64 lines
3.4 KiB
C
64 lines
3.4 KiB
C
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#ifndef LOGISTIC_REGRESSION_TRAINER_H
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#define LOGISTIC_REGRESSION_TRAINER_H
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#include <stdio.h>
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#include <stdlib.h>
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#include <math.h>
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#include "averaged_perceptron_tagger.h"
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#include "collections.h"
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#include "features.h"
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#include "ftrl.h"
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#include "logistic_regression.h"
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#include "minibatch.h"
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#include "sparse_matrix.h"
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#include "string_utils.h"
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#include "stochastic_gradient_descent.h"
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#include "tokens.h"
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#include "trie.h"
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/**
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* Helper struct for training logistic regression model
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*/
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typedef enum {
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LOGISTIC_REGRESSION_OPTIMIZER_SGD,
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LOGISTIC_REGRESSION_OPTIMIZER_FTRL
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} logistic_regression_optimizer_type;
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typedef struct logistic_regression_trainer {
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trie_t *feature_ids; // Trie mapping features to array indices
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size_t num_features; // Number of features
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khash_t(str_uint32) *label_ids; // Hashtable mapping labels to array indices
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size_t num_labels; // Number of labels
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double_matrix_t *gradient; // Gradient matrix to be reused
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khash_t(int_uint32) *unique_columns; // Unique columns set
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uint32_array *batch_columns; // Unique columns as array
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double_matrix_t *batch_weights; // Weights updated in this batch
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uint32_t epochs; // Number of epochs
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logistic_regression_optimizer_type optimizer_type; // Trainer type
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union {
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sgd_trainer_t *sgd; // Stochastic (ok, minibatch) gradient descent
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ftrl_trainer_t *ftrl; // Follow-the-regularized-leader (FTRL) Proximal
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} optimizer;
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} logistic_regression_trainer_t;
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logistic_regression_trainer_t *logistic_regression_trainer_init_sgd(trie_t *feature_ids, khash_t(str_uint32) *label_ids, bool fit_intercept, regularization_type_t reg_type, double lambda, double gamma_0);
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logistic_regression_trainer_t *logistic_regression_trainer_init_ftrl(trie_t *feature_ids, khash_t(str_uint32) *label_ids, double lambda1, double lambda2, double alpha, double beta);
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bool logistic_regression_trainer_reset_params_sgd(logistic_regression_trainer_t *self, double lambda, double gamma_0);
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bool logistic_regression_trainer_reset_params_ftrl(logistic_regression_trainer_t *self, double alpha, double beta, double lambda1, double lambda2);
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bool logistic_regression_trainer_train_minibatch(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
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double logistic_regression_trainer_minibatch_cost(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
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double logistic_regression_trainer_minibatch_cost_regularized(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
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double logistic_regression_trainer_regularization_cost(logistic_regression_trainer_t *self, size_t m);
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double_matrix_t *logistic_regression_trainer_get_weights(logistic_regression_trainer_t *self);
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double_matrix_t *logistic_regression_trainer_get_regularized_weights(logistic_regression_trainer_t *self);
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double_matrix_t *logistic_regression_trainer_final_weights(logistic_regression_trainer_t *self);
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sparse_matrix_t *logistic_regression_trainer_final_weights_sparse(logistic_regression_trainer_t *self);
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void logistic_regression_trainer_destroy(logistic_regression_trainer_t *self);
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#endif
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