), nrounds = structure(list(id = "nrounds", type = "integer", len = 1L,
     lower = 1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE,
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     0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x80, 0xdf,
     0x4f, 0x2d, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00,
     0x00, 0x00, 0x3b, 0x00, 0x00, 0x80, 0x82, 0xe2, 0x47, 0x3b, 0x01, 0x00, 0x00,
     0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,
     0x9a, 0x99, 0x99, 0xbe, 0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x32, 0xa4, 0xf3, 0x3e, 0x02, 0x00,
     0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
     0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x8c, 0xaf, 0xf8, 0xbe, 0xc7,
     0x92, 0xac, 0x41, 0x00, 0x00, 0x50, 0x41, 0x25, 0x49, 0x92, 0x3d, 0x00, 0x00,
     0x00, 0x00, 0xef, 0xd4, 0x14, 0x41, 0x00, 0x00, 0xe8, 0x40, 0xd9, 0x64, 0x93,
     0x3f, 0x02, 0x00, 0x00, 0x00, 0x90, 0xb9, 0x43, 0x40, 0x00, 0x00, 0xb8, 0x40,
     0x68, 0x2f, 0xa1, 0xbf, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x80, 0x3f, 0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0xc8, 0x40, 0xd4, 0x08, 0xcb, 0x3f, 0x00, 0x00, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x3f, 0x00, 0x00, 0x00, 0x80,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x88, 0x40, 0xf4,
     0x3c, 0xcf, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
     0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x6e, 0x69, 0x74, 0x65, 0x72, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     0x00, 0x30)), niter = 1, evaluation_log = structure(list(iter = 1, train_error = 0.076923), .Names = c("iter",
     "train_error"), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x7fd924009378>),
     call = xgb.train(params = params, data = dtrain, nrounds = nrounds, watchlist = watchlist,
     verbose = verbose, print_every_n = print_every_n, early_stopping_rounds = early_stopping_rounds,
     maximize = maximize, save_period = save_period, save_name = save_name, xgb_model = xgb_model,
     callbacks = callbacks, objective = ..1), params = structure(list(objective = "binary:logistic",
     silent = 1), .Names = c("objective", "silent")), callbacks = structure(list(
     cb.print.evaluation = structure(function (env = parent.frame())
     {
     if (length(env$bst_evaluation) == 0 || period == 0 || NVL(env$rank, 0) !=
     0)
     return()
     i <- env$iteration
     if ((i - 1)%%period == 0 || i == env$begin_iteration || i == env$end_iteration) {
     msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
     cat(msg, "\n")
     }
     }, call = cb.print.evaluation(period = print_every_n), name = "cb.print.evaluation"),
     cb.evaluation.log = structure(function (env = parent.frame(), finalize = FALSE)
     {
     if (is.null(mnames))
     init(env)
     if (finalize)
     return(finalizer(env))
     ev <- env$bst_evaluation
