[parser] Averaged perceptron training

src/averaged_perceptron_trainer.h

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+/*
+averaged_perceptron_trainer.h
+-----------------------------
+
+Trainer for a generic averaged perceptron model.
+
+The averaged perceptron uses a simple online error-driven
+learning algorithm. Given some features and the true label,
+it predicts the expected label under the current weights. If
+it guess correctly, there's nothing to do and it moves
+on to the next example. If it predicted the wrong answer, it
+makes the following updates to its weights:
+
+weights[feature][predicted] -= 1.0
+weights[feature][actual] += 1.0
+
+This seems overly simplistic, and it is. This is the regular
+perceptron update rule. On the more difficult cases, this model
+would tend to overfit by spending a lot of time fiddling with the
+weights for the few cases it got wrong and building the whole model
+around those few cases. The averaged perceptron is one way to account
+for this and build a more robust model. 
+
+
+Paper: [Collins, 2002] Discriminative Training Methods for Hidden Markov Models: 
+                       Theory and Experiments with Perceptron Algorithms
+
+Link: http://www.cs.columbia.edu/~mcollins/papers/tagperc.pdf
+*/
+#ifndef AVERAGED_PERCEPTRON_TRAINER_H
+#define AVERAGED_PERCEPTRON_TRAINER_H
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "averaged_perceptron.h"
+#include "collections.h"
+#include "string_utils.h"
+#include "trie.h"
+
+typedef struct class_weight {
+    double value;
+    double total;
+    uint64_t last_updated;
+} class_weight_t;
+
+#define NULL_WEIGHT (class_weight_t){0.0, 0.0, 0}
+
+KHASH_MAP_INIT_INT(class_weights, class_weight_t)
+
+KHASH_MAP_INIT_INT(feature_class_weights, khash_t(class_weights) *) 
+
+typedef struct averaged_perceptron_trainer {
+    uint32_t num_features;
+    uint32_t num_classes;
+    uint64_t num_updates;
+    uint64_t num_errors;
+    trie_t *features;
+    khash_t(str_uint32) *classes;
+    cstring_array *class_strings;
+    // {feature_id => {class_id => class_weight_t}}
+    khash_t(feature_class_weights) *weights;
+    double_array *scores;
+} averaged_perceptron_trainer_t;
+
+averaged_perceptron_trainer_t *averaged_perceptron_trainer_new(void);
+
+uint32_t averaged_perceptron_trainer_predict(averaged_perceptron_trainer_t *self, cstring_array *features);
+bool averaged_perceptron_trainer_train_example(averaged_perceptron_trainer_t *trainer, cstring_array *features, char *label);
+
+averaged_perceptron_t *averaged_perceptron_trainer_finalize(averaged_perceptron_trainer_t *self);
+
+void averaged_perceptron_trainer_destroy(averaged_perceptron_trainer_t *self);
+
+#endif