Keep only Marc's current source version
Change-Id: Id7dcbf6163b77d9833b264c5e6947174a20f8b85
diff --git a/word2vec.c b/word2vec.c
deleted file mode 100644
index fbf96a1..0000000
--- a/word2vec.c
+++ /dev/null
@@ -1,1291 +0,0 @@
-// Copyright 2013 Google Inc. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <math.h>
-#include <pthread.h>
-#include "collocatordb.h"
-
-#define MAX_STRING 100
-#define EXP_TABLE_SIZE 1000
-#define MAX_EXP 6
-#define MAX_SENTENCE_LENGTH 1000
-#define MAX_CODE_LENGTH 40
-
-const int vocab_hash_size = 30000000; // Maximum 30 * 0.7 = 21M words in the vocabulary
-
-typedef float real; // Precision of float numbers
-
-struct vocab_word {
- long long cn;
- int *point;
- char *word, *code, codelen;
-};
-
-char train_file[MAX_STRING], output_file[MAX_STRING];
-char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
-struct vocab_word *vocab;
-int binary = 0, type = 1, debug_mode = 2, window = 5, min_count = 5, num_threads = 12, min_reduce = 1;
-int *vocab_hash;
-long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100;
-long long train_words = 0, word_count_actual = 0, iter = 5, file_size = 0, classes = 0;
-real alpha = 0.025, starting_alpha, sample = 1e-3;
-real *syn0, *syn1, *syn1neg, *syn1nce, *expTable;
-clock_t start;
-
-real *syn1_window, *syn1neg_window, *syn1nce_window;
-int w_offset, window_layer_size;
-
-int window_hidden_size = 500;
-real *syn_window_hidden, *syn_hidden_word, *syn_hidden_word_neg, *syn_hidden_word_nce;
-
-int hs = 0, negative = 5;
-const int table_size = 1e8;
-int *table;
-
-//constrastive negative sampling
-char negative_classes_file[MAX_STRING];
-int *word_to_group;
-int *group_to_table; //group_size*table_size
-int class_number;
-
-//nce
-real* noise_distribution;
-int nce = 0;
-
-//param caps
-real CAP_VALUE = 50;
-int cap = 0;
-
-COLLOCATORS *cdb = null;
-
-void capParam(real* array, int index){
- if(array[index] > CAP_VALUE)
- array[index] = CAP_VALUE;
- else if(array[index] < -CAP_VALUE)
- array[index] = -CAP_VALUE;
-}
-
-real hardTanh(real x){
- if(x>=1){
- return 1;
- }
- else if(x<=-1){
- return -1;
- }
- else{
- return x;
- }
-}
-
-real dHardTanh(real x, real g){
- if(x > 1 && g > 0){
- return 0;
- }
- if(x < -1 && g < 0){
- return 0;
- }
- return 1;
-}
-
-void InitUnigramTable() {
- int a, i;
- long long train_words_pow = 0;
- real d1, power = 0.75;
- table = (int *)malloc(table_size * sizeof(int));
- for (a = 0; a < vocab_size; a++) train_words_pow += pow(vocab[a].cn, power);
- i = 0;
- d1 = pow(vocab[i].cn, power) / (real)train_words_pow;
- for (a = 0; a < table_size; a++) {
- table[a] = i;
- if (a / (real)table_size > d1) {
- i++;
- d1 += pow(vocab[i].cn, power) / (real)train_words_pow;
- }
- if (i >= vocab_size) i = vocab_size - 1;
- }
-
- noise_distribution = (real *)calloc(vocab_size, sizeof(real));
- for (a = 0; a < vocab_size; a++) noise_distribution[a] = pow(vocab[a].cn, power)/(real)train_words_pow;
-}
-
-// Reads a single word from a file, assuming space + tab + EOL to be word boundaries
-void ReadWord(char *word, FILE *fin) {
- int a = 0, ch;
- while (!feof(fin)) {
- ch = fgetc(fin);
- if (ch == 13) continue;
- if ((ch == ' ') || (ch == '\t') || (ch == '\n')) {
- if (a > 0) {
- if (ch == '\n') ungetc(ch, fin);
- break;
- }
- if (ch == '\n') {
- strcpy(word, (char *)"</s>");
- return;
- } else continue;
- }
- word[a] = ch;
- a++;
- if (a >= MAX_STRING - 1) a--; // Truncate too long words
- }
- word[a] = 0;
-}
-
-// Returns hash value of a word
-int GetWordHash(char *word) {
- unsigned long long a, hash = 0;
- for (a = 0; a < strlen(word); a++) hash = hash * 257 + word[a];
- hash = hash % vocab_hash_size;
- return hash;
-}
-
-// Returns position of a word in the vocabulary; if the word is not found, returns -1
-int SearchVocab(char *word) {
- unsigned int hash = GetWordHash(word);
- while (1) {
- if (vocab_hash[hash] == -1) return -1;
- if (!strcmp(word, vocab[vocab_hash[hash]].word)) return vocab_hash[hash];
- hash = (hash + 1) % vocab_hash_size;
- }
- return -1;
-}
-
-// Reads a word and returns its index in the vocabulary
-int ReadWordIndex(FILE *fin) {
- char word[MAX_STRING];
- ReadWord(word, fin);
- if (feof(fin)) return -1;
- return SearchVocab(word);
-}
-
-// Adds a word to the vocabulary
-int AddWordToVocab(char *word) {
- unsigned int hash, length = strlen(word) + 1;
- if (length > MAX_STRING) length = MAX_STRING;
- vocab[vocab_size].word = (char *)calloc(length, sizeof(char));
- strcpy(vocab[vocab_size].word, word);
- vocab[vocab_size].cn = 0;
- vocab_size++;
- // Reallocate memory if needed
- if (vocab_size + 2 >= vocab_max_size) {
- vocab_max_size += 1000;
- vocab = (struct vocab_word *)realloc(vocab, vocab_max_size * sizeof(struct vocab_word));
- }
- hash = GetWordHash(word);
- while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
- vocab_hash[hash] = vocab_size - 1;
- return vocab_size - 1;
-}
-
-// Used later for sorting by word counts
-int VocabCompare(const void *a, const void *b) {
- return ((struct vocab_word *)b)->cn - ((struct vocab_word *)a)->cn;
-}
-
-// Sorts the vocabulary by frequency using word counts
-void SortVocab() {
- int a, size;
- unsigned int hash;
- // Sort the vocabulary and keep </s> at the first position
- qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);
- for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
- size = vocab_size;
- train_words = 0;
- for (a = 0; a < size; a++) {
- // Words occuring less than min_count times will be discarded from the vocab
- if ((vocab[a].cn < min_count) && (a != 0)) {
- vocab_size--;
- free(vocab[a].word);
- } else {
- // Hash will be re-computed, as after the sorting it is not actual
- hash=GetWordHash(vocab[a].word);
- while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
- vocab_hash[hash] = a;
- train_words += vocab[a].cn;
- }
- }
- vocab = (struct vocab_word *)realloc(vocab, (vocab_size + 1) * sizeof(struct vocab_word));
- // Allocate memory for the binary tree construction
- for (a = 0; a < vocab_size; a++) {
- vocab[a].code = (char *)calloc(MAX_CODE_LENGTH, sizeof(char));
- vocab[a].point = (int *)calloc(MAX_CODE_LENGTH, sizeof(int));
- }
-}
-
-// Reduces the vocabulary by removing infrequent tokens
-void ReduceVocab() {
- int a, b = 0;
- unsigned int hash;
- for (a = 0; a < vocab_size; a++) if (vocab[a].cn > min_reduce) {
- vocab[b].cn = vocab[a].cn;
- vocab[b].word = vocab[a].word;
- b++;
- } else free(vocab[a].word);
- vocab_size = b;
- for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
- for (a = 0; a < vocab_size; a++) {
- // Hash will be re-computed, as it is not actual
- hash = GetWordHash(vocab[a].word);
- while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
- vocab_hash[hash] = a;
- }
- fflush(stdout);
- min_reduce++;
-}
-
-// Create binary Huffman tree using the word counts
-// Frequent words will have short uniqe binary codes
-void CreateBinaryTree() {
- long long a, b, i, min1i, min2i, pos1, pos2, point[MAX_CODE_LENGTH];
- char code[MAX_CODE_LENGTH];
- long long *count = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
- long long *binary = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
- long long *parent_node = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
- for (a = 0; a < vocab_size; a++) count[a] = vocab[a].cn;
- for (a = vocab_size; a < vocab_size * 2; a++) count[a] = 1e15;
- pos1 = vocab_size - 1;
- pos2 = vocab_size;
- // Following algorithm constructs the Huffman tree by adding one node at a time
- for (a = 0; a < vocab_size - 1; a++) {
- // First, find two smallest nodes 'min1, min2'
- if (pos1 >= 0) {
- if (count[pos1] < count[pos2]) {
- min1i = pos1;
- pos1--;
- } else {
- min1i = pos2;
- pos2++;
- }
- } else {
- min1i = pos2;
- pos2++;
- }
- if (pos1 >= 0) {
- if (count[pos1] < count[pos2]) {
- min2i = pos1;
- pos1--;
- } else {
- min2i = pos2;
- pos2++;
- }
- } else {
- min2i = pos2;
- pos2++;
- }
- count[vocab_size + a] = count[min1i] + count[min2i];
- parent_node[min1i] = vocab_size + a;
- parent_node[min2i] = vocab_size + a;
- binary[min2i] = 1;
- }
- // Now assign binary code to each vocabulary word
- for (a = 0; a < vocab_size; a++) {
- b = a;
- i = 0;
- while (1) {
- code[i] = binary[b];
- point[i] = b;
- i++;
- b = parent_node[b];
- if (b == vocab_size * 2 - 2) break;
- }
- vocab[a].codelen = i;
- vocab[a].point[0] = vocab_size - 2;
- for (b = 0; b < i; b++) {
- vocab[a].code[i - b - 1] = code[b];
- vocab[a].point[i - b] = point[b] - vocab_size;
- }
- }
- free(count);
- free(binary);
- free(parent_node);
-}
-
-void LearnVocabFromTrainFile() {
- char word[MAX_STRING];
- FILE *fin;
- long long a, i;
- for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
- fin = fopen(train_file, "rb");
- if (fin == NULL) {
- printf("ERROR: training data file not found!\n");
- exit(1);
- }
- vocab_size = 0;
- AddWordToVocab((char *)"</s>");
- while (1) {
- ReadWord(word, fin);
- if (feof(fin)) break;
- train_words++;
- if ((debug_mode > 1) && (train_words % 100000 == 0)) {
- printf("%lldK%c", train_words / 1000, 13);
- fflush(stdout);
- }
- i = SearchVocab(word);
- if (i == -1) {
- a = AddWordToVocab(word);
- vocab[a].cn = 1;
- } else vocab[i].cn++;
- if (vocab_size > vocab_hash_size * 0.7) ReduceVocab();
- }
- SortVocab();
- if (debug_mode > 0) {
- printf("Vocab size: %lld\n", vocab_size);
- printf("Words in train file: %lld\n", train_words);
- }
- file_size = ftell(fin);
- fclose(fin);
-}
-
-void SaveVocab() {
- long long i;
- FILE *fo = fopen(save_vocab_file, "wb");
- for (i = 0; i < vocab_size; i++) fprintf(fo, "%s %lld\n", vocab[i].word, vocab[i].cn);
- fclose(fo);
-}
-
-void ReadVocab() {
- long long a, i = 0;
- char c;
- char word[MAX_STRING];
- FILE *fin = fopen(read_vocab_file, "rb");
- if (fin == NULL) {
- printf("Vocabulary file not found\n");
- exit(1);
- }
- for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
- vocab_size = 0;
- while (1) {
- ReadWord(word, fin);
- if (feof(fin)) break;
- a = AddWordToVocab(word);
- fscanf(fin, "%lld%c", &vocab[a].cn, &c);
- i++;
- }
- SortVocab();
- if (debug_mode > 0) {
- printf("Vocab size: %lld\n", vocab_size);
- printf("Words in train file: %lld\n", train_words);
- }
- fin = fopen(train_file, "rb");
- if (fin == NULL) {
- printf("ERROR: training data file not found!\n");
- exit(1);
- }
- fseek(fin, 0, SEEK_END);
- file_size = ftell(fin);
- fclose(fin);
-}
-
-void InitClassUnigramTable() {
- long long a,c;
- printf("loading class unigrams \n");
- FILE *fin = fopen(negative_classes_file, "rb");
- if (fin == NULL) {
- printf("ERROR: class file not found!\n");
- exit(1);
- }
- word_to_group = (int *)malloc(vocab_size * sizeof(int));
- for(a = 0; a < vocab_size; a++) word_to_group[a] = -1;
- char class[MAX_STRING];
- char prev_class[MAX_STRING];
- prev_class[0] = 0;
- char word[MAX_STRING];
- class_number = -1;
- while (1) {
- if (feof(fin)) break;
- ReadWord(class, fin);
- ReadWord(word, fin);
- int word_index = SearchVocab(word);
- if (word_index != -1){
- if(strcmp(class, prev_class) != 0){
- class_number++;
- strcpy(prev_class, class);
- }
- word_to_group[word_index] = class_number;
- }
- ReadWord(word, fin);
- }
- class_number++;
- fclose(fin);
-
- group_to_table = (int *)malloc(table_size * class_number * sizeof(int));
- long long train_words_pow = 0;
- real d1, power = 0.75;
-
- for(c = 0; c < class_number; c++){
- long long offset = c * table_size;
- train_words_pow = 0;
- for (a = 0; a < vocab_size; a++) if(word_to_group[a] == c) train_words_pow += pow(vocab[a].cn, power);
- int i = 0;
- while(word_to_group[i]!=c && i < vocab_size) i++;
- d1 = pow(vocab[i].cn, power) / (real)train_words_pow;
- for (a = 0; a < table_size; a++) {
- //printf("index %lld , word %d\n", a, i);
- group_to_table[offset + a] = i;
- if (a / (real)table_size > d1) {
- i++;
- while(word_to_group[i]!=c && i < vocab_size) i++;
- d1 += pow(vocab[i].cn, power) / (real)train_words_pow;
- }
- if (i >= vocab_size) while(word_to_group[i]!=c && i >= 0) i--;
- }
- }
-}
-
