Annotated Word2Vec

Word2Vec.c

//  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>

// Maximum Word Length
#define MAX_STRING 100 
// Expotenial Table Size, Provides 1000 Temporary Results
#define EXP_TABLE_SIZE 1000
// From -6 (exp^-6 / (exp^-6 + 1)) to 6 (exp^6 / (exp^6 + 1))
#define MAX_EXP 6
// Maximum Sentence Length
#define MAX_SENTENCE_LENGTH 1000
// Maximum Huffman Code Length
#define MAX_CODE_LENGTH 40

// Hash, Linear Probing, Open Addressing, Fill Factor = 0.7
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; // Word Frequency
  int *point; //Path From Root To The Word in Huffman Tree, Store Every Index of Non-Leaf Node 
  char *word, *code, codelen; //Word, Huffman Code, Code Length

// Definition of Training File, Ouput File
char train_file[MAX_STRING], output_file[MAX_STRING];
// Definition of Output Vocabulary File, Reading Vocabulary File 
char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];

// Deinition of Vocabulary Struct
struct vocab_word *vocab;
// binary 0 -> vectors.bin Output Binary Stream; binary 1 -> Output Text Format
// cbow 1 -> Using Cbow, cbow 0 -> Skip-gram
// debug_mode > 0 -> Print Information When Loading Completed; debug_mode > 1 -> Print Information During Traning Process;
// window Size of Window. Indicate the Maximum scope of 'sum' in Cbow; 'max space between words（w1,w2,p(w1 | w2)）' in Skip-gram
// min_count Number of Minimum Word Frequency
// num_threads Thread Number
// min_reduce Minimum Word Frequency in ReduceVocab, Becauce Hash Table is Limited
int binary = 0, cbow = 0, debug_mode = 2, window = 5, min_count = 5, num_threads = 1, min_reduce = 1;
int *vocab_hash;//Vocabulary Hash; Index: Hash Value; Content: Position of Word in Hash; a[word_hash] = word index in vocab 
// vocab_max_size Maximum Vocabulary Size
// vocab_size Current Vocabulary Size, Extend 1000 per each time When Approximate vocab_max_size
// layer1_size Nodes Number in Hidden Layer
long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100;
// train_words Training Words Number(Accumulation of Word Frequency)
// word_count_actual Trained Words Number
// file_size Traning File Size, Get Through 'ftell'
// classes Number of Classes in Test Process
long long train_words = 0, word_count_actual = 0, file_size = 0, classes = 0;
// alpha Learning Rate in BP Algorithm, Adjust in Training Process
// starting_alpha Strating Alpha Value
// sample Parameter of Subsampling. Subsampling: Decline High-Frequency Word With Probability, Deafault = 0 -> No Subsampling
real alpha = 0.025, starting_alpha, sample = 0;
// syn0 Concatenate word vectors
// syn1 Weight from Hidden Layer to Non-Leaf Node in Huffman Tree in Hierarchical Softmax
// syn1neg Weight from Hidden Layer to Class in Negative Sampling
// expTable Exponential Cashing Table
real *syn0, *syn1, *syn1neg, *expTable;
// start Staring Time, Compute How Many Words Trained Per Second
clock_t start;

// hs Hierarchical Softmax and Negative Sampling Mark
int hs = 1, negative = 0;
// table_size 
// Sampling Table Size
// table 
// table Sampling Table
const int table_size = 1e8;
int *table;

// Generating Sampling Table According Word Frequency
void InitUnigramTable() {
  int a, i;
  long long train_words_pow = 0;
  real d1, power = 0.75; //Probability is Proportional to Word Freq to the power of 'power'
  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;//Probability of First Word
  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;//Process The Last One to Avoid Crossing Boundary
  }
}

// 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;
    // ASCII Value of 8,9,10 and 13 are 'Backspace', 'Tab', 'New Line' and 'Enter'
    if ((ch == ' ') || (ch == '\t') || (ch == '\n')) { //Seperator of Word
        if (ch == '\n') ungetc(ch, fin); //Push Back a Character to Input Stream
        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]; // How Hash compute?
  hash = hash % vocab_hash_size;
  return hash;
}

