text: //  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 <Rcpp.h>
  using namespace Rcpp;
  
  #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 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, 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 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 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;
  // }
  
  
  
  // void tmcn_word2vec(std::string train_file0,
  //                    std::string output_file0,
  //                    int binary0,
  //                    int size0)
  // {
  //   int i;
  //   binary = binary0;
  //
  //   strcpy(train_file, train_file0.c_str());
  //   strcpy(output_file, output_file0.c_str());
  //
  //   layer1_size = size0;
  //
  //   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();
  // }
  
  
  //' @useDynLib word2vecr
  // [[Rcpp::export]]
  void CWrapper_word2vec(std::string train_file_,
                         std::string output_file_,
                         int binary_ = 1,
                         int size_ = 100)
  {
    word_count_actual = 0;
    // tmcn_word2vec(train_file, output_file, binary, size);
    int i;
    binary = binary_;
  
    strcpy(train_file, train_file_.c_str());
    strcpy(output_file, output_file_.c_str());
  
    layer1_size = size_;
  
    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();
  }
  
  
  