-void InitNet() {
- long long a, b;
- unsigned long long next_random = 1;
- window_layer_size = layer1_size*window*2;
- a = posix_memalign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));
- if (syn0 == NULL) {printf("Memory allocation failed\n"); exit(1);}
-
- if (hs) {
- a = posix_memalign((void **)&syn1, 128, (long long)vocab_size * layer1_size * sizeof(real));
- if (syn1 == NULL) {printf("Memory allocation failed\n"); exit(1);}
- a = posix_memalign((void **)&syn1_window, 128, (long long)vocab_size * window_layer_size * sizeof(real));
- if (syn1_window == NULL) {printf("Memory allocation failed\n"); exit(1);}
- a = posix_memalign((void **)&syn_hidden_word, 128, (long long)vocab_size * window_hidden_size * sizeof(real));
- if (syn_hidden_word == NULL) {printf("Memory allocation failed\n"); exit(1);}
-
- for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
- syn1[a * layer1_size + b] = 0;
- for (a = 0; a < vocab_size; a++) for (b = 0; b < window_layer_size; b++)
- syn1_window[a * window_layer_size + b] = 0;
- for (a = 0; a < vocab_size; a++) for (b = 0; b < window_hidden_size; b++)
- syn_hidden_word[a * window_hidden_size + b] = 0;
- }
- if (negative>0) {
- a = posix_memalign((void **)&syn1neg, 128, (long long)vocab_size * layer1_size * sizeof(real));
- if (syn1neg == NULL) {printf("Memory allocation failed\n"); exit(1);}
- a = posix_memalign((void **)&syn1neg_window, 128, (long long)vocab_size * window_layer_size * sizeof(real));
- if (syn1neg_window == NULL) {printf("Memory allocation failed\n"); exit(1);}
- a = posix_memalign((void **)&syn_hidden_word_neg, 128, (long long)vocab_size * window_hidden_size * sizeof(real));
- if (syn_hidden_word_neg == NULL) {printf("Memory allocation failed\n"); exit(1);}
-
- for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
- syn1neg[a * layer1_size + b] = 0;
- for (a = 0; a < vocab_size; a++) for (b = 0; b < window_layer_size; b++)
- syn1neg_window[a * window_layer_size + b] = 0;
- for (a = 0; a < vocab_size; a++) for (b = 0; b < window_hidden_size; b++)
- syn_hidden_word_neg[a * window_hidden_size + b] = 0;
- }
- if (nce>0) {
- a = posix_memalign((void **)&syn1nce, 128, (long long)vocab_size * layer1_size * sizeof(real));
- if (syn1nce == NULL) {printf("Memory allocation failed\n"); exit(1);}
- a = posix_memalign((void **)&syn1nce_window, 128, (long long)vocab_size * window_layer_size * sizeof(real));
- if (syn1nce_window == NULL) {printf("Memory allocation failed\n"); exit(1);}
- a = posix_memalign((void **)&syn_hidden_word_nce, 128, (long long)vocab_size * window_hidden_size * sizeof(real));
- if (syn_hidden_word_nce == NULL) {printf("Memory allocation failed\n"); exit(1);}
-
- for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
- syn1nce[a * layer1_size + b] = 0;
- for (a = 0; a < vocab_size; a++) for (b = 0; b < window_layer_size; b++)
- syn1nce_window[a * window_layer_size + b] = 0;
- for (a = 0; a < vocab_size; a++) for (b = 0; b < window_hidden_size; b++)
- syn_hidden_word_nce[a * window_hidden_size + b] = 0;
- }
- for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++) {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- syn0[a * layer1_size + b] = (((next_random & 0xFFFF) / (real)65536) - 0.5) / layer1_size;
- }
-
- a = posix_memalign((void **)&syn_window_hidden, 128, window_hidden_size * window_layer_size * sizeof(real));
- if (syn_window_hidden == NULL) {printf("Memory allocation failed\n"); exit(1);}
- for (a = 0; a < window_hidden_size * window_layer_size; a++){
- next_random = next_random * (unsigned long long)25214903917 + 11;
- syn_window_hidden[a] = (((next_random & 0xFFFF) / (real)65536) - 0.5) / (window_hidden_size*window_layer_size);
- }
-
- CreateBinaryTree();
-}
-
-void *TrainModelThread(void *id) {
- long long a, b, d, cw, word, last_word, sentence_length = 0, sentence_position = 0;
- long long word_count = 0, last_word_count = 0, sen[MAX_SENTENCE_LENGTH + 1];
- long long l1, l2, c, target, label, local_iter = iter;
- unsigned long long next_random = (long long)id;
- real f, g;
- clock_t now;
- int input_len_1 = layer1_size;
- int window_offset = -1;
- if(type == 2 || type == 4){
- input_len_1=window_layer_size;
- }
- real *neu1 = (real *)calloc(input_len_1, sizeof(real));
- real *neu1e = (real *)calloc(input_len_1, sizeof(real));
-
- int input_len_2 = 0;
- if(type == 4){
- input_len_2 = window_hidden_size;
- }
- real *neu2 = (real *)calloc(input_len_2, sizeof(real));
- real *neu2e = (real *)calloc(input_len_2, sizeof(real));
-
- FILE *fi = fopen(train_file, "rb");
- fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
- while (1) {
- if (word_count - last_word_count > 10000) {
- word_count_actual += word_count - last_word_count;
- last_word_count = word_count;
- if ((debug_mode > 1)) {
- now=clock();
- printf("%cAlpha: %f Progress: %.2f%% Words/thread/sec: %.2fk ", 13, alpha,
- word_count_actual / (real)(iter * train_words + 1) * 100,
- word_count_actual / ((real)(now - start + 1) / (real)CLOCKS_PER_SEC * 1000));
- fflush(stdout);
- }
- alpha = starting_alpha * (1 - word_count_actual / (real)(iter * train_words + 1));
- if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;
- }
- if (sentence_length == 0) {
- while (1) {
- word = ReadWordIndex(fi);
- if (feof(fi)) break;
- if (word == -1) continue;
- word_count++;
- if (word == 0) break;
- // The subsampling randomly discards frequent words while keeping the ranking same
- if (sample > 0) {
- real ran = (sqrt(vocab[word].cn / (sample * train_words)) + 1) * (sample * train_words) / vocab[word].cn;
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if (ran < (next_random & 0xFFFF) / (real)65536) continue;
- }
- sen[sentence_length] = word;
- sentence_length++;
- if (sentence_length >= MAX_SENTENCE_LENGTH) break;
- }
- sentence_position = 0;
- }
- if (feof(fi) || (word_count > train_words / num_threads)) {
- word_count_actual += word_count - last_word_count;
- local_iter--;
- if (local_iter == 0) break;
- word_count = 0;
- last_word_count = 0;
- sentence_length = 0;
- fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
- continue;
- }
- word = sen[sentence_position];
- if (word == -1) continue;
- for (c = 0; c < input_len_1; c++) neu1[c] = 0;
- for (c = 0; c < input_len_1; c++) neu1e[c] = 0;
- for (c = 0; c < input_len_2; c++) neu2[c] = 0;
- for (c = 0; c < input_len_2; c++) neu2e[c] = 0;
- next_random = next_random * (unsigned long long)25214903917 + 11;
- b = next_random % window;
- if (type == 0) { //train the cbow architecture
- // in -> hidden
- cw = 0;
- for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size];
- cw++;
- }
- if (cw) {
- for (c = 0; c < layer1_size; c++) neu1[c] /= cw;
- if (hs) for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * layer1_size;
- // Propagate hidden -> output
- for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];
- if (f <= -MAX_EXP) continue;
- else if (f >= MAX_EXP) continue;
- else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
- if(cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1, c + l2);
- }
- // NEGATIVE SAMPLING
- if (negative > 0) for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
- for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
- for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
- if (cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1neg, c + l2);
- }
- // Noise Contrastive Estimation
- if (nce > 0) for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
-
- for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1nce[c + l2];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else {
- f = exp(f);
- g = (label - f/(noise_distribution[target]*nce + f)) * alpha;
- }
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1nce[c + l2];
- for (c = 0; c < layer1_size; c++) syn1nce[c + l2] += g * neu1[c];
- if(cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1nce,c + l2);
- }
- // hidden -> in
- for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c];
- }
- }
- } else if(type==1) { //train skip-gram
- for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- l1 = last_word * layer1_size;
- for (c = 0; c < layer1_size; c++) neu1e[c] = 0;
- // HIERARCHICAL SOFTMAX
- if (hs) for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * layer1_size;
- // Propagate hidden -> output
- for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1[c + l2];
- if (f <= -MAX_EXP) continue;
- else if (f >= MAX_EXP) continue;
- else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * syn0[c + l1];
- if (cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1, c + l2);
- }
- // NEGATIVE SAMPLING
- if (negative > 0) for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
- for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1neg[c + l2];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
- for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * syn0[c + l1];
- if (cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1neg, c + l2);
- }
- //Noise Contrastive Estimation
- if (nce > 0) for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
- for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1nce[c + l2];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else {
- f = exp(f);
- g = (label - f/(noise_distribution[target]*nce + f)) * alpha;
- }
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1nce[c + l2];
- for (c = 0; c < layer1_size; c++) syn1nce[c + l2] += g * syn0[c + l1];
- if (cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1nce, c + l2);
- }
- // Learn weights input -> hidden
- for (c = 0; c < layer1_size; c++) syn0[c + l1] += neu1e[c];
- }
- }
- else if(type == 2){ //train the cwindow architecture
- // in -> hidden
- cw = 0;
- for (a = 0; a < window * 2 + 1; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- window_offset = a*layer1_size;
- if (a > window) window_offset-=layer1_size;
- for (c = 0; c < layer1_size; c++) neu1[c+window_offset] += syn0[c + last_word * layer1_size];
- cw++;
- }
- if (cw) {
- if (hs) for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * window_layer_size;
- // Propagate hidden -> output
- for (c = 0; c < window_layer_size; c++) f += neu1[c] * syn1_window[c + l2];
- if (f <= -MAX_EXP) continue;
- else if (f >= MAX_EXP) continue;
- else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < window_layer_size; c++) neu1e[c] += g * syn1_window[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < window_layer_size; c++) syn1_window[c + l2] += g * neu1[c];
- if (cap == 1) for (c = 0; c < window_layer_size; c++) capParam(syn1_window, c + l2);
- }
- // NEGATIVE SAMPLING
- if (negative > 0) for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < window_layer_size; c++) f += neu1[c] * syn1neg_window[c + l2];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
- for (c = 0; c < window_layer_size; c++) neu1e[c] += g * syn1neg_window[c + l2];
- for (c = 0; c < window_layer_size; c++) syn1neg_window[c + l2] += g * neu1[c];
- if(cap == 1) for (c = 0; c < window_layer_size; c++) capParam(syn1neg_window, c + l2);
- }
- // Noise Contrastive Estimation
- if (nce > 0) for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < window_layer_size; c++) f += neu1[c] * syn1nce_window[c + l2];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else {
- f = exp(f);
- g = (label - f/(noise_distribution[target]*nce + f)) * alpha;
- }
- for (c = 0; c < window_layer_size; c++) neu1e[c] += g * syn1nce_window[c + l2];
- for (c = 0; c < window_layer_size; c++) syn1nce_window[c + l2] += g * neu1[c];
- if(cap == 1) for (c = 0; c < window_layer_size; c++) capParam(syn1nce_window, c + l2);
- }
- // hidden -> in
- for (a = 0; a < window * 2 + 1; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- window_offset = a * layer1_size;
- if(a > window) window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c + window_offset];
- }
- }
- }
- else if (type == 3){ //train structured skip-gram
- for (a = 0; a < window * 2 + 1; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- l1 = last_word * layer1_size;