// Returns position of a word in the vocabulary; if the word is not found, returns -1
// Linear Probing, Open Addressing
int SearchVocab(char *word) {
  unsigned int hash = GetWordHash(word);
  while (1) {
    if (vocab_hash[hash] == -1) return -1; // None error
    if (!strcmp(word, vocab[vocab_hash[hash]].word)) return vocab_hash[hash]; //Return Index in Vacabulary Table
    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 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)); //Store Words
  strcpy(vocab[vocab_size].word, word);
  vocab[vocab_size].cn = 0; // Default Value = 0
  vocab_size++; //Current Words in Vocabulary + 1
  // Reallocate Memory if Needed
  if (vocab_size + 2 >= vocab_max_size) {
    vocab_max_size += 1000; // Increase 1000 Each Time
    vocab = (struct vocab_word *)realloc(vocab, vocab_max_size * sizeof(struct vocab_word));
  }
  hash = GetWordHash(word); // Get Hash Value
  while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size; // Linear Probing
  vocab_hash[hash] = vocab_size - 1; // Record Position in Vocabulary Table
  return vocab_size - 1; // Return Its Position in Vocabulary Table

// 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
  // Reserve 'Enter' at First Position
  qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare); // Quick Sort on Vocabulary Table
  for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1; // Quick Sort Messes Up Hash, Rebuild Later
  size = vocab_size;
  train_words = 0; //  Training Words Number(Accumulation of Word Frequency)
  for (a = 0; a < size; a++) {
    // Words Cccuring Less Than min_count Times Will Be Discarded From the vocab
    if (vocab[a].cn < min_count) { 
      vocab_size--;
      free(vocab[vocab_size].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; // Accumulation of Word Frequency
    }
  }
  vocab = (struct vocab_word *)realloc(vocab, (vocab_size + 1) * sizeof(struct vocab_word)); // Collect Spare Memory
  // 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; // B Remaining Words, Both of Them > min_reduce
  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]; // Assign Code
      point[i] = b; // Assign Code Through Path, Itself First
      i++;
      b = parent_node[b];
      if (b == vocab_size * 2 - 2) break;
    }
    // Notice that Point Is One Layer Deeper Than Code At The Same Posiiton
    vocab[a].codelen = i; // Not Count for Root
    vocab[a].point[0] = vocab_size - 2; // Reverse Order，Assign First One as Root (2*vocab_size - 2 - vocab_size)
    for (b = 0; b < i; b++) { // Reverse Order
      vocab[a].code[i - b - 1] = code[b]; // No Root, Left Child = 0, Right Child = 1
      vocab[a].point[i - b] = point[b] - vocab_size; // Notice Last Element In point Array Is Negative, Is Meaningless
    }
  }
  free(count);
  free(binary);
  free(parent_node);
}

// Loading Training File To Vocabulary Table
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>"); // Append 'Enter' First
  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) { // If Not Exist, Add It to Hash Table
      a = AddWordToVocab(word);
      vocab[a].cn = 1;
    } else vocab[i].cn++; // Otherwise, Word Frequency + 1
    if (vocab_size > vocab_hash_size * 0.7) ReduceVocab(); // Extend Size If Needed
  }
  SortVocab(); // Sort ALl Word According Word Frequeny
  if (debug_mode > 0) { 
    printf("Vocab size: %lld\n", vocab_size);
    printf("Words in train file: %lld\n", train_words);
  }
  file_size = ftell(fin); // File Size
  fclose(fin);
}

// Output Word and Frequency to File
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);
}


// Loading Vocabulary File To Vocabulary Table
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"); // Open File to Get File Size 
  if (fin == NULL) {
    printf("ERROR: training data file not found!\n");
    exit(1);
  }
  fseek(fin, 0, SEEK_END);
  file_size = ftell(fin);
  fclose(fin);
}


