word2vec

//  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>#define MAX_STRING 100#define EXP_TABLE_SIZE 1000#define MAX_EXP 6#define MAX_SENTENCE_LENGTH 1000#define MAX_CODE_LENGTH 40const int vocab_hash_size = 30000000;  // Maximum 30 * 0.7 = 21M words in the vocabularytypedef float real;                    // Precision of float numbersstruct 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, cbow = 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, *expTable;clock_t start;int hs = 0, negative = 5;const int table_size = 1e8;int *table;void InitUnigramTable() {  int a, i;  double train_words_pow = 0;  double 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) / train_words_pow;  for (a = 0; a < table_size; a++) {    table[a] = i;    if (a / (double)table_size > d1) {      i++;      d1 += pow(vocab[i].cn, power) / train_words_pow;    }    if (i >= vocab_size) i = vocab_size - 1;  }}// Reads a single word from a file, assuming space + tab + EOL to be word boundariesvoid 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 wordint 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 -1int 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 vocabularyint ReadWordIndex(FILE *fin) {  char word[MAX_STRING];  ReadWord(word, fin);  if (feof(fin)) return -1;  return SearchVocab(word);}// Adds a word to the vocabularyint 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 countsint 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 countsvoid 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 tokensvoid 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 codesvoid 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 InitNet() {  long long a, b;  unsigned long long next_random = 1;  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);}    for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)     syn1[a * layer1_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);}    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 < 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;  }  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;  real *neu1 = (real *)calloc(layer1_size, sizeof(real));  real *neu1e = (real *)calloc(layer1_size, 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 < layer1_size; c++) neu1[c] = 0;    for (c = 0; c < layer1_size; c++) neu1e[c] = 0;    next_random = next_random * (unsigned long long)25214903917 + 11;    b = next_random % window;    if (cbow) {  //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];        }        // 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;            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];        }        // 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 {  //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];        }        // 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;            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];        }        // Learn weights input -> hidden        for (c = 0; c < layer1_size; c++) syn0[c + l1] += neu1e[c];      }    }    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) InitUnigramTable();  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-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-cbow <int>\n");    printf("\t\tUse the continuous bag of words model; default is 1 (use 0 for skip-gram model)\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 -cbow 1 -iter 3\n\n");    return 0;  }  output_file[0] = 0;  save_vocab_file[0] = 0;  read_vocab_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 *)"-cbow", argc, argv)) > 0) cbow = atoi(argv[i + 1]);  if (cbow) alpha = 0.05;  if ((i = ArgPos((char *)"-alpha", argc, argv)) > 0) alpha = atof(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 *)"-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]);  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;}