diff options
| -rw-r--r-- | Makefile | 128 | ||||
| -rw-r--r-- | include/crossval.h | 14 | ||||
| -rw-r--r-- | include/gensvm.h (renamed from include/msvmmaj.h) | 18 | ||||
| -rw-r--r-- | include/gensvm_init.h | 28 | ||||
| -rw-r--r-- | include/gensvm_io.h | 30 | ||||
| -rw-r--r-- | include/gensvm_kernel.h | 38 | ||||
| -rw-r--r-- | include/gensvm_lapack.h (renamed from include/msvmmaj_lapack.h) | 8 | ||||
| -rw-r--r-- | include/gensvm_matrix.h (renamed from include/msvmmaj_matrix.h) | 8 | ||||
| -rw-r--r-- | include/gensvm_pred.h | 32 | ||||
| -rw-r--r-- | include/gensvm_sv.h (renamed from include/msvmmaj_sv.h) | 10 | ||||
| -rw-r--r-- | include/gensvm_train.h | 31 | ||||
| -rw-r--r-- | include/gensvm_train_dataset.h (renamed from include/msvmmaj_train_dataset.h) | 32 | ||||
| -rw-r--r-- | include/globals.h | 4 | ||||
| -rw-r--r-- | include/libGenSVM.h | 42 | ||||
| -rw-r--r-- | include/libMSVMMaj.h | 42 | ||||
| -rw-r--r-- | include/msvmmaj_init.h | 28 | ||||
| -rw-r--r-- | include/msvmmaj_io.h | 30 | ||||
| -rw-r--r-- | include/msvmmaj_kernel.h | 38 | ||||
| -rw-r--r-- | include/msvmmaj_pred.h | 32 | ||||
| -rw-r--r-- | include/msvmmaj_train.h | 31 | ||||
| -rw-r--r-- | include/timer.h | 4 | ||||
| -rw-r--r-- | include/types.h | 4 | ||||
| -rw-r--r-- | include/util.h | 12 | ||||
| -rw-r--r-- | src/crossval.c | 24 | ||||
| -rw-r--r-- | src/gensvm_init.c (renamed from src/msvmmaj_init.c) | 66 | ||||
| -rw-r--r-- | src/gensvm_io.c (renamed from src/msvmmaj_io.c) | 36 | ||||
| -rw-r--r-- | src/gensvm_kernel.c (renamed from src/msvmmaj_kernel.c) | 48 | ||||
| -rw-r--r-- | src/gensvm_lapack.c (renamed from src/msvmmaj_lapack.c) | 4 | ||||
| -rw-r--r-- | src/gensvm_matrix.c (renamed from src/msvmmaj_matrix.c) | 4 | ||||
| -rw-r--r-- | src/gensvm_pred.c (renamed from src/msvmmaj_pred.c) | 46 | ||||
| -rw-r--r-- | src/gensvm_sv.c (renamed from src/msvmmaj_sv.c) | 12 | ||||
| -rw-r--r-- | src/gensvm_train.c (renamed from src/msvmmaj_train.c) | 74 | ||||
| -rw-r--r-- | src/gensvm_train_dataset.c (renamed from src/msvmmaj_train_dataset.c) | 144 | ||||
| -rw-r--r-- | src/libGenSVM.c (renamed from src/libMSVMMaj.c) | 62 | ||||
| -rw-r--r-- | src/predGenSVM.c (renamed from src/predMSVMMaj.c) | 50 | ||||
| -rw-r--r-- | src/trainGenSVM.c (renamed from src/trainMSVMMaj.c) | 78 | ||||
| -rw-r--r-- | src/trainGenSVMdataset.c (renamed from src/trainMSVMMajdataset.c) | 42 | ||||
| -rw-r--r-- | src/util.c | 24 |
38 files changed, 679 insertions, 679 deletions
@@ -1,102 +1,102 @@ VERSION=0.1 CC=gcc -CFLAGS=-Wall -O3 -DVERSION=$(VERSION) -g +CFLAGS=-Wall -O3 -DVERSION=$(VERSION) INCLUDE= -Iinclude LIB= -Llib -EXECS=trainMSVMMaj trainMSVMMajdataset +EXECS=trainGenSVM trainGenSVMdataset .PHONY: all clean tar -all: lib/libmsvmmaj.a $(EXECS) +all: lib/libgensvm.a $(EXECS) override LDFLAGS+=-lcblas -llapack -lm -lib/libmsvmmaj.a: \ +lib/libgensvm.a: \ src/crossval.o \ - src/libMSVMMaj.o \ - src/msvmmaj_init.o \ - src/msvmmaj_io.o \ - src/msvmmaj_kernel.o \ - src/msvmmaj_lapack.o \ - src/msvmmaj_matrix.o \ - src/msvmmaj_pred.o \ - src/msvmmaj_sv.o \ - src/msvmmaj_train.o \ - src/msvmmaj_train_dataset.o \ + src/libGenSVM.o \ + src/gensvm_init.o \ + src/gensvm_io.o \ + src/gensvm_kernel.o \ + src/gensvm_lapack.o \ + src/gensvm_matrix.o \ + src/gensvm_pred.o \ + src/gensvm_sv.o \ + src/gensvm_train.o \ + src/gensvm_train_dataset.o \ src/strutil.o \ src/timer.o \ src/util.o - @ar rcs lib/libmsvmmaj.a \ + @ar rcs lib/libgensvm.a \ src/crossval.o \ - src/libMSVMMaj.o \ - src/msvmmaj_init.o \ - src/msvmmaj_io.o \ - src/msvmmaj_matrix.o \ - src/msvmmaj_kernel.o \ - src/msvmmaj_lapack.o \ - src/msvmmaj_pred.o \ - src/msvmmaj_sv.o \ - src/msvmmaj_train.o \ - src/msvmmaj_train_dataset.o \ + src/libGenSVM.o \ + src/gensvm_init.o \ + src/gensvm_io.o \ + src/gensvm_matrix.o \ + src/gensvm_kernel.o \ + src/gensvm_lapack.o \ + src/gensvm_pred.o \ + src/gensvm_sv.o \ + src/gensvm_train.o \ + src/gensvm_train_dataset.o \ src/strutil.o \ src/timer.o \ src/util.o - @echo libmsvmmaj.a... + @echo libgensvm.a... -trainMSVMMaj: src/trainMSVMMaj.c lib/libmsvmmaj.a - @$(CC) -o trainMSVMMaj src/trainMSVMMaj.c $(CFLAGS) $(INCLUDE) $(LIB)\ - -lmsvmmaj $(LDFLAGS) - @echo trainMSVMMaj... +trainGenSVM: src/trainGenSVM.c lib/libgensvm.a + @$(CC) -o trainGenSVM src/trainGenSVM.c $(CFLAGS) $(INCLUDE) $(LIB)\ + -lgensvm $(LDFLAGS) + @echo trainGenSVM... -trainMSVMMajdataset: src/trainMSVMMajdataset.c lib/libmsvmmaj.a - @$(CC) -o trainMSVMMajdataset src/trainMSVMMajdataset.c $(CFLAGS) $(INCLUDE) $(LIB) -lmsvmmaj $(LDFLAGS) - @echo trainMSVMMajdataset... +trainGenSVMdataset: src/trainGenSVMdataset.c lib/libgensvm.a + @$(CC) -o trainGenSVMdataset src/trainGenSVMdataset.c $(CFLAGS) $(INCLUDE) $(LIB) -lgensvm $(LDFLAGS) + @echo trainGenSVMdataset... -predMSVMMaj: src/predMSVMMaj.c lib/libmsvmmaj.a - @$(CC) -o predMSVMMaj src/predMSVMMaj.c $(CFLAGS) $(INCLUDE) $(LIB) -lmsvmmaj $(LDFLAGS) - @echo predMSVMMaj... +predGenSVM: src/predGenSVM.c lib/libgensvm.a + @$(CC) -o predGenSVM src/predGenSVM.c $(CFLAGS) $(INCLUDE) $(LIB) -lgensvm $(LDFLAGS) + @echo predGenSVM... src/crossval.o: @$(CC) -c -o src/crossval.o src/crossval.c $(CFLAGS) $(INCLUDE) @echo crossval.o... -src/msvmmaj_kernel.o: - @$(CC) -c -o src/msvmmaj_kernel.o src/msvmmaj_kernel.c $(CFLAGS) $(INCLUDE) - @echo msvmmaj_kernel.o... +src/gensvm_kernel.o: + @$(CC) -c -o src/gensvm_kernel.o src/gensvm_kernel.c $(CFLAGS) $(INCLUDE) + @echo gensvm_kernel.o... -src/libMSVMMaj.o: - @$(CC) -c -o src/libMSVMMaj.o src/libMSVMMaj.c $(CFLAGS) $(INCLUDE) - @echo libMSVMMaj.o... +src/libGenSVM.o: + @$(CC) -c -o src/libGenSVM.o src/libGenSVM.c $(CFLAGS) $(INCLUDE) + @echo libGenSVM.o... -src/msvmmaj_matrix.o: - @$(CC) -c -o src/msvmmaj_matrix.o src/msvmmaj_matrix.c $(CFLAGS) $(INCLUDE) - @echo msvmmaj_matrix.o... +src/gensvm_matrix.o: + @$(CC) -c -o src/gensvm_matrix.o src/gensvm_matrix.c $(CFLAGS) $(INCLUDE) + @echo gensvm_matrix.o... -src/msvmmaj_init.o: - @$(CC) -c -o src/msvmmaj_init.o src/msvmmaj_init.c $(CFLAGS) $(INCLUDE) - @echo msvmmaj_init.o... +src/gensvm_init.o: + @$(CC) -c -o src/gensvm_init.o src/gensvm_init.c $(CFLAGS) $(INCLUDE) + @echo gensvm_init.o... -src/msvmmaj_io.o: - @$(CC) -c -o $@ src/msvmmaj_io.c $(CFLAGS) $(INCLUDE) +src/gensvm_io.o: + @$(CC) -c -o $@ src/gensvm_io.c $(CFLAGS) $(INCLUDE) -src/msvmmaj_pred.o: - @$(CC) -c -o src/msvmmaj_pred.o src/msvmmaj_pred.c $(CFLAGS) $(INCLUDE) - @echo msvmmaj_pred.o... +src/gensvm_pred.o: + @$(CC) -c -o src/gensvm_pred.o src/gensvm_pred.c $(CFLAGS) $(INCLUDE) + @echo gensvm_pred.o... -src/msvmmaj_sv.o: - @$(CC) -c -o src/msvmmaj_sv.o src/msvmmaj_sv.c $(CFLAGS) $(INCLUDE) - @echo msvmmaj_sv.o... +src/gensvm_sv.o: + @$(CC) -c -o src/gensvm_sv.o src/gensvm_sv.c $(CFLAGS) $(INCLUDE) + @echo gensvm_sv.o... -src/msvmmaj_train.o: - @$(CC) -c -o src/msvmmaj_train.o src/msvmmaj_train.c $(CFLAGS) $(INCLUDE) - @echo msvmmaj_train.o... +src/gensvm_train.o: + @$(CC) -c -o src/gensvm_train.o src/gensvm_train.c $(CFLAGS) $(INCLUDE) + @echo gensvm_train.o... -src/msvmmaj_train_dataset.o: - @$(CC) -c -o src/msvmmaj_train_dataset.o src/msvmmaj_train_dataset.c $(CFLAGS) $(INCLUDE) - @echo msvmmaj_train_dataset.o... +src/gensvm_train_dataset.o: + @$(CC) -c -o src/gensvm_train_dataset.o src/gensvm_train_dataset.c $(CFLAGS) $(INCLUDE) + @echo gensvm_train_dataset.o... -src/msvmmaj_lapack.o: - @$(CC) -c -o src/msvmmaj_lapack.o src/msvmmaj_lapack.c $(CFLAGS) $(INCLUDE) +src/gensvm_lapack.o: + @$(CC) -c -o src/gensvm_lapack.o src/gensvm_lapack.c $(CFLAGS) $(INCLUDE) @echo mylapack.o... src/strutil.o: diff --git a/include/crossval.h b/include/crossval.h index 0dff0b9..fa3cca7 100644 --- a/include/crossval.h +++ b/include/crossval.h @@ -6,20 +6,20 @@ * * @details * Contains function declarations for functions needed for performing cross - * validation on MajData structures. + * validation on GenData structures. * */ -#ifndef CROSSVAL_H -#define CROSSVAL_H +#ifndef GENSVM_CROSSVAL_H +#define GENSVM_CROSSVAL_H #include "globals.h" // forward delaration -struct MajData; +struct GenData; -void msvmmaj_make_cv_split(long N, long folds, long *cv_idx); -void msvmmaj_get_tt_split(struct MajData *full_data, struct MajData *train_data, - struct MajData *test_data, long *cv_idx, long fold_idx); +void gensvm_make_cv_split(long N, long folds, long *cv_idx); +void gensvm_get_tt_split(struct GenData *full_data, struct GenData *train_data, + struct GenData *test_data, long *cv_idx, long fold_idx); #endif diff --git a/include/msvmmaj.h b/include/gensvm.h index 3d04f30..1231c29 100644 --- a/include/msvmmaj.h +++ b/include/gensvm.h @@ -1,22 +1,22 @@ /** - * @file msvmmaj.h + * @file gensvm.h * @author Gertjan van den Burg * @date August, 2013 * @brief Definitions for common structures * * @details - * Contains documentation and declarations of MajModel and MajData. + * Contains documentation and declarations of GenModel and GenData. * */ -#ifndef MSVMMAJ_H -#define MSVMMAJ_H +#ifndef GENSVM_H +#define GENSVM_H #include "globals.h" #include "types.h" /** - * @brief A structure to represent a single MSVMMaj model. + * @brief A structure to represent a single GenSVM model. * * @param weight_idx which weights to use (1 = unit, 2 = group) * @param K number of classes in the dataset @@ -45,7 +45,7 @@ * @TODO * change R to int, it's a binary matrix */ -struct MajModel { +struct GenModel { int weight_idx; long K; long n; @@ -80,11 +80,11 @@ struct MajModel { * @param *Z pointer to augmented data matrix * @param *RAW pointer to augmented raw data matrix * @param *J pointer to regularization vector - * @param kerneltype kerneltype used in MajData::Z - * @param *kernelparam kernel parameters used in MajData::Z + * @param kerneltype kerneltype used in GenData::Z + * @param *kernelparam kernel parameters used in GenData::Z * */ -struct MajData { +struct GenData { long K; long n; long m; diff --git a/include/gensvm_init.h b/include/gensvm_init.h new file mode 100644 index 0000000..544c960 --- /dev/null +++ b/include/gensvm_init.h @@ -0,0 +1,28 @@ +/** + * @file gensvm_init.h + * @author Gertjan van den Burg + * @date January, 2014 + * @brief Header file for gensvm_init.c + * + * @details + * Contains function declarations for the initialization functions for + * GenModel and GenData structures. + */ + +#ifndef GENSVM_INIT_H +#define GENSVM_INIT_H + +// forward declaration +struct GenData; +struct GenModel; + +struct GenModel *gensvm_init_model(); + +struct GenData *gensvm_init_data(); + +void gensvm_allocate_model(struct GenModel *model); +void gensvm_reallocate_model(struct GenModel *model, long n, long m); +void gensvm_free_model(struct GenModel *model); +void gensvm_free_data(struct GenData *data); + +#endif diff --git a/include/gensvm_io.h b/include/gensvm_io.h new file mode 100644 index 0000000..35b6a5a --- /dev/null +++ b/include/gensvm_io.h @@ -0,0 +1,30 @@ +/** + * @file gensvm_io.h + * @author Gertjan van den Burg + * @date January, 2014 + * @brief Header files for gensvm_io.c + * + * @details + * Function declarations for input/output functions. + * + */ + +#ifndef GENSVM_IO_H +#define GENSVM_IO_H + +#include "globals.h" + +// forward declarations +struct GenData; +struct GenModel; + +// function declarations +void gensvm_read_data(struct GenData *dataset, char *data_file); + +void gensvm_read_model(struct GenModel *model, char *model_filename); +void