// Net Initialzation
void InitNet() {
  long long a, b;
  // posix_memalign() Return Number of 'size' Dynamic Address When Allocate Successful
  // syn0 Word Vectors
  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) { // Hierarchical Softmax
     // Using syn1 in Hierarchical Softmax
    a = posix_memalign((void **)&syn1, 128, (long long)vocab_size * layer1_size * sizeof(real));
    if (syn1 == NULL) {printf("Memory allocation failed\n"); exit(1);}
    for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
     syn1[a * layer1_size + b] = 0;
  }
  if (negative>0) { // Negative Sampling
    // Using syn1neg in Negative Sampling
    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 (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
     syn1neg[a * layer1_size + b] = 0;
  }
  for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
   syn0[a * layer1_size + b] = (rand() / (real)RAND_MAX - 0.5) / layer1_size; // Random Intialization of syn0
  CreateBinaryTree(); // Creating Huffman Tree
}

void *TrainModelThread(void *id) {
  // word Add Word to sen; Represent Current Word When Sentence Completed
  // last_word Prior Word, Used for Window Scan
  // sentence_length Current Sentence Length
  // sentence_position Current Word's Index In Sentence
  long long a, b, d, word, last_word, sentence_length = 0, sentence_position = 0;
  // word_count Trained Word Count
  // sen Sentence Array
  long long word_count = 0, last_word_count = 0, sen[MAX_SENTENCE_LENGTH + 1];
  // l1 Starting Position of word in Concatenated Word Vectors in NS 
  // l2 Starting Position of word in Concatenated Word Vectors in CBOW
  // c Count Mark
  // target Current Sample in NS
  // label urrent Sample's Label in NS
  long long l1, l2, c, target, label;
  // id Receiveing When Create Thread For Random Number Generating
  unsigned long long next_random = (long long)id;
  // f e^x / (1/e^x)，Probability of Current Code = 0 in HS; Probability of label = 1 in NS
  // g error(|f - True Value|) * Learning Rate
  real f, g;
  // Starting Time
  clock_t now;
  real *neu1 = (real *)calloc(layer1_size, sizeof(real)); // Hidden Node
  real *neu1e = (real *)calloc(layer1_size, sizeof(real)); // Error Accumulation
  FILE *fi = fopen(train_file, "rb");
  fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET); // Distribute File to Each Thread
  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)(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)(train_words + 1)); // Adjusting Learning Rate
      if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001; // Lower Bound of Learning Rate
    }
    if (sentence_length == 0) { // If Current Sentence Length = 0
      while (1) {
        word = ReadWordIndex(fi);
        if (feof(fi)) break; // Till End of File
        if (word == -1) continue; // Word Not Exist
        word_count++;
        if (word == 0) break; // is 'Enter'
        // The subsampling randomly discards frequent words while keeping the ranking same
        // Discard High Frequency Words with High Probability And Vice Versa
        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)) break; // If Arrive End of File, Then Exit
    if (word_count > train_words / num_threads) break; // If Complete Assigned Work, Then Exit
    word = sen[sentence_position]; // Pick First Word of Sentence
    if (word == -1) continue; // If Word Not Exist, Then Continue
    // Reset Error Accumulation 
    for (c = 0; c < layer1_size; c++) neu1[c] = 0; 
    for (c = 0; c < layer1_size; c++) neu1e[c] = 0;
    next_random = next_random * (unsigned long long)25214903917 + 11;
    b = next_random % window; // b is Random Number which from 0 to window-1， Representing Acutal Window Size Currently

    // CBOW
    if (cbow) {  //Train the Cbow Architecture
      // in -> hidden
      // Accumulate word vecotr in Current Window to Hidden Node
      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; // Word Not Exist
        for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size];
      }

      // HS
      if (hs) for (d = 0; d < vocab[word].codelen; d++) { 
        f = 0;
        l2 = vocab[word].point[d] * layer1_size; // Node Through Path
        // Propagate hidden -> output
        // Prepare For Computing f
        for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];
        // Discard if Cross Boundries
         // if (f <= -MAX_EXP) continue; 
        // else if (f >= MAX_EXP) continue;
        if (f <= -MAX_EXP) f = 0; 
        else if (f >= MAX_EXP) f = 1; 
        // Find Cash Value From Expoential Table
        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
        // Update Weight Which From Hidden Layer to Non-Leaf Node in Huffman tree
        for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
      }