     if (!is.null(env$bst_evaluation_err))
     ev <- c(ev, env$bst_evaluation_err)
     env$evaluation_log <- c(env$evaluation_log, list(c(iter = env$iteration,
     ev)))
     }, call = cb.evaluation.log(), name = "cb.evaluation.log"), cb.save.model = structure(function (env = parent.frame())
     {
     if (is.null(env$bst))
     stop("'save_model' callback requires the 'bst' booster object in its calling frame")
     if ((save_period > 0 && (env$iteration - env$begin_iteration)%%save_period ==
     0) || (save_period == 0 && env$iteration == env$end_iteration))
     xgb.save(env$bst, sprintf(save_name, env$iteration))
     }, call = cb.save.model(save_period = save_period, save_name = save_name), name = "cb.save.model")), .Names = c("cb.print.evaluation",
     "cb.evaluation.log", "cb.save.model"))), .Names = c("handle", "raw", "niter",
     "evaluation_log", "call", "params", "callbacks"), class = "xgb.Booster"), task.desc = structure(list(
     id = "binary", type = "classif", target = "Class", size = 52L, n.feat = structure(c(60L,
     0L, 0L), .Names = c("numerics", "factors", "ordered")), has.missings = FALSE,
     has.weights = FALSE, has.blocking = FALSE, class.levels = c("M", "R"), positive = "M",
     negative = "R"), .Names = c("id", "type", "target", "size", "n.feat", "has.missings",
     "has.weights", "has.blocking", "class.levels", "positive", "negative"), class = c("TaskDescClassif",
     "TaskDescSupervised", "TaskDesc")), subset = 1:52, features = c("V1", "V2", "V3",
     "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
     "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28",
     "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
     "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52",
     "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60"), factor.levels = structure(list(
     Class = c("M", "R")), .Names = "Class"), time = 0.0459999999999923), .Names = c("learner",
     "learner.model", "task.desc", "subset", "features", "factor.levels", "time"), class = "WrappedModel")), .Names = "next.model", class = c("DownsampleModel",
     "ChainModel", "WrappedModel")), task.desc = structure(list(id = "binary", type = "classif",
     target = "Class", size = 208L, n.feat = structure(c(60L, 0L, 0L), .Names = c("numerics",
     "factors", "ordered")), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE,
     class.levels = c("M", "R"), positive = "M", negative = "R"), .Names = c("id",
     "type", "target", "size", "n.feat", "has.missings", "has.weights", "has.blocking",
     "class.levels", "positive", "negative"), class = c("TaskDescClassif", "TaskDescSupervised",
     "TaskDesc")), subset = c(45L, 137L, 82L, 56L, 188L, 133L, 11L, 76L, 91L, 106L, 132L,
     48L, 72L, 207L, 149L, 28L, 143L, 97L, 186L, 198L, 122L, 127L, 27L, 65L, 75L, 203L,
     157L, 146L, 79L, 51L, 205L, 128L, 1L, 24L, 155L, 144L, 89L, 187L, 174L, 8L, 86L,