gensvm_write_model(struct GenModel *model, char *output_filename); + +void gensvm_write_predictions(struct GenData *data, long *predy, + char *output_filename); + +#endif diff --git a/include/gensvm_kernel.h b/include/gensvm_kernel.h new file mode 100644 index 0000000..bf46bbc --- /dev/null +++ b/include/gensvm_kernel.h @@ -0,0 +1,38 @@ +/** + * @file gensvm_kernel.h + * @author Gertjan van den Burg + * @date January, 2014 + * @brief Header file for kernel functionality + * + * @details + * Contains function declarations for computing the kernel matrix + * in nonlinear MSVMGen. Additional kernel functions should be + * included here and in gensvm_kernel.c + * + */ + +#ifndef GENSVM_KERNEL_H +#define GENSVM_KERNEL_H + +#include "globals.h" + +// forward declarations +struct GenData; +struct GenModel; + +// function declarations +void gensvm_make_kernel(struct GenModel *model, struct GenData *data); + +long gensvm_make_eigen(double *K, long n, double **P, double **Lambda); + +void gensvm_make_crosskernel(struct GenModel *model, + struct GenData *data_train, struct GenData *data_test, + double **K2); + +double gensvm_compute_rbf(double *x1, double *x2, double *kernelparam, + long n); +double gensvm_compute_poly(double *x1, double *x2, double *kernelparam, + long n); +double gensvm_compute_sigmoid(double *x1, double *x2, double *kernelparam, + long n); +#endif diff --git a/include/msvmmaj_lapack.h b/include/gensvm_lapack.h index 6ea1122..7ac4fc9 100644 --- a/include/msvmmaj_lapack.h +++ b/include/gensvm_lapack.h @@ -1,16 +1,16 @@ /** - * @file msvmmaj_lapack.h + * @file gensvm_lapack.h * @author Gertjan van den Burg * @date August, 2013 - * @brief Header file for msvmmaj_lapack.c + * @brief Header file for gensvm_lapack.c * * @details * Function declarations for external LAPACK functions * */ -#ifndef MSVMMAJ_LAPACK_H -#define MSVMMAJ_LAPACK_H +#ifndef GENSVM_LAPACK_H +#define GENSVM_LAPACK_H #include "globals.h" diff --git a/include/msvmmaj_matrix.h b/include/gensvm_matrix.h index db64303..01be3da 100644 --- a/include/msvmmaj_matrix.h +++ b/include/gensvm_matrix.h @@ -1,16 +1,16 @@ /** - * @file msvmmaj_matrix.h + * @file gensvm_matrix.h * @author Gertjan van den Burg * @date August, 2013 - * @brief Header file for msvmmaj_matrix.c + * @brief Header file for gensvm_matrix.c * * @details * Contains function declarations for functions useful for dealing with matrices. * */ -#ifndef MSVMMAJ_MATRIX_H -#define MSVMMAJ_MATRIX_H +#ifndef GENSVM_MATRIX_H +#define GENSVM_MATRIX_H #include "globals.h" diff --git a/include/gensvm_pred.h b/include/gensvm_pred.h new file mode 100644 index 0000000..0cce20b --- /dev/null +++ b/include/gensvm_pred.h @@ -0,0 +1,32 @@ +/** + * @file gensvm_pred.h + * @author Gertjan van den Burg + * @date August, 2013 + * @brief Header file for gensvm_pred.c + * + * @details + * Contains function declarations for prediction functions. + * + */ + +#ifndef GENSVM_PRED_H +#define GENSVM_PRED_H + +#include "globals.h" + +// forward declarations +struct GenData; +struct GenModel; + +// function declarations +void gensvm_predict_labels(struct GenData *data_test, + struct GenData *data_train, struct GenModel *model, + long *predy); +void gensvm_predict_labels_linear(struct GenData *data, + struct GenModel *model, long *predy); +void gensvm_predict_labels_kernel(struct GenData *data_test, + struct GenData *data_train, struct GenModel *model, + long *predy); +double gensvm_prediction_perf(struct GenData *data, long *perdy); + +#endif diff --git a/include/msvmmaj_sv.h b/include/gensvm_sv.h index e37ffc4..5664f83 100644 --- a/include/msvmmaj_sv.h +++ b/include/gensvm_sv.h @@ -1,19 +1,19 @@ /** - * @file msvmmaj_sv.h + * @file gensvm_sv.h * @author Gertjan van den Burg * @date May, 2014 - * @brief Header file for msvmmaj_sv.c + * @brief Header file for gensvm_sv.c * * @details * Contains function declarations for functions used to count support vectors. * */ -#ifndef MSVMMAJ_SV_H -#define MSVMMAJ_SV_H +#ifndef GENSVM_SV_H +#define GENSVM_SV_H #include "globals.h" -long msvmmaj_num_sv(struct MajModel *model, struct MajData *data); +long gensvm_num_sv(struct GenModel *model, struct GenData *data); #endif diff --git a/include/gensvm_train.h b/include/gensvm_train.h new file mode 100644 index 0000000..f59359d --- /dev/null +++ b/include/gensvm_train.h @@ -0,0 +1,31 @@ +/** + * @file gensvm_train.h + * @author Gertjan van den Burg + * @date August, 2013 + * @brief Header file for gensvm_train.c + * + * @details + * Contains function declarations for functions used to train a single + * GenModel. + * + */ + +#ifndef GENSVM_TRAIN_H +#define GENSVM_TRAIN_H + +#include "globals.h" + +//forward declarations +struct GenData; +struct GenModel; + +// function declarations +void gensvm_optimize(struct GenModel *model, struct GenData *data); + +double gensvm_get_loss(struct GenModel *model, struct GenData *data, + double *ZV); + +void gensvm_get_update(struct GenModel *model, struct GenData *data, + double *B, double *ZAZ, double *ZAZV, double *ZAZVT); + +#endif diff --git a/include/msvmmaj_train_dataset.h b/include/gensvm_train_dataset.h index 95e2c74..299bc52 100644 --- a/include/msvmmaj_train_dataset.h +++ b/include/gensvm_train_dataset.h @@ -1,5 +1,5 @@ /** - * @file msvmmaj_train_dataset.h + * @file gensvm_train_dataset.h * @author Gertjan van den Burg * @date August, 2013 * @brief Structs and functions necessary for the grid search @@ -12,8 +12,8 @@ * */ -#ifndef MSVMMAJ_TRAIN_DATASET_H -#define MSVMMAJ_TRAIN_DATASET_H +#ifndef GENSVM_TRAIN_DATASET_H +#define GENSVM_TRAIN_DATASET_H #include "globals.h" #include "types.h" @@ -23,13 +23,13 @@ * * @param folds number of folds in cross validation * @param ID numeric id of the task in the queue - * @param weight_idx parameter for the MajModel - * @param p parameter for the MajModel - * @param kappa parameter for the MajModel - * @param lambda parameter for the MajModel - * @param epsilon parameter for the MajModel - * @param kerneltype parameter for the MajModel - * @param *kernelparam parameters for the MajModel + * @param weight_idx parameter for the GenModel + * @param p parameter for the GenModel + * @param kappa parameter for the GenModel + * @param lambda parameter for the GenModel + * @param epsilon parameter for the GenModel + * @param kerneltype parameter for the GenModel + * @param *kernelparam parameters for the GenModel * @param *train_data pointer to the training data * @param *test_data pointer to the test data (if any) * @param performance performance after cross validation @@ -44,8 +44,8 @@ struct Task { double lambda; double epsilon; double *kernelparam; - struct MajData *train_data; - struct MajData *test_data; + struct GenData *train_data; + struct GenData *test_data; double performance; }; @@ -120,7 +120,7 @@ struct Training { }; void make_queue(struct Training *training, struct Queue *queue, - struct MajData *train_data, struct MajData *test_data); + struct GenData *train_data, struct GenData *test_data); struct Task *get_next_task(struct Queue *q); void start_training_tt(struct Queue *q); @@ -129,11 +129,11 @@ void free_queue(struct Queue *q); void consistency_repeats(struct Queue *q, long repeats, TrainType traintype); -double cross_validation(struct MajModel *model, struct MajData *data, +double cross_validation(struct GenModel *model, struct GenData *data, long folds); -void make_model_from_task(struct Task *task, struct MajModel *model); -void copy_model(struct MajModel *from, struct MajModel *to); +void make_model_from_task(struct Task *task, struct GenModel *model); +void copy_model(struct GenModel *from, struct GenModel *to); void print_progress_string(struct Task *task, long N); #endif diff --git a/include/globals.h b/include/globals.h index 55fb6c4..46cc3d2 100644 --- a/include/globals.h +++ b/include/globals.h @@ -16,8 +16,8 @@ * */ -#ifndef MSVMMAJ_GLOBALS_H -#define MSVMMAJ_GLOBALS_H +#ifndef GENSVM_GLOBALS_H +#define GENSVM_GLOBALS_H #include <stdio.h> #include <stdlib.h> diff --git a/include/libGenSVM.h b/include/libGenSVM.h new file mode 100644 index 0000000..cfa2815 --- /dev/null +++ b/include/libGenSVM.h @@ -0,0 +1,42 @@ +/** + * @file libGenSVM.h + * @author Gertjan van den Burg + * @date August, 2013 + * @brief Header file for the core GenSVM library libGenSVM.c + * + * @details + * The core computational routines for GenSVM are defined in libGenSVM.c. + * This file contains function declarations for these functions. + * + */ + +/** + * @todo + * rename this file and libGenSVM.c to correspond with the lowercase convention. + * Also change the name of the include guard. + */ +#ifndef LIBGENSVM_H +#define LIBGENSVM_H + +#include "globals.h" + +// forward declarations +struct GenData; +struct GenModel; + +// function declarations +void gensvm_simplex_gen(long K, double *U); +void gensvm_category_matrix(struct GenModel *model, struct GenData *data); +void gensvm_simplex_diff(struct GenModel *model, struct GenData *dataset); + +void gensvm_calculate_errors(struct GenModel *model, struct GenData *data, + double *ZV); +void gensvm_calculate_huber(struct GenModel *model); + +void gensvm_step_doubling(struct GenModel *model); + +void gensvm_seed_model_V(struct GenModel *from_model, + struct GenModel *to_model, struct GenData *data); +void gensvm_initialize_weights(struct GenData *data, struct GenModel *model); + +#endif diff --git a/include/libMSVMMaj.h b/include/libMSVMMaj.h deleted file mode 100644 index a9bd789..0000000 --- a/include/libMSVMMaj.h +++ /dev/null @@ -1,42 +0,0 @@ -/** - * @file libMSVMMaj.h - * @author Gertjan van den Burg - * @date August, 2013 - * @brief Header file for the core MSVMMaj library libMSVMMaj.c - * - * @details - * The core computational routines for MSVMMaj are defined in libMSVMMaj.c. - * This file contains function declarations for these functions. - * - */ - -/** - * @todo - * rename this file and libMSVMMaj.c to correspond with the lowercase convention. - * Also change the name of the include guard. - */ -#ifndef LIBMSVMMAJ_H -#define LIBMSVMMAJ_H - -#include "globals.h" - -// forward declarations -struct MajData; -struct MajModel; - -// function declarations -void msvmmaj_simplex_gen(long K, double *U); -void msvmmaj_category_matrix(struct MajModel *model, struct MajData *data); -void msvmmaj_simplex_diff(struct MajModel *model, struct MajData *dataset); - -void msvmmaj_calculate_errors(struct MajModel *model, struct MajData *data, - double *ZV); -void msvmmaj_calculate_huber(struct MajModel *model); - -void msvmmaj_step_doubling(struct MajModel *model); - -void msvmmaj_seed_model_V(struct MajModel *from_model, - struct MajModel *to_model, struct MajData *data); -void msvmmaj_initialize_weights(struct MajData *data, struct MajModel *model); - -#endif diff --git a/include/msvmmaj_init.h b/include/msvmmaj_init.h deleted file mode 100644 index 281214c..0000000 --- a/include/msvmmaj_init.h +++ /dev/null @@ -1,28 +0,0 @@ -/** - * @file msvmmaj_init.h - * @author Gertjan van den Burg - * @date January, 2014 - * @brief Header file for msvmmaj_init.c - * - * @details - * Contains function declarations for the initialization functions for - * MajModel and MajData structures. - */ - -#ifndef MSVMMAJ_INIT_H -#define MSVMMAJ_INIT_H - -// forward declaration -struct MajData; -struct MajModel; - -struct MajModel *msvmmaj_init_model(); - -struct