      // NEGATIVE SAMPLING
      if (negative > 0) for (d = 0; d < negative + 1; d++) {
        if (d == 0) { // Output 1 in Current Class
          target = word;
          label = 1;
        } else { 
          next_random = next_random * (unsigned long long)25214903917 + 11;
          target = table[(next_random >> 16) % table_size];
          if (target == 0) target = next_random % (vocab_size - 1) + 1;
          if (target == word) continue;
          label = 0;
        }
        l2 = target * layer1_size; 
        f = 0;
        for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];
        if (f > MAX_EXP) g = (label - 1) * alpha;
        else if (f < -MAX_EXP) g = (label - 0) * alpha;
        else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
        for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
        for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
      }
      // hidden -> in
      // Update word vectors According Error Accumulation In Hidden Layer
      for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
        c = sentence_position - window + a;
        if (c < 0) continue;
        if (c >= sentence_length) continue;
        last_word = sen[c];
        if (last_word == -1) continue;
        for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c];
      }
    } else {  //train skip-gram
      for (a = b; a < window * 2 + 1 - b; a++) if (a != window) { // Prdict Context Word
        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; 
        // Reset Error Accumulation = 0
        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
          // Dot Product of Predict Word and Non-Leaf Node in Huffman Tree
          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;
          if (f <= -MAX_EXP) f = 0;
          else if (f >= MAX_EXP) f = 1;
          else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
          // 'g' is the gradient multiplied by the learning rate
          g = (1 - vocab[word].code[d] - f) * alpha;
          // Propagate errors output -> hidden
          for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
          // Learn weights hidden -> output
          for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * syn0[c + l1];
        }
        // NEGATIVE SAMPLING
        if (negative > 0) for (d = 0; d < negative + 1; d++) {
          if (d == 0) {
            target = word;
            label = 1;
          } else {
            next_random = next_random * (unsigned long long)25214903917 + 11;
            target = table[(next_random >> 16) % table_size];
            if (target == 0) target = next_random % (vocab_size - 1) + 1;
            if (target == word) continue;
            label = 0;
          }
          l2 = target * layer1_size;
          f = 0;
          for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1neg[c + l2];
          // Same As CBOW
          if (f > MAX_EXP) g = (label - 1) * alpha;
          else if (f < -MAX_EXP) g = (label - 0) * alpha;
          else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
          for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
          for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * syn0[c + l1];
        }
        // Learn weights input -> hidden
        for (c = 0; c < layer1_size; c++) syn0[c + l1] += neu1e[c];
      }
    }
    sentence_position++;
    if (sentence_position >= sentence_length) {
      sentence_length = 0;
      continue;
    }
  }
  fclose(fi);
  free(neu1);
  free(neu1e);
  pthread_exit(NULL);
}

void TrainModel() {
  long a, b, c, d;
  FILE *fo;
  // Create Multiple Thread
  pthread_t *pt = (pthread_t *)malloc(num_threads * sizeof(pthread_t));
  printf("Starting training using file %s\n", train_file);
  starting_alpha = alpha;
  // Load From Vacabulary File First, Or Load From Traning File Otherwise
  if (read_vocab_file[0] != 0) ReadVocab(); else LearnVocabFromTrainFile();
  // Ouput to Vocabulary File with Word And Frequency
  if (save_vocab_file[0] != 0) SaveVocab();
  if (output_file[0] == 0) return;
  InitNet(); // Network Initialization
  if (negative > 0) InitUnigramTable(); // Sampling According Word Frequency
  start = clock(); // Staring 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);