     38L, 130L, 109L, 99L, 125L, 12L, 2L, 200L, 134L, 42L, 6L, 165L, 199L, 84L, 177L,
     14L, 4L, 190L, 129L, 185L, 62L, 70L, 40L, 196L, 150L, 32L, 171L, 17L, 160L, 112L,
     16L, 33L, 147L, 41L, 197L, 136L, 105L, 58L, 167L, 23L, 57L, 31L, 181L, 22L, 113L,
     119L, 96L, 103L, 151L, 178L, 21L, 115L, 102L, 107L, 121L, 34L, 5L, 126L, 117L, 15L,
     67L, 153L, 60L), features = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9",
     "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21",
     "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33",
     "V34", "V35", "V36", "V37", "V38", "V39", "V40", "V41", "V42", "V43", "V44", "V45",
     "V46", "V47", "V48", "V49", "V50", "V51", "V52", "V53", "V54", "V55", "V56", "V57",
     "V58", "V59", "V60"), factor.levels = structure(list(Class = c("M", "R")), .Names = "Class"),
     time = 0.0600000000000023), .Names = c("learner", "learner.model", "task.desc",
     "subset", "features", "factor.levels", "time"), class = c("DownsampleModel", "BaseWrapperModel",
     "WrappedModel")), .newdata = structure(list(V1 = c(0.0262, 0.0317, 0.0223, 0.0164,
     0.0079, 0.0192, 0.027, 0.0126, 0.0293, 0.0201, 0.01, 0.0189, 0.0311, 0.0206, 0.0094,
     0.0123, 0.0211, 0.0093, 0.0408, 0.0308, 0.019, 0.0119, 0.0131, 0.0087, 0.0293, 0.0132,
     0.0225, 0.013, 0.0086, 0.0067, 0.0176, 0.0368, 0.0195, 0.0065, 0.0208, 0.0139, 0.0239,
     0.0336, 0.0108, 0.0229, 0.0409, 0.0378, 0.0188, 0.0856, 0.0235, 0.0253, 0.026, 0.0459,
     0.0025, 0.0491, 0.0201, 0.0629, 0.0162, 0.0428, 0.0264, 0.021, 0.0283, 0.0414, 0.0228,
     0.0261, 0.0249, 0.027, 0.0443, 0.1083, 0.043, 0.0731, 0.0164, 0.0412, 0.0707, 0.0299,
     0.0654, 0.0231, 0.0233, 0.0211, 0.0201, 0.0107, 0.0258, 0.0305, 0.0217, 0.0072, 0.0221,
     0.0137, 0.0015, 0.013, 0.0179, 0.018, 0.0191, 0.0294, 0.0197, 0.0394, 0.0423, 0.0095,
     0.0096, 0.0089, 0.0156, 0.0315, 0.0056, 0.0203, 0.0392, 0.0131, 0.0335, 0.0187, 0.0522,
     0.026), V2 = c(0.0582, 0.0956, 0.0375, 0.0173, 0.0086, 0.0607, 0.0092, 0.0149, 0.0644,
     0.0026, 0.0275, 0.0308, 0.0491, 0.0132, 0.0166, 0.0022, 0.0319, 0.0269, 0.0653, 0.0339,
     0.0038, 0.0582, 0.0068, 0.0046, 0.0378, 0.008, 0.0019, 6e-04, 0.0215, 0.0096, 0.0172,
     0.0403, 0.0142, 0.0122, 0.0186, 0.0222, 0.0189, 0.0294, 0.0086, 0.0369, 0.0421, 0.0318,
     0.037, 0.0454, 0.0291, 0.0808, 0.0192, 0.0437, 0.0309, 0.0279, 0.0423, 0.1065, 0.0253,
     0.0555, 0.0071, 0.0121, 0.0599, 0.0436, 0.0106, 0.0266, 0.0119, 0.0163, 0.0446, 0.107,
     0.0902, 0.1249, 0.0627, 0.1135, 0.1252, 0.0688, 0.0649, 0.0315, 0.0394, 0.0128, 0.0178,
     0.0453, 0.0433, 0.0363, 0.0152, 0.0027, 0.0065, 0.0297, 0.0186, 0.012, 0.0136, 0.0444,
     0.0173, 0.0123, 0.0394, 0.042, 0.0321, 0.0308, 0.0404, 0.0274, 0.021, 0.0252, 0.0267,
     0.0121, 0.0108, 0.0387, 0.0258, 0.0346, 0.0437, 0.0363), V3 = c(0.1099, 0.1321, 0.0484,
