// Copyright 2013 Google Inc. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <pthread.h>
#define MAX_STRING 100
#define EXP_TABLE_SIZE 1000
#define MAX_EXP 6
#define MAX_SENTENCE_LENGTH 1000
#define MAX_CODE_LENGTH 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;// The frequence of the word occurance
int *point; // Parent nodes sequence in up-to-down order, the index of root node is vocab_size*2-2
char *word, *code, codelen;// Code means is 0-1 sequence in up-to-down order
};
char train_file[MAX_STRING], output_file[MAX_STRING];
char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
struct vocab_word *vocab; // sorted vocabulary
int binary = 0, cbow = 0, debug_mode = 2, window = 5, min_count = 5, num_threads = 1, min_reduce = 1;
int *vocab_hash; // this is used to find the index in uppper vocab array according the hash
long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100; // layer1_size mean hidden layer neurons number
long long train_words = 0, word_count_actual = 0, file_size = 0, classes = 0;
real alpha = 0.025, starting_alpha, sample = 0;
real *syn0, *syn1, *syn1neg, *expTable;
clock_t start;
int hs = 1, negative = 0;
const int table_size = 1e8;
int *table;
void InitUnigramTable() // the computed table is used for negative sampling
{
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;
// If train_words_pow is quite large, some low frequency words will be ignored
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;
}
}
// 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 index of a word in the vocabulary, not hash; 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) // initial vocab_max_size is 1000
{
vocab_max_size += 1000;
vocab = (struct vocab_word *)realloc(vocab, vocab_max_size * sizeof(struct vocab_word));
}
hash = GetWordHash(word);
// if hash collides
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, and re-compute the hash
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)
{
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;
}
}
// Allocate memory for the binary tree construction
vocab = (struct vocab_word *)realloc(vocab, (vocab_size + 1) * sizeof(struct vocab_word));
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;
for (a = 0; a < vocab_size - 1; a++) // Following algorithm constructs the Huffman tree by adding one node at a time
{
// 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 of min1i and min2i is node vocab_size+a
parent_node[min2i] = vocab_size + a;
binary[min2i] = 1; // binary[min1i] = 0
}
// 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]; // binary[0] is the code of the leaf node
point[i] = b; // point[0] is the index of the leaf node itself
i++;
b = parent_node[b]; // Get the parent node index of node b
if (b == vocab_size * 2 - 2) break; // The root node index vocab_size*2-2
}
vocab[a].codelen = i;
vocab[a].point[0] = vocab_size - 2; // The index of root node is vocab_size*2-2, here we can see that the point value parent_node-vocab_size
for (b = 0; b < i; b++)
{
vocab[a].code[i - b - 1] = code[b]; // code is the binary code of the word in huffman tree
vocab[a].point[i - b] = point[b] - vocab_size; // this is tricky
}
}
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; // here vocab_hash_size is 30000000
fin = fopen(train_file, "rb");
if (fin == NULL)
{
printf("ERROR: training data file not found!\n");
exit(1);
}
vocab_size = 0;
AddWordToVocab((char *)"</s>"); // this is the first word in vocab
while (1)
{
ReadWord(word, fin);
if (feof(fin)) break;
train_words++;
if ((debug_mode > 1) && (train_words % 100000 == 0)) // output the progress
{
printf("%lldK%c", train_words / 1000, 13);
fflush(stdout);
}
i = SearchVocab(word);
if (i == -1) // it's a new word
{
a = AddWordToVocab(word);// add word to the vocab and return the sequence id since first word added
vocab[a].cn = 1;
}
else vocab[i].cn++;