MajData *msvmmaj_init_data(); - -void msvmmaj_allocate_model(struct MajModel *model); -void msvmmaj_reallocate_model(struct MajModel *model, long n, long m); -void msvmmaj_free_model(struct MajModel *model); -void msvmmaj_free_data(struct MajData *data); - -#endif diff --git a/include/msvmmaj_io.h b/include/msvmmaj_io.h deleted file mode 100644 index 99fb4dc..0000000 --- a/include/msvmmaj_io.h +++ /dev/null @@ -1,30 +0,0 @@ -/** - * @file msvmmaj_io.h - * @author Gertjan van den Burg - * @date January, 2014 - * @brief Header files for msvmmaj_io.c - * - * @details - * Function declarations for input/output functions. - * - */ - -#ifndef MSVMMAJ_IO_H -#define MSVMMAJ_IO_H - -#include "globals.h" - -// forward declarations -struct MajData; -struct MajModel; - -// function declarations -void msvmmaj_read_data(struct MajData *dataset, char *data_file); - -void msvmmaj_read_model(struct MajModel *model, char *model_filename); -void msvmmaj_write_model(struct MajModel *model, char *output_filename); - -void msvmmaj_write_predictions(struct MajData *data, long *predy, - char *output_filename); - -#endif diff --git a/include/msvmmaj_kernel.h b/include/msvmmaj_kernel.h deleted file mode 100644 index d4f169a..0000000 --- a/include/msvmmaj_kernel.h +++ /dev/null @@ -1,38 +0,0 @@ -/** - * @file msvmmaj_kernel.h - * @author Gertjan van den Burg - * @date January, 2014 - * @brief Header file for kernel functionality - * - * @details - * Contains function declarations for computing the kernel matrix - * in nonlinear MSVMMaj. Additional kernel functions should be - * included here and in msvmmaj_kernel.c - * - */ - -#ifndef MSVMMAJ_KERNEL_H -#define MSVMMAJ_KERNEL_H - -#include "globals.h" - -// forward declarations -struct MajData; -struct MajModel; - -// function declarations -void msvmmaj_make_kernel(struct MajModel *model, struct MajData *data); - -long msvmmaj_make_eigen(double *K, long n, double **P, double **Lambda); - -void msvmmaj_make_crosskernel(struct MajModel *model, - struct MajData *data_train, struct MajData *data_test, - double **K2); - -double msvmmaj_compute_rbf(double *x1, double *x2, double *kernelparam, - long n); -double msvmmaj_compute_poly(double *x1, double *x2, double *kernelparam, - long n); -double msvmmaj_compute_sigmoid(double *x1, double *x2, double *kernelparam, - long n); -#endif diff --git a/include/msvmmaj_pred.h b/include/msvmmaj_pred.h deleted file mode 100644 index c274cfa..0000000 --- a/include/msvmmaj_pred.h +++ /dev/null @@ -1,32 +0,0 @@ -/** - * @file msvmmaj_pred.h - * @author Gertjan van den Burg - * @date August, 2013 - * @brief Header file for msvmmaj_pred.c - * - * @details - * Contains function declarations for prediction functions. - * - */ - -#ifndef MSVMMAJ_PRED_H -#define MSVMMAJ_PRED_H - -#include "globals.h" - -// forward declarations -struct MajData; -struct MajModel; - -// function declarations -void msvmmaj_predict_labels(struct MajData *data_test, - struct MajData *data_train, struct MajModel *model, - long *predy); -void msvmmaj_predict_labels_linear(struct MajData *data, - struct MajModel *model, long *predy); -void msvmmaj_predict_labels_kernel(struct MajData *data_test, - struct MajData *data_train, struct MajModel *model, - long *predy); -double msvmmaj_prediction_perf(struct MajData *data, long *perdy); - -#endif diff --git a/include/msvmmaj_train.h b/include/msvmmaj_train.h deleted file mode 100644 index 835100f..0000000 --- a/include/msvmmaj_train.h +++ /dev/null @@ -1,31 +0,0 @@ -/** - * @file msvmmaj_train.h - * @author Gertjan van den Burg - * @date August, 2013 - * @brief Header file for msvmmaj_train.c - * - * @details - * Contains function declarations for functions used to train a single - * MajModel. - * - */ - -#ifndef MSVMMAJ_TRAIN_H -#define MSVMMAJ_TRAIN_H - -#include "globals.h" - -//forward declarations -struct MajData; -struct MajModel; - -// function declarations -void msvmmaj_optimize(struct MajModel *model, struct MajData *data); - -double msvmmaj_get_loss(struct MajModel *model, struct MajData *data, - double *ZV); - -void msvmmaj_get_update(struct MajModel *model, struct MajData *data, - double *B, double *ZAZ, double *ZAZV, double *ZAZVT); - -#endif diff --git a/include/timer.h b/include/timer.h index d4af649..a1b60a7 100644 --- a/include/timer.h +++ b/include/timer.h @@ -9,8 +9,8 @@ * */ -#ifndef MSVMMAJ_TIMER_H -#define MSVMMAJ_TIMER_H +#ifndef GENSVM_TIMER_H +#define GENSVM_TIMER_H #include "globals.h" diff --git a/include/types.h b/include/types.h index f6d008b..1cbcba0 100644 --- a/include/types.h +++ b/include/types.h @@ -9,8 +9,8 @@ * */ -#ifndef MSVMMAJ_TYPES_H -#define MSVMMAJ_TYPES_H +#ifndef GENSVM_TYPES_H +#define GENSVM_TYPES_H /** * @brief Implementation of true and false diff --git a/include/util.h b/include/util.h index 375a9c2..fe8d2a3 100644 --- a/include/util.h +++ b/include/util.h @@ -9,18 +9,18 @@ * */ -#ifndef MSVMMAJ_UTIL_H -#define MSVMMAJ_UTIL_H +#ifndef GENSVM_UTIL_H +#define GENSVM_UTIL_H #include "globals.h" // forward declarations -struct MajData; -struct MajModel; +struct GenData; +struct GenModel; // function declarations -int msvmmaj_check_argv(int argc, char **argv, char *str); -int msvmmaj_check_argv_eq(int argc, char **argv, char *str); +int gensvm_check_argv(int argc, char **argv, char *str); +int gensvm_check_argv_eq(int argc, char **argv, char *str); void note(const char *fmt,...); diff --git a/src/crossval.c b/src/crossval.c index 1b5a592..85f9341 100644 --- a/src/crossval.c +++ b/src/crossval.c @@ -6,16 +6,16 @@ * * @details * This file contains functions for performing cross validation. The funtion - * msvmmaj_make_cv_split() creates a cross validation vector for non-stratified - * cross validation. The function msvmmaj_get_tt_split() creates a train and + * gensvm_make_cv_split() creates a cross validation vector for non-stratified + * cross validation. The function gensvm_get_tt_split() creates a train and * test dataset from a given dataset and a pre-determined CV partition vector. * See individual function documentation for details. * */ #include "crossval.h" -#include "msvmmaj.h" -#include "msvmmaj_matrix.h" +#include "gensvm.h" +#include "gensvm_matrix.h" /** * @brief Create a cross validation split vector @@ -35,7 +35,7 @@ * for each observation on exit * */ -void msvmmaj_make_cv_split(long N, long folds, long *cv_idx) +void gensvm_make_cv_split(long N, long folds, long *cv_idx) { long i, j, idx; @@ -71,23 +71,23 @@ void msvmmaj_make_cv_split(long N, long folds, long *cv_idx) * @brief Create train and test datasets for a CV split * * @details - * Given a MajData structure for the full dataset, a previously created + * Given a GenData structure for the full dataset, a previously created * cross validation split vector and a fold index, a training and test dataset * are created. * - * @param[in] full_data a MajData structure for the entire + * @param[in] full_data a GenData structure for the entire * dataset - * @param[in,out] train_data an initialized MajData structure which + * @param[in,out] train_data an initialized GenData structure which * on exit contains the training dataset - * @param[in,out] test_data an initialized MajData structure which + * @param[in,out] test_data an initialized GenData structure which * on exit contains the test dataset * @param[in] cv_idx a vector of cv partitions created by - * msvmmaj_make_cv_split() + * gensvm_make_cv_split() * @param[in] fold_idx index of the fold which becomes the * test dataset */ -void msvmmaj_get_tt_split(struct MajData *full_data, struct MajData *train_data, - struct MajData *test_data, long *cv_idx, long fold_idx) +void gensvm_get_tt_split(struct GenData *full_data, struct GenData *train_data, + struct GenData *test_data, long *cv_idx, long fold_idx) { long i, j, k, l, test_n, train_n; diff --git a/src/msvmmaj_init.c b/src/gensvm_init.c index 0fedfe7..b3f214e 100644 --- a/src/msvmmaj_init.c +++ b/src/gensvm_init.c @@ -1,12 +1,12 @@ /** - * @file msvmmaj_init.c + * @file gensvm_init.c * @author Gertjan van den Burg * @date January 7, 2014 * @brief Functions for initializing model and data structures * * @details - * This file contains functions for initializing a MajModel instance - * and a MajData instance. In addition, default values for these + * This file contains functions for initializing a GenModel instance + * and a GenData instance. In addition, default values for these * structures are defined here (and only here). Functions for allocating * memory for the model structure and freeing of the model and data structures * are also included. @@ -15,21 +15,21 @@ #include <math.h> -#include "msvmmaj.h" -#include "msvmmaj_init.h" +#include "gensvm.h" +#include "gensvm_init.h" /** - * @brief Initialize a MajModel structure + * @brief Initialize a GenModel structure * * @details - * A MajModel structure is initialized and the default value for the + * A GenModel structure is initialized and the default value for the * parameters are set. A pointer to the initialized model is returned. * - * @returns initialized MajModel + * @returns initialized GenModel */ -struct MajModel *msvmmaj_init_model() +struct GenModel *gensvm_init_model() { - struct MajModel *model = Malloc(struct MajModel, 1); + struct GenModel *model = Malloc(struct GenModel, 1); // set default values model->p = 1.0; @@ -56,18 +56,18 @@ struct MajModel *msvmmaj_init_model() } /** - * @brief Initialize a MajData structure + * @brief Initialize a GenData structure * * @details - * A MajData structure is initialized and default values are set. + * A GenData structure is initialized and default values are set. * A pointer to the initialized data is returned. * - * @returns initialized MajData + * @returns initialized GenData * */ -struct MajData *msvmmaj_init_data() +struct GenData *gensvm_init_data() { - struct MajData *data = Malloc(struct MajData, 1); + struct GenData *data = Malloc(struct GenData, 1); data->J = NULL; data->y = NULL; data->Z = NULL; @@ -81,16 +81,16 @@ struct MajData *msvmmaj_init_data() } /** - * @brief Allocate memory for a MajModel + * @brief Allocate memory for a GenModel * * @details - * This function can be used to allocate the memory needed for a MajModel. All + * This function can be used to allocate the memory needed for a GenModel. All * arrays in the model are specified and initialized to 0. * - * @param[in] model MajModel to allocate + * @param[in] model GenModel to allocate * */ -void msvmmaj_allocate_model(struct MajModel *model) +void gensvm_allocate_model(struct GenModel *model) { long n = model->n; long m = model->m; @@ -158,19 +158,19 @@ void msvmmaj_allocate_model(struct MajModel *model) } /** - * @brief Reallocate memory for MajModel + * @brief Reallocate memory for GenModel * * @details - * This function can be used to reallocate existing memory for a MajModel, + * This function can be used to reallocate existing memory for a GenModel, * upon a change in the model dimensions. This is used in combination with * kernels. * - * @param[in] model MajModel to reallocate - * @param[in] n new value of MajModel->n - * @param[in] m new value of MajModel->m + * @param[in] model GenModel to reallocate + * @param[in] n new value of GenModel->n + * @param[in] m new value of GenModel->m * */ -void msvmmaj_reallocate_model(struct MajModel *model, long n, long m) +void