124 lines
3.6 KiB
C
124 lines
3.6 KiB
C
#include <math.h>
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#include "logistic_regression.h"
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#include "logistic.h"
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#include "file_utils.h"
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bool logistic_regression_model_expectation_sparse(sparse_matrix_t *theta, sparse_matrix_t *x, double_matrix_t *p_y) {
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if (theta == NULL || x == NULL || p_y == NULL) {
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if (theta == NULL) log_error("theta = NULL\n");
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if (x == NULL) log_error("x = NULL\n");
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if (p_y == NULL) log_error("p_y = NULL\n");
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return false;
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}
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if (sparse_matrix_dot_sparse(x, theta, p_y) != 0) {
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log_error("x->m = %" PRIu32 ", x->n = %" PRIu32 ", theta->m = %" PRIu32 ", theta->n = %" PRIu32 ", p_y->m = %zu, p_y->n = %zu\n", x->m, x->n, theta->m, theta->n, p_y->m, p_y->n);
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return false;
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}
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softmax_matrix(p_y);
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return true;
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}
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bool logistic_regression_model_expectation(double_matrix_t *theta, sparse_matrix_t *x, double_matrix_t *p_y) {
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if (theta == NULL || x == NULL || p_y == NULL) {
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if (theta == NULL) log_error("theta = NULL\n");
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if (x == NULL) log_error("x = NULL\n");
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if (p_y == NULL) log_error("p_y = NULL\n");
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return false;
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}
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if (sparse_matrix_dot_dense(x, theta, p_y) != 0) {
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log_error("x->m = %" PRIu32 ", x->n = %" PRIu32 ", theta->m = %zu, theta->n = %zu, p_y->m = %zu, p_y->n = %zu\n", x->m, x->n, theta->m, theta->n, p_y->m, p_y->n);
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return false;
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}
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softmax_matrix(p_y);
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return true;
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}
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double logistic_regression_cost_function(double_matrix_t *theta, sparse_matrix_t *x, uint32_array *y, double_matrix_t *p_y) {
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size_t m = x->m;
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size_t n = x->n;
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if (m != y->n) {
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log_error("m = %zu, y->n = %zu\n", m, y->n);
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return -1.0;
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}
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if (!double_matrix_resize_aligned(p_y, x->m, theta->n, 16)) {
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log_error("resize_aligned failed\n");
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return -1.0;
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}
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double_matrix_zero(p_y);
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if (!logistic_regression_model_expectation(theta, x, p_y)) {
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log_error("model expectation failed\n");
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return -1.0;
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}
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double *expected_values = p_y->values;
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double cost = 0.0;
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for (size_t i = 0; i < p_y->m; i++) {
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uint32_t y_i = y->a[i];
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double value = double_matrix_get(p_y, i, y_i);
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cost += log(value);
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}
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cost *= -(1.0 / m);
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return cost;
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}
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bool logistic_regression_gradient(double_matrix_t *theta, double_matrix_t *gradient, sparse_matrix_t *x, uint32_array *y, double_matrix_t *p_y) {
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size_t m = x->m;
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size_t n = x->n;
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if (m != y->n || theta->m != gradient->m || theta->n != gradient->n) return false;
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if (!double_matrix_resize_aligned(p_y, x->m, theta->n, 16)) {
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return false;
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}
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double_matrix_zero(p_y);
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if (!logistic_regression_model_expectation(theta, x, p_y)) {
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return false;
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}
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size_t num_features = n;
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size_t num_classes = p_y->n;
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uint32_t i, j;
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double residual;
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uint32_t row;
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uint32_t col;
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double data;
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double_matrix_zero(gradient);
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double *theta_values = theta->values;
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double *predicted_values = p_y->values;
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double *gradient_values = gradient->values;
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// gradient = -(1. / m) * x.T.dot(y - p_y)
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sparse_matrix_foreach(x, row, col, data, {
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uint32_t y_i = y->a[row];
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for (j = 0; j < num_classes; j++) {
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double class_prob = double_matrix_get(p_y, row, j);
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double residual = (y_i == j ? 1.0 : 0.0) - class_prob;
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double gradient_update = data * residual;
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double_matrix_add_scalar(gradient, col, j, gradient_update);
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}
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})
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double scale = -1.0 / m;
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double_matrix_mul(gradient, scale);
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return true;
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}
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