if (vocab_size > vocab_hash_size * 0.7) ReduceVocab(); // remove some low frequency words
}
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);
}
// read vocab processed before
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); // then the hash address is returned
fscanf(fin, "%lld%c", &vocab[a].cn, &c);
i++;
}
SortVocab();// sort the vocab with respect to the word frequency
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);
}
// initial connection weights, syn1 and syn1neg set to 0 and syn0 ranging from -0.5 to 0.5
void InitConnectionWeights()
{
long long a, b;
a = posix_memalign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn0 == NULL)
{
printf("Memory allocation failed\n"); exit(1);
}
if (hs)
{
a = posix_memalign((void **)&syn1, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn1 == NULL)
{
printf("Memory allocation failed\n"); exit(1);
}
for (b = 0; b < layer1_size; b++)
for (a = 0; a < vocab_size; a++)
syn1[a * layer1_size + b] = 0;
}
if (negative>0)
{
a = posix_memalign((void **)&syn1neg, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn1neg == NULL)
{
printf("Memory allocation failed\n"); exit(1);
}
for (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; // ranging from -0.5 to 0.5, the mean is 0
}
// Before this step, all the vocabs are sorted by frequency and encoded with Huffman tree, the connection weights are also initialed
void *TrainModelThread(void *id)
{
long long a, b, d; // counters
long long word; // the target word
long long last_word; // words surrounds the target word
long long sentence_length = 0, sentence_position = 0; //
long long word_count = 0; // number of words have read from train file
long long last_word_count = 0; // before this, the number of words read from train file
long long sen[MAX_SENTENCE_LENGTH + 1]; // read 1000 words from file and only one processed each round
long long l1, l2, c, target, label; // explained latter
unsigned long long next_random = (long long)id, multiplier=25214903917; // initially, next_random is the id of worker thread, search wiki with keyword LCG for more info
real f, g; // explained latter
clock_t now;
real *neu1 = (real *)calloc(layer1_size, sizeof(real)); // this is the hidden layer
real *neu1e = (real *)calloc(layer1_size, sizeof(real));
FILE *fi = fopen(train_file, "rb");
fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
while (1) // Each round, only one word is trained, read 1000 words if previous words are all trained.
{
if (word_count - last_word_count > 10000) // print the progress
{
word_count_actual += word_count - last_word_count; // Some frequent words are discarded, however, which need to be counted as well.
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));
if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;
}
if (sentence_length == 0) // read MAX_SENTENCE_LENGTH = 1000 words from file, sub-sampling is applied
{
while (1)
{
word = ReadWordIndex(fi);
if (feof(fi)) break;
if (word == -1) continue;
word_count++; // including the subsampled words
if (word == 0) break;
// The sub-sampling randomly discards frequent words while keeping the ranking same
if (sample > 0)
{
// ran = (0.01*sqrt(x)+0.0001)/x, x = vocab[word].cn/train_words, x = 0.0001, ran -> 0.4, x = 1, ran - > 0.0101
real ran = (sqrt(vocab[word].cn / (sample * train_words)) + 1) * (sample * train_words) / vocab[word].cn;
next_random = next_random * multiplier + 11;
if (ran < (next_random & 0xFFFF) / (real)65536) continue; // if x is too large, ran will be small, there are good chances the word will be ignored
}
sen[sentence_length] = word;
sentence_length++; // exclude the subsampled words
if (sentence_length >= MAX_SENTENCE_LENGTH) break; // MAX_SENTENCE_LENGTH = 1000
}
sentence_position = 0; // pointer of the sentence
}
if (feof(fi)) break;
if (word_count > train_words / num_threads) break; // if the thread has done trainning the words assigned to it, will exits.
