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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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14
src/stochastic_gradient_descent.c
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@@ -1,6 +1,6 @@
#include "stochastic_gradient_descent.h"
bool stochastic_gradient_descent(matrix_t *theta, matrix_t *gradient, double gamma) {
bool stochastic_gradient_descent(double_matrix_t *theta, double_matrix_t *gradient, double gamma) {
if (gradient->m != theta->m || gradient->n != theta->n) {
return false;
}
@@ -8,10 +8,10 @@ bool stochastic_gradient_descent(matrix_t *theta, matrix_t *gradient, double gam
size_t m = gradient->m;
size_t n = gradient->n;
return matrix_sub_matrix_times_scalar(theta, gradient, gamma);
return double_matrix_sub_matrix_times_scalar(theta, gradient, gamma);
}
bool stochastic_gradient_descent_sparse(matrix_t *theta, matrix_t *gradient, uint32_array *update_indices, double gamma) {
bool stochastic_gradient_descent_sparse(double_matrix_t *theta, double_matrix_t *gradient, uint32_array *update_indices, double gamma) {
if (gradient->m != theta->m || gradient->n != theta->n) {
return false;
}
@@ -90,7 +90,7 @@ static inline void regularize_row(double *theta_i, size_t n, double lambda, uint
double_array_mul(theta_i, update, n);
}
bool stochastic_gradient_descent_regularize_weights(matrix_t *theta, uint32_array *update_indices, uint32_array *last_updated, uint32_t t, double lambda, double gamma_0) {
bool stochastic_gradient_descent_regularize_weights(double_matrix_t *theta, uint32_array *update_indices, uint32_array *last_updated, uint32_t t, double lambda, double gamma_0) {
if (lambda > 0.0) {
uint32_t *updates = last_updated->a;
@@ -101,7 +101,7 @@ bool stochastic_gradient_descent_regularize_weights(matrix_t *theta, uint32_arra
for (size_t i = 0; i < batch_rows; i++) {
uint32_t row = rows[i];
double *theta_i = matrix_get_row(theta, row);
double *theta_i = double_matrix_get_row(theta, row);
uint32_t last_updated = updates[row];
double gamma_t = stochastic_gradient_descent_gamma_t(gamma_0, lambda, t - last_updated);
@@ -114,14 +114,14 @@ bool stochastic_gradient_descent_regularize_weights(matrix_t *theta, uint32_arra
return true;
}
inline bool stochastic_gradient_descent_finalize_weights(matrix_t *theta, uint32_array *last_updated, uint32_t t, double lambda, double gamma_0) {
inline bool stochastic_gradient_descent_finalize_weights(double_matrix_t *theta, uint32_array *last_updated, uint32_t t, double lambda, double gamma_0) {
if (lambda > 0.0) {
uint32_t *updates = last_updated->a;
size_t m = theta->m;
size_t n = theta->n;
for (size_t i = 0; i < m; i++) {
double *theta_i = matrix_get_row(theta, i);
double *theta_i = double_matrix_get_row(theta, i);
uint32_t last_updated = updates[i];
double gamma_t = stochastic_gradient_descent_gamma_t(gamma_0, lambda, t - last_updated);