- window_offset = a * layer1_size;
- if(a > window) window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++) neu1e[c] = 0;
- // HIERARCHICAL SOFTMAX
- if (hs) for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * window_layer_size;
- // Propagate hidden -> output
- for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1_window[c + l2 + window_offset];
- if (f <= -MAX_EXP) continue;
- else if (f >= MAX_EXP) continue;
- else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1_window[c + l2 + window_offset];
- // Learn weights hidden -> output
- for (c = 0; c < layer1_size; c++) syn1[c + l2 + window_offset] += g * syn0[c + l1];
- if(cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1, c + l2 + window_offset);
- }
- // NEGATIVE SAMPLING
- if (negative > 0) for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1neg_window[c + l2 + window_offset];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg_window[c + l2 + window_offset];
- for (c = 0; c < layer1_size; c++) syn1neg_window[c + l2 + window_offset] += g * syn0[c + l1];
- if(cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1neg_window, c + l2 + window_offset);
- }
- // Noise Constrastive Estimation
- if (nce > 0) for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1nce_window[c + l2 + window_offset];
- if (f > MAX_EXP) g = (label - 1) * alpha;
- else if (f < -MAX_EXP) g = (label - 0) * alpha;
- else {
- f = exp(f);
- g = (label - f/(noise_distribution[target]*nce + f)) * alpha;
- }
- for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1nce_window[c + l2 + window_offset];
- for (c = 0; c < layer1_size; c++) syn1nce_window[c + l2 + window_offset] += g * syn0[c + l1];
- if (cap == 1) for (c = 0; c < layer1_size; c++) capParam(syn1nce_window, c + l2 + window_offset);
- }
- // Learn weights input -> hidden
- for (c = 0; c < layer1_size; c++) {syn0[c + l1] += neu1e[c]; if(syn0[c + l1] > 50) syn0[c + l1] = 50; if(syn0[c + l1] < -50) syn0[c + l1] = -50;}
- }
- }
- else if(type == 4){ //training senna
- // in -> hidden
- cw = 0;
- for (a = 0; a < window * 2 + 1; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- window_offset = a*layer1_size;
- if (a > window) window_offset-=layer1_size;
- for (c = 0; c < layer1_size; c++) neu1[c+window_offset] += syn0[c + last_word * layer1_size];
- cw++;
- }
- if (cw) {
- for (a = 0; a < window_hidden_size; a++){
- c = a*window_layer_size;
- for(b = 0; b < window_layer_size; b++){
- neu2[a] += syn_window_hidden[c + b] * neu1[b];
- }
- }
- if (hs) for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * window_hidden_size;
- // Propagate hidden -> output
- for (c = 0; c < window_hidden_size; c++) f += hardTanh(neu2[c]) * syn_hidden_word[c + l2];
- if (f <= -MAX_EXP) continue;
- else if (f >= MAX_EXP) continue;
- else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < window_hidden_size; c++) neu2e[c] += dHardTanh(neu2[c],g) * g * syn_hidden_word[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < window_hidden_size; c++) syn_hidden_word[c + l2] += dHardTanh(neu2[c],g) * g * neu2[c];
- }
- // NEGATIVE SAMPLING
- if (negative > 0) for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random * (unsigned long long)25214903917 + 11;
- if(word_to_group != NULL && word_to_group[word] != -1){
- target = word;
- while(target == word) {
- target = group_to_table[word_to_group[word]*table_size + (next_random >> 16) % table_size];
- next_random = next_random * (unsigned long long)25214903917 + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- }
- else{
- target = table[(next_random >> 16) % table_size];
- }
- if (target == 0) target = next_random % (vocab_size - 1) + 1;
- if (target == word) continue;
- label = 0;
- }
- l2 = target * window_hidden_size;
- f = 0;
- for (c = 0; c < window_hidden_size; c++) f += hardTanh(neu2[c]) * syn_hidden_word_neg[c + l2];
- if (f > MAX_EXP) g = (label - 1) * alpha / negative;
- else if (f < -MAX_EXP) g = (label - 0) * alpha / negative;
- else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha / negative;
- for (c = 0; c < window_hidden_size; c++) neu2e[c] += dHardTanh(neu2[c],g) * g * syn_hidden_word_neg[c + l2];
- for (c = 0; c < window_hidden_size; c++) syn_hidden_word_neg[c + l2] += dHardTanh(neu2[c],g) * g * neu2[c];
- }
- for (a = 0; a < window_hidden_size; a++)
- for(b = 0; b < window_layer_size; b++)
- neu1e[b] += neu2e[a] * syn_window_hidden[a*window_layer_size + b];
- for (a = 0; a < window_hidden_size; a++)
- for(b = 0; b < window_layer_size; b++)
- syn_window_hidden[a*window_layer_size + b] += neu2e[a] * neu1[b];
- // hidden -> in
- for (a = 0; a < window * 2 + 1; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- window_offset = a * layer1_size;
- if(a > window) window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c + window_offset];
- }
- }
- } else if(type == 5) {
- for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- printf("storing %s %s - %d\n", vocab[word].word, vocab[last_word].word, a - window);
- cw++;
- }
- }
- else{
- printf("unknown type %i", type);
- exit(0);
- }
- sentence_position++;
- if (sentence_position >= sentence_length) {
- sentence_length = 0;
- continue;
- }
- }
- fclose(fi);
- free(neu1);
- free(neu1e);
- pthread_exit(NULL);
-}
-
-void TrainModel() {
- long a, b, c, d;
- FILE *fo;
- pthread_t *pt = (pthread_t *)malloc(num_threads * sizeof(pthread_t));
- printf("Starting training using file %s\n", train_file);
- starting_alpha = alpha;
- if (read_vocab_file[0] != 0) ReadVocab(); else LearnVocabFromTrainFile();
- if (save_vocab_file[0] != 0) SaveVocab();
- if (output_file[0] == 0) return;
- InitNet();
- if (negative > 0 || nce > 0) InitUnigramTable();
- if (negative_classes_file[0] != 0) InitClassUnigramTable();
- start = clock();
- for (a = 0; a < num_threads; a++) pthread_create(&pt[a], NULL, TrainModelThread, (void *)a);
- for (a = 0; a < num_threads; a++) pthread_join(pt[a], NULL);
- fo = fopen(output_file, "wb");
- if (classes == 0) {
- // Save the word vectors
- fprintf(fo, "%lld %lld\n", vocab_size, layer1_size);
- for (a = 0; a < vocab_size; a++) {
- fprintf(fo, "%s ", vocab[a].word);
- if (binary) for (b = 0; b < layer1_size; b++) fwrite(&syn0[a * layer1_size + b], sizeof(real), 1, fo);
- else for (b = 0; b < layer1_size; b++) fprintf(fo, "%lf ", syn0[a * layer1_size + b]);
- fprintf(fo, "\n");
- }
- } else {
- // Run K-means on the word vectors
- int clcn = classes, iter = 10, closeid;
- int *centcn = (int *)malloc(classes * sizeof(int));
- int *cl = (int *)calloc(vocab_size, sizeof(int));
- real closev, x;
- real *cent = (real *)calloc(classes * layer1_size, sizeof(real));
- for (a = 0; a < vocab_size; a++) cl[a] = a % clcn;
- for (a = 0; a < iter; a++) {
- for (b = 0; b < clcn * layer1_size; b++) cent[b] = 0;
- for (b = 0; b < clcn; b++) centcn[b] = 1;
- for (c = 0; c < vocab_size; c++) {
- for (d = 0; d < layer1_size; d++) cent[layer1_size * cl[c] + d] += syn0[c * layer1_size + d];
- centcn[cl[c]]++;
- }
- for (b = 0; b < clcn; b++) {
- closev = 0;
- for (c = 0; c < layer1_size; c++) {
- cent[layer1_size * b + c] /= centcn[b];
- closev += cent[layer1_size * b + c] * cent[layer1_size * b + c];
- }
- closev = sqrt(closev);
- for (c = 0; c < layer1_size; c++) cent[layer1_size * b + c] /= closev;
- }
- for (c = 0; c < vocab_size; c++) {
- closev = -10;
- closeid = 0;
- for (d = 0; d < clcn; d++) {
- x = 0;
- for (b = 0; b < layer1_size; b++) x += cent[layer1_size * d + b] * syn0[c * layer1_size + b];
- if (x > closev) {
- closev = x;
- closeid = d;
- }
- }
- cl[c] = closeid;
- }
- }
- // Save the K-means classes
- for (a = 0; a < vocab_size; a++) fprintf(fo, "%s %d\n", vocab[a].word, cl[a]);
- free(centcn);
- free(cent);
- free(cl);
- }
- fclose(fo);
-}
-
-int ArgPos(char *str, int argc, char **argv) {
- int a;
- for (a = 1; a < argc; a++) if (!strcmp(str, argv[a])) {
- if (a == argc - 1) {
- printf("Argument missing for %s\n", str);
- exit(1);
- }
- return a;
- }
- return -1;
-}
-
-int main(int argc, char **argv) {
- int i;
- if (argc == 1) {
- printf("WORD VECTOR estimation toolkit v 0.1c\n\n");
- printf("Options:\n");
- printf("Parameters for training:\n");
- printf("\t-train <file>\n");
- printf("\t\tUse text data from <file> to train the model\n");
- printf("\t-output <file>\n");
- printf("\t\tUse <file> to save the resulting word vectors / word clusters\n");
- printf("\t-size <int>\n");
- printf("\t\tSet size of word vectors; default is 100\n");
- printf("\t-window <int>\n");
- printf("\t\tSet max skip length between words; default is 5\n");
- printf("\t-sample <float>\n");
- printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency in the training data\n");
- printf("\t\twill be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)\n");
- printf("\t-hs <int>\n");
- printf("\t\tUse Hierarchical Softmax; default is 0 (not used)\n");
- printf("\t-negative <int>\n");
- printf("\t\tNumber of negative examples; default is 5, common values are 3 - 10 (0 = not used)\n");
- printf("\t-negative-classes <file>\n");
- printf("\t\tNegative classes to sample from\n");
- printf("\t-nce <int>\n");
- printf("\t\tNumber of negative examples for nce; default is 0, common values are 3 - 10 (0 = not used)\n");
- printf("\t-threads <int>\n");
- printf("\t\tUse <int> threads (default 12)\n");
- printf("\t-iter <int>\n");
- printf("\t\tRun more training iterations (default 5)\n");
- printf("\t-min-count <int>\n");
- printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
- printf("\t-alpha <float>\n");
- printf("\t\tSet the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW\n");
- printf("\t-classes <int>\n");
- printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
- printf("\t-debug <int>\n");
- printf("\t\tSet the debug mode (default = 2 = more info during training)\n");
- printf("\t-binary <int>\n");
- printf("\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");
- printf("\t-save-vocab <file>\n");
- printf("\t\tThe vocabulary will be saved to <file>\n");
- printf("\t-read-vocab <file>\n");
- printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
- printf("\t-type <int>\n");
- printf("\t\tType of embeddings (0 for cbow, 1 for skipngram, 2 for cwindow, 3 for structured skipngram, 4 for senna type, 5 for store positional bigramms)\n");
- printf("\t-cap <int>\n");
- printf("\t\tlimit the parameter values to the range [-50, 50]; default is 0 (off)\n");
- printf("\nExamples:\n");
- printf("./word2vec -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -type 1 -iter 3\n\n");
- return 0;
- }
- output_file[0] = 0;
- save_vocab_file[0] = 0;
- read_vocab_file[0] = 0;
- negative_classes_file[0] = 0;
- if ((i = ArgPos((char *)"-size", argc, argv)) > 0) layer1_size = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(train_file, argv[i + 1]);
- if ((i = ArgPos((char *)"-save-vocab", argc, argv)) > 0) strcpy(save_vocab_file, argv[i + 1]);
- if ((i = ArgPos((char *)"-read-vocab", argc, argv)) > 0) strcpy(read_vocab_file, argv[i + 1]);
- if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-binary", argc, argv)) > 0) binary = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-type", argc, argv)) > 0) type = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
- if ((i = ArgPos((char *)"-window", argc, argv)) > 0) window = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-sample", argc, argv)) > 0) sample = atof(argv[i + 1]);
- if ((i = ArgPos((char *)"-hs", argc, argv)) > 0) hs = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-negative", argc, argv)) > 0) negative = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-negative-classes", argc, argv)) > 0) strcpy(negative_classes_file, argv[i + 1]);
- if ((i = ArgPos((char *)"-nce", argc, argv)) > 0) nce = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-threads", argc, argv)) > 0) num_threads = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-iter", argc, argv)) > 0) iter = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-classes", argc, argv)) > 0) classes = atoi(argv[i + 1]);