     0.0347, 0.0055, 0.0378, 0.0145, 0.0641, 0.039, 0.0138, 0.019, 0.0197, 0.0692, 0.0533,
     0.0398, 0.0196, 0.0415, 0.0217, 0.0397, 0.0202, 0.0642, 0.0623, 0.0308, 0.0081, 0.0257,
     0.0188, 0.0075, 0.0088, 0.0242, 0.0024, 0.0501, 0.0317, 0.0181, 0.0068, 0.0131, 0.0089,
     0.0466, 0.0476, 0.0058, 0.004, 0.0573, 0.0423, 0.0953, 0.0382, 0.0749, 0.0507, 0.0254,
     0.0347, 0.0171, 0.0592, 0.0554, 0.1526, 0.0262, 0.0708, 0.0342, 0.0203, 0.0656, 0.0447,
     0.013, 0.0223, 0.0277, 0.0341, 0.0235, 0.0257, 0.0833, 0.1665, 0.0738, 0.0518, 0.1447,
     0.0992, 0.0737, 0.017, 0.0416, 0.0015, 0.0274, 0.0289, 0.0547, 0.0214, 0.0346, 0.0089,
     0.0164, 0.0116, 0.0289, 0.0436, 0.0408, 0.0476, 0.0291, 0.0117, 0.0384, 0.0446, 0.0709,
     0.0539, 0.0682, 0.0248, 0.0282, 0.0167, 0.0221, 0.038, 0.0267, 0.0329, 0.0398, 0.0168,
     0.018, 0.0136), V4 = c(0.1083, 0.1408, 0.0475, 0.007, 0.025, 0.0774, 0.0278, 0.1732,
     0.0173, 0.0062, 0.0371, 0.0622, 0.0831, 0.0569, 0.0359, 0.0206, 0.0286, 0.0339, 0.0604,
     0.0889, 0.0452, 0.06, 0.0311, 0.023, 0.0062, 0.0141, 0.0097, 0.0456, 0.0445, 0.0058,
     0.0285, 0.0293, 0.0406, 0.0108, 0.0211, 0.0108, 0.044, 0.0539, 0.046, 0.0375, 0.013,
     0.035, 0.0824, 0.0203, 0.0519, 0.0244, 0.0061, 0.0456, 0.0228, 0.127, 0.0783, 0.1229,
     0.0386, 0.0618, 0.0793, 0.1036, 0.0229, 0.0844, 0.0842, 0.0749, 0.076, 0.0247, 0.1008,
     0.0837, 0.0813, 0.1496, 0.0608, 0.0232, 0.1644, 0.1021, 0.1132, 0.0226, 0.0547, 0.045,
     0.0232, 0.0713, 0.0681, 0.0227, 0.0346, 0.0061, 0.0487, 0.0082, 0.0195, 0.0624, 0.0633,
     0.0698, 0.0301, 0.0113, 0.0076, 0.0551, 0.0108, 0.0411, 0.0688, 0.0237, 0.0596, 0.0479,
     0.0561, 0.0128, 0.0257, 0.0078, 0.057, 0.0177, 0.0292, 0.0272), V5 = c(0.0974, 0.1674,
     0.0647, 0.0187, 0.0344, 0.1388, 0.0412, 0.2565, 0.0476, 0.0133, 0.0416, 0.008, 0.0079,
     0.0647, 0.0681, 0.018, 0.0121, 0.0305, 0.0496, 0.157, 0.0333, 0.1397, 0.0085, 0.0586,
     0.013, 0.0436, 0.0445, 0.0525, 0.0667, 0.0197, 0.0262, 0.082, 0.0391, 0.0217, 0.061,
     0.0215, 0.0657, 0.0794, 0.0752, 0.0455, 0.0183, 0.1787, 0.0249, 0.0385, 0.0227, 0.1724,
     0.0352, 0.0067, 0.0434, 0.1772, 0.062, 0.1437, 0.0645, 0.1215, 0.1043, 0.1675, 0.0839,
     0.0419, 0.1117, 0.1364, 0.1218, 0.0822, 0.2252, 0.0748, 0.0165, 0.1443, 0.0233, 0.0646,
     0.1693, 0.08, 0.2482, 0.041, 0.0993, 0.0711, 0.0724, 0.1075, 0.0784, 0.0456, 0.0484,
     0.042, 0.0519, 0.0241, 0.0515, 0.0428, 0.0596, 0.1615, 0.0463, 0.0497, 0.0251, 0.0597,
     0.107, 0.0613, 0.0887, 0.0224, 0.0462, 0.0902, 0.0936, 0.0537, 0.041, 0.0721, 0.0529,
     0.0393, 0.0351, 0.0214), V6 = c(0.228, 0.171, 0.0591, 0.0671, 0.0546, 0.0809, 0.0757,
     0.2559, 0.0816, 0.0151, 0.0201, 0.0789, 0.02, 0.1432, 0.0706, 0.0492, 0.0438, 0.1172,
     0.1817, 0.175, 0.069, 0.1883, 0.0767, 0.0682, 0.0612, 0.0668, 0.0906, 0.0778, 0.0771,
     0.0618, 0.0351, 0.1342, 0.0249, 0.0284, 0.0613, 0.0136, 0.0742, 0.0804, 0.0887, 0.1452,