word = sen[sentence_position]; // get the target word
if (word == -1) continue;
for (c = 0; c < layer1_size; c++) neu1[c] = 0; // initial the hidden layer
for (c = 0; c < layer1_size; c++) neu1e[c] = 0; // initial the back-propagation error
next_random = next_random * multiplier + 11;
b = next_random % window; // so the window will be dynamically changed for each word
if (cbow) //train the cbow architecture of a single word sen[sentence_position]
{
// compute the hidden layer
for (a = b; a < window * 2 + 1 - b; a++)
{
if (a != window)// scan the words around it, itself excluded
{
// sentence_position < c < sentence_position+window+1-b, c != sentence_position, if c=sentence_position, a=window
// If sentence_position < b, only 'sentence_position' words will be scanned
c = sentence_position - window + a;
if (c < 0) continue;
// If sentence_position > sentence_length - b, only sentence_length - sentence_position words will be scanned
if (c >= sentence_length) continue;
last_word = sen[c];
if (last_word == -1) continue; // if last word not exists in vocab, then continue
for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size]; // hidden layer not squashed
// syn0 is vocab_size*layer1_size matrix, last_word is the index of last word, it is greater than 0 and less than vocab_size
// neu1 matrix is the result of vector_neighbors.dot(syn0), where neighbors_vector is a 1*vocab_size vector,
// and the column value is 1 if it's the neighbor of sen[sentence_position], otherwise 0, same as one-hot presentation
// Given that syn0 is a matrix of vocab_size*layer1_size, neu1 is all the sum of syn0[nearby_words * layer_size],
// which means that all the neighbor word vectors are summed up and become one vector
}
}
if (hs) // Train with correct result, hidden -> output
{
for (d = 0; d < vocab[word].codelen; d++)// iterate all the parent nodes of word in up-to-down order, first node is the root node
{
f = 0;
l2 = vocab[word].point[d] * layer1_size; // find the predicted label in output layer by index 'vocab[word].point[d]'
// matrix dot production neu1.dot(syn1[l2]), syn1[l2] means l2 row of matrix syn1
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];
// if uppper statement expressed as output = neu1.dot(syn1), f = output[vocab[word].point[d]]
// if -inft --> f --> 0, then 0 --> xx --> 500, then 0 --> f' --> 1/2, otherwise 0 --> f --> +inft, then 1/2 --> f' --> 1
if (f <= -MAX_EXP || f >= MAX_EXP) continue;
else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]; // xx = 500 + f*500/MAX_EXP, f=x/(1+x), processed by activation function
// 'g' is the gradient multiplied by the learning rate, and 1 -vocal[word].code[d] - f can be considered as the gradient since they are much close
// if code is 0, f are expected to be 1 and thus code 0 means positive, I mean if code is 0, predicted label should be 1
// if true label = 1 which mean 1 - vocab[word].code[d] = 1, when f < 1, means g > 0, then syn1[c + l2] increased, and vice versa.
g = (1 - vocab[word].code[d] - f) * alpha;
// Learn weights hidden -> output, use hidden layer neu1 update connection weights syn1
// If vocab[word].code[d]=1 and f = 1, g < 0, means that f is too large, and how to reduce f? make neu1.dot(syn1[l2]) less,
// so syn1[l2] = syn1[l2] + g*neu1 will help?, yes, of course, here f = neu1.dot(syn1[l2]+g*neu1)=neu1.dot(syn1[l2])+g*neu1.dot(neu1),
// if g < 0, f is decreased, and vice versa.
