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[language_classification] Automatic hyperparameter optimization using either the cross-validation set or two distinct subsets of the training set

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Al
2016-01-17 21:11:37 -05:00
parent af5689ee52
commit f808f74271
6 changed files with 299 additions and 112 deletions
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36
src/logistic_regression.c
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@@ -18,7 +18,6 @@ bool logistic_regression_model_expectation(matrix_t *theta, sparse_matrix_t *x,
return true;
}
double logistic_regression_cost_function(matrix_t *theta, sparse_matrix_t *x, uint32_array *y, matrix_t *p_y, double lambda) {
size_t m = x->m;
size_t n = x->n;
@@ -139,28 +138,29 @@ static bool logistic_regression_gradient_params(matrix_t *theta, matrix_t *gradi
// Update the only the relevant columns in x
if (regularize && x_cols != NULL) {
size_t batch_rows = x_cols->n;
uint32_t *cols = x_cols->a;
for (i = 0; i < batch_rows; i++) {
col = cols[i];
if (regularize) {
size_t num_rows = num_features;
uint32_t *cols = NULL;
if (x_cols != NULL) {
cols = x_cols->a;
num_rows = x_cols->n;
}
for (i = 0; i < num_rows; i++) {
col = x_cols != NULL ? cols[i] : i;
for (j = 0; j < num_classes; j++) {
size_t idx = i * num_classes + j;
size_t idx = col * num_classes + j;
double theta_ij = theta_values[idx];
double reg_value = theta_ij * lambda;
gradient_values[idx] += reg_value;
double reg_update = theta_ij * lambda;
double current_value = gradient_values[idx];
double updated_value = current_value + reg_update;
if (fabs(updated_value) == fabs(current_value)) {
gradient_values[idx] = updated_value;
}
}
}
} else if (regularize) {
for (i = 1; i < num_features; i++) {
for (j = 0; j < num_classes; j++) {
size_t idx = i * num_classes + j;
double theta_ij = theta_values[idx];
double reg_value = theta_ij * lambda;
gradient_values[idx] += reg_value;
}
}
}
return true;