26 lines
1.0 KiB
C
26 lines
1.0 KiB
C
/*
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Stochastic gradient descent implementation
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Based on Leon Bottou's Stochastic Gradient Descent Tricks:
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http://leon.bottou.org/publications/pdf/tricks-2012.pdf
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Learning rate calculated as:
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gamma_t = gamma_0(1 + gamma_0 * lambda * t)^-1
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*/
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#ifndef STOCHASTIC_GRADIENT_DESCENT_H
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#define STOCHASTIC_GRADIENT_DESCENT_H
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#include <stdlib.h>
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#include <stdbool.h>
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#include "matrix.h"
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bool stochastic_gradient_descent(double_matrix_t *theta, double_matrix_t *gradient, double gamma);
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bool stochastic_gradient_descent_sparse(double_matrix_t *theta, double_matrix_t *gradient, uint32_array *update_indices, double gamma);
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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);
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bool stochastic_gradient_descent_finalize_weights(double_matrix_t *theta, uint32_array *last_updated, uint32_t t, double lambda, double gamma_0);
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double stochastic_gradient_descent_gamma_t(double gamma_0, double lambda, uint32_t t);
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#endif |