- if ((i = ArgPos((char *)"-cap", argc, argv)) > 0) cap = atoi(argv[i + 1]);
- if (type==0 || type==2 || type==4) alpha = 0.05;
- if (type==5) {
- sample = 0;
- cdb = open_collocators(output_file);
- }
- if ((i = ArgPos((char *)"-alpha", argc, argv)) > 0) alpha = atof(argv[i + 1]);
- vocab = (struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word));
- vocab_hash = (int *)calloc(vocab_hash_size, sizeof(int));
- expTable = (real *)malloc((EXP_TABLE_SIZE + 1) * sizeof(real));
- for (i = 0; i < EXP_TABLE_SIZE; i++) {
- expTable[i] = exp((i / (real)EXP_TABLE_SIZE * 2 - 1) * MAX_EXP); // Precompute the exp() table
- expTable[i] = expTable[i] / (expTable[i] + 1); // Precompute f(x) = x / (x + 1)
- }
- TrainModel();
- return 0;
-}
-
diff --git a/word2vecExt1.c b/word2vecExt1.c
deleted file mode 100644
index a2d5cdc..0000000
--- a/word2vecExt1.c
+++ /dev/null
@@ -1,2182 +0,0 @@
-// Copyright 2013 Google Inc. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <locale.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <unistd.h>
-#include <math.h>
-#include <pthread.h>
-#include <collocatordb.h>
-
-#define MAX_STRING 100
-#define EXP_TABLE_SIZE 1000
-#define MAX_EXP 6
-#define MAX_SENTENCE_LENGTH 1000
-#define MAX_CC 100
-#define MAX_CODE_LENGTH 40
-
-const int vocab_hash_size = 30000000; // Maximum 30 * 0.7 = 21M words in the vocabulary
-
-typedef float real; // Precision of float numbers
-
-struct vocab_word {
- long long cn;
- int *point;
- char *word, *code, codelen;
-};
-
-char train_file[MAX_STRING], output_file[MAX_STRING];
-char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
-char save_net_file[MAX_STRING], read_net_file[MAX_STRING];
-char magic_stop_file[MAX_STRING];
-
-struct vocab_word *vocab;
-int binary = 0, type = 1, debug_mode = 2, window = 5, min_count = 5,
- num_threads = 12, min_reduce = 1;
-int *vocab_hash;
-long long *threadPos;
-int *threadIters;
-long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100;
-long long train_words = 0, word_count_actual = 0, iter = 5, file_size = 0,
- classes = 0;
-real alpha = 0.025, starting_alpha, sample = 1e-3;
-real *syn0, *syn1, *syn1neg, *syn1nce, *expTable;
-real avgWordLength=0;
-clock_t start, start_clock;
-
-real *syn1_window, *syn1neg_window, *syn1nce_window;
-int w_offset, window_layer_size;
-
-int window_hidden_size = 500;
-real *syn_window_hidden, *syn_hidden_word, *syn_hidden_word_neg,
- *syn_hidden_word_nce;
-
-int hs = 0, negative = 5;
-const int table_size = 1e8;
-int *table;
-
-long cc = 0;
-long tc = 1;
-
-//constrastive negative sampling
-char negative_classes_file[MAX_STRING];
-int *word_to_group;
-int *group_to_table; //group_size*table_size
-int class_number;
-
-//nce
-real* noise_distribution;
-int nce = 0;
-
-//param caps
-real CAP_VALUE = 50;
-int cap = 0;
-
-COLLOCATORDB *cdb = NULL;
-
-void capParam(real* array, int index) {
- if (array[index] > CAP_VALUE)
- array[index] = CAP_VALUE;
- else if (array[index] < -CAP_VALUE)
- array[index] = -CAP_VALUE;
-}
-
-real hardTanh(real x) {
- if (x >= 1) {
- return 1;
- } else if (x <= -1) {
- return -1;
- } else {
- return x;
- }
-}
-
-real dHardTanh(real x, real g) {
- if (x > 1 && g > 0) {
- return 0;
- }
- if (x < -1 && g < 0) {
- return 0;
- }
- return 1;
-}
-
-void InitUnigramTable() {
- int a, i;
- long long train_words_pow = 0;
- real d1, power = 0.75;
- table = (int *) malloc(table_size * sizeof(int));
- for (a = 0; a < vocab_size; a++)
- train_words_pow += pow(vocab[a].cn, power);
- i = 0;
- d1 = pow(vocab[i].cn, power) / (real) train_words_pow;
- for (a = 0; a < table_size; a++) {
- table[a] = i;
- if (a / (real) table_size > d1) {
- i++;
- d1 += pow(vocab[i].cn, power) / (real) train_words_pow;
- }
- if (i >= vocab_size)
- i = vocab_size - 1;
- }
-
- noise_distribution = (real *) calloc(vocab_size, sizeof(real));
- for (a = 0; a < vocab_size; a++)
- noise_distribution[a] = pow(vocab[a].cn, power)
- / (real) train_words_pow;
-}
-
-// Reads a single word from a file, assuming space + tab + EOL to be word boundaries
-void ReadWord(char *word, FILE *fin) {
- int a = 0, ch;
- while (!feof(fin)) {
- ch = fgetc(fin);
- if (ch == 13)
- continue;
- if ((ch == ' ') || (ch == '\t') || (ch == '\n')) {
- if (a > 0) {
- if (ch == '\n')
- ungetc(ch, fin);
- break;
- }
- if (ch == '\n') {
- strcpy(word, (char *) "</s>");
- return;
- } else
- continue;
- }
- word[a] = ch;
- a++;
- if (a >= MAX_STRING - 1)
- a--; // Truncate too long words
- }
- word[a] = 0;
-}
-
-// Returns hash value of a word
-int GetWordHash(char *word) {
- unsigned long long a, hash = 0;
- for (a = 0; a < strlen(word); a++)
- hash = hash * 257 + word[a];
- hash = hash % vocab_hash_size;
- return hash;
-}
-
-// Returns position of a word in the vocabulary; if the word is not found, returns -1
-int SearchVocab(char *word) {
- unsigned int hash = GetWordHash(word);
- while (1) {
- if (vocab_hash[hash] == -1)
- return -1;
- if (!strcmp(word, vocab[vocab_hash[hash]].word))
- return vocab_hash[hash];
- hash = (hash + 1) % vocab_hash_size;
- }
- return -1;
-}
-
-// Reads a word and returns its index in the vocabulary
-int ReadWordIndex(FILE *fin) {
- char word[MAX_STRING];
- ReadWord(word, fin);
- if (feof(fin))
- return -1;
- return SearchVocab(word);
-}
-
-// Adds a word to the vocabulary
-int AddWordToVocab(char *word) {
- unsigned int hash, length = strlen(word) + 1;
- if (length > MAX_STRING)
- length = MAX_STRING;
- vocab[vocab_size].word = (char *) calloc(length, sizeof(char));
- strcpy(vocab[vocab_size].word, word);
- vocab[vocab_size].cn = 0;
- vocab_size++;
- // Reallocate memory if needed
- if (vocab_size + 2 >= vocab_max_size) {
- vocab_max_size += 1000;
- vocab = (struct vocab_word *) realloc(vocab,
- vocab_max_size * sizeof(struct vocab_word));
- }
- hash = GetWordHash(word);
- while (vocab_hash[hash] != -1)
- hash = (hash + 1) % vocab_hash_size;
- vocab_hash[hash] = vocab_size - 1;
- return vocab_size - 1;
-}
-
-// Used later for sorting by word counts
-int VocabCompare(const void *a, const void *b) {
- return ((struct vocab_word *) b)->cn - ((struct vocab_word *) a)->cn;
-}
-
-// Sorts the vocabulary by frequency using word counts
-void SortVocab() {
- int a, size;
- unsigned int hash;
- // Sort the vocabulary and keep </s> at the first position
- qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);
- for (a = 0; a < vocab_hash_size; a++)
- vocab_hash[a] = -1;
- size = vocab_size;
- train_words = 0;
- for (a = 0; a < size; a++) {
- avgWordLength += vocab[a].cn * (strlen(vocab[a].word) + 1);
- // Words occuring less than min_count times will be discarded from the vocab
- if ((vocab[a].cn < min_count) && (a != 0)) {
- vocab_size--;
- free(vocab[a].word);
- } else {
- // Hash will be re-computed, as after the sorting it is not actual
- hash = GetWordHash(vocab[a].word);
- while (vocab_hash[hash] != -1)
- hash = (hash + 1) % vocab_hash_size;
- vocab_hash[hash] = a;
- train_words += vocab[a].cn;
- }
- }
- avgWordLength /= train_words;
- vocab = (struct vocab_word *) realloc(vocab,
- (vocab_size + 1) * sizeof(struct vocab_word));
- // Allocate memory for the binary tree construction
- for (a = 0; a < vocab_size; a++) {
- vocab[a].code = (char *) calloc(MAX_CODE_LENGTH, sizeof(char));
- vocab[a].point = (int *) calloc(MAX_CODE_LENGTH, sizeof(int));
- }
-}
-
-// Reduces the vocabulary by removing infrequent tokens
-void ReduceVocab() {
- int a, b = 0;
- unsigned int hash;
- for (a = 0; a < vocab_size; a++)
- if (vocab[a].cn > min_reduce) {
- vocab[b].cn = vocab[a].cn;
- vocab[b].word = vocab[a].word;
- b++;
- } else
- free(vocab[a].word);
- vocab_size = b;
- for (a = 0; a < vocab_hash_size; a++)
- vocab_hash[a] = -1;
- for (a = 0; a < vocab_size; a++) {
- // Hash will be re-computed, as it is not actual
- hash = GetWordHash(vocab[a].word);
- while (vocab_hash[hash] != -1)
- hash = (hash + 1) % vocab_hash_size;
- vocab_hash[hash] = a;
- }
- fflush(stdout);
- min_reduce++;
-}
-
-// Create binary Huffman tree using the word counts
-// Frequent words will have short uniqe binary codes
-void CreateBinaryTree() {
- long long a, b, i, min1i, min2i, pos1, pos2, point[MAX_CODE_LENGTH];
- char code[MAX_CODE_LENGTH];
- long long *count = (long long *) calloc(vocab_size * 2 + 1,
- sizeof(long long));
- long long *binary = (long long *) calloc(vocab_size * 2 + 1,
- sizeof(long long));
- long long *parent_node = (long long *) calloc(vocab_size * 2 + 1,
- sizeof(long long));
- // todo: this needs to operate on a sorted copy of vocab[a].cn if we use local counts
- for (a = 0; a < vocab_size; a++)
- count[a] = vocab[a].cn;
- for (a = vocab_size; a < vocab_size * 2; a++)
- count[a] = 1e15;
- pos1 = vocab_size - 1;
- pos2 = vocab_size;
- // Following algorithm constructs the Huffman tree by adding one node at a time
- for (a = 0; a < vocab_size - 1; a++) {
- // First, find two smallest nodes 'min1, min2'
- if (pos1 >= 0) {
- if (count[pos1] < count[pos2]) {
- min1i = pos1;
- pos1--;
- } else {
- min1i = pos2;
- pos2++;
- }
- } else {
- min1i = pos2;
- pos2++;
- }
- if (pos1 >= 0) {
- if (count[pos1] < count[pos2]) {
- min2i = pos1;
- pos1--;
- } else {
- min2i = pos2;
- pos2++;
- }
- } else {
- min2i = pos2;
- pos2++;
- }
- count[vocab_size + a] = count[min1i] + count[min2i];
- parent_node[min1i] = vocab_size + a;
- parent_node[min2i] = vocab_size + a;
- binary[min2i] = 1;
- }
- // Now assign binary code to each vocabulary word
- for (a = 0; a < vocab_size; a++) {
- b = a;
- i = 0;
- while (1) {
- code[i] = binary[b];
- point[i] = b;
- i++;
- b = parent_node[b];
- if (b == vocab_size * 2 - 2)
- break;
- }
- vocab[a].codelen = i;
- vocab[a].point[0] = vocab_size - 2;
- for (b = 0; b < i; b++) {
- vocab[a].code[i - b - 1] = code[b];
- vocab[a].point[i - b] = point[b] - vocab_size;
- }
- }
- free(count);
- free(binary);
- free(parent_node);
-}
-
-void LearnVocabFromTrainFile() {
- char word[MAX_STRING];
- FILE *fin;
- long long a, i;
- for (a = 0; a < vocab_hash_size; a++)
- vocab_hash[a] = -1;
- fin = fopen(train_file, "rb");
- if (fin == NULL) {
- printf("ERROR: training data file not found!\n");
- exit(1);
- }
- vocab_size = 0;
- AddWordToVocab((char *) "</s>");
- while (1) {
- ReadWord(word, fin);
- if (feof(fin))
- break;
- train_words++;
- if ((debug_mode > 1) && (train_words % 100000 == 0)) {
- printf("%lldK%c", train_words / 1000, 13);
- fflush(stdout);
- }
- i = SearchVocab(word);
- if (i == -1) {
- a = AddWordToVocab(word);
- vocab[a].cn = 1;
- } else
- vocab[i].cn++;
- if (vocab_size > vocab_hash_size * 0.7)
- ReduceVocab();
- }
- SortVocab();
- if (debug_mode > 0) {
- printf("Vocab size: %'lld\n", vocab_size);
- printf("Words in train file: %'lld\n", train_words);
- }
- file_size = ftell(fin);
- fclose(fin);
-}
-
-void SaveVocab() {
- long long i;
- FILE *fo = fopen(save_vocab_file, "wb");
- for (i = 0; i < vocab_size; i++)
- fprintf(fo, "%s %lld\n", vocab[i].word, vocab[i].cn);
- fclose(fo);
-}
-
-void ReadVocab() {
- long long a, i = 0;
- char c;
- char word[MAX_STRING];
- FILE *fin = fopen(read_vocab_file, "rb");
- if (fin == NULL) {
- printf("Vocabulary file not found\n");
- exit(1);
- }
- for (a = 0; a < vocab_hash_size; a++)
- vocab_hash[a] = -1;
- vocab_size = 0;
- while (1) {
- ReadWord(word, fin);
- if (feof(fin))
- break;
- a = AddWordToVocab(word);
- fscanf(fin, "%lld%c", &vocab[a].cn, &c);
- i++;
- }
- fclose(fin);
- fin = fopen(train_file, "rb");
- if (fin == NULL) {
- printf("ERROR: training data file not found!\n");
- exit(1);
- }
- fseek(fin, 0, SEEK_END);
- file_size = ftell(fin);
- fclose(fin);
- SortVocab();
- if (debug_mode > 0) {
- printf("Vocab size: %'lld\n", vocab_size);
- printf("Words in vocab's train file: %'lld\n", train_words);
- printf("Avg. word length in vocab's train file: %.2f\n", avgWordLength);
- }
- train_words = file_size / avgWordLength;
- // PF: so even with tc=0, alpha will be appropriately adapted?
