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;
-}
-