diff options
Diffstat (limited to 'utils/training/estimate_k_mixture_model.cpp')
| -rw-r--r-- | utils/training/estimate_k_mixture_model.cpp | 159 |
1 files changed, 159 insertions, 0 deletions
diff --git a/utils/training/estimate_k_mixture_model.cpp b/utils/training/estimate_k_mixture_model.cpp new file mode 100644 index 0000000..c0fa03f --- /dev/null +++ b/utils/training/estimate_k_mixture_model.cpp @@ -0,0 +1,159 @@ +/* + * libpinyin + * Library to deal with pinyin. + * + * Copyright (C) 2011 Peng Wu <alexepico@gmail.com> + * + * This program is free software; you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation; either version 2 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program; if not, write to the Free Software + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. + */ + +#include <locale.h> +#include "pinyin_internal.h" +#include "k_mixture_model.h" + +static const gchar * bigram_filename = "k_mixture_model_ngram.db"; +static const gchar * deleted_bigram_filename = "k_mixture_model_deleted_ngram.db"; + +static GOptionEntry entries[] = +{ + {"bigram-file", 0, 0, G_OPTION_ARG_FILENAME, &bigram_filename, "the bigram file", NULL}, + {"deleted-bigram-file", 0, 0, G_OPTION_ARG_FILENAME, &deleted_bigram_filename, "the deleted bigram file", NULL}, + {NULL} +}; + + +parameter_t compute_interpolation(KMixtureModelSingleGram * deleted_bigram, + KMixtureModelBigram * unigram, + KMixtureModelSingleGram * bigram){ + bool success; + parameter_t lambda = 0, next_lambda = 0.6; + parameter_t epsilon = 0.001; + + KMixtureModelMagicHeader magic_header; + assert(unigram->get_magic_header(magic_header)); + assert(0 != magic_header.m_total_freq); + + while (fabs(lambda - next_lambda) > epsilon){ + lambda = next_lambda; + next_lambda = 0; + parameter_t numerator = 0; + parameter_t part_of_denominator = 0; + + FlexibleBigramPhraseArray array = g_array_new(FALSE, FALSE, sizeof(KMixtureModelArrayItemWithToken)); + deleted_bigram->retrieve_all(array); + + for ( size_t i = 0; i < array->len; ++i){ + KMixtureModelArrayItemWithToken * item = &g_array_index(array, KMixtureModelArrayItemWithToken, i); + //get the phrase token + phrase_token_t token = item->m_token; + guint32 deleted_count = item->m_item.m_WC; + + { + parameter_t elem_poss = 0; + KMixtureModelArrayHeader array_header; + KMixtureModelArrayItem array_item; + if ( bigram && bigram->get_array_item(token, array_item) ){ + assert(bigram->get_array_header(array_header)); + assert(0 != array_header.m_WC); + elem_poss = array_item.m_WC / (parameter_t) array_header.m_WC; + } + numerator = lambda * elem_poss; + } + + { + parameter_t elem_poss = 0; + KMixtureModelArrayHeader array_header; + if (unigram->get_array_header(token, array_header)){ + elem_poss = array_header.m_freq / (parameter_t) magic_header.m_total_freq; + } + part_of_denominator = (1 - lambda) * elem_poss; + } + if (0 == (numerator + part_of_denominator)) + continue; + + next_lambda += deleted_count * (numerator / (numerator + part_of_denominator)); + } + KMixtureModelArrayHeader header; + assert(deleted_bigram->get_array_header(header)); + assert(0 != header.m_WC); + next_lambda /= header.m_WC; + + g_array_free(array, TRUE); + } + lambda = next_lambda; + return lambda; +} + +int main(int argc, char * argv[]){ + setlocale(LC_ALL, ""); + + GError * error = NULL; + GOptionContext * context; + + context = g_option_context_new("- estimate k mixture model"); + g_option_context_add_main_entries(context, entries, NULL); + if (!g_option_context_parse(context, &argc, &argv, &error)) { + g_print("option parsing failed:%s\n", error->message); + exit(EINVAL); + } + + /* TODO: magic header signature check here. */ + KMixtureModelBigram unigram(K_MIXTURE_MODEL_MAGIC_NUMBER); + unigram.attach(bigram_filename, ATTACH_READONLY); + + KMixtureModelBigram bigram(K_MIXTURE_MODEL_MAGIC_NUMBER); + bigram.attach(bigram_filename, ATTACH_READONLY); + + KMixtureModelBigram deleted_bigram(K_MIXTURE_MODEL_MAGIC_NUMBER); + deleted_bigram.attach(deleted_bigram_filename, ATTACH_READONLY); + + GArray * deleted_items = g_array_new(FALSE, FALSE, sizeof(phrase_token_t)); + deleted_bigram.get_all_items(deleted_items); + + parameter_t lambda_sum = 0; + int lambda_count = 0; + + for( size_t i = 0; i < deleted_items->len; ++i ){ + phrase_token_t * token = &g_array_index(deleted_items, phrase_token_t, i); + KMixtureModelSingleGram * single_gram = NULL; + bigram.load(*token, single_gram); + + KMixtureModelSingleGram * deleted_single_gram = NULL; + deleted_bigram.load(*token, deleted_single_gram); + + KMixtureModelArrayHeader array_header; + if (single_gram) + assert(single_gram->get_array_header(array_header)); + KMixtureModelArrayHeader deleted_array_header; + assert(deleted_single_gram->get_array_header(deleted_array_header)); + + if ( 0 != deleted_array_header.m_WC ) { + parameter_t lambda = compute_interpolation(deleted_single_gram, &unigram, single_gram); + + printf("token:%d lambda:%f\n", *token, lambda); + + lambda_sum += lambda; + lambda_count ++; + } + + if (single_gram) + delete single_gram; + delete deleted_single_gram; + } + + printf("average lambda:%f\n", (lambda_sum/lambda_count)); + g_array_free(deleted_items, TRUE); + return 0; +} |