- if(debug_mode > 0)
- printf("Estimated words in train file: %'lld\n", train_words);
- if (tc > 0) {
- // recalculate counts for the current corpus
- // adapted from LearnVocabFromTrainFile()
- // note that we don't sort or rehash the vocabulary again, we only adapt vocab[.].cn.
- fin = fopen(train_file, "rb");
- if (fin == NULL) {
- printf("ERROR: training data file not found!\n");
- exit(1);
- }
- // reset vocabulary counts
- for (a = 0; a < vocab_size; a++)
- vocab[a].cn = 0;
- train_words = 0;
- while (1) {
- ReadWord(word, fin);
- if (feof(fin))
- break;
- if ((debug_mode > 1) && (train_words % 100000 == 0)) {
- printf("%lldK%c", train_words / 1000, 13);
- fflush(stdout);
- }
- i = SearchVocab(word);
- // the word must be in the vocabulary but we don't issue a warning,
- // because it may have been cut off due to minfreq.
- if (i >= 0) {
- vocab[i].cn++;
- train_words++;
- }
- }
- // we cannot have 0 counts.
- for (a = 0; a < vocab_size; a++) {
- if(vocab[a].cn == 0) {
- vocab[a].cn = 1;
- train_words++;
- }
- }
- if (debug_mode > 0) {
- printf("Vocab size: %lld\n", vocab_size);
- printf("Words in current train file: %'lld\n", train_words);
- }
- fseek(fin, 0, SEEK_END);
- file_size = ftell(fin);
- fclose(fin);
- }
-}
-
-void InitClassUnigramTable() {
- // TODO: this probably needs to be adapted for dealing with subcorpus adjusted vocabulary counts
- long long a, c;
- printf("loading class unigrams \n");
- FILE *fin = fopen(negative_classes_file, "rb");
- if (fin == NULL) {
- printf("ERROR: class file not found!\n");
- exit(1);
- }
- word_to_group = (int *) malloc(vocab_size * sizeof(int));
- for (a = 0; a < vocab_size; a++)
- word_to_group[a] = -1;
- char class[MAX_STRING];
- char prev_class[MAX_STRING];
- prev_class[0] = 0;
- char word[MAX_STRING];
- class_number = -1;
- while (1) {
- if (feof(fin))
- break;
- ReadWord(class, fin);
- ReadWord(word, fin);
- int word_index = SearchVocab(word);
- if (word_index != -1) {
- if (strcmp(class, prev_class) != 0) {
- class_number++;
- strcpy(prev_class, class);
- }
- word_to_group[word_index] = class_number;
- }
- ReadWord(word, fin);
- }
- class_number++;
- fclose(fin);
-
- group_to_table = (int *) malloc(table_size * class_number * sizeof(int));
- long long train_words_pow = 0;
- real d1, power = 0.75;
-
- for (c = 0; c < class_number; c++) {
- long long offset = c * table_size;
- train_words_pow = 0;
- for (a = 0; a < vocab_size; a++)
- if (word_to_group[a] == c)
- train_words_pow += pow(vocab[a].cn, power);
- int i = 0;
- while (word_to_group[i] != c && i < vocab_size)
- i++;
- d1 = pow(vocab[i].cn, power) / (real) train_words_pow;
- for (a = 0; a < table_size; a++) {
- //printf("index %lld , word %d\n", a, i);
- group_to_table[offset + a] = i;
- if (a / (real) table_size > d1) {
- i++;
- while (word_to_group[i] != c && i < vocab_size)
- i++;
- d1 += pow(vocab[i].cn, power) / (real) train_words_pow;
- }
- if (i >= vocab_size)
- while (word_to_group[i] != c && i >= 0)
- i--;
- }
- }
-}
-
-void SaveArgs(int argc, char **argv) {
- unsigned int i;
- char args_file[MAX_STRING];
- strcpy(args_file, output_file);
- strcat(args_file, ".args");
- FILE *fargs = fopen(args_file, "w");
- if (fargs == NULL) {
- printf("Cannot save args to %s.\n", args_file);
- return;
- }
-
- for(i=1; i<argc; i++)
- fprintf(fargs, "%s ", argv[i]);
-
- fprintf(fargs, "\n");
- fclose(fargs);
-
- return;
-}
-
-void SaveNet() {
- if (type == 4 || negative <= 0) {
- fprintf(stderr,
- "save-net only supported for type 0,1,2,3 with negative sampling\n");
- return;
- }
-
- FILE *fnet = fopen(save_net_file, "wb");
- if (fnet == NULL) {
- printf("Net parameter file not found\n");
- exit(1);
- }
- fwrite(syn0, sizeof(real), vocab_size * layer1_size, fnet);
- if (type == 0 || type == 1) {
- fwrite(syn1neg, sizeof(real), vocab_size * layer1_size, fnet);
- }
- if (type == 2 || type == 3) {
- fwrite(syn1neg_window, sizeof(real), vocab_size * window_layer_size, fnet);
- }
- fclose(fnet);
-}
-
-void InitNet() {
- long long a, b;
- unsigned long long next_random = 1;
- long long read;
-
- window_layer_size = layer1_size * window * 2;
- a = posix_memalign((void **) &syn0, 128,
- (long long) vocab_size * layer1_size * sizeof(real));
- if (syn0 == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
-
- if (hs) {
- a = posix_memalign((void **) &syn1, 128,
- (long long) vocab_size * layer1_size * sizeof(real));
- if (syn1 == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- a = posix_memalign((void **) &syn1_window, 128,
- (long long) vocab_size * window_layer_size * sizeof(real));
- if (syn1_window == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- a = posix_memalign((void **) &syn_hidden_word, 128,
- (long long) vocab_size * window_hidden_size * sizeof(real));
- if (syn_hidden_word == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
-
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < layer1_size; b++)
- syn1[a * layer1_size + b] = 0;
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < window_layer_size; b++)
- syn1_window[a * window_layer_size + b] = 0;
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < window_hidden_size; b++)
- syn_hidden_word[a * window_hidden_size + b] = 0;
- }
- if (negative > 0) {
- if (type == 0 || type == 1) {
- a = posix_memalign((void **) &syn1neg, 128,
- (long long) vocab_size * layer1_size * sizeof(real));
- if (syn1neg == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < layer1_size; b++)
- syn1neg[a * layer1_size + b] = 0;
- } else if (type == 2 || type == 3) {
- a = posix_memalign((void **) &syn1neg_window, 128,
- (long long) vocab_size * window_layer_size * sizeof(real));
- if (syn1neg_window == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < window_layer_size; b++)
- syn1neg_window[a * window_layer_size + b] = 0;
- } else if (type == 4) {
- a = posix_memalign((void **) &syn_hidden_word_neg, 128,
- (long long) vocab_size * window_hidden_size * sizeof(real));
- if (syn_hidden_word_neg == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < window_hidden_size; b++)
- syn_hidden_word_neg[a * window_hidden_size + b] = 0;
- }
- }
- if (nce > 0) {
- a = posix_memalign((void **) &syn1nce, 128,
- (long long) vocab_size * layer1_size * sizeof(real));
- if (syn1nce == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- a = posix_memalign((void **) &syn1nce_window, 128,
- (long long) vocab_size * window_layer_size * sizeof(real));
- if (syn1nce_window == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- a = posix_memalign((void **) &syn_hidden_word_nce, 128,
- (long long) vocab_size * window_hidden_size * sizeof(real));
- if (syn_hidden_word_nce == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
-
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < layer1_size; b++)
- syn1nce[a * layer1_size + b] = 0;
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < window_layer_size; b++)
- syn1nce_window[a * window_layer_size + b] = 0;
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < window_hidden_size; b++)
- syn_hidden_word_nce[a * window_hidden_size + b] = 0;
- }
-
- if (type == 4) {
- a = posix_memalign((void **) &syn_window_hidden, 128,
- window_hidden_size * window_layer_size * sizeof(real));
- if (syn_window_hidden == NULL) {
- printf("Memory allocation failed\n");
- exit(1);
- }
- for (a = 0; a < window_hidden_size * window_layer_size; a++) {
- next_random = next_random * (unsigned long long) 25214903917 + 11;
- syn_window_hidden[a] = (((next_random & 0xFFFF) / (real) 65536)
- - 0.5) / (window_hidden_size * window_layer_size);
- }
- }
-
- if (read_net_file[0] == 0) {
- for (a = 0; a < vocab_size; a++)
- for (b = 0; b < layer1_size; b++) {
- next_random = next_random * (unsigned long long) 25214903917
- + 11;
- syn0[a * layer1_size + b] = (((next_random & 0xFFFF)
- / (real) 65536) - 0.5) / layer1_size;
- }
- } else if ((type == 0 || type == 1) && negative > 0) {
- FILE *fnet = fopen(read_net_file, "rb");
- if (fnet == NULL) {
- printf("Net parameter file not found\n");
- exit(1);
- }
- printf("vocab-size: %lld, layer1_size: %lld\n",
- vocab_size, layer1_size);
- read = fread(syn0, sizeof(real), vocab_size * layer1_size, fnet);
- if (read != vocab_size * layer1_size) {
- fprintf(stderr, "read-net failed %lld\n", read);
- exit(-1);
- }
- read = fread(syn1neg, sizeof(real),
- vocab_size * layer1_size, fnet);
- if (read != (long long) vocab_size * layer1_size) {
- fprintf(stderr, "read-net failed, read %lld, expected: %lld\n",
- read,
- (long long) sizeof(real) * vocab_size * layer1_size);
- exit(-1);
- }
- fgetc(fnet);
- if (!feof(fnet)) {
- fprintf(stderr,
- "Remaining bytes in net-file after read-net. File position: %ld\n",
- ftell(fnet));
- exit(-1);
- }
- fclose(fnet);
- } else if ((type == 2 || type == 3) && negative > 0) {
- FILE *fnet = fopen(read_net_file, "rb");
- if (fnet == NULL) {
- printf("Net parameter file not found\n");
- exit(1);
- }
- printf("vocab-size: %lld, layer1_size: %lld, window_layer_size %d\n",
- vocab_size, layer1_size, window_layer_size);
- read = fread(syn0, sizeof(real), vocab_size * layer1_size, fnet);
- if (read != vocab_size * layer1_size) {
- fprintf(stderr, "read-net failed %lld\n", read);
- exit(-1);
- }
- read = fread(syn1neg_window, sizeof(real),
- vocab_size * window_layer_size, fnet);
- if (read != (long long) vocab_size * window_layer_size) {
- fprintf(stderr, "read-net failed, read %lld, expected: %lld\n",
- read,
- (long long) sizeof(real) * vocab_size * window_layer_size);
- exit(-1);
- }
- fgetc(fnet);
- if (!feof(fnet)) {
- fprintf(stderr,
- "Remaining bytes in net-file after read-net. File position: %ld\n",
- ftell(fnet));
- exit(-1);
- }
- fclose(fnet);
- } else {
- fprintf(stderr,
- "read-net only supported for type 3 with negative sampling\n");
- exit(-1);
- }
-
- CreateBinaryTree();
-}
-
-char *currentDateTime(char *buf, real offset) {
- time_t t;
- time(&t);
- t += (long) offset;
- struct tm tstruct;
- tstruct = *localtime(&t);
- strftime(buf, 80, "%c", &tstruct);
- return buf;
-}
-
-void *MonitorThread(void *id) {
- char *timebuf = malloc(80);;
- int i, n=num_threads;
- long long sum;
- sleep(1);
- while(n > 0) {
- sleep(1);
- sum = n = 0;
- for(i=0; i < num_threads; i++) {
- if(threadPos[i] >= 0) {
- sum += (iter - threadIters[i]) * file_size / num_threads + threadPos[i] - (file_size / num_threads) * i;
- n++;
- } else {
- sum += iter * file_size / num_threads;
- }
- }
- if(n == 0)
- break;
- real finished_portion = (real) sum / (float) (file_size * iter);
- long long now = time(NULL);
- long long elapsed = (now - start);
- long long ttg = ((1.0 / finished_portion) * (real) elapsed - elapsed);
-