     0.1019, 0.1635, 0.0488, 0.0534, 0.0834, 0.3823, 0.0701, 0.089, 0.1224, 0.1908, 0.0871,
     0.119, 0.0472, 0.1524, 0.0783, 0.0418, 0.1673, 0.1215, 0.1506, 0.1513, 0.1538, 0.1256,
     0.2611, 0.1125, 0.0277, 0.277, 0.1048, 0.1124, 0.0844, 0.0629, 0.1257, 0.0116, 0.1515,
     0.1563, 0.0833, 0.1019, 0.125, 0.0665, 0.0526, 0.0865, 0.0849, 0.0253, 0.0817, 0.0349,
     0.0808, 0.0887, 0.069, 0.0998, 0.0629, 0.1416, 0.0973, 0.1039, 0.0932, 0.0845, 0.0779,
     0.1057, 0.1146, 0.0874, 0.0491, 0.1341, 0.1091, 0.163, 0.1171, 0.0338), V7 = c(0.2431,
     0.0731, 0.0753, 0.1056, 0.0528, 0.0568, 0.1026, 0.2947, 0.0993, 0.0541, 0.0314, 0.144,
     0.0981, 0.1344, 0.102, 0.0033, 0.1299, 0.145, 0.1178, 0.092, 0.0901, 0.1422, 0.0771,
     0.0993, 0.0895, 0.0609, 0.0889, 0.0931, 0.0499, 0.0432, 0.0362, 0.1161, 0.0892, 0.0527,
     0.0612, 0.0659, 0.138, 0.1136, 0.1015, 0.2211, 0.1054, 0.0887, 0.1424, 0.214, 0.0677,
     0.3729, 0.1263, 0.1798, 0.1947, 0.2217, 0.1201, 0.0884, 0.1056, 0.1543, 0.1417, 0.0723,
     0.1154, 0.2002, 0.1776, 0.1316, 0.1192, 0.1323, 0.2061, 0.3322, 0.0569, 0.2555, 0.1338,
     0.1787, 0.0715, 0.013, 0.1797, 0.0223, 0.1674, 0.1518, 0.1232, 0.1606, 0.1296, 0.0939,
     0.0773, 0.1182, 0.0812, 0.0279, 0.1005, 0.0384, 0.209, 0.0596, 0.0576, 0.1326, 0.0747,
     0.0956, 0.0961, 0.1016, 0.0955, 0.1488, 0.1365, 0.1024, 0.0706, 0.1021, 0.1053, 0.1626,
     0.1709, 0.2028, 0.1257, 0.0655), V8 = c(0.3771, 0.1401, 0.0098, 0.0697, 0.0958, 0.0219,
     0.1138, 0.411, 0.0315, 0.021, 0.0651, 0.1451, 0.1016, 0.2041, 0.0893, 0.0398, 0.139,
     0.0638, 0.1024, 0.1353, 0.1454, 0.1447, 0.064, 0.0717, 0.1107, 0.0131, 0.0655, 0.0941,
     0.0906, 0.0951, 0.0535, 0.0663, 0.0973, 0.0575, 0.0506, 0.0954, 0.1099, 0.1228, 0.0494,
     0.1188, 0.107, 0.0817, 0.1972, 0.311, 0.2002, 0.3583, 0.108, 0.1741, 0.1661, 0.0768,
     0.2707, 0.0907, 0.1388, 0.0391, 0.1176, 0.0828, 0.1098, 0.1516, 0.0997, 0.1654, 0.1229,
     0.1584, 0.1668, 0.459, 0.2057, 0.1712, 0.0644, 0.2407, 0.0947, 0.0813, 0.0989, 0.0805,
     0.1513, 0.1206, 0.1298, 0.2119, 0.1729, 0.0972, 0.0862, 0.0999, 0.1833, 0.013, 0.0124,
     0.0446, 0.3465, 0.1071, 0.1103, 0.1117, 0.0578, 0.0802, 0.1323, 0.1394, 0.214, 0.1224,
     0.078, 0.1209, 0.0996, 0.0852, 0.169, 0.1902, 0.1684, 0.1694, 0.1178, 0.14), V9 = c(0.5598,
     0.2083, 0.0684, 0.0962, 0.1009, 0.1037, 0.0794, 0.4983, 0.0736, 0.0505, 0.1896, 0.1789,
     0.2025, 0.1571, 0.0381, 0.0791, 0.0695, 0.074, 0.0583, 0.1593, 0.074, 0.0487, 0.0726,
     0.0576, 0.0973, 0.0899, 0.1624, 0.1711, 0.1229, 0.0836, 0.0258, 0.0155, 0.084, 0.1054,
     0.0989, 0.0786, 0.1384, 0.1235, 0.0472, 0.075, 0.2302, 0.1779, 0.1873, 0.2837, 0.2876,
     0.3429, 0.1523, 0.1598, 0.1368, 0.1246, 0.1206, 0.2107, 0.0598, 0.061, 0.0453, 0.0494,
     0.137, 0.0818, 0.1428, 0.1864, 0.2119, 0.2017, 0.1801, 0.5526, 0.3887, 0.0466, 0.1522,
