50 lines
2.1 KiB
C
50 lines
2.1 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 "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 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 *weights; // Matrix of logistic regression weights
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double_matrix_t *gradient; // Gradient matrix to be reused
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khash_t(int_set) *unique_columns; // Unique columns set
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uint32_array *batch_columns; // Unique columns as array
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uint32_array *last_updated; // Array of length N indicating the last time each feature was updated
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double lambda; // Regularization weight
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uint32_t iters; // Number of iterations, used to decay learning rate
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uint32_t epochs; // Number of epochs
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double gamma_0; // Initial learning rate
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} logistic_regression_trainer_t;
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logistic_regression_trainer_t *logistic_regression_trainer_init(trie_t *feature_ids, khash_t(str_uint32) *label_ids, double gamma_0, double lambda);
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bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
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double logistic_regression_trainer_batch_cost(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
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bool logistic_regression_trainer_finalize(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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