- printf("\rAlpha: %.3f Done: %.2f%% with %.2fKB/s TE: %llds TTG: %llds ETA: %s\033[K",
- alpha,
- finished_portion * 100,
- (float) sum / elapsed / 1000,
- elapsed,
- ttg,
- currentDateTime(timebuf, ttg)
- );
- fflush(stdout);
- }
- pthread_exit(NULL);
-}
-
-void *TrainModelThread(void *id) {
- long long a, b, d, cw, word, last_word, sentence_length = 0,
- sentence_position = 0;
- long long word_count = 0, last_word_count = 0, sen[MAX_SENTENCE_LENGTH + 1];
- long long l1, l2, c, target, label, local_iter = iter;
- unsigned long long next_random = (long long) id;
- real f, g;
- int input_len_1 = layer1_size;
- int window_offset = -1;
- if (type == 2 || type == 4) {
- input_len_1 = window_layer_size;
- }
- real *neu1 = (real *) calloc(input_len_1, sizeof(real));
- real *neu1e = (real *) calloc(input_len_1, sizeof(real));
- threadIters[(long) id] = iter;
-
- int input_len_2 = 0;
- if (type == 4) {
- input_len_2 = window_hidden_size;
- }
- real *neu2 = (real *) calloc(input_len_2, sizeof(real));
- real *neu2e = (real *) calloc(input_len_2, sizeof(real));
-
- FILE *fi = fopen(train_file, "rb");
- long long start_pos = file_size / (long long) num_threads * (long long) id;
- long long end_pos = file_size / (long long) num_threads * (long long) (id + 1) -1;
- long long current_pos = start_pos;
- long long last_pos = start_pos;;
- fseek(fi, start_pos, SEEK_SET);
- while (1) {
- if (word_count - last_word_count > 10000) {
- // if ((current_pos - last_pos > 100000)) {
- // PF: changed back, because it seems that alpha is not correctly adjusted otherwise.
- word_count_actual += word_count - last_word_count;
- last_pos = current_pos;
- last_word_count = word_count;
- alpha = starting_alpha
- * (1 - word_count_actual / (real) (iter * train_words + 1));
- if (alpha < starting_alpha * 0.0001)
- alpha = starting_alpha * 0.0001;
- }
- if (sentence_length == 0) {
- while (1) {
- word = ReadWordIndex(fi);
- if (feof(fi))
- break;
- if (word == -1)
- continue;
- word_count++;
- if (word == 0)
- break;
- // The subsampling randomly discards frequent words while keeping the ranking same
- if (sample > 0) {
- real ran = (sqrt(vocab[word].cn / (sample * train_words))
- + 1) * (sample * train_words) / vocab[word].cn;
- next_random = next_random * (unsigned long long) 25214903917
- + 11;
- if (ran < (next_random & 0xFFFF) / (real) 65536) {
- if (type == 3) // in structured skipgrams
- word = -2; // keep the window position correct
- else
- continue;
- }
- }
- sen[sentence_length] = word;
- sentence_length++;
- if (sentence_length >= MAX_SENTENCE_LENGTH)
- break;
- }
- sentence_position = 0;
- }
- current_pos = threadPos[(long) id] = ftell(fi);
- if (feof(fi) || current_pos >= end_pos ) {
- word_count_actual += word_count - last_word_count;
- threadIters[(long) id]--;
- local_iter--;
- if (local_iter == 0)
- break;
- if (magic_stop_file[0] && access(magic_stop_file, F_OK ) != -1) {
- printf("Magic stop file %s found. Stopping traing ...\n", magic_stop_file);
- break;
- }
- word_count = 0;
- last_word_count = 0;
- sentence_length = 0;
- fseek(fi, file_size / (long long) num_threads * (long long) id,
- SEEK_SET);
- continue;
- }
- word = sen[sentence_position];
- while (word == -2 && sentence_position<sentence_length)
- word = sen[++sentence_position];
- if (sentence_position>=sentence_length) {
- sentence_length=0;
- continue;
- }
- if (word < 0)
- continue;
- for (c = 0; c < input_len_1; c++)
- neu1[c] = 0;
- for (c = 0; c < input_len_1; c++)
- neu1e[c] = 0;
- for (c = 0; c < input_len_2; c++)
- neu2[c] = 0;
- for (c = 0; c < input_len_2; c++)
- neu2e[c] = 0;
- next_random = next_random * (unsigned long long) 25214903917 + 11;
- b = next_random % window;
- if (type == 0) { //train the cbow architecture
- // in -> hidden
- cw = 0;
- for (a = b; a < window * 2 + 1 - b; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word == -1)
- continue;
- for (c = 0; c < layer1_size; c++)
- neu1[c] += syn0[c + last_word * layer1_size];
- cw++;
- }
- if (cw) {
- for (c = 0; c < layer1_size; c++)
- neu1[c] /= cw;
- if (hs)
- for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * layer1_size;
- // Propagate hidden -> output
- for (c = 0; c < layer1_size; c++)
- f += neu1[c] * syn1[c + l2];
- if (f <= -MAX_EXP)
- continue;
- else if (f >= MAX_EXP)
- continue;
- else
- f = expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < layer1_size; c++)
- neu1e[c] += g * syn1[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < layer1_size; c++)
- syn1[c + l2] += g * neu1[c];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1, c + l2);
- }
- // NEGATIVE SAMPLING
- if (negative > 0)
- for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target = group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16) % table_size];
- next_random = next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target =
- table[(next_random >> 16) % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
- for (c = 0; c < layer1_size; c++)
- f += neu1[c] * syn1neg[c + l2];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else
- g = (label
- - expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))])
- * alpha;
- for (c = 0; c < layer1_size; c++)
- neu1e[c] += g * syn1neg[c + l2];
- for (c = 0; c < layer1_size; c++)
- syn1neg[c + l2] += g * neu1[c];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1neg, c + l2);
- }
- // Noise Contrastive Estimation
- if (nce > 0)
- for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target = group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16) % table_size];
- next_random = next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- } else {
- target =
- table[(next_random >> 16) % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
-
- for (c = 0; c < layer1_size; c++)
- f += neu1[c] * syn1nce[c + l2];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else {
- f = exp(f);
- g =
- (label
- - f
- / (noise_distribution[target]
- * nce + f)) * alpha;
- }
- for (c = 0; c < layer1_size; c++)
- neu1e[c] += g * syn1nce[c + l2];
- for (c = 0; c < layer1_size; c++)
- syn1nce[c + l2] += g * neu1[c];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1nce, c + l2);
- }
- // hidden -> in
- for (a = b; a < window * 2 + 1 - b; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word == -1)
- continue;
- for (c = 0; c < layer1_size; c++)
- syn0[c + last_word * layer1_size] += neu1e[c];
- }
- }
- } else if (type == 1) { //train skip-gram
- for (a = b; a < window * 2 + 1 - b; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word == -1)
- continue;
- l1 = last_word * layer1_size;
- for (c = 0; c < layer1_size; c++)
- neu1e[c] = 0;
- // HIERARCHICAL SOFTMAX
- if (hs)
- for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * layer1_size;
- // Propagate hidden -> output
- for (c = 0; c < layer1_size; c++)
- f += syn0[c + l1] * syn1[c + l2];
- if (f <= -MAX_EXP)
- continue;
- else if (f >= MAX_EXP)
- continue;
- else
- f = expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < layer1_size; c++)
- neu1e[c] += g * syn1[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < layer1_size; c++)
- syn1[c + l2] += g * syn0[c + l1];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1, c + l2);
- }
- // NEGATIVE SAMPLING
- if (negative > 0)
- for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target =
- group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16)
- % table_size];
- next_random =
- next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target = table[(next_random >> 16)
- % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
- for (c = 0; c < layer1_size; c++)
- f += syn0[c + l1] * syn1neg[c + l2];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else
- g =
- (label
- - expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE
- / MAX_EXP / 2))])
- * alpha;
- for (c = 0; c < layer1_size; c++)
- neu1e[c] += g * syn1neg[c + l2];
- for (c = 0; c < layer1_size; c++)
- syn1neg[c + l2] += g * syn0[c + l1];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1neg, c + l2);
- }
- //Noise Contrastive Estimation
- if (nce > 0)
- for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target =
- group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16)
- % table_size];
- next_random =
- next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target = table[(next_random >> 16)
- % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * layer1_size;
- f = 0;
- for (c = 0; c < layer1_size; c++)
- f += syn0[c + l1] * syn1nce[c + l2];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else {
- f = exp(f);
- g = (label
- - f
- / (noise_distribution[target]
- * nce + f)) * alpha;
- }
- for (c = 0; c < layer1_size; c++)
- neu1e[c] += g * syn1nce[c + l2];
- for (c = 0; c < layer1_size; c++)
- syn1nce[c + l2] += g * syn0[c + l1];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1nce, c + l2);
- }
- // Learn weights input -> hidden
- for (c = 0; c < layer1_size; c++)
- syn0[c + l1] += neu1e[c];
- }
- } else if (type == 2) { //train the cwindow architecture
- // in -> hidden
- cw = 0;
- for (a = 0; a < window * 2 + 1; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word == -1)
- continue;
- window_offset = a * layer1_size;
- if (a > window)
- window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++)
- neu1[c + window_offset] += syn0[c
- + last_word * layer1_size];
- cw++;
- }
- if (cw) {
- if (hs)
- for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * window_layer_size;
- // Propagate hidden -> output
- for (c = 0; c < window_layer_size; c++)
- f += neu1[c] * syn1_window[c + l2];
- if (f <= -MAX_EXP)
- continue;
- else if (f >= MAX_EXP)
- continue;
- else
- f = expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < window_layer_size; c++)
- neu1e[c] += g * syn1_window[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < window_layer_size; c++)
- syn1_window[c + l2] += g * neu1[c];
- if (cap == 1)
- for (c = 0; c < window_layer_size; c++)
- capParam(syn1_window, c + l2);
- }
- // NEGATIVE SAMPLING
- if (negative > 0)
- for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target = group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16) % table_size];
- next_random = next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target =
- table[(next_random >> 16) % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < window_layer_size; c++)
- f += neu1[c] * syn1neg_window[c + l2];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else
- g = (label
- - expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))])
- * alpha;
- for (c = 0; c < window_layer_size; c++)
- neu1e[c] += g * syn1neg_window[c + l2];
- for (c = 0; c < window_layer_size; c++)