     0.2682, 0.1583, 0.1761, 0.246, 0.2365, 0.1723, 0.1666, 0.2085, 0.3061, 0.2794, 0.2535,
     0.1451, 0.1976, 0.2228, 0.0489, 0.1168, 0.1318, 0.5276, 0.3175, 0.2423, 0.2984, 0.1357,
     0.1618, 0.2462, 0.2592, 0.2546, 0.1569, 0.1038, 0.1241, 0.1673, 0.1136, 0.2105, 0.261,
     0.1865, 0.2328, 0.1258, 0.1843), V10 = c(0.6194, 0.3513, 0.1487, 0.0251, 0.124, 0.1186,
     0.152, 0.592, 0.086, 0.1097, 0.2668, 0.2522, 0.0767, 0.1573, 0.1328, 0.0475, 0.0568,
     0.136, 0.2176, 0.2795, 0.0349, 0.0864, 0.0901, 0.0818, 0.0751, 0.0922, 0.1452, 0.1483,
     0.1185, 0.118, 0.0474, 0.0506, 0.1191, 0.1109, 0.1093, 0.1015, 0.1376, 0.0842, 0.0393,
     0.1631, 0.2259, 0.2053, 0.1806, 0.2751, 0.3674, 0.2197, 0.163, 0.1408, 0.143, 0.2028,
     0.0279, 0.3597, 0.1334, 0.0113, 0.0945, 0.0686, 0.1767, 0.1975, 0.2227, 0.2013, 0.2531,
     0.2122, 0.3083, 0.5966, 0.7106, 0.1114, 0.078, 0.2058, 0.1247, 0.0998, 0.3422, 0.2461,
     0.2078, 0.1345, 0.272, 0.2936, 0.2954, 0.3127, 0.211, 0.2318, 0.181, 0.0874, 0.1476,
     0.1375, 0.5965, 0.2918, 0.3134, 0.3473, 0.1695, 0.2558, 0.2696, 0.3745, 0.2952, 0.2119,
     0.1567, 0.1533, 0.1859, 0.1747, 0.2471, 0.3193, 0.266, 0.2684, 0.2529, 0.2354), V11 = c(0.6333,
     0.1786, 0.1156, 0.0801, 0.1097, 0.1237, 0.1675, 0.5832, 0.0414, 0.0841, 0.3376, 0.2607,
     0.1767, 0.2327, 0.1303, 0.1152, 0.0869, 0.2132, 0.2459, 0.3336, 0.1459, 0.2143, 0.075,
     0.1315, 0.0528, 0.1445, 0.1442, 0.1532, 0.0775, 0.0978, 0.0526, 0.0906, 0.1522, 0.0937,
     0.1063, 0.1261, 0.0938, 0.0357, 0.1106, 0.2709, 0.2373, 0.3135, 0.2139, 0.2707, 0.2974,
     0.2653, 0.103, 0.2693, 0.0994, 0.0947, 0.2251, 0.5466, 0.2969, 0.1255, 0.1132, 0.1125,
     0.1995, 0.2309, 0.2621, 0.289, 0.2855, 0.221, 0.3794, 0.5304, 0.7342, 0.1739, 0.1791,
     0.1546, 0.234, 0.0523, 0.2128, 0.2245, 0.1239, 0.0785, 0.2188, 0.3104, 0.2506, 0.2192,
     0.2343, 0.2472, 0.2549, 0.11, 0.2118, 0.2026, 0.6254, 0.3273, 0.4786, 0.4231, 0.1734,
     0.3078, 0.3412, 0.4229, 0.4025, 0.3003, 0.2476, 0.2128, 0.2481, 0.2198, 0.268, 0.3468,
     0.3188, 0.3108, 0.2716, 0.272), V12 = c(0.706, 0.0658, 0.1654, 0.1056, 0.1215, 0.1601,
     0.137, 0.5419, 0.0472, 0.0942, 0.3282, 0.371, 0.2555, 0.1785, 0.0273, 0.052, 0.1935,
     0.3738, 0.3332, 0.294, 0.3473, 0.372, 0.0844, 0.1862, 0.1209, 0.1475, 0.0948, 0.11,
     0.1101, 0.0909, 0.1854, 0.2545, 0.1322, 0.0827, 0.1179, 0.0828, 0.0259, 0.0689, 0.1412,
     0.3358, 0.3323, 0.3118, 0.1523, 0.0946, 0.0837, 0.3223, 0.2187, 0.3259, 0.225, 0.2497,
     0.2615, 0.5205, 0.4754, 0.2473, 0.084, 0.1741, 0.2869, 0.3025, 0.3109, 0.365, 0.2961,
     0.2399, 0.5364, 0.2251, 0.5033, 0.316, 0.2681, 0.2671, 0.1764, 0.0904, 0.1377, 0.152,
     0.0236, 0.0367, 0.3037, 0.3431, 0.2601, 0.2621, 0.2087, 0.288, 0.2984, 0.1084, 0.2575,
     0.2389, 0.4507, 0.3035, 0.5239, 0.5044, 0.247, 0.3404, 0.4292, 0.4499, 0.5148, 0.3094,
     0.2783, 0.2536, 0.2712, 0.2721, 0.3049, 0.3738, 0.3553, 0.2933, 0.2374, 0.2442),