for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
// Propagate errors output -> hidden, this is used to update connection weights syn0
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
}
}
if (negative > 0) // Consider negative to be 5, train with error results
{
for (d = 0; d < negative + 1; d++)
{
if (d == 0)
{
target = word;
label = 1; // the value can be whatever you like
}else
{
next_random = next_random * multiplier + 11;
target = table[(next_random >> 16) % table_size]; // table_size is 1e8
if (target == 0) target = next_random % (vocab_size - 1) + 1; // target 0 is '<s>'
if (target == word) continue;
label = 0; // the value can be whatever you like
}
l2 = target * layer1_size;
f = 0;
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];
// the prediction label is expected to be 0 as we define above
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];
}
}
for (a = b; a < window * 2 + 1 - b; a++)
{
if (a != window) // not the target word itself
{
c = sentence_position - window + a;
if (c < 0) continue;
if (c >= sentence_length) continue;
last_word = sen[c];// Get a word around the specific word
if (last_word == -1) continue;
for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c]; // mini-batch gradient descent
}
}
}
else //train skip-gram
{
for (a = b; a < window * 2 + 1 - b; a++)
{
if (a != window)
{
c = sentence_position - window + a;
if (c < 0) continue;
if (c >= sentence_length) continue;
last_word = sen[c];
if (last_word == -1) continue;
l1 = last_word * layer1_size; // syn0[l1] is the weights for word 'last_word'
for (c = 0; c < layer1_size; c++) neu1e[c] = 0;
if (hs)
{
for (d = 0; d < vocab[word].codelen; d++)
{
f = 0;
l2 = vocab[word].point[d] * layer1_size; // syn1[l2] is the weights for word vocab[word].point[d], the parent of 'last_word'
for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1[c + l2]; // Only one word given, so input is a vector likes [0,1,0,0,0,...,0]
// processed by activation function
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 = (1 - vocab[word].code[d] - f) * alpha; // 'g' is the gradient multiplied by the learning rate
// 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 (negative > 0)
{
for (d = 0; d < negative + 1; d++)
{
if (d == 0)
{
target = word;
label = 1;
}
else
{
next_random = next_random * multiplier + 11;
target = table[(next_random >> 16) % table_size];
if (target == 0) target = next_random % (vocab_size - 1) + 1;
if (target == word) continue;
label = 0;
}
l2 = target * layer1_size;
f = 0;
for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1neg[c + l2];
if (f > MAX_EXP) g = (label - 1) * alpha;
else if (f < -MAX_EXP) g = (label - 0) * alpha;
else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * syn0[c + l1];
}
}
// Learn weights input -> hidden
for (c = 0; c < layer1_size; c++) syn0[c + l1] += neu1e[c];
}
}
}
sentence_position++;
if (sentence_position >= sentence_length)
{
sentence_length = 0;
}
}
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;
// Below is quite important.
CreateBinaryTree(); // build the huffman tree of the vocab
InitConnectionWeights(); // initial neural network connection weights
if (negative > 0) InitUnigramTable(); // the computed table is used for negative sampling, frequent words will be chosen
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) // output directly
{
// 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 // clustering using k-means
{
// Run K-means on the word vectors
int clcn = classes, iter = 10, closeid; // What does classes stand for? Maybe divide into 'classes' clusters.
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; // At first assign each word to random cluster
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");
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");
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");
printf("\t\tUse Hierarchical Softmax; default is 1 (0 = not used)\n");
printf("\t-negative <int>\n");
printf("\t\tNumber of negative examples; default is 0, common values are 5 - 10 (0 = not used)\n");
printf("\t-threads <int>\n");
printf("\t\tUse <int> threads (default 1)\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\n");
printf("\t-classes <int>\n");
printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
printf("\t-debug <int>\n");
printf("\t\tSet the debug mode (default = 2 = more info during training)\n");
printf("\t-binary <int>\n");
printf("\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");
printf("\t-save-vocab <file>\n");
printf("\t\tThe vocabulary will be saved to <file>\n");
printf("\t-read-vocab <file>\n");
printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
printf("\t-cbow <int>\n");
printf("\t\tUse the continuous bag of words model; default is 0 (skip-gram model)\n");
printf("\nExamples:\n");
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;
}
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, si
// Precompute f(x) = x / (x + 1), f(x) = 1 / (1/x + 1)zeof(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++) // EXP_TABLE_SIZE=1000 and MAX_EXP is 6
{
//expTable[i] = exp((i -500)/ 500 * 6), ranging e^-6 ~ e^6
expTable[i] = exp((i / (real)EXP_TABLE_SIZE * 2 - 1) * MAX_EXP);
// Precompute the exp() table, if 0 --> i --> 500, -6 --> exp --> 0, 0 --> x --> 1/2, then f(x) --> 0
// otherwise, if 500 --> i --> 1000, then 1/2 --> f(x) --> 1
expTable[i] = expTable[i] / (expTable[i] + 1);
}
TrainModel();
return 0;
}