Note: In this file, LiblineaR refers to this R package, while 'LIBLINEAR' refers to the original C/C++ library.
2.10-8 : 2017/02/13
1. Upgraded to 'LIBLINEAR' version 2.10, including cross-validation procedure to find parameter C.
- two extra arguments in the LiblineaR function.
2. Argument bias from LiblineaR function can now be given as a numeric, allowing to tune the value of the additional bias feature. This change is backwards-compatible with the previous use of booleans.
1.94-2: 2015/01/30
1. Cleaned DESCRIPTION
2. library(SparseM) replaced by 'require' or 'requireNamespace' as appropriate.
1.94-1: 2015/01/30
1. Upgraded to 'LIBLINEAR' version 1.94, including support vector *regression*
- argument labels of function LiblineaR was renamed as target. The old naming is still accepted with a warning
- argument type of function LiblineaR may take extra values (11,12,13)
- extra argument svr_eps of function LiblineaR for the tolerance of regression loss
- if class labels are -1 and 1, ensure that positive decision values represent the class 1
- enriched examples, with cases of regression
2. Fix CITATION file format to satisfy CRAN requirements
3. Cleaning and reformatting of man pages
4. Fixing a bug in class labels ordering
1.80-11: 2014/09/18
1. Refactored C code in order to facilitate upgrades of 'LIBLINEAR'.
2. Fix memory leak in predictLinear (the buffer x wasn't freed)
1.80-10: 2014/09/14
1. Shortened examples runtime.
2. Uniformized notations in DESCRIPTION file for LiblineaR (this package) and 'LIBLINEAR' (the C/C++ library wrapped by this package).
1.80-9: 2014/09/12
1. Replaced all expressions:
rand()%(a-b);
by
GetRNGstate();
(int) (unif_rand()*(a - b))%(a - b);
PutRNGstate();
in linear.cpp, where a and b might take different values.
1.80-8: 2014/09/11
1. Corrected a bug in memory allocation for sparse matrices:
src/trainLinear.c
These changes are to be credited to Christian Wolf.
2. Added a PACKAGE argument to speed up call to .C(...):
R/LiblineaR.R
R/predict.R
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