- syn1neg_window[c + l2] += g * neu1[c];
- if (cap == 1)
- for (c = 0; c < window_layer_size; c++)
- capParam(syn1neg_window, c + l2);
- }
- // Noise Contrastive Estimation
- if (nce > 0)
- for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target = group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16) % table_size];
- next_random = next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target =
- table[(next_random >> 16) % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < window_layer_size; c++)
- f += neu1[c] * syn1nce_window[c + l2];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else {
- f = exp(f);
- g =
- (label
- - f
- / (noise_distribution[target]
- * nce + f)) * alpha;
- }
- for (c = 0; c < window_layer_size; c++)
- neu1e[c] += g * syn1nce_window[c + l2];
- for (c = 0; c < window_layer_size; c++)
- syn1nce_window[c + l2] += g * neu1[c];
- if (cap == 1)
- for (c = 0; c < window_layer_size; c++)
- capParam(syn1nce_window, c + l2);
- }
- // hidden -> in
- for (a = 0; a < window * 2 + 1; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word == -1)
- continue;
- window_offset = a * layer1_size;
- if (a > window)
- window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++)
- syn0[c + last_word * layer1_size] += neu1e[c
- + window_offset];
- }
- }
- } else if (type == 3) { //train structured skip-gram
- for (a = 0; a < window * 2 + 1; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word < 0)
- continue;
- l1 = last_word * layer1_size;
- window_offset = a * layer1_size;
- if (a > window)
- window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++)
- neu1e[c] = 0;
- // HIERARCHICAL SOFTMAX
- if (hs)
- for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * window_layer_size;
- // Propagate hidden -> output
- for (c = 0; c < layer1_size; c++)
- f += syn0[c + l1]
- * syn1_window[c + l2 + window_offset];
- if (f <= -MAX_EXP)
- continue;
- else if (f >= MAX_EXP)
- continue;
- else
- f = expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < layer1_size; c++)
- neu1e[c] += g
- * syn1_window[c + l2 + window_offset];
- // Learn weights hidden -> output
- for (c = 0; c < layer1_size; c++)
- syn1[c + l2 + window_offset] += g
- * syn0[c + l1];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1, c + l2 + window_offset);
- }
- // NEGATIVE SAMPLING
- if (negative > 0)
- for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target =
- group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16)
- % table_size];
- next_random =
- next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target = table[(next_random >> 16)
- % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < layer1_size; c++)
- f +=
- syn0[c + l1]
- * syn1neg_window[c + l2
- + window_offset];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else
- g =
- (label
- - expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE
- / MAX_EXP / 2))])
- * alpha;
- if(debug_mode > 2 && ((long long) id) == 0) {
- printf("negative sampling %lld for input (word) %s (#%lld), target (last word) %s returned %s (#%lld), ", d, vocab[word].word, word, vocab[last_word].word, vocab[target].word, target);
- printf("label %lld, a %lld, gain %.4f\n", label, a-window, g);
- }
- for (c = 0; c < layer1_size; c++)
- neu1e[c] +=
- g
- * syn1neg_window[c + l2
- + window_offset];
- for (c = 0; c < layer1_size; c++)
- syn1neg_window[c + l2 + window_offset] += g
- * syn0[c + l1];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1neg_window,
- c + l2 + window_offset);
- }
- // Noise Constrastive Estimation
- if (nce > 0)
- for (d = 0; d < nce + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target =
- group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16)
- % table_size];
- next_random =
- next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target = table[(next_random >> 16)
- % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * window_layer_size;
- f = 0;
- for (c = 0; c < layer1_size; c++)
- f +=
- syn0[c + l1]
- * syn1nce_window[c + l2
- + window_offset];
- if (f > MAX_EXP)
- g = (label - 1) * alpha;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha;
- else {
- f = exp(f);
- g = (label
- - f
- / (noise_distribution[target]
- * nce + f)) * alpha;
- }
- for (c = 0; c < layer1_size; c++)
- neu1e[c] +=
- g
- * syn1nce_window[c + l2
- + window_offset];
- for (c = 0; c < layer1_size; c++)
- syn1nce_window[c + l2 + window_offset] += g
- * syn0[c + l1];
- if (cap == 1)
- for (c = 0; c < layer1_size; c++)
- capParam(syn1nce_window,
- c + l2 + window_offset);
- }
- // Learn weights input -> hidden
- for (c = 0; c < layer1_size; c++) {
- syn0[c + l1] += neu1e[c];
- if (syn0[c + l1] > 50)
- syn0[c + l1] = 50;
- if (syn0[c + l1] < -50)
- syn0[c + l1] = -50;
- }
- }
- } else if (type == 4) { //training senna
- // in -> hidden
- cw = 0;
- for (a = 0; a < window * 2 + 1; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word == -1)
- continue;
- window_offset = a * layer1_size;
- if (a > window)
- window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++)
- neu1[c + window_offset] += syn0[c
- + last_word * layer1_size];
- cw++;
- }
- if (cw) {
- for (a = 0; a < window_hidden_size; a++) {
- c = a * window_layer_size;
- for (b = 0; b < window_layer_size; b++) {
- neu2[a] += syn_window_hidden[c + b] * neu1[b];
- }
- }
- if (hs)
- for (d = 0; d < vocab[word].codelen; d++) {
- f = 0;
- l2 = vocab[word].point[d] * window_hidden_size;
- // Propagate hidden -> output
- for (c = 0; c < window_hidden_size; c++)
- f += hardTanh(neu2[c]) * syn_hidden_word[c + l2];
- if (f <= -MAX_EXP)
- continue;
- else if (f >= MAX_EXP)
- continue;
- else
- f = expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- // 'g' is the gradient multiplied by the learning rate
- g = (1 - vocab[word].code[d] - f) * alpha;
- // Propagate errors output -> hidden
- for (c = 0; c < window_hidden_size; c++)
- neu2e[c] += dHardTanh(neu2[c], g) * g
- * syn_hidden_word[c + l2];
- // Learn weights hidden -> output
- for (c = 0; c < window_hidden_size; c++)
- syn_hidden_word[c + l2] += dHardTanh(neu2[c], g) * g
- * neu2[c];
- }
- // NEGATIVE SAMPLING
- if (negative > 0)
- for (d = 0; d < negative + 1; d++) {
- if (d == 0) {
- target = word;
- label = 1;
- } else {
- next_random = next_random
- * (unsigned long long) 25214903917 + 11;
- if (word_to_group != NULL
- && word_to_group[word] != -1) {
- target = word;
- while (target == word) {
- target = group_to_table[word_to_group[word]
- * table_size
- + (next_random >> 16) % table_size];
- next_random = next_random
- * (unsigned long long) 25214903917
- + 11;
- }
- //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word);
- } else {
- target =
- table[(next_random >> 16) % table_size];
- }
- if (target == 0)
- target = next_random % (vocab_size - 1) + 1;
- if (target == word)
- continue;
- label = 0;
- }
- l2 = target * window_hidden_size;
- f = 0;
- for (c = 0; c < window_hidden_size; c++)
- f += hardTanh(neu2[c])
- * syn_hidden_word_neg[c + l2];
- if (f > MAX_EXP)
- g = (label - 1) * alpha / negative;
- else if (f < -MAX_EXP)
- g = (label - 0) * alpha / negative;
- else
- g = (label
- - expTable[(int) ((f + MAX_EXP)
- * (EXP_TABLE_SIZE / MAX_EXP / 2))])
- * alpha / negative;
- for (c = 0; c < window_hidden_size; c++)
- neu2e[c] += dHardTanh(neu2[c], g) * g
- * syn_hidden_word_neg[c + l2];
- for (c = 0; c < window_hidden_size; c++)
- syn_hidden_word_neg[c + l2] += dHardTanh(neu2[c], g)
- * g * neu2[c];
- }
- for (a = 0; a < window_hidden_size; a++)
- for (b = 0; b < window_layer_size; b++)
- neu1e[b] += neu2e[a]
- * syn_window_hidden[a * window_layer_size + b];
- for (a = 0; a < window_hidden_size; a++)
- for (b = 0; b < window_layer_size; b++)
- syn_window_hidden[a * window_layer_size + b] += neu2e[a]
- * neu1[b];
- // hidden -> in
- for (a = 0; a < window * 2 + 1; a++)
- if (a != window) {
- c = sentence_position - window + a;
- if (c < 0)
- continue;
- if (c >= sentence_length)
- continue;
- last_word = sen[c];
- if (last_word == -1)
- continue;
- window_offset = a * layer1_size;
- if (a > window)
- window_offset -= layer1_size;
- for (c = 0; c < layer1_size; c++)
- syn0[c + last_word * layer1_size] += neu1e[c
- + window_offset];
- }
- }
- } else if(type == 5) {
- for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
- c = sentence_position - window + a;
- if (c < 0) continue;
- if (c >= sentence_length) continue;
- last_word = sen[c];
- if (last_word == -1) continue;
- inc_collocator(cdb, word, last_word, a - window);
- // printf("%2d: storing %s %s - %d\n", id, vocab[word].word, vocab[last_word].word, (int) a - window);
- // cw++;
- }
- } else {
- printf("unknown type %i", type);
- exit(0);
- }
- sentence_position++;
- if (sentence_position >= sentence_length) {
- sentence_length = 0;
- continue;
- }
- }
- fclose(fi);
- free(neu1);
- free(neu1e);
- threadPos[(long) id] = -1;
- pthread_exit(NULL);
-}
-
-void ShowCollocations() {
- long a, b, c, d, e, window_offset, target, max_target = 0, maxmax_target;
- real f, max_f, maxmax_f;
- real *target_sums, bestf[MAX_CC], worstbest;
- long besti[MAX_CC];
- int N = 10, bestp[MAX_CC];
- a = posix_memalign((void **) &target_sums, 128, vocab_size * sizeof(real));
-
- for (d = cc; d < vocab_size; d++) {
- for (b = 0; b < vocab_size; b++)
- target_sums[b] = 0;
- for (b = 0; b < N; b++)
- bestf[b] = -1;
- worstbest = -1;
-
- maxmax_f = -1;
- maxmax_target = 0;
- for (a = window * 2 + 1; a >=0; a--) {
- if (a != window) {
- max_f = -1;
- window_offset = a * layer1_size;
- if (a > window)
- window_offset -= layer1_size;
- for(target = 0; target < vocab_size; target ++) {
- if(target == d)
- continue;
- f = 0;
- for (c = 0; c < layer1_size; c++)
- f += syn0[d* layer1_size + c] * syn1neg_window[target * window_layer_size + window_offset + c];
- if (f < -MAX_EXP)
- continue;
- else if (f > MAX_EXP)
- continue;
- else
- f = expTable[(int) ((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
- if(f > max_f) {
- max_f = f;
- max_target = target;
- }
- target_sums[target] += (1-target_sums[target]) * f;
- if(f > worstbest) {
- for (b = 0; b < N; b++) {
- if (f > bestf[b]) {
- for (e = N - 1; e > b; e--) {
- bestf[e] = bestf[e - 1];
- besti[e] = besti[e - 1];
- bestp[e] = bestp[e - 1];
- }
- bestf[b] = f;
- besti[b] = target;
- bestp[b] = window-a;
- break;
- }
- }
- worstbest = bestf[N - 1];
- }
- }
- printf("%s (%.2f) ", vocab[max_target].word, max_f);
- if (max_f > maxmax_f) {
- maxmax_f = max_f;
- maxmax_target = max_target;
- }
- } else {
- printf("\x1b[1m%s\x1b[0m ", vocab[d].word);
- }
- }
- max_f = -1;
- for (b = 0; b < vocab_size; b++) {
- if (target_sums[b] > max_f) {
- max_f = target_sums[b];
- max_target = b;
- }
- }