     V13 = c(0.5544, 0.0513, 0.3833, 0.1266, 0.1874, 0.352, 0.1361, 0.5472, 0.0835,
     0.1204, 0.2432, 0.3906, 0.2812, 0.1507, 0.0644, 0.1192, 0.1478, 0.3738, 0.3087,
     0.1608, 0.3197, 0.2665, 0.1226, 0.2789, 0.1763, 0.2087, 0.0618, 0.089, 0.1042,
     0.0656, 0.104, 0.1464, 0.1434, 0.092, 0.1291, 0.0493, 0.1499, 0.1705, 0.2202,
     0.4091, 0.3827, 0.3686, 0.1975, 0.102, 0.1912, 0.5582, 0.1542, 0.4545, 0.2444,
     0.2209, 0.177, 0.5127, 0.5677, 0.3011, 0.0717, 0.271, 0.3275, 0.3938, 0.2859,
     0.351, 0.3341, 0.2964, 0.6173, 0.2402, 0.3, 0.3249, 0.1788, 0.3141, 0.2284, 0.2655,
     0.4032, 0.1732, 0.1771, 0.1227, 0.2959, 0.2456, 0.2249, 0.2419, 0.1645, 0.2126,
     0.2624, 0.1094, 0.2354, 0.2112, 0.3693, 0.3033, 0.4393, 0.5237, 0.3141, 0.34,
     0.3682, 0.5404, 0.4901, 0.2743, 0.2896, 0.2686, 0.2934, 0.2105, 0.2863, 0.3055,
     0.3116, 0.2275, 0.1878, 0.1665), V14 = c(0.532, 0.3752, 0.3598, 0.089, 0.3383,
     0.4479, 0.1345, 0.5314, 0.0938, 0.042, 0.1268, 0.2672, 0.2722, 0.1916, 0.0712,
     0.1943, 0.1871, 0.2673, 0.2613, 0.3335, 0.2823, 0.2113, 0.1619, 0.2579, 0.2039,
     0.2558, 0.1641, 0.1236, 0.0853, 0.0593, 0.0948, 0.1272, 0.1244, 0.0911, 0.1591,
     0.0848, 0.2851, 0.3257, 0.2976, 0.44, 0.484, 0.3885, 0.4844, 0.4519, 0.504, 0.6916,
     0.263, 0.5785, 0.3239, 0.3195, 0.3709, 0.5395, 0.569, 0.3747, 0.1968, 0.3087,
     0.3769, 0.505, 0.3316, 0.3495, 0.4287, 0.4061, 0.7842, 0.2689, 0.1951, 0.2164,
     0.1039, 0.2904, 0.3115, 0.3099, 0.5684, 0.3099, 0.3115, 0.2614, 0.2059, 0.1887,
     0.2115, 0.2179, 0.1689, 0.0708, 0.1893, 0.1023, 0.1334, 0.1444, 0.2864, 0.2587,
     0.344, 0.4398, 0.3297, 0.3951, 0.394, 0.4303, 0.4127, 0.2547, 0.2956, 0.2803,
     0.2637, 0.1727, 0.2294, 0.1926, 0.1965, 0.0994, 0.0983, 0.0336), V15 = c(0.6479,
     0.5419, 0.1713, 0.0198, 0.3227, 0.3769, 0.2144, 0.4981, 0.1466, 0.0031, 0.1278,
     0.2716, 0.3227, 0.2061, 0.1204, 0.184, 0.1994, 0.2333, 0.3232, 0.4985, 0.0166,
     0.1103, 0.2317, 0.224, 0.2727, 0.2603, 0.0708, 0.1197, 0.0456, 0.0832, 0.0912,
     0.1223, 0.0653, 0.1487, 0.168, 0.1514, 0.5743, 0.4602, 0.4116, 0.5485, 0.6812,
     0.585, 0.7298, 0.6737, 0.6352, 0.7943, 0.294, 0.4471, 0.3039, 0.334, 0.4533,
     0.6558, 0.6421, 0.452, 0.2633, 0.3575, 0.4169, 0.5872, 0.3755, 0.4325, 0.5205,
     0.5095, 0.8392, 0.6646, 0.2767, 0.2031, 0.198, 0.3531, 0.4725, 0.352, 0.2398,
     0.438, 0.499, 0.428, 0.0906, 0.1184, 0.127, 0.1159, 0.165, 0.1194, 0.0668, 0.0601,
     0.0092, 0.0742, 0.1635, 0.1682, 0.2869, 0.3236, 0.2759, 0.3352, 0.2965, 0.3333,
     0.3575, 0.187, 0.3189, 0.1886, 0.188, 0.204, 0.1165, 0.1385, 0.178, 0.1801, 0.0683,
     0.1302), V16 = c(0.6931, 0.544, 0.1136, 0.1133, 0.2723, 0.5761, 0.5354, 0.6985,
     0.0809, 0.0162, 0.4441, 0.4183, 0.3463, 0.2307, 0.0717, 0.2077, 0.3283, 0.5367,
     0.3731, 0.7295, 0.0572, 0.1136, 0.2934, 0.2568, 0.2321, 0.1985, 0.0844, 0.1145,
