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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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24
src/logistic_regression_trainer.c
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@@ -13,7 +13,7 @@ void logistic_regression_trainer_destroy(logistic_regression_trainer_t *self) {
}
if (self->weights != NULL) {
matrix_destroy(self->weights);
double_matrix_destroy(self->weights);
}
if (self->last_updated != NULL) {
@@ -29,7 +29,7 @@ void logistic_regression_trainer_destroy(logistic_regression_trainer_t *self) {
}
if (self->gradient != NULL) {
matrix_destroy(self->gradient);
double_matrix_destroy(self->gradient);
}
free(self);
@@ -48,9 +48,9 @@ logistic_regression_trainer_t *logistic_regression_trainer_init(trie_t *feature_
trainer->label_ids = label_ids;
trainer->num_labels = kh_size(label_ids);
trainer->weights = matrix_new_zeros(trainer->num_features, trainer->num_labels);
trainer->weights = double_matrix_new_zeros(trainer->num_features, trainer->num_labels);
trainer->gradient = matrix_new_zeros(trainer->num_features, trainer->num_labels);
trainer->gradient = double_matrix_new_zeros(trainer->num_features, trainer->num_labels);
trainer->unique_columns = kh_init(int_set);
trainer->batch_columns = uint32_array_new_size(trainer->num_features);
@@ -70,14 +70,14 @@ exit_trainer_created:
}
static matrix_t *model_expectation(sparse_matrix_t *x, matrix_t *theta) {
matrix_t *p_y = matrix_new_zeros(x->m, theta->n);
static double_matrix_t *model_expectation(sparse_matrix_t *x, double_matrix_t *theta) {
double_matrix_t *p_y = double_matrix_new_zeros(x->m, theta->n);
if (p_y == NULL) return NULL;
if(logistic_regression_model_expectation(theta, x, p_y)) {
return p_y;
} else {
matrix_destroy(p_y);
double_matrix_destroy(p_y);
return NULL;
}
}
@@ -88,11 +88,11 @@ double logistic_regression_trainer_batch_cost(logistic_regression_trainer_t *sel
sparse_matrix_t *x = feature_matrix(self->feature_ids, features);
uint32_array *y = label_vector(self->label_ids, labels);
matrix_t *p_y = matrix_new_zeros(x->m, n);
double_matrix_t *p_y = double_matrix_new_zeros(x->m, n);
double cost = logistic_regression_cost_function(self->weights, x, y, p_y, self->lambda);
matrix_destroy(p_y);
double_matrix_destroy(p_y);
uint32_array_destroy(y);
sparse_matrix_destroy(x);
return cost;
@@ -103,12 +103,12 @@ bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self
size_t n = self->weights->n;
// Optimize
matrix_t *gradient = self->gradient;
double_matrix_t *gradient = self->gradient;
sparse_matrix_t *x = feature_matrix(self->feature_ids, features);
uint32_array *y = label_vector(self->label_ids, labels);
matrix_t *p_y = matrix_new_zeros(x->m, n);
double_matrix_t *p_y = double_matrix_new_zeros(x->m, n);
bool ret = false;
@@ -135,7 +135,7 @@ bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self
self->iters++;
exit_matrices_created:
matrix_destroy(p_y);
double_matrix_destroy(p_y);
uint32_array_destroy(y);
sparse_matrix_destroy(x);
return ret;