gensvm_reallocate_model(struct GenModel *model, long n, long m) { long K = model->K; @@ -237,16 +237,16 @@ void msvmmaj_reallocate_model(struct MajModel *model, long n, long m) } /** - * @brief Free allocated MajModel struct + * @brief Free allocated GenModel struct * * @details - * Simply free a previously allocated MajModel by freeing all its component + * Simply free a previously allocated GenModel by freeing all its component * arrays. Note that the model struct itself is also freed here. * - * @param[in] model MajModel to free + * @param[in] model GenModel to free * */ -void msvmmaj_free_model(struct MajModel *model) +void gensvm_free_model(struct GenModel *model) { free(model->W); free(model->t); @@ -264,16 +264,16 @@ void msvmmaj_free_model(struct MajModel *model) } /** - * @brief Free allocated MajData struct + * @brief Free allocated GenData struct * * @details - * Simply free a previously allocated MajData struct by freeing all its + * Simply free a previously allocated GenData struct by freeing all its * components. Note that the data struct itself is also freed here. * - * @param[in] data MajData struct to free + * @param[in] data GenData struct to free * */ -void msvmmaj_free_data(struct MajData *data) +void gensvm_free_data(struct GenData *data) { free(data->Z); free(data->y); diff --git a/src/msvmmaj_io.c b/src/gensvm_io.c index 8a09b3d..546ecd5 100644 --- a/src/msvmmaj_io.c +++ b/src/gensvm_io.c @@ -1,5 +1,5 @@ /** - * @file msvmmaj_io.c + * @file gensvm_io.c * @author Gertjan van den Burg * @date January, 2014 * @brief Functions for input and output of data and model files @@ -10,9 +10,9 @@ * */ -#include "msvmmaj.h" -#include "msvmmaj_io.h" -#include "msvmmaj_matrix.h" +#include "gensvm.h" +#include "gensvm_io.h" +#include "gensvm_matrix.h" #include "strutil.h" #include "timer.h" @@ -30,10 +30,10 @@ * Make sure that this function allows datasets without class labels for * testing. * - * @param[in,out] dataset initialized MajData struct + * @param[in,out] dataset initialized GenData struct * @param[in] data_file filename of the data file. */ -void msvmmaj_read_data(struct MajData *dataset, char *data_file) +void gensvm_read_data(struct GenData *dataset, char *data_file) { FILE *fid; long i, j; @@ -125,16 +125,16 @@ void msvmmaj_read_data(struct MajData *dataset, char *data_file) * @brief Read model from file * * @details - * Read a MajModel from a model file. The MajModel struct must have been + * Read a GenModel from a model file. The GenModel struct must have been * initalized elswhere. The model file is expected to follow the @ref * spec_model_file. The easiest way to generate a model file is through - * msvmmaj_write_model(), which can for instance be used in trainMSVMMaj.c. + * gensvm_write_model(), which can for instance be used in trainGenSVM.c. * - * @param[in,out] model initialized MajModel + * @param[in,out] model initialized GenModel * @param[in] model_filename filename of the model file * */ -void msvmmaj_read_model(struct MajModel *model, char *model_filename) +void gensvm_read_model(struct GenModel *model, char *model_filename) { long i, j, nr = 0; FILE *fid; @@ -202,16 +202,16 @@ void msvmmaj_read_model(struct MajModel *model, char *model_filename) * @brief Write model to file * * @details - * Write a MajModel to a file. The current time is specified in the file in + * Write a GenModel to a file. The current time is specified in the file in * UTC + offset. The model file further corresponds to the @ref * spec_model_file. * - * @param[in] model MajModel which contains an estimate for - * MajModel::V + * @param[in] model GenModel which contains an estimate for + * GenModel::V * @param[in] output_filename the output file to write the model to * */ -void msvmmaj_write_model(struct MajModel *model, char *output_filename) +void gensvm_write_model(struct GenModel *model, char *output_filename) { FILE *fid; long i, j; @@ -227,7 +227,7 @@ void msvmmaj_write_model(struct MajModel *model, char *output_filename) get_time_string(timestr); // Write output to file - fprintf(fid, "Output file for MSVMMaj (version %1.1f)\n", VERSION); + fprintf(fid, "Output file for GenSVM (version %1.1f)\n", VERSION); fprintf(fid, "Generated on: %s\n\n", timestr); fprintf(fid, "Model:\n"); fprintf(fid, "p = %15.16f\n", model->p); @@ -262,15 +262,15 @@ void msvmmaj_write_model(struct MajModel *model, char *output_filename) * Write the given predictions to an output file, such that the resulting file * corresponds to the @ref spec_data_file. * - * @param[in] data MajData with the original instances + * @param[in] data GenData with the original instances * @param[in] predy predictions of the class labels of the - * instances in the given MajData. Note that the + * instances in the given GenData. Note that the * order of the instances is assumed to be the * same. * @param[in] output_filename the file to which the predictions are written * */ -void msvmmaj_write_predictions(struct MajData *data, long *predy, +void gensvm_write_predictions(struct GenData *data, long *predy, char *output_filename) { long i, j; diff --git a/src/msvmmaj_kernel.c b/src/gensvm_kernel.c index 8f757c5..55cfa03 100644 --- a/src/msvmmaj_kernel.c +++ b/src/gensvm_kernel.c @@ -1,8 +1,8 @@ /** - * @file msvmmaj_kernel.c + * @file gensvm_kernel.c * @author Gertjan van den Burg * @date October 18, 2013 - * @brief Defines main functions for use of kernels in MSVMMaj. + * @brief Defines main functions for use of kernels in GenSVM. * * @details * Functions for constructing different kernels using user-supplied @@ -13,10 +13,10 @@ #include <math.h> -#include "msvmmaj.h" -#include "msvmmaj_kernel.h" -#include "msvmmaj_lapack.h" -#include "msvmmaj_matrix.h" +#include "gensvm.h" +#include "gensvm_kernel.h" +#include "gensvm_lapack.h" +#include "gensvm_matrix.h" #include "util.h" /** @@ -25,11 +25,11 @@ * Create a kernel matrix based on the specified kerneltype. Kernel parameters * are assumed to be specified in the model. * - * @param[in] model MajModel specifying the parameters - * @param[in] data MajData specifying the data. + * @param[in] model GenModel specifying the parameters + * @param[in] data GenData specifying the data. * */ -void msvmmaj_make_kernel(struct MajModel *model, struct MajData *data) +void gensvm_make_kernel(struct GenModel *model, struct GenData *data) { long i, j; // Determine if a kernel needs to be computed. This is not the case if @@ -95,17 +95,17 @@ void msvmmaj_make_kernel(struct MajModel *model, struct MajData *data) x1 = &data->RAW[i*(data->m+1)+1]; x2 = &data->RAW[j*(data->m+1)+1]; if (model->kerneltype == K_POLY) - value = msvmmaj_compute_poly(x1, x2, + value = gensvm_compute_poly(x1, x2, model->kernelparam, data->m); else if (model->kerneltype == K_RBF) - value = msvmmaj_compute_rbf(x1, x2, + value = gensvm_compute_rbf(x1, x2, model->kernelparam, data->m); else if (model->kerneltype == K_SIGMOID) - value = msvmmaj_compute_sigmoid(x1, x2, + value = gensvm_compute_sigmoid(x1, x2, model->kernelparam, data->m); else { fprintf(stderr, "Unknown kernel type in " - "msvmmaj_make_kernel\n"); + "gensvm_make_kernel\n"); exit(1); } matrix_set(K, n, i, j, value); @@ -115,7 +115,7 @@ void msvmmaj_make_kernel(struct MajModel *model, struct MajData *data) double *P = NULL; double *Sigma = NULL; - long num_eigen = msvmmaj_make_eigen(K, n, &P, &Sigma); + long num_eigen = gensvm_make_eigen(K, n, &P, &Sigma); //printf("num eigen: %li\n", num_eigen); data->m = num_eigen; @@ -171,7 +171,7 @@ void msvmmaj_make_kernel(struct MajModel *model, struct MajData *data) * tbd * */ -long msvmmaj_make_eigen(double *K, long n, double **P, double **Sigma) +long gensvm_make_eigen(double *K, long n, double **P, double **Sigma) { int M, status, LWORK, *IWORK, *IFAIL; long i, j, num_eigen, cutoff_idx; @@ -278,8 +278,8 @@ long msvmmaj_make_eigen(double *K, long n, double **P, double **Sigma) return num_eigen; } -void msvmmaj_make_crosskernel(struct MajModel *model, - struct MajData *data_train, struct MajData *data_test, +void gensvm_make_crosskernel(struct GenModel *model, + struct GenData *data_train, struct GenData *data_test, double **K2) { long i, j; @@ -302,20 +302,20 @@ void msvmmaj_make_crosskernel(struct MajModel *model, x1 = &data_test->RAW[i*(m+1)+1]; x2 = &data_train->RAW[j*(m+1)+1]; if (model->kerneltype == K_POLY) - value = msvmmaj_compute_poly(x1, x2, + value = gensvm_compute_poly(x1, x2, model->kernelparam, m); else if (model->kerneltype == K_RBF) - value = msvmmaj_compute_rbf(x1, x2, + value = gensvm_compute_rbf(x1, x2, model->kernelparam, m); else if (model->kerneltype == K_SIGMOID) - value = msvmmaj_compute_sigmoid(x1, x2, + value = gensvm_compute_sigmoid(x1, x2, model->kernelparam, m); else { fprintf(stderr, "Unknown kernel type in " - "msvmmaj_make_crosskernel\n"); + "gensvm_make_crosskernel\n"); exit(1); } matrix_set((*K2), n_train, i, j, value); @@ -344,7 +344,7 @@ void msvmmaj_make_crosskernel(struct MajModel *model, * @param[in] n length of the vectors x1 and x2 * @returns kernel evaluation */ -double msvmmaj_compute_rbf(double *x1, double *x2, double *kernelparam, long n) +double gensvm_compute_rbf(double *x1, double *x2, double *kernelparam, long n) { long i; double value = 0.0; @@ -372,7 +372,7 @@ double msvmmaj_compute_rbf(double *x1, double *x2, double *kernelparam, long n) * @param[in] n length of the vectors x1 and x2 * @returns kernel evaluation */ -double msvmmaj_compute_poly(double *x1, double *x2, double *kernelparam, long n) +double gensvm_compute_poly(double *x1, double *x2, double *kernelparam, long n) { long i; double value = 0.0; @@ -400,7 +400,7 @@ double msvmmaj_compute_poly(double *x1, double *x2, double *kernelparam, long n) * @param[in] n length of the vectors x1 and x2 * @returns kernel evaluation */ -double msvmmaj_compute_sigmoid(double *x1, double *x2, double *kernelparam, long n) +double gensvm_compute_sigmoid(double *x1, double *x2, double *kernelparam, long n) { long i; double value = 0.0; diff --git a/src/msvmmaj_lapack.c b/src/gensvm_lapack.c index 34bd132..6f50340 100644 --- a/src/msvmmaj_lapack.c +++ b/src/gensvm_lapack.c @@ -1,5 +1,5 @@ /** - * @file msvmmaj_lapack.c + * @file gensvm_lapack.c * @author Gertjan van den Burg * @date August 9, 2013 * @brief Utility functions for interacting with LAPACK @@ -9,7 +9,7 @@ * to use LAPACK functions from liblapack. */ -#include "msvmmaj_lapack.h" +#include "gensvm_lapack.h" /** * @brief Solve AX = B where A is symmetric positive definite. diff --git a/src/msvmmaj_matrix.c b/src/gensvm_matrix.c index 9e1be04..43f284f 100644 --- a/src/msvmmaj_matrix.c +++ b/src/gensvm_matrix.c @@ -1,5 +1,5 @@ /** - * @file msvmmaj_matrix.c + * @file gensvm_matrix.c * @author Gertjan van den Burg * @date August, 2013 * @brief Functions facilitating matrix access @@ -12,7 +12,7 @@ * */ -#include "msvmmaj_matrix.h" +#include "gensvm_matrix.h" #include "util.h" /** diff --git a/src/msvmmaj_pred.c b/src/gensvm_pred.c index ea1ebfe..f331116 100644 --- a/src/msvmmaj_pred.c +++ b/src/gensvm_pred.c @@ -1,5 +1,5 @@ /** - * @file msvmmaj_pred.c + * @file gensvm_pred.c * @author Gertjan van den Burg * @date August 9, 2013 * @brief Main functions for predicting class labels.. @@ -13,22 +13,22 @@ #include <cblas.h> -#include "libMSVMMaj.h" -#include "msvmmaj.h" -#include "msvmmaj_kernel.h" -#include "msvmmaj_matrix.h" -#include "msvmmaj_pred.h" +#include "libGenSVM.h" +#include "gensvm.h" +#include "gensvm_kernel.h" +#include "gensvm_matrix.h" +#include "gensvm_pred.h" #include "util.h" // testing -void msvmmaj_predict_labels(struct MajData *data_test, - struct MajData *data_train, struct MajModel *model, +void gensvm_predict_labels(struct GenData *data_test, + struct GenData *data_train, struct GenModel *model, long *predy) { if (model->kerneltype == K_LINEAR) - msvmmaj_predict_labels_linear(data_test, model, predy); + gensvm_predict_labels_linear(data_test, model, predy); else - msvmmaj_predict_labels_kernel(data_test, data_train, model, + gensvm_predict_labels_kernel(data_test, data_train, model, predy); } @@ -42,12 +42,12 @@ void msvmmaj_predict_labels(struct MajData *data_test, * norm. The nearest simplex vertex determines the predicted class label, * which is recorded in predy. * - * @param[in] data MajData to predict labels for - * @param[in] model MajModel with optimized V + * @param[in] data GenData to predict labels for + * @param[in] model GenModel with optimized V * @param[out] predy pre-allocated vector to record predictions in */ -void msvmmaj_predict_labels_linear(struct MajData *data, - struct MajModel *model, long *predy) +void gensvm_predict_labels_linear(struct GenData *data, + struct GenModel *model, long *predy) { long i, j, k, label; double norm, min_dist; @@ -61,11 +61,11 @@ void msvmmaj_predict_labels_linear(struct MajData *data, double *U = Calloc(double, K*(K-1)); // Get the simplex matrix - msvmmaj_simplex_gen(K, U); + gensvm_simplex_gen(K, U); // Generate the simplex-space vectors cblas_dgemm( - CblasRowMajor, + CblasRowGenor, CblasNoTrans, CblasNoTrans, n, @@ -104,8 +104,8 @@ void msvmmaj_predict_labels_linear(struct MajData *data, free(S); } -void msvmmaj_predict_labels_kernel(struct MajData *data_test, - struct MajData *data_train, struct MajModel *model, +void gensvm_predict_labels_kernel(struct GenData *data_test, + struct GenData *data_train, struct GenModel *model, long *predy) { long i, j, k, label; @@ -117,14 +117,14 @@ void msvmmaj_predict_labels_kernel(struct MajData *data_test, long K = model->K; double *K2 = NULL; - msvmmaj_make_crosskernel(model, data_train, data_test, &K2); + gensvm_make_crosskernel(model, data_train, data_test, &K2); double *S = Calloc(double, K-1); double *ZV = Calloc(double, n_test*(r+1)); double *KPS = Calloc(double, n_test*(r+1)); double *U = Calloc(double, K*(K-1)); - msvmmaj_simplex_gen(K, U); + gensvm_simplex_gen(K, U); // were doing the computations explicitly since P is included in // data_train->Z. Might want to look at this some more if it turns out @@ -147,7 +147,7 @@ void msvmmaj_predict_labels_kernel(struct MajData *data_test, } cblas_dgemm( - CblasRowMajor, + CblasRowGenor, CblasNoTrans, CblasNoTrans, n_test, @@ -195,12 +195,12 @@ void msvmmaj_predict_labels_kernel(struct MajData *data_test, * of correctly classified samples and dividing by the total number of * samples, multiplying by 100. * - * @param[in] data the MajData dataset with known labels + * @param[in] data the GenData dataset with known labels * @param[in] predy the predicted class labels * * @returns percentage correctly classified. */ -double msvmmaj_prediction_perf(struct MajData *data, long *predy) +double gensvm_prediction_perf(struct GenData *data, long *predy) { long i, correct = 0; double performance; diff --git a/src/msvmmaj_sv.c b/src/gensvm_sv.c index 1358d4e..787b869 100644 --- a/src/msvmmaj_sv.c +++ b/src/gensvm_sv.c @@ -1,5 +1,5 @@ /** - * @file msvmmaj_sv.c + * @file gensvm_sv.c * @author Gertjan van den Burg * @date May, 2014 * @brief Calculate the number of support vectors @@ -10,8 +10,8 @@ * */ -#include "msvmmaj.h" -#include "msvmmaj_matrix.h" +#include "gensvm.h" +#include "gensvm_matrix.h" /** * @brief Calculate the number of support vectors in a model @@ -22,12 +22,12 @@ * other classes). All objects for which this is not the case are thus support * vectors. * - * @param[in] model MajModel with solution - * @param[in] data MajData to be used + * @param[in] model GenModel with solution + * @param[in] data GenData to be used * @return number of support vectors with this solution * */ -long msvmmaj_num_sv(struct MajModel *model, struct MajData *data) +long gensvm_num_sv(struct GenModel *model, struct GenData *data) { long i, j, num_correct, num_sv = 0; double value; diff --git a/src/msvmmaj_train.c b/src/gensvm_train.c index 09b00ee..9deac80 100644 --- a/src/msvmmaj_train.c +++ b/src/gensvm_train.c @@ -1,8 +1,8 @@ /** - * @file msvmmaj_train.c + * @file gensvm_train.c * @author Gertjan van den Burg * @date August 9, 2013 - * @brief Main functions for training the MSVMMaj solution. + * @brief Main functions for training the GenSVM solution. * * @details * Contains update and loss functions used to actually find @@ -13,12 +13,12 @@ #include <math.h> #include <cblas.h> -#include "libMSVMMaj.h" -#include "msvmmaj.h" -#include "msvmmaj_lapack.h" -#include "msvmmaj_matrix.h" -#include "msvmmaj_sv.h" -#include "msvmmaj_train.h" +#include "libGenSVM.h" +#include "gensvm.h" +#include "gensvm_lapack.h" +#include "gensvm_matrix.h" +#include "gensvm_sv.h" +#include "gensvm_train.h" #include "util.h" /** @@ -27,23 +27,23 @@ #define MAX_ITER 1000000000 /** - * @brief The main training loop for MSVMMaj + * @brief The main training loop for GenSVM * * @details * This function is the main training function. This function * handles the optimization of the model with the given model parameters, with - * the data given. On return the matrix MajModel::V contains the optimal + * the data given. On return the matrix GenModel::V contains the optimal * weight matrix. * * In this function, step doubling is used in the majorization algorithm after - * a burn-in of 50 iterations. If the training is finished, MajModel::t and - * MajModel::W are extracted from MajModel::V. + * a burn-in of 50 iterations. If the training is finished, GenModel::t and + * GenModel::W are extracted from GenModel::V. * - * @param[in,out] model the MajModel to be trained. Contains optimal + * @param[in,out] model the GenModel to be trained. Contains optimal * V on exit. - * @param[in] data the MajData to train the model with. + * @param[in] data the GenData to train the model with. */ -void msvmmaj_optimize(struct MajModel *model, struct MajData *data) +void gensvm_optimize(struct GenModel *model, struct GenData *data) { long i, j, it = 0; double L, Lbar, value; @@ -70,23 +70,23 @@ void msvmmaj_optimize(struct MajModel *model, struct MajData *data) note("\tepsilon = %g\n", model->epsilon); note("\n"); - msvmmaj_simplex_gen(model->K, model->U); - msvmmaj_simplex_diff(model, data); - msvmmaj_category_matrix(model, data); + gensvm_simplex_gen(model->K, model->U); + gensvm_simplex_diff(model, data); + gensvm_category_matrix(model, data); - L = msvmmaj_get_loss(model, data, ZV); + L = gensvm_get_loss(model, data, ZV); Lbar = L + 2.0*model->epsilon*L; while ((it < MAX_ITER) && (Lbar - L)/L > model->epsilon) { // ensure V contains newest V and Vbar contains V from // previous - msvmmaj_get_update(model, data, B, ZAZ, ZAZV, ZAZVT); + gensvm_get_update(model, data, B, ZAZ, ZAZV, ZAZVT); if (it > 50) - msvmmaj_step_doubling(model); + gensvm_step_doubling(model); Lbar = L; - L = msvmmaj_get_loss(model, data, ZV); + L = gensvm_get_loss(model, data, ZV); if (it%100 == 0) note("iter = %li, L = %15.16f, Lbar = %15.16f, " @@ -99,7 +99,7 @@ void msvmmaj_optimize(struct MajModel *model, struct MajData *data) note("optimization finished, iter = %li, loss = %15.16f, " "rel. diff. = %15.16f\n", it-1, L, (Lbar - L)/L); - note("number of support vectors: %li\n", msvmmaj_num_sv(model, data)); + note("number of support vectors: %li\n", gensvm_num_sv(model, data)); model->training_error = (Lbar - L)/L; @@ -126,14 +126,14 @@ void msvmmaj_optimize(struct MajModel *model, struct MajData *data) * given model. Note that the matrix ZV is passed explicitly to avoid having * to reallocate memory at every step. * - * @param[in] model MajModel structure which holds the current + * @param[in] model GenModel structure which holds the current * estimate V - * @param[in] data MajData structure + * @param[in] data GenData structure * @param[in,out] ZV pre-allocated matrix ZV which is updated on * output * @returns the current value of the loss function */ -double msvmmaj_get_loss(struct MajModel *model, struct MajData *data, +double gensvm_get_loss(struct GenModel *model, struct GenData *data, double *ZV) { long i, j; @@ -143,8 +143,8 @@ double msvmmaj_get_loss(struct MajModel *model, struct MajData *data, double value, rowvalue, loss = 0.0; - msvmmaj_calculate_errors(model, data, ZV); - msvmmaj_calculate_huber(model); + gensvm_calculate_errors(model, data, ZV); + gensvm_calculate_huber(model); for (i=0; i<n; i++) { rowvalue = 0; @@ -183,9 +183,9 @@ double msvmmaj_get_loss(struct MajModel *model, struct MajData *data, * recalculating the majorization coefficients for all instances and all * classes, and solving a linear system to find V. * - * Because the function msvmmaj_get_update() is always called after a call to - * msvmmaj_get_loss() with the same MajModel::V, it is unnecessary to calculate - * the updated errors MajModel::Q and MajModel::H here too. This saves on + * Because the function gensvm_get_update() is always called after a call to + * gensvm_get_loss() with the same GenModel::V, it is unnecessary to calculate + * the updated errors GenModel::Q and GenModel::H here too. This saves on * computation time. * * In calculating the majorization coefficients we calculate the elements of a @@ -233,7 +233,7 @@ double msvmmaj_get_loss(struct MajModel *model, struct MajData *data, * @param [in] ZAZV pre-allocated matrix used in system solving * @param [in] ZAZVT pre-allocated matrix used in system solving */ -void msvmmaj_get_update(struct MajModel *model, struct MajData *data, double *B, +void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, double *ZAZ, double *ZAZV, double *ZAZVT) { int status, class; @@ -384,7 +384,7 @@ void msvmmaj_get_update(struct MajModel *model, struct MajData *data, double *B, // Note that the use of dsym is faster than dspr, even // though dspr uses less memory. cblas_dsyr( - CblasRowMajor, + CblasRowGenor, CblasUpper, m+1, Avalue, @@ -394,7 +394,7 @@ void msvmmaj_get_update(struct MajModel *model, struct MajData *data, double *B, m+1); } // Copy upper to lower (necessary because we need to switch - // to Col-Major order for LAPACK). + // to Col-Genor order for LAPACK). /* for (i=0; i<m+1; i++) for (j=0; j<m+1; j++) @@ -404,7 +404,7 @@ void msvmmaj_get_update(struct MajModel *model, struct MajData *data, double *B, // Calculate the right hand side of the system we // want to solve. cblas_dsymm( - CblasRowMajor, + CblasRowGenor, CblasLeft, CblasUpper, m+1, @@ -419,7 +419,7 @@ void msvmmaj_get_update(struct MajModel *model, struct MajData *data, double *B, K-1); cblas_dgemm( - CblasRowMajor, + CblasRowGenor, CblasTrans, CblasNoTrans, m+1, @@ -445,7 +445,7 @@ void msvmmaj_get_update(struct MajModel *model, struct MajData *data, double *B, } // For the LAPACK call we need to switch to Column- - // Major order. This is unnecessary for the matrix + // Genor order. This is unnecessary for the matrix // ZAZ because it is symmetric. The matrix ZAZV // must be converted however. for (i=0; i<m+1; i++) diff --git a/src/msvmmaj_train_dataset.c b/src/gensvm_train_dataset.c index 26c684c..3034bb4 100644 --- a/src/msvmmaj_train_dataset.c +++ b/src/gensvm_train_dataset.c @@ -1,11 +1,11 @@ /** - * @file msvmmaj_train_dataset.c + * @file gensvm_train_dataset.c * @author Gertjan van den Burg * @date January, 2014 * @brief Functions for finding the optimal parameters for the dataset * * @details - * The MSVMMaj algorithm takes a number of parameters. The functions in + * The GenSVM algorithm takes a number of parameters. The functions in * this file are used to find the optimal parameters. */ @@ -13,18 +13,18 @@ #include <time.h> #include "crossval.h" -#include "libMSVMMaj.h" -#include "msvmmaj.h" -#include "msvmmaj_init.h" -#include "msvmmaj_kernel.h" -#include "msvmmaj_matrix.h" -#include "msvmmaj_train.h" -#include "msvmmaj_train_dataset.h" -#include "msvmmaj_pred.h" +#include "libGenSVM.h" +#include "gensvm.h" +#include "gensvm_init.h" +#include "gensvm_kernel.h" +#include "gensvm_matrix.h" +#include "gensvm_train.h" +#include "gensvm_train_dataset.h" +#include "gensvm_pred.h" #include "util.h" #include "timer.h" -extern FILE *MSVMMAJ_OUTPUT_FILE; +extern FILE *GENSVM_OUTPUT_FILE; /** * @brief Initialize a Queue from a Training instance @@ -34,19 +34,19 @@ extern FILE *MSVMMAJ_OUTPUT_FILE; * creates all tasks that need to be performed and adds these to * a Queue. Each task contains a pointer to the train and test datasets * which are supplied. Note that the tasks are created in a specific order of - * the parameters, to ensure that the MajModel::V of a previous parameter - * set provides the best possible initial estimate of MajModel::V for the next + * the parameters, to ensure that the GenModel::V of a previous parameter + * set provides the best possible initial estimate of GenModel::V for the next * parameter set. * * @param[in] training Training struct describing the grid search * @param[in] queue pointer to a Queue that will be used to * add the tasks to - * @param[in] train_data MajData of the training set - * @param[in] test_data MajData of the test set + * @param[in] train_data GenData of the training set + * @param[in] test_data GenData of the test set * */ void make_queue(struct Training *training, struct Queue *queue, - struct MajData *train_data, struct MajData *test_data) + struct GenData *train_data, struct GenData *test_data) { long i, j, k; long N, cnt = 0; @@ -293,7 +293,7 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) long i, r, N; double p, pi, pr, pt, boundary, *time, *std, *mean, *perf; struct Queue *nq = Malloc(struct Queue, 1); - struct MajModel *model = msvmmaj_init_model(); + struct GenModel *model = gensvm_init_model(); struct Task *task; clock_t loop_s, loop_e; @@ -334,8 +334,8 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) model->n = 0; model->m = task->train_data->m; model->K = task->train_data->K; - msvmmaj_allocate_model(model); - msvmmaj_seed_model_V(NULL, model, task->train_data); + gensvm_allocate_model(model); + gensvm_seed_model_V(NULL, model, task->train_data); } time[i] = 0.0; @@ -356,7 +356,7 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) note("%3.3f\t", p); // this is done because if we reuse the V it's not a // consistency check - msvmmaj_seed_model_V(NULL, model, task->train_data); + gensvm_seed_model_V(NULL, model, task->train_data); } for (r=0; r<repeats; r++) { std[i] += pow(matrix_get( @@ -422,55 +422,55 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) * * @details * This is an implementation of cross validation which uses the optimal - * parameters MajModel::V of a previous fold as initial conditions for - * MajModel::V of the next fold. An initial seed for V can be given through the + * parameters GenModel::V of a previous fold as initial conditions for + * GenModel::V of the next fold. An initial seed for V can be given through the * seed_model parameter. If seed_model is NULL, random starting values are * used. * - * @param[in] model MajModel with the configuration to train - * @param[in] seed_model MajModel with a seed for MajModel::V - * @param[in] data MajData with the dataset + * @param[in] model GenModel with the configuration to train + * @param[in] seed_model GenModel with a seed for GenModel::V + * @param[in] data GenData with the dataset * @param[in] folds number of cross validation folds * @returns performance (hitrate) of the configuration on * cross validation */ -double cross_validation(struct MajModel *model, struct MajData *data, +double cross_validation(struct GenModel *model, struct GenData *data, long folds) { FILE *fid; long f, *predy; double performance, total_perf = 0; - struct MajData *train_data, *test_data; + struct GenData *train_data, *test_data; long *cv_idx = Calloc(long, data->n); - train_data = msvmmaj_init_data(); - test_data = msvmmaj_init_data(); + train_data = gensvm_init_data(); + test_data = gensvm_init_data(); // create splits - msvmmaj_make_cv_split(data->n, folds, cv_idx); + gensvm_make_cv_split(data->n, folds, cv_idx); for (f=0; f<folds; f++) { - msvmmaj_get_tt_split(data, train_data, test_data, cv_idx, f); + gensvm_get_tt_split(data, train_data, test_data, cv_idx, f); - msvmmaj_make_kernel(model, train_data); + gensvm_make_kernel(model, train_data); // reallocate the model if necessary for the new train split - msvmmaj_reallocate_model(model, train_data->n, train_data->m); + gensvm_reallocate_model(model, train_data->n, train_data->m); - msvmmaj_initialize_weights(train_data, model); + gensvm_initialize_weights(train_data, model); // train the model (without output) - fid = MSVMMAJ_OUTPUT_FILE; - MSVMMAJ_OUTPUT_FILE = NULL; - msvmmaj_optimize(model, train_data); - MSVMMAJ_OUTPUT_FILE = fid; + fid = GENSVM_OUTPUT_FILE; + GENSVM_OUTPUT_FILE = NULL; + gensvm_optimize(model, train_data); + GENSVM_OUTPUT_FILE = fid; // calculate prediction performance on test set predy = Calloc(long, test_data->n); - msvmmaj_predict_labels(test_data, train_data, model, predy); - performance = msvmmaj_prediction_perf(test_data, predy); + gensvm_predict_labels(test_data, train_data, model, predy); + performance = gensvm_prediction_perf(test_data, predy); total_perf += performance * test_data->n; free(predy); @@ -495,7 +495,7 @@ double cross_validation(struct MajModel *model, struct MajData *data, * Given a Queue of Task struct to be trained, a grid search is launched to * find the optimal parameter configuration. As is also done within * cross_validation(), the optimal weights of one parameter set are used as - * initial estimates for MajModel::V in the next parameter set. Note that to + * initial estimates for GenModel::V in the next parameter set. Note that to * optimally exploit this feature of the optimization algorithm, the order in * which tasks are considered is important. This is considered in * make_queue(). @@ -508,14 +508,14 @@ void start_training_cv(struct Queue *q) { double perf, current_max = 0; struct Task *task = get_next_task(q); - struct MajModel *model = msvmmaj_init_model(); + struct GenModel *model = gensvm_init_model(); clock_t main_s, main_e, loop_s, loop_e; model->n = 0; model->m = task->train_data->m; model->K = task->train_data->K; - msvmmaj_allocate_model(model); - msvmmaj_seed_model_V(NULL, model, task->train_data); + gensvm_allocate_model(model); + gensvm_seed_model_V(NULL, model, task->train_data); main_s = clock(); while (task) { @@ -539,7 +539,7 @@ void start_training_cv(struct Queue *q) note("\nTotal elapsed time: %8.8f seconds\n", elapsed_time(main_s, main_e)); - msvmmaj_free_model(model); + gensvm_free_model(model); } /** @@ -570,15 +570,15 @@ void start_training_tt(struct Queue *q) double total_perf, current_max = 0; struct Task *task = get_next_task(q); - struct MajModel *seed_model = msvmmaj_init_model(); + struct GenModel *seed_model = gensvm_init_model(); clock_t main_s, main_e; clock_t loop_s, loop_e; seed_model->m = task->train_data->m; seed_model->K = task->train_data->K; - msvmmaj_allocate_model(seed_model); - msvmmaj_seed_model_V(NULL, seed_model, task->train_data); + gensvm_allocate_model(seed_model); + gensvm_seed_model_V(NULL, seed_model, task->train_data); main_s = clock(); while (task) { @@ -587,31 +587,31 @@ void start_training_tt(struct Queue *q) c+1, q->N, task->weight_idx, task->epsilon, task->p, task->kappa, task->lambda); loop_s = clock(); - struct MajModel *model = msvmmaj_init_model(); + struct GenModel *model = gensvm_init_model(); make_model_from_task(task, model); model->n = task->train_data->n; model->m = task->train_data->m; model->K = task->train_data->K; - msvmmaj_allocate_model(model); - msvmmaj_initialize_weights(task->train_data, model); - msvmmaj_seed_model_V(seed_model, model, task->train_data); + gensvm_allocate_model(model); + gensvm_initialize_weights(task->train_data, model); + gensvm_seed_model_V(seed_model, model, task->train_data); - fid = MSVMMAJ_OUTPUT_FILE; - MSVMMAJ_OUTPUT_FILE = NULL; - msvmmaj_optimize(model, task->train_data); - MSVMMAJ_OUTPUT_FILE = fid; + fid = GENSVM_OUTPUT_FILE; + GENSVM_OUTPUT_FILE = NULL; + gensvm_optimize(model, task->train_data); + GENSVM_OUTPUT_FILE = fid; predy = Calloc(long, task->test_data->n); - msvmmaj_predict_labels(task->test_data, task->train_data, + gensvm_predict_labels(task->test_data, task->train_data, model, predy); if (task->test_data->y != NULL) - total_perf = msvmmaj_prediction_perf(task->test_data, + total_perf = gensvm_prediction_perf(task->test_data, predy); - msvmmaj_seed_model_V(model, seed_model, task->train_data); + gensvm_seed_model_V(model, seed_model, task->train_data); - msvmmaj_free_model(model); + gensvm_free_model(model); free(predy); note("."); loop_e = clock(); @@ -626,7 +626,7 @@ void start_training_tt(struct Queue *q) note("\nTotal elapsed time: %8.8f seconds\n", elapsed_time(main_s, main_e)); free(task); - msvmmaj_free_model(seed_model); + gensvm_free_model(seed_model); } /** @@ -651,16 +651,16 @@ void free_queue(struct Queue *q) } /** - * @brief Copy parameters from Task to MajModel + * @brief Copy parameters from Task to GenModel * * @details - * A Task struct only contains the parameters of the MajModel to be estimated. + * A Task struct only contains the parameters of the GenModel to be estimated. * This function is used to copy these parameters. * * @param[in] task Task instance with parameters - * @param[in,out] model MajModel to which the parameters are copied + * @param[in,out] model GenModel to which the parameters are copied */ -void make_model_from_task(struct Task *task, struct MajModel *model) +void make_model_from_task(struct Task *task, struct GenModel *model) { // copy basic model parameters model->weight_idx = task->weight_idx; @@ -675,16 +675,16 @@ void make_model_from_task(struct Task *task, struct MajModel *model) } /** - * @brief Copy model parameters between two MajModel structs + * @brief Copy model parameters between two GenModel structs * * @details - * The parameters copied are MajModel::weight_idx, MajModel::epsilon, - * MajModel::p, MajModel::kappa, and MajModel::lambda. + * The parameters copied are GenModel::weight_idx, GenModel::epsilon, + * GenModel::p, GenModel::kappa, and GenModel::lambda. * - * @param[in] from MajModel to copy parameters from - * @param[in,out] to MajModel to copy parameters to + * @param[in] from GenModel to copy parameters from + * @param[in,out] to GenModel to copy parameters to */ -void copy_model(struct MajModel *from, struct MajModel *to) +void copy_model(struct GenModel *from, struct GenModel *to) { to->weight_idx = from->weight_idx; to->epsilon = from->epsilon; @@ -719,7 +719,7 @@ void copy_model(struct MajModel *from, struct MajModel *to) * * @details * To track the progress of the grid search the parameters of the current task - * are written to the output specified in MSVMMAJ_OUTPUT_FILE. Since the + * are written to the output specified in GENSVM_OUTPUT_FILE. Since the * parameters differ with the specified kernel, this function writes a * parameter string depending on which kernel is used. * diff --git a/src/libMSVMMaj.c b/src/libGenSVM.c index df422c0..bb48673 100644 --- a/src/libMSVMMaj.c +++ b/src/libGenSVM.c @@ -1,23 +1,23 @@ /** - * @file libMSVMMaj.c + * @file libGenSVM.c * @author Gertjan van den Burg * @date August 8, 2013 - * @brief Main functions for the MSVMMaj algorithm + * @brief Main functions for the GenSVM algorithm * * @details * The functions in this file are all functions needed * to calculate the optimal separation boundaries for * a multiclass classification problem, using the - * MSVMMaj algorithm. + * GenSVM algorithm. * */ #include <cblas.h> #include <math.h> -#include "libMSVMMaj.h" -#include "msvmmaj.h" -#include "msvmmaj_matrix.h" +#include "libGenSVM.h" +#include "gensvm.h" +#include "gensvm_matrix.h" inline double rnd() { return (double) rand()/0x7FFFFFFF; } @@ -34,7 +34,7 @@ inline double rnd() { return (double) rand()/0x7FFFFFFF; } * @param[in] K number of classes * @param[in,out] U simplex matrix of size K * (K-1) */ -void msvmmaj_simplex_gen(long K, double *U) +void gensvm_simplex_gen(long K, double *U) { long i, j; for (i=0; i<K; i++) { @@ -58,11 +58,11 @@ void msvmmaj_simplex_gen(long K, double *U) * except at the column corresponding to the label of instance i, there the * element is 0. * - * @param[in,out] model corresponding MajModel - * @param[in] dataset corresponding MajData + * @param[in,out] model corresponding GenModel + * @param[in] dataset corresponding GenData * */ -void msvmmaj_category_matrix(struct MajModel *model, struct MajData *dataset) +void gensvm_category_matrix(struct GenModel *model, struct GenData *dataset) { long i, j; long n = model->n; @@ -88,11 +88,11 @@ void msvmmaj_category_matrix(struct MajModel *model, struct MajData *dataset) * other rows of the simplex matrix are calculated. These difference vectors * are stored in a matrix, which is one horizontal slice of the 3D matrix. * - * @param[in,out] model the corresponding MajModel - * @param[in] data the corresponding MajData + * @param[in,out] model the corresponding GenModel + * @param[in] data the corresponding GenData * */ -void msvmmaj_simplex_diff(struct MajModel *model, struct MajData *data) +void gensvm_simplex_diff(struct GenModel *model, struct GenData *data) { long i, j, k; double value; @@ -120,14 +120,14 @@ void msvmmaj_simplex_diff(struct MajModel *model, struct MajData *data) * allocated. In addition, the matrix ZV is calculated here. It is assigned * to a pre-allocated block of memory, which is passed to this function. * - * @param[in,out] model the corresponding MajModel - * @param[in] data the corresponding MajData + * @param[in,out] model the corresponding GenModel + * @param[in] data the corresponding GenData * @param[in,out] ZV a pointer to a memory block for ZV. On exit * this block is updated with the new ZV matrix - * calculated with MajModel::V. + * calculated with GenModel::V. * */ -void msvmmaj_calculate_errors(struct MajModel *model, struct MajData *data, +void gensvm_calculate_errors(struct GenModel *model, struct GenData *data, double *ZV) { long i, j, k; @@ -138,7 +138,7 @@ void msvmmaj_calculate_errors(struct MajModel *model, struct MajData *data, long K = model->K; cblas_dgemm( - CblasRowMajor, + CblasRowGenor, CblasNoTrans, CblasNoTrans, n, @@ -181,9 +181,9 @@ void msvmmaj_calculate_errors(struct MajModel *model, struct MajData *data, * \end{dcases} * @f] * - * @param[in,out] model the corresponding MajModel + * @param[in,out] model the corresponding GenModel */ -void msvmmaj_calculate_huber(struct MajModel *model) +void gensvm_calculate_huber(struct GenModel *model) { long i, j; double q, value; @@ -213,11 +213,11 @@ void msvmmaj_calculate_huber(struct MajModel *model) * significant improvement in the number of iterations necessary * because the seeded model V is closer to the optimal V. * - * @param[in] from_model MajModel from which to copy V - * @param[in,out] to_model MajModel to which V will be copied + * @param[in] from_model GenModel from which to copy V + * @param[in,out] to_model GenModel to which V will be copied */ -void msvmmaj_seed_model_V(struct MajModel *from_model, - struct MajModel *to_model, struct MajData *data) +void gensvm_seed_model_V(struct GenModel *from_model, + struct GenModel *to_model, struct GenData *data) { long i, j, k; double cmin, cmax, value; @@ -255,14 +255,14 @@ void msvmmaj_seed_model_V(struct MajModel *from_model, * @brief Use step doubling * * @details - * Step doubling can be used to speed up the Majorization algorithm. Instead + * Step doubling can be used to speed up the Genorization algorithm. Instead * of using the value at the minimimum of the majorization function, the value * ``opposite'' the majorization point is used. This can essentially cut the * number of iterations necessary to reach the minimum in half. * - * @param[in] model MajModel containing the augmented parameters + * @param[in] model GenModel containing the augmented parameters */ -void msvmmaj_step_doubling(struct MajModel *model) +void gensvm_step_doubling(struct GenModel *model) { long i, j; double value; @@ -295,12 +295,12 @@ void msvmmaj_step_doubling(struct MajModel *model) * where @f$ n_k @f$ is the number of instances in group @f$ k @f$ and * @f$ y_i = k @f$. * - * @param[in] data MajData with the dataset - * @param[in,out] model MajModel with the weight specification. On - * exit MajModel::rho contains the instance + * @param[in] data GenData with the dataset + * @param[in,out] model GenModel with the weight specification. On + * exit GenModel::rho contains the instance * weights. */ -void msvmmaj_initialize_weights(struct MajData *data, struct MajModel *model) +void gensvm_initialize_weights(struct GenData *data, struct GenModel *model) { long *groups; long i; diff --git a/src/predMSVMMaj.c b/src/predGenSVM.c index 3dfcf08..7fac2ef 100644 --- a/src/predMSVMMaj.c +++ b/src/predGenSVM.c @@ -5,7 +5,7 @@ */ /** - * @file predMSVMMaj.c + * @file predGenSVM.c * @author Gertjan van den Burg * @date January, 2014 * @brief Command line interface for predicting class labels @@ -15,24 +15,24 @@ * determining the predictive performance of a pre-determined model on a given * test dataset. The predictive performance can be written to the screen or * the predicted class labels can be written to a specified output file. This - * is done using msvmmaj_write_predictions(). + * is done using gensvm_write_predictions(). * * The specified model file must follow the specification given in - * msvmmaj_write_model(). + * gensvm_write_model(). * * For usage information, see the program help function. * */ -#include "msvmmaj.h" -#include "msvmmaj_init.h" -#include "msvmmaj_io.h" -#include "msvmmaj_pred.h" +#include "gensvm.h" +#include "gensvm_init.h" +#include "gensvm_io.h" +#include "gensvm_pred.h" #include "util.h" #define MINARGS 3 -extern FILE *MSVMMAJ_OUTPUT_FILE; +extern FILE *GENSVM_OUTPUT_FILE; // function declarations void exit_with_help(); @@ -45,8 +45,8 @@ void parse_command_line(int argc, char **argv, */ void exit_with_help() { - printf("This is MSVMMaj, version %1.1f\n\n", VERSION); - printf("Usage: predMSVMMaj [options] test_data_file model_file\n"); + printf("This is GenSVM, version %1.1f\n\n", VERSION); + printf("Usage: predGenSVM [options] test_data_file model_file\n"); printf("Options:\n"); printf("-o output_file : write output to file\n"); printf("-q : quiet mode (no output)\n"); @@ -54,7 +54,7 @@ void exit_with_help() } /** - * @brief Main interface function for predMSVMMaj + * @brief Main interface function for predGenSVM * * @details * Main interface for the command line program. A given model file is read and @@ -80,17 +80,17 @@ int main(int argc, char **argv) char model_filename[MAX_LINE_LENGTH]; char output_filename[MAX_LINE_LENGTH];; - if (argc < MINARGS || msvmmaj_check_argv(argc, argv, "-help") - || msvmmaj_check_argv_eq(argc, argv, "-h") ) + if (argc < MINARGS || gensvm_check_argv(argc, argv, "-help") + || gensvm_check_argv_eq(argc, argv, "-h") ) exit_with_help(); parse_command_line(argc, argv, input_filename, output_filename, model_filename); // read the data and model - struct MajModel *model = msvmmaj_init_model(); - struct MajData *data = msvmmaj_init_data(); - msvmmaj_read_data(data, input_filename); - msvmmaj_read_model(model, model_filename); + struct GenModel *model = gensvm_init_model(); + struct GenData *data = gensvm_init_data(); + gensvm_read_data(data, input_filename); + gensvm_read_model(model, model_filename); // check if the number of attributes in data equals that in model if (data->m != model->m) { @@ -107,21 +107,21 @@ int main(int argc, char **argv) // predict labels and performance if test data has labels predy = Calloc(long, data->n); - msvmmaj_predict_labels(data, model, predy); + gensvm_predict_labels(data, model, predy); if (data->y != NULL) { - performance = msvmmaj_prediction_perf(data, predy); + performance = gensvm_prediction_perf(data, predy); note("Predictive performance: %3.2f%%\n", performance); } // if output file is specified, write predictions to it - if (msvmmaj_check_argv_eq(argc, argv, "-o")) { - msvmmaj_write_predictions(data, predy, output_filename); + if (gensvm_check_argv_eq(argc, argv, "-o")) { + gensvm_write_predictions(data, predy, output_filename); note("Predictions written to: %s\n", output_filename); } // free the model, data, and predictions - msvmmaj_free_model(model); - msvmmaj_free_data(data); + gensvm_free_model(model); + gensvm_free_data(data); free(predy); return 0; @@ -150,7 +150,7 @@ void parse_command_line(int argc, char **argv, char *input_filename, { int i; - MSVMMAJ_OUTPUT_FILE = stdout; + GENSVM_OUTPUT_FILE = stdout; for (i=1; i<argc; i++) { if (argv[i][0] != '-') break; @@ -161,7 +161,7 @@ void parse_command_line(int argc, char **argv, char *input_filename, strcpy(output_filename, argv[i]); break; case 'q': - MSVMMAJ_OUTPUT_FILE = NULL; + GENSVM_OUTPUT_FILE = NULL; i--; break; default: diff --git a/src/trainMSVMMaj.c b/src/trainGenSVM.c index 5377b43..eb75f5d 100644 --- a/src/trainMSVMMaj.c +++ b/src/trainGenSVM.c @@ -1,34 +1,34 @@ /** - * @file trainMSVMMaj.c + * @file trainGenSVM.c * @author Gertjan van den Burg * @date August, 2013 - * @brief Command line interface for training a single model with MSVMMaj + * @brief Command line interface for training a single model with GenSVM * * @details * This is a command line program for training a single model on a given * dataset. To run a grid search over a number of parameter configurations, - * see trainMSVMMajdataset.c. + * see trainGenSVMdataset.c. * */ #include <time.h> #include <math.h> -#include "msvmmaj_kernel.h" -#include "libMSVMMaj.h" -#include "msvmmaj.h" -#include "msvmmaj_io.h" -#include "msvmmaj_init.h" -#include "msvmmaj_train.h" +#include "gensvm_kernel.h" +#include "libGenSVM.h" +#include "gensvm.h" +#include "gensvm_io.h" +#include "gensvm_init.h" +#include "gensvm_train.h" #include "util.h" #define MINARGS 2 -extern FILE *MSVMMAJ_OUTPUT_FILE; +extern FILE *GENSVM_OUTPUT_FILE; // function declarations void exit_with_help(); -void parse_command_line(int argc, char **argv, struct MajModel *model, +void parse_command_line(int argc, char **argv, struct GenModel *model, char *input_filename, char *output_filename, char *model_filename); /** @@ -36,8 +36,8 @@ void parse_command_line(int argc, char **argv, struct MajModel *model, */ void exit_with_help() { - printf("This is MSVMMaj, version %1.1f\n\n", VERSION); - printf("Usage: trainMSVMMaj [options] training_data_file\n"); + printf("This is GenSVM, version %1.1f\n\n", VERSION); + printf("Usage: trainGenSVM [options] training_data_file\n"); printf("Options:\n"); printf("-c coef : coefficient for the polynomial and sigmoid kernel\n"); printf("-d degree : degree for the polynomial kernel\n"); @@ -60,11 +60,11 @@ void exit_with_help() } /** - * @brief Main interface function for trainMSVMMaj + * @brief Main interface function for trainGenSVM * * @details * Main interface for the command line program. A given dataset file is read - * and a MSVMMaj model is trained on this data. By default the progress of the + * and a GenSVM model is trained on this data. By default the progress of the * computations are written to stdout. See for full options of the program the * help function. * @@ -78,17 +78,17 @@ int main(int argc, char **argv) char model_filename[MAX_LINE_LENGTH]; char output_filename[MAX_LINE_LENGTH]; - struct MajModel *model = msvmmaj_init_model(); - struct MajData *data = msvmmaj_init_data(); + struct GenModel *model = gensvm_init_model(); + struct GenData *data = gensvm_init_data(); - if (argc < MINARGS || msvmmaj_check_argv(argc, argv, "-help") - || msvmmaj_check_argv_eq(argc, argv, "-h") ) + if (argc < MINARGS || gensvm_check_argv(argc, argv, "-help") + || gensvm_check_argv_eq(argc, argv, "-h") ) exit_with_help(); parse_command_line(argc, argv, model, input_filename, output_filename, model_filename); // read data file - msvmmaj_read_data(data, input_filename); + gensvm_read_data(data, input_filename); // copy dataset parameters to model model->n = data->n; @@ -97,40 +97,40 @@ int main(int argc, char **argv) model->data_file = input_filename; // allocate model - msvmmaj_allocate_model(model); + gensvm_allocate_model(model); // initialize kernel (if necessary) - msvmmaj_make_kernel(model, data); + gensvm_make_kernel(model, data); // reallocate model and initialize weights - msvmmaj_reallocate_model(model, data->n, data->m); - msvmmaj_initialize_weights(data, model); + gensvm_reallocate_model(model, data->n, data->m); + gensvm_initialize_weights(data, model); // seed the random number generator (only place in programs is in // command line interfaces) srand(time(NULL)); - if (msvmmaj_check_argv_eq(argc, argv, "-m")) { - struct MajModel *seed_model = msvmmaj_init_model(); - msvmmaj_read_model(seed_model, model_filename); - msvmmaj_seed_model_V(seed_model, model, data); - msvmmaj_free_model(seed_model); + if (gensvm_check_argv_eq(argc, argv, "-m")) { + struct GenModel *seed_model = gensvm_init_model(); + gensvm_read_model(seed_model, model_filename); + gensvm_seed_model_V(seed_model, model, data); + gensvm_free_model(seed_model); } else { - msvmmaj_seed_model_V(NULL, model, data); + gensvm_seed_model_V(NULL, model, data); } // start training - msvmmaj_optimize(model, data); + gensvm_optimize(model, data); // write_model to file - if (msvmmaj_check_argv_eq(argc, argv, "-o")) { - msvmmaj_write_model(model, output_filename); + if (gensvm_check_argv_eq(argc, argv, "-o")) { + gensvm_write_model(model, output_filename); note("Output written to %s\n", output_filename); } // free model and data - msvmmaj_free_model(model); - msvmmaj_free_data(data); + gensvm_free_model(model); + gensvm_free_data(data); return 0; } @@ -140,7 +140,7 @@ int main(int argc, char **argv) * * @details * Process the command line arguments for the model parameters, and record - * them in the specified MajModel. An input filename for the dataset is read + * them in the specified GenModel. An input filename for the dataset is read * and if specified an output filename and a model filename for the seed * model. * @@ -155,7 +155,7 @@ int main(int argc, char **argv) * filename * */ -void parse_command_line(int argc, char **argv, struct MajModel *model, +void parse_command_line(int argc, char **argv, struct GenModel *model, char *input_filename, char *output_filename, char *model_filename) { int i; @@ -163,7 +163,7 @@ void parse_command_line(int argc, char **argv, struct MajModel *model, degree = 2.0, coef = 0.0; - MSVMMAJ_OUTPUT_FILE = stdout; + GENSVM_OUTPUT_FILE = stdout; // parse options for (i=1; i<argc; i++) { @@ -206,7 +206,7 @@ void parse_command_line(int argc, char **argv, struct MajModel *model, model->kerneltype = atoi(argv[i]); break; case 'q': - MSVMMAJ_OUTPUT_FILE = NULL; + GENSVM_OUTPUT_FILE = NULL; i--; break; default: diff --git a/src/trainMSVMMajdataset.c b/src/trainGenSVMdataset.c index b9d9180..2882c8f 100644 --- a/src/trainMSVMMajdataset.c +++ b/src/trainGenSVMdataset.c @@ -1,5 +1,5 @@ /** - * @file trainMSVMMajdataset.c + * @file trainGenSVMdataset.c * @author Gertjan van den Burg * @date January, 2014 * @brief Command line interface for the grid search program @@ -22,18 +22,18 @@ #include <time.h> #include "crossval.h" -#include "msvmmaj.h" -#include "msvmmaj_io.h" -#include "msvmmaj_init.h" -#include "msvmmaj_pred.h" -#include "msvmmaj_train.h" -#include "msvmmaj_train_dataset.h" +#include "gensvm.h" +#include "gensvm_io.h" +#include "gensvm_init.h" +#include "gensvm_pred.h" +#include "gensvm_train.h" +#include "gensvm_train_dataset.h" #include "strutil.h" #include "util.h" #define MINARGS 2 -extern FILE *MSVMMAJ_OUTPUT_FILE; +extern FILE *GENSVM_OUTPUT_FILE; // function declarations void exit_with_help(); @@ -45,8 +45,8 @@ void read_training_from_file(char *input_filename, struct Training *training); */ void exit_with_help() { - printf("This is MSVMMaj, version %1.1f\n\n", VERSION); - printf("Usage: trainMSVMMajdataset [options] training_file\n"); + printf("This is GenSVM, version %1.1f\n\n", VERSION); + printf("Usage: trainGenSVMdataset [options] training_file\n"); printf("Options:\n"); printf("-h | -help : print this help.\n"); printf("-q : quiet mode (no output)\n"); @@ -55,7 +55,7 @@ void exit_with_help() } /** - * @brief Main interface function for trainMSVMMajdataset + * @brief Main interface function for trainGenSVMdataset * * @details * Main interface for the command line program. A given training file which @@ -75,11 +75,11 @@ int main(int argc, char **argv) char input_filename[MAX_LINE_LENGTH]; struct Training *training = Malloc(struct Training, 1); - struct MajData *train_data = Malloc(struct MajData, 1); - struct MajData *test_data = Malloc(struct MajData, 1); + struct GenData *train_data = Malloc(struct GenData, 1); + struct GenData *test_data = Malloc(struct GenData, 1); - if (argc < MINARGS || msvmmaj_check_argv(argc, argv, "-help") - || msvmmaj_check_argv_eq(argc, argv, "-h") ) + if (argc < MINARGS || gensvm_check_argv(argc, argv, "-help") + || gensvm_check_argv_eq(argc, argv, "-h") ) exit_with_help(); parse_command_line(argc, argv, input_filename); @@ -88,10 +88,10 @@ int main(int argc, char **argv) read_training_from_file(input_filename, training); note("Reading data from %s\n", training->train_data_file); - msvmmaj_read_data(train_data, training->train_data_file); + gensvm_read_data(train_data, training->train_data_file); if (training->traintype == TT) { note("Reading data from %s\n", training->test_data_file); - msvmmaj_read_data(test_data, training->test_data_file); + gensvm_read_data(test_data, training->test_data_file); } note("Creating queue\n"); @@ -113,8 +113,8 @@ int main(int argc, char **argv) free_queue(q); free(training); - msvmmaj_free_data(train_data); - msvmmaj_free_data(test_data); + gensvm_free_data(train_data); + gensvm_free_data(test_data); note("Done.\n"); return 0; @@ -139,7 +139,7 @@ void parse_command_line(int argc, char **argv, char *input_filename) { int i; - MSVMMAJ_OUTPUT_FILE = stdout; + GENSVM_OUTPUT_FILE = stdout; for (i=1; i<argc; i++) { if (argv[i][0] != '-') break; @@ -147,7 +147,7 @@ void parse_command_line(int argc, char **argv, char *input_filename) exit_with_help(); switch (argv[i-1][1]) { case 'q': - MSVMMAJ_OUTPUT_FILE = NULL; + GENSVM_OUTPUT_FILE = NULL; i--; break; default: @@ -13,11 +13,11 @@ #include "util.h" -FILE *MSVMMAJ_OUTPUT_FILE; ///< The #MSVMMAJ_OUTPUT_FILE specifies the +FILE *GENSVM_OUTPUT_FILE; ///< The #GENSVM_OUTPUT_FILE specifies the ///< output stream to which all output is ///< written. This is done through the ///< internal (!) - ///< function msvmmaj_print_string(). The + ///< function gensvm_print_string(). The ///< advantage of using a global output ///< stream variable is that the output can ///< temporarily be suppressed by importing @@ -40,7 +40,7 @@ FILE *MSVMMAJ_OUTPUT_FILE; ///< The #MSVMMAJ_OUTPUT_FILE specifies the * @returns index of the string in the arguments if found, 0 * otherwise */ -int msvmmaj_check_argv(int argc, char **argv, char *str) +int gensvm_check_argv(int argc, char **argv, char *str) { int i; int arg_str = 0; @@ -69,7 +69,7 @@ int msvmmaj_check_argv(int argc, char **argv, char *str) * @returns index of the command line argument that corresponds to * the string, 0 if none matches. */ -int msvmmaj_check_argv_eq(int argc, char **argv, char *str) +int gensvm_check_argv_eq(int argc, char **argv, char *str) { int i; int arg_str = 0; @@ -88,19 +88,19 @@ int msvmmaj_check_argv_eq(int argc, char **argv, char *str) * * @details * This function is used to print a given string to the output stream - * specified by #MSVMMAJ_OUTPUT_FILE. The stream is flushed after the string - * is written to the stream. If #MSVMMAJ_OUTPUT_FILE is NULL, nothing is + * specified by #GENSVM_OUTPUT_FILE. The stream is flushed after the string + * is written to the stream. If #GENSVM_OUTPUT_FILE is NULL, nothing is * written. Note that this function is only used by note(), it should never be * used directly. * * @param[in] s string to write to the stream * */ -static void msvmmaj_print_string(const char *s) +static void gensvm_print_string(const char *s) { - if (MSVMMAJ_OUTPUT_FILE != NULL) { - fputs(s, MSVMMAJ_OUTPUT_FILE); - fflush(MSVMMAJ_OUTPUT_FILE); + if (GENSVM_OUTPUT_FILE != NULL) { + fputs(s, GENSVM_OUTPUT_FILE); + fflush(GENSVM_OUTPUT_FILE); } } @@ -111,7 +111,7 @@ static void msvmmaj_print_string(const char *s) * This function is a replacement of fprintf(), such that the output stream * does not have to be specified at each function call. The functionality is * exactly the same however. Writing the formatted string to the output stream - * is handled by msvmmaj_print_string(). + * is handled by gensvm_print_string(). * * @param[in] fmt String format * @param[in] ... variable argument list for the string format @@ -124,5 +124,5 @@ void note(const char *fmt,...) va_start(ap,fmt); vsprintf(buf,fmt,ap); va_end(ap); - (*msvmmaj_print_string)(buf); + (*gensvm_print_string)(buf); } |