diff options
Diffstat (limited to 'src/libMSVMMaj.c')
| -rw-r--r-- | src/libMSVMMaj.c | 120 |
1 files changed, 102 insertions, 18 deletions
diff --git a/src/libMSVMMaj.c b/src/libMSVMMaj.c index 64c7a26..202e228 100644 --- a/src/libMSVMMaj.c +++ b/src/libMSVMMaj.c @@ -1,10 +1,33 @@ +/** + * @file libMSVMMaj.c + * @author Gertjan van den Burg (burg@ese.eur.nl) + * @date August 8, 2013 + * @brief Main functions for the MSVMMaj 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. + * + */ + #include "libMSVMMaj.h" -/* - Generate the simplex matrix. A pointer to the matrix - must be given, and the matrix must have been - allocated. -*/ +/** + * @name simplex_gen + * @brief Generate matrix of simplex vertex coordinates + * @ingroup libMSVMMaj + * + * Generate the simplex matrix. Each row of the created + * matrix contains the coordinate vector of a single + * vertex of the K-simplex in K-1 dimensions. The simplex + * generated is a special simplex with edges of length 1. + * The simplex matrix U must already have been allocated. + * + * @param [in] K number of classes + * @param [in,out] U simplex matrix of size K * (K-1) + */ void simplex_gen(long K, double *U) { long i, j; @@ -21,7 +44,7 @@ void simplex_gen(long K, double *U) } } -/* +/*! Generate the category matrix R. The category matrix has 1's everywhere except at the column corresponding to the label of instance i. */ @@ -40,6 +63,9 @@ void category_matrix(struct Model *model, struct Data *dataset) } } +/*! + * Simplex diff + */ void simplex_diff(struct Model *model, struct Data *data) { long i, j, k; @@ -59,7 +85,7 @@ void simplex_diff(struct Model *model, struct Data *data) } } -/* +/*! Calculate the errors Q based on the current value of V. It is assumed that the memory for Q has already been allocated. In addition, the matrix ZV is calculated here. It is assigned to a @@ -103,7 +129,7 @@ void calculate_errors(struct Model *model, struct Data *data, double *ZV) } } -/* +/*! Calculate the Huber hinge errors for each error in the matrix Q. */ void calculate_huber(struct Model *model) @@ -125,7 +151,7 @@ void calculate_huber(struct Model *model) } } -/* +/*! Calculate the value of the loss function based on the current estimate of V. */ @@ -167,9 +193,46 @@ double get_msvmmaj_loss(struct Model *model, struct Data *data, double *ZV) return loss; } -/* + +/** + * @name seed_model_V + * @brief seed the matrix V from an existing model or using rand + * @ingroup libMSVMMaj + * + * The matrix V must be seeded before the main_loop() can start. + * This can be done by either seeding it with random numbers or + * using the solution from a previous model on the same dataset + * as initial seed. The latter option usually allows for a + * significant improvement in the number of iterations necessary + * because the seeded model V is closer to the optimal V. + * + * @param [in] from_model model from which to copy V + * @param [in,out] to_model model to which V will be copied + */ +void seed_model_V(struct Model *from_model, struct Model *to_model) +{ + long i, j; + long m = to_model->m; + long K = to_model->K; + double value; + + if (from_model == NULL) { + for (i=0; i<m+1; i++) + for (j=0; j<K-1; j++) + matrix_set(to_model->V, K-1, i, j, -1.0+2.0*rnd()); + } else { + for (i=0; i<m+1; i++) + for (j=0; j<K-1; j++) { + value = matrix_get(from_model->V, K-1, i, j); + matrix_set(to_model->V, K-1, i, j, value); + } + } +} + +/*! Training loop is defined here. */ + void main_loop(struct Model *model, struct Data *data) { long i, j, it = 0; @@ -203,12 +266,6 @@ void main_loop(struct Model *model, struct Data *data) simplex_diff(model, data); category_matrix(model, data); - // Initialize V - for (i=0; i<m+1; i++) - for (j=0; j<K-1; j++) - matrix_set(model->V, K-1, i, j, 1.0); - //matrix_set(model->V, K-1, i, j, -1.0+2.0*rnd()); - L = get_msvmmaj_loss(model, data, ZV); Lbar = L + 2.0*model->epsilon*L; @@ -244,6 +301,10 @@ void main_loop(struct Model *model, struct Data *data) free(ZAZVT); } +/*! + * Step doubling + */ + void step_doubling(struct Model *model) { long i, j; @@ -253,12 +314,16 @@ void step_doubling(struct Model *model) for (i=0; i<m+1; i++) { for (j=0; j<K-1; j++) { - matrix_mult(model->V, K-1, i, j, 2.0); + matrix_mul(model->V, K-1, i, j, 2.0); matrix_add(model->V, K-1, i, j, -matrix_get(model->Vbar, K-1, i, j)); } } } +/*! + * dposv + */ + int dposv(char UPLO, int N, int NRHS, double *A, int LDA, double *B, int LDB) { @@ -269,6 +334,10 @@ int dposv(char UPLO, int N, int NRHS, double *A, int LDA, double *B, return INFO; } +/*! + * dsysv + */ + int dsysv(char UPLO, int N, int NRHS, double *A, int LDA, int *IPIV, double *B, int LDB, double *WORK, int LWORK) { @@ -280,6 +349,10 @@ int dsysv(char UPLO, int N, int NRHS, double *A, int LDA, int *IPIV, return INFO; } +/*! + * msvmmaj_update + */ + void msvmmaj_update(struct Model *model, struct Data *data, double *B, double *ZAZ, double *ZAZV, double *ZAZVT) { @@ -526,6 +599,10 @@ void msvmmaj_update(struct Model *model, struct Data *data, } } +/*! + * initialize_weights + */ + void initialize_weights(struct Data *data, struct Model *model) { long *groups; @@ -550,9 +627,12 @@ void initialize_weights(struct Data *data, struct Model *model) fprintf(stderr, "Unknown weight specification.\n"); exit(1); } - } +/*! + * predict_labels + */ + void predict_labels(struct Data *data, struct Model *model, long *predy) { long i, j, k, label; @@ -609,6 +689,10 @@ void predict_labels(struct Data *data, struct Model *model, long *predy) free(S); } +/*! + * prediction_perf + */ + double prediction_perf(struct Data *data, long *predy) { long i, correct = 0; |