bool gen_unigram(FILE * output, FacadePhraseIndex * phrase_index) {
fprintf(output, "\\1-gram\n");
for ( size_t i = 0; i < PHRASE_INDEX_LIBRARY_COUNT; i++) {
PhraseIndexRange range;
int result = phrase_index->get_range(i, range);
if (ERROR_OK != result )
continue;
PhraseItem item;
for (phrase_token_t token = range.m_range_begin;
token < range.m_range_end; token++) {
int result = phrase_index->get_phrase_item(token, item);
if ( result == ERROR_NO_ITEM )
continue;
assert( result == ERROR_OK);
size_t freq = item.get_unigram_frequency();
if ( 0 == freq )
continue;
char * phrase = taglib_token_to_string(phrase_index, token);
if ( phrase )
fprintf(output, "\\item %d %s count %ld\n", token, phrase, freq);
g_free(phrase);
}
}
return true;
}
bool gen_bigram(FILE * output, FacadePhraseIndex * phrase_index, Bigram * bigram){
fprintf(output, "\\2-gram\n");
/* Retrieve all user items. */
GArray * items = g_array_new(FALSE, FALSE, sizeof(phrase_token_t));
bigram->get_all_items(items);
PhraseItem item;
for(size_t i = 0; i < items->len; i++){
phrase_token_t token = g_array_index(items, phrase_token_t, i);
SingleGram * single_gram = NULL;
bigram->load(token, single_gram);
BigramPhraseWithCountArray array = g_array_new(FALSE, FALSE, sizeof(BigramPhraseItemWithCount));
single_gram->retrieve_all(array);
for(size_t j = 0; j < array->len; j++) {
BigramPhraseItemWithCount * item = &g_array_index(array, BigramPhraseItemWithCount, j);
char * word1 = taglib_token_to_string(phrase_index, token);
char * word2 = taglib_token_to_string(phrase_index, item->m_token);
guint32 freq = item->m_count;
if ( word1 && word2)
fprintf(output, "\\item %d %s %d %s count %d\n",
token, word1, item->m_token, word2, freq);
g_free(word1); g_free(word2);
}
g_array_free(array, TRUE);
delete single_gram;
}
g_array_free(items, TRUE);
return true;
}
bool print_k_mixture_model_array_items(FILE * output,
KMixtureModelBigram * bigram,
FacadePhraseIndex * phrase_index){
fprintf(output, "\\2-gram\n");
GArray * items = g_array_new(FALSE, FALSE, sizeof(phrase_token_t));
bigram->get_all_items(items);
for (size_t i = 0; i < items->len; ++i) {
phrase_token_t * token = &g_array_index(items, phrase_token_t, i);
KMixtureModelSingleGram * single_gram = NULL;
assert(bigram->load(*token, single_gram));
FlexibleBigramPhraseArray array = g_array_new
(FALSE, FALSE, sizeof(KMixtureModelArrayItemWithToken));
single_gram->retrieve_all(array);
for (size_t m = 0; m < array->len; ++m){
KMixtureModelArrayItemWithToken * item = &g_array_index(array, KMixtureModelArrayItemWithToken, m);
char * word1 = taglib_token_to_string(phrase_index, *token);
char * word2 = taglib_token_to_string(phrase_index, item->m_token);
if (word1 && word2)
fprintf(output, "\\item %s %s count %d T %d N_n_0 %d n_1 %d Mr %d\n",
word1, word2, item->m_item.m_WC, item->m_item.m_WC,
item->m_item.m_N_n_0, item->m_item.m_n_1,
item->m_item.m_Mr);
g_free(word1); g_free(word2);
}
g_array_free(array, TRUE);
delete single_gram;
}
g_array_free(items, TRUE);
return true;
}
bool print_k_mixture_model_array_headers(FILE * output,
KMixtureModelBigram * bigram,
FacadePhraseIndex * phrase_index){
fprintf(output, "\\1-gram\n");
GArray * items = g_array_new(FALSE, FALSE, sizeof(phrase_token_t));
bigram->get_all_items(items);
for (size_t i = 0; i < items->len; ++i) {
phrase_token_t * token = &g_array_index(items, phrase_token_t, i);
KMixtureModelArrayHeader array_header;
assert(bigram->get_array_header(*token, array_header));
char * phrase = taglib_token_to_string(phrase_index, *token);
if ( phrase )
fprintf(output, "\\item %s count %d freq %d\n",
phrase, array_header.m_WC, array_header.m_freq);
g_free(phrase);
}
return true;
}