     0.1304, 0.1297, 0.1688, 0.1669, 0.089, 0.1666, 0.1918, 0.1396, 0.8278, 0.6225,
     0.4754, 0.7213, 0.7555, 0.7868, 0.7807, 0.6699, 0.6804, 0.7152, 0.2978, 0.2231,
     0.241, 0.3323, 0.5553, 0.8705, 0.7487, 0.5392, 0.4191, 0.4998, 0.5036, 0.661,
     0.4499, 0.5398, 0.6087, 0.5512, 0.9016, 0.6632, 0.3737, 0.258, 0.3234, 0.5079,
     0.5543, 0.3892, 0.4331, 0.5595, 0.6707, 0.6122, 0.161, 0.208, 0.1193, 0.1237,
     0.1967, 0.2808, 0.2666, 0.0906, 0.1951, 0.1533, 0.0422, 0.1308, 0.3889, 0.2956,
     0.2056, 0.2252, 0.3172, 0.3496, 0.3447, 0.1452, 0.1892, 0.1485, 0.1405, 0.1786,
     0.2127, 0.2122, 0.2794, 0.22, 0.1503, 0.1708), V17 = c(0.6759, 0.515, 0.0349,
     0.2826, 0.3943, 0.6426, 0.683, 0.8292, 0.1179, 0.0624, 0.6795, 0.6988, 0.5395,
     0.236, 0.1224, 0.1956, 0.6861, 0.7312, 0.4203, 0.735, 0.2164, 0.1934, 0.3526,
     0.2933, 0.2676, 0.2394, 0.259, 0.2137, 0.269, 0.2038, 0.1568, 0.1424, 0.1226,
     0.1268, 0.1615, 0.1066, 0.8669, 0.7327, 0.539, 0.8137, 0.9522, 0.9739, 0.7906,
     0.7066, 0.7505, 0.3512, 0.0699, 0.2164, 0.0367, 0.278, 0.4616, 0.9786, 0.8999,
     0.6588, 0.505, 0.6011, 0.618, 0.7417, 0.4765, 0.6237, 0.7236, 0.6613, 1, 0.1674,
     0.2507, 0.1796, 0.3748, 0.4639, 0.5386, 0.3962, 0.5954, 0.682, 0.7655, 0.7435,
     0.18, 0.2736, 0.1794, 0.0886, 0.2934, 0.4221, 0.4274, 0.1313, 0.3685, 0.3052,
     0.1785, 0.2803, 0.442, 0.3286, 0.1162, 0.2086, 0.2825, 0.3426, 0.3068, 0.1457,
     0.173, 0.216, 0.2028, 0.1318, 0.2062, 0.2758, 0.287, 0.2732, 0.1723, 0.2177),
     V18 = c(0.7551, 0.4262, 0.3796, 0.3234, 0.6432, 0.679, 0.56, 0.7839, 0.2179,
     0.2127, 0.7051, 0.5733, 0.7911, 0.1299, 0.2349, 0.163, 0.5814, 0.7659, 0.5364,
     0.8253, 0.4563, 0.4142, 0.3657, 0.2991, 0.2934, 0.3134, 0.2679, 0.2838, 0.2947,
     0.3811, 0.0375, 0.1285, 0.1846, 0.1374, 0.1647, 0.1923, 0.8131, 0.7843, 0.6279,
     0.9185, 0.9826, 1, 0.6122, 0.5632, 0.6595, 0.2008, 0.1401, 0.3201, 0.1672, 0.2975,
     0.3797, 0.9335, 1, 0.7113, 0.6711, 0.647, 0.8025, 0.8006, 0.6254, 0.6876, 0.7577,
     0.6804, 0.8911, 0.0837, 0.2507, 0.2422, 0.2586, 0.1859, 0.3746, 0.2449, 0.5772,
     0.6164, 0.8485, 0.813, 0.218, 0.3274, 0.2185, 0.1755, 0.3709, 0.5279, 0.6291,
     0.2758, 0.4646, 0.4116, 0.4394, 0.4519, 0.3892, 0.3231, 0.1884, 0.2248, 0.305,
     0.2851, 0.2945, 0.2429, 0.2226, 0.2417, 0.2613, 0.226, 0.2222, 0.4576, 0.3969,
     0.2862, 0.2339, 0.3175), V19 = c(0.8929, 0.2024, 0.7401, 0.3238, 0.7271, 0.7157,
     0.3093, 0.8215, 0.3326, 0.3436, 0.7966, 0.2226, 0.9064, 0.3812, 0.3684, 0.1218,
     0.25, 0.6271, 0.7062, 0.8793, 0.3819, 0.3279, 0.3221, 0.3924, 0.3295, 0.4077,
     0.3094, 0.364, 0.3669, 0.4451, 0.1316, 0.1857, 0.388, 0.1095, 0.1397, 0.2991,
     0.9045, 0.7988, 0.706, 1, 0.8871, 0.9843, 0.42, 0.3785, 0.4509, 0.2676, 0.299,
     0.2915, 0.3038, 0.2948, 0.345, 0.7917, 0.969, 0.7602, 0.7922, 0.8067, 0.9333,
     0.8456, 0.7304, 0.7329, 0.7726, 0.652, 0.8753, 0