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[math] Generic dense matrix implementation using BLAS calls for matrix-matrix multiplication if available

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Al
2016-08-06 00:40:01 -04:00
parent d4a792f33c
commit 46cd725c13
19 changed files with 469 additions and 425 deletions
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10
src/language_classifier_train.c
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@@ -13,7 +13,7 @@
#include "sparse_matrix.h"
#include "sparse_matrix_utils.h"
#define LANGUAGE_CLASSIFIER_FEATURE_COUNT_THRESHOLD 1.0
#define LANGUAGE_CLASSIFIER_FEATURE_COUNT_THRESHOLD 5.0
#define LANGUAGE_CLASSIFIER_LABEL_COUNT_THRESHOLD 100
#define LOG_BATCH_INTERVAL 10
@@ -134,13 +134,13 @@ double compute_cv_accuracy(logistic_regression_trainer_t *trainer, char *filenam
uint32_t correct = 0;
uint32_t total = 0;
matrix_t *p_y = matrix_new_zeros(LANGUAGE_CLASSIFIER_DEFAULT_BATCH_SIZE, trainer->num_labels);
double_matrix_t *p_y = double_matrix_new_zeros(LANGUAGE_CLASSIFIER_DEFAULT_BATCH_SIZE, trainer->num_labels);
while ((minibatch = language_classifier_data_set_get_minibatch(data_set, trainer->label_ids)) != NULL) {
sparse_matrix_t *x = feature_matrix(trainer->feature_ids, minibatch->features);
uint32_array *y = label_vector(trainer->label_ids, minibatch->labels);
matrix_resize(p_y, x->m, trainer->num_labels);
double_matrix_resize(p_y, x->m, trainer->num_labels);
if (!logistic_regression_model_expectation(trainer->weights, x, p_y)) {
log_error("Predict cv batch failed\n");
@@ -149,7 +149,7 @@ double compute_cv_accuracy(logistic_regression_trainer_t *trainer, char *filenam
double *row;
for (size_t i = 0; i < p_y->m; i++) {
row = matrix_get_row(p_y, i);
row = double_matrix_get_row(p_y, i);
int64_t predicted = double_array_argmax(row, p_y->n);
if (predicted < 0) {
@@ -171,7 +171,7 @@ double compute_cv_accuracy(logistic_regression_trainer_t *trainer, char *filenam
}
language_classifier_data_set_destroy(data_set);
matrix_destroy(p_y);
double_matrix_destroy(p_y);
double accuracy = (double)correct / total;
return accuracy;