1.80-7: 2013/06/24
1. Modification of the following files in order to support sparse matrices:
R/LiblineaR.R
R/predict.R
src/predictLinear.c
src/trainLinear.c
These changes are mainly to be credited to Kai-Hsiang Hsu, from the Department of Computer Science of the National Taiwan University.
2. Addition of examples to reflect the use of sparse matrices in the following file:
man/LiblineaR.Rd
1.80-6: 2013/03/26
1. Corrected a memory mapping bug in predictLinear.c
1.80-5: 2013/03/25
1. Suppress printing to stdout in linear.cpp, tron.cpp
2. Suppress the use of exit(1); in predictLinear.c
3. Correct a bug when retrieving weights from C to R for multi-class models
1.80-4: 2011/04/21
1. Correct bugs in linear.cpp (update of solve_l1r_l2_svc and solve_l1r_lr)
1.80-3: 2011/04/20
1. Add:
extern "C"
before each function below :
//
// Interface function
//
in linear.cpp
2. Suppress useless functions:
- save_model
- load_model
in:
linear.cpp
linear.h
3. change all "fprintf" into Rprintf in :
linear.cpp
trainLinear.c
predictLinear.c
4. change:
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
into
#define Malloc(type,n) (type *)Calloc(n,type)
in:
trainLinear.c
linear.cpp
5. replace all the malloc((n)*sizeof(type)) by Calloc(n,type) in predictLinear.c
5. replace all the realloc(*p,size) by Realloc(*p,n,type) in linear.cpp
7. replace all free() by Free() in:
trainLinear.c
predictLinear.c
linear.cpp
8. Add
#include
#include
#include
in linear.cpp
1.80-2: 2011/04/12
1. Incorporate changes from LIBLINEAR versions 1.51 to 1.80:
- Use set_print_string_function to set the print function
- Add free_model_content and free_and_destroy_model functions (avoid memory problem if users declare a model variable)
- Add check_probability_model (consistent with libsvm)
- A new solver: coordinate descent for dual logistic regression
- New optimization method for l1-regularized logistic regression
- linear.cpp:
* Use 1-norm stopping condition for l1-regularized solvers
* newton_iter < l/10 replaced by newton_iter <= l/10 in l2r_lr_dual (for l < 10)
2. predict.LiblineaR function can return additional information:
- probabilities (only for logistic regression models)
- decision values