  // Training Done, To Save
  fo = fopen(output_file, "wb");

  if (classes == 0) { // Save word vectors
    // Save the word vectors
    fprintf(fo, "%lld %lld\n", vocab_size, layer1_size); // Dim of vector
    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 0.1b\n\n");
    printf("Options:\n");
    printf("Parameters for training:\n");
    printf("\t-train <file>\n"); // Specify Trainging File
    printf("\t\tUse text data from <file> to train the model\n");
    printf("\t-output <file>\n"); // Specify Vocabulary File
    printf("\t\tUse <file> to save the resulting word vectors / word clusters\n");
    printf("\t-size <int>\n"); // Dim of word vector，Corresponds to layer1_size， Defualt Value = 100
    printf("\t\tSet size of word vectors; default is 100\n"); // Context Window Size
    printf("\t-window <int>\n"); 
    printf("\t\tSet max skip length between words; default is 5\n");
    printf("\t-sample <float>\n"); // Decline Probability in Sub-sampling
    printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency");
    printf(" in the training data will be randomly down-sampled; default is 0 (off), useful value is 1e-5\n");
    printf("\t-hs <int>\n"); // Using HS, Default Value = 1
    printf("\t\tUse Hierarchical Softmax; default is 1 (0 = not used)\n");
    printf("\t-negative <int>\n"); // Sample Numbers in NS
    printf("\t\tNumber of negative examples; default is 0, common values are 5 - 10 (0 = not used)\n");
    printf("\t-threads <int>\n"); // Thread Number
    printf("\t\tUse <int> threads (default 1)\n");
    printf("\t-min-count <int>\n"); // Threshold of Minimum Word Number 
    printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
    printf("\t-alpha <float>\n"); // Starting Learning Rate, Default Value = 0.025
    printf("\t\tSet the starting learning rate; default is 0.025\n");
    printf("\t-classes <int>\n"); // Number of Output Word Class, Default Value = 0 -> No Output
    printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
    printf("\t-debug <int>\n"); // Debug Parameter
    printf("\t\tSet the debug mode (default = 2 = more info during training)\n");
    printf("\t-binary <int>\n"); // binary 0 -> vectors.bin Output Binary Stream; binary 1 -> Output Text Format
    printf("\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");
    printf("\t-save-vocab <file>\n"); // Vocabulary File
    printf("\t\tThe vocabulary will be saved to <file>\n");
    printf("\t-read-vocab <file>\n"); // Vocabulary Loading File
    printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
    printf("\t-cbow <int>\n"); // Using CBOW
    printf("\t\tUse the continuous bag of words model; default is 0 (skip-gram model)\n");
    printf("\nExamples:\n"); // Examples
    printf("./word2vec -train data.txt -output vec.txt -debug 2 -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -cbow 1\n\n");
    return 0;
  }
  // File Name Is Null
  output_file[0] = 0;
  save_vocab_file[0] = 0;
  read_vocab_file[0] = 0;
  if ((i = ArgPos((char *)"-size", argc, argv)) > 0) layer1_size = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(train_file, argv[i + 1]);
  if ((i = ArgPos((char *)"-save-vocab", argc, argv)) > 0) strcpy(save_vocab_file, argv[i + 1]);
  if ((i = ArgPos((char *)"-read-vocab", argc, argv)) > 0) strcpy(read_vocab_file, argv[i + 1]);
  if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-binary", argc, argv)) > 0) binary = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-cbow", argc, argv)) > 0) cbow = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-alpha", argc, argv)) > 0) alpha = atof(argv[i + 1]);
  if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
  if ((i = ArgPos((char *)"-window", argc, argv)) > 0) window = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-sample", argc, argv)) > 0) sample = atof(argv[i + 1]);
  if ((i = ArgPos((char *)"-hs", argc, argv)) > 0) hs = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-negative", argc, argv)) > 0) negative = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-threads", argc, argv)) > 0) num_threads = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
  if ((i = ArgPos((char *)"-classes", argc, argv)) > 0) classes = atoi(argv[i + 1]);

  vocab = (struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word));
  vocab_hash = (int *)calloc(vocab_hash_size, sizeof(int));
  expTable = (real *)malloc((EXP_TABLE_SIZE + 1) * sizeof(real));
  // f Value from e^-6 to e^6 
  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;
}