- printf(" – max sum: %s (%.2f), max resp.: \x1b[1m%s\x1b[0m (%.2f)\n",
- vocab[max_target].word, max_f, vocab[maxmax_target].word,
- maxmax_f);
- for (b = 0; b < N && bestf[b] > -1; b++)
- printf("%-32s %.2f %d\n", vocab[besti[b]].word, bestf[b], bestp[b]);
- printf("\n");
- }
-}
-
-void TrainModel() {
- long a, b, c, d;
- FILE *fo;
- pthread_t *pt = (pthread_t *) malloc(num_threads * sizeof(pthread_t));
- threadPos = malloc(num_threads * sizeof(long long));
- threadIters = malloc(num_threads * sizeof(int));
- char *timebuf = malloc(80);
- printf("Starting training using file %s\n", train_file);
- starting_alpha = alpha;
- if (read_vocab_file[0] != 0)
- ReadVocab();
- else
- LearnVocabFromTrainFile();
- if (save_vocab_file[0] != 0)
- SaveVocab();
- if (output_file[0] == 0)
- return;
- InitNet();
- if (cc > 0)
- ShowCollocations();
- if (negative > 0 || nce > 0)
- InitUnigramTable();
- if (negative_classes_file[0] != 0)
- InitClassUnigramTable();
- start = time(NULL);
- start_clock = clock();
- for (a = 0; a < num_threads; a++)
- pthread_create(&pt[a], NULL, TrainModelThread, (void *) a);
- if(debug_mode > 1)
- pthread_create(&pt[num_threads], NULL, MonitorThread, (void *) a);
- for (a = 0; a < num_threads; a++)
- pthread_join(pt[a], NULL);
- if(debug_mode > 1) {
- pthread_join(pt[num_threads], NULL);
- clock_t now = time(NULL);
- clock_t now_clock = clock();
- printf("\nFinished: %s - user: %lds - real: %lds\n", currentDateTime(timebuf, 0), (now_clock - start_clock) / CLOCKS_PER_SEC, now - start);
- if(type == 5) // don't save vectorsmfor classic collocators
- return;
- printf("Saving vectors to %s ...", output_file);
- fflush(stdout);
- }
- fo = fopen(output_file, "wb");
- if (classes == 0) {
- // Save the word vectors
- fprintf(fo, "%lld %lld\n", vocab_size, layer1_size);
- for (a = 0; a < vocab_size; a++) {
- fprintf(fo, "%s ", vocab[a].word);
- if (binary)
- for (b = 0; b < layer1_size; b++)
- fwrite(&syn0[a * layer1_size + b], sizeof(real), 1, fo);
- else
- for (b = 0; b < layer1_size; b++)
- fprintf(fo, "%lf ", syn0[a * layer1_size + b]);
- fprintf(fo, "\n");
- }
- if(debug_mode > 1)
- fprintf(stderr, "\n");
- } else {
- // Run K-means on the word vectors
- int clcn = classes, iter = 10, closeid;
- int *centcn = (int *) malloc(classes * sizeof(int));
- int *cl = (int *) calloc(vocab_size, sizeof(int));
- real closev, x;
- real *cent = (real *) calloc(classes * layer1_size, sizeof(real));
- for (a = 0; a < vocab_size; a++)
- cl[a] = a % clcn;
- for (a = 0; a < iter; a++) {
- for (b = 0; b < clcn * layer1_size; b++)
- cent[b] = 0;
- for (b = 0; b < clcn; b++)
- centcn[b] = 1;
- for (c = 0; c < vocab_size; c++) {
- for (d = 0; d < layer1_size; d++)
- cent[layer1_size * cl[c] + d] += syn0[c * layer1_size + d];
- centcn[cl[c]]++;
- }
- for (b = 0; b < clcn; b++) {
- closev = 0;
- for (c = 0; c < layer1_size; c++) {
- cent[layer1_size * b + c] /= centcn[b];
- closev += cent[layer1_size * b + c]
- * cent[layer1_size * b + c];
- }
- closev = sqrt(closev);
- for (c = 0; c < layer1_size; c++)
- cent[layer1_size * b + c] /= closev;
- }
- for (c = 0; c < vocab_size; c++) {
- closev = -10;
- closeid = 0;
- for (d = 0; d < clcn; d++) {
- x = 0;
- for (b = 0; b < layer1_size; b++)
- x += cent[layer1_size * d + b]
- * syn0[c * layer1_size + b];
- if (x > closev) {
- closev = x;
- closeid = d;
- }
- }
- cl[c] = closeid;
- }
- }
- // Save the K-means classes
- for (a = 0; a < vocab_size; a++)
- fprintf(fo, "%s %d\n", vocab[a].word, cl[a]);
- free(centcn);
- free(cent);
- free(cl);
- }
- fclose(fo);
- if (save_net_file[0] != 0)
- SaveNet();
-}
-
-int ArgPos(char *str, int argc, char **argv) {
- int a;
- for (a = 1; a < argc; a++)
- if (!strcmp(str, argv[a])) {
- if (a == argc - 1) {
- printf("Argument missing for %s\n", str);
- exit(1);
- }
- return a;
- }
- return -1;
-}
-
-void print_help() {
- printf("WORD VECTOR estimation toolkit v 0.1c\n\n");
- printf("Options:\n");
- printf("Parameters for training:\n");
- printf("\t-train <file>\n");
- printf("\t\tUse text data from <file> to train the model\n");
- printf("\t-output <file>\n");
- printf(
- "\t\tUse <file> to save the resulting word vectors / word clusters\n");
- printf("\t-size <int>\n");
- printf("\t\tSet size of word vectors; default is 100\n");
- printf("\t-window <int>\n");
- printf("\t\tSet max skip length between words; default is 5\n");
- printf("\t-sample <float>\n");
- printf(
- "\t\tSet threshold for occurrence of words. Those that appear with higher frequency in the training data\n");
- printf(
- "\t\twill be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)\n");
- printf("\t-hs <int>\n");
- printf("\t\tUse Hierarchical Softmax; default is 0 (not used)\n");
- printf("\t-negative <int>\n");
- printf(
- "\t\tNumber of negative examples; default is 5, common values are 3 - 10 (0 = not used)\n");
- printf("\t-negative-classes <file>\n");
- printf("\t\tNegative classes to sample from\n");
- printf("\t-nce <int>\n");
- printf(
- "\t\tNumber of negative examples for nce; default is 0, common values are 3 - 10 (0 = not used)\n");
- printf("\t-threads <int>\n");
- printf("\t\tUse <int> threads (default 12)\n");
- printf("\t-iter <int>\n");
- printf("\t\tRun more training iterations (default 5)\n");
- printf("\t-min-count <int>\n");
- printf(
- "\t\tThis will discard words that appear less than <int> times; default is 5\n");
- printf("\t-alpha <float>\n");
- printf(
- "\t\tSet the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW\n");
- printf("\t-classes <int>\n");
- printf(
- "\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
- printf("\t-debug <int>\n");
- printf(
- "\t\tSet the debug mode (default = 2 = more info during training)\n");
- printf("\t-binary <int>\n");
- printf(
- "\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");
- printf("\t-save-vocab <file>\n");
- printf("\t\tThe vocabulary will be saved to <file>\n");
- printf("\t-read-vocab <file>\n");
- printf(
- "\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
- printf("\t-train-counts <int>\n");
- printf(
- "\t\tUse word counts of actual corpus rather than vocabulary counts; default is 1 (on)\n");
- printf("\t-read-net <file>\n");
- printf(
- "\t\tThe net parameters will be read from <file>, not initialized randomly\n");
- printf("\t-save-net <file>\n");
- printf("\t\tThe net parameters will be saved to <file>\n");
- printf("\t-magic-stop-file <file>\n");
- printf("\t\tIf the magic file <file> exists training will stop after the current cycle.\n");
- printf("\t-show-cc <int>\n");
- printf("\t\tShow words with their collocators starting from word rank <int>. Depends on -read-vocab and -read-net.\n");
- printf("\t-type <int>\n");
- printf(
- "\t\tType of embeddings (0 for cbow, 1 for skipngram, 2 for cwindow, 3 for structured skipngram, 4 for senna type, 5 for store positional bigramms)\n");
- printf("\t-cap <int>\n");
- printf(
- "\t\tlimit the parameter values to the range [-50, 50]; default is 0 (off)\n");
- printf("\nExamples:\n");
- printf(
- "./word2vec -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -type 1 -iter 3\n\n");
-}
-
-int main(int argc, char **argv) {
- int i;
- setlocale(LC_ALL, "");
- if (argc == 1) {
- print_help();
- return 0;
- }
- output_file[0] = 0;
- save_vocab_file[0] = 0;
- read_vocab_file[0] = 0;
- save_net_file[0] = 0;
- read_net_file[0] = 0;
- negative_classes_file[0] = 0;
- if ((i = ArgPos((char *) "-h", argc, argv)) > 0) {
- print_help();
- return(0);
- }
- if ((i = ArgPos((char *) "-help", argc, argv)) > 0) {
- print_help();
- return(0);
- }
- if ((i = ArgPos((char *) "-size", argc, argv)) > 0)
- layer1_size = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-train", argc, argv)) > 0)
- strcpy(train_file, argv[i + 1]);
- if ((i = ArgPos((char *) "-save-vocab", argc, argv)) > 0)
- strcpy(save_vocab_file, argv[i + 1]);
- if ((i = ArgPos((char *) "-read-vocab", argc, argv)) > 0)
- strcpy(read_vocab_file, argv[i + 1]);
- if ((i = ArgPos((char *) "-train-counts", argc, argv)) > 0)
- tc = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-save-net", argc, argv)) > 0)
- strcpy(save_net_file, argv[i + 1]);
- if ((i = ArgPos((char *) "-read-net", argc, argv)) > 0)
- strcpy(read_net_file, argv[i + 1]);
- if ((i = ArgPos((char *) "-magic-stop-file", argc, argv)) > 0) {
- strcpy(magic_stop_file, argv[i + 1]);
- if (access(magic_stop_file, F_OK ) != -1) {
- printf("ERROR: magic stop file %s must not exist at start.\n", magic_stop_file);
- exit(1);
- }
- }
- if ((i = ArgPos((char *) "-debug", argc, argv)) > 0)
- debug_mode = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-binary", argc, argv)) > 0)
- binary = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-show-cc", argc, argv)) > 0)
- cc = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-type", argc, argv)) > 0)
- type = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-output", argc, argv)) > 0)
- strcpy(output_file, argv[i + 1]);
- if ((i = ArgPos((char *) "-window", argc, argv)) > 0)
- window = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-sample", argc, argv)) > 0)
- sample = atof(argv[i + 1]);
- if ((i = ArgPos((char *) "-hs", argc, argv)) > 0)
- hs = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-negative", argc, argv)) > 0)
- negative = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-negative-classes", argc, argv)) > 0)
- strcpy(negative_classes_file, argv[i + 1]);
- if ((i = ArgPos((char *) "-nce", argc, argv)) > 0)
- nce = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-threads", argc, argv)) > 0)
- num_threads = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-iter", argc, argv)) > 0)
- iter = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-min-count", argc, argv)) > 0)
- min_count = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-classes", argc, argv)) > 0)
- classes = atoi(argv[i + 1]);
- if ((i = ArgPos((char *) "-cap", argc, argv)) > 0)
- cap = atoi(argv[i + 1]);
- if (type == 0 || type == 2 || type == 4)
- alpha = 0.05;
- if (type==5) {
- sample = 0;
- cdb = open_collocatordb_for_write(output_file);
- }
- if ((i = ArgPos((char *) "-alpha", argc, argv)) > 0)
- alpha = atof(argv[i + 1]);
- vocab = (struct vocab_word *) calloc(vocab_max_size,
- sizeof(struct vocab_word));
- vocab_hash = (int *) calloc(vocab_hash_size, sizeof(int));
- expTable = (real *) malloc((EXP_TABLE_SIZE + 1) * sizeof(real));
- for (i = 0; i < EXP_TABLE_SIZE; i++) {
- expTable[i] = exp((i / (real) EXP_TABLE_SIZE * 2 - 1) * MAX_EXP); // Precompute the exp() table
- expTable[i] = expTable[i] / (expTable[i] + 1); // Precompute f(x) = x / (x + 1)
- }
- SaveArgs(argc, argv);
- TrainModel();
- return 0;
-}
-