[置顶] word2vec源码解析之word2vec.c
word2vec源码解析之word2vec.c
最近研究了一下google的开源项目word2vector，http://code.google.com/p/word2vec/。
其实这玩意算是神经网络在文本挖掘的一项成功应用。
//下面是我对word2vec.c的注释
//详细算法可以参考论文，或者看这篇博客 http://www.cnblogs.com/downtjs/p/3784440.html
// 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;//词频
int *point;//huffman编码对应内节点的路径
char *word, *code, codelen;//huffman编码
};
char train_file[MAX_STRING], output_file[MAX_STRING];
char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
struct vocab_word *vocab;
int binary = 0, cbow = 0, debug_mode = 2, window = 5, min_count = 5, num_threads = 1, 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, 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;
//每个单词的能量分布表，table在负样本抽样中用到
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++) //遍历词汇表，统计词的能量总值train_words_pow，指数power应该是缩小值的吧。
train_words_pow += pow(vocab[a].cn, power);
i = 0;
d1 = pow(vocab[i].cn, power) / (real)train_words_pow;//表示已遍历的词的能量值占总能力值的比例
for (a = 0; a < table_size; a++)//遍历table。a表示table的位置，i表示词汇表的位置
{
table[a] = i;//单词i占用table的a位置
//table反映的是一个单词能量的分布，一个单词能量越大，所占用的table的位置越多
if (a / (real)table_size > d1)
{
i++;//移动到下一个词
d1 += pow(vocab[i].cn, power) / (real)train_words_pow;
}
if (i >= vocab_siInitNetze) i = vocab_size - 1;
}
}
// Reads a single word from a file, assuming space + tab + EOL to be word boundaries
//从文件中读取一个词
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返回一个词的hash值，一个词跟hash值一一对应（可能冲突）
int GetWordHash(char *word)
{
unsigned long long a, hash = 0;
for (a = 0; a < strlen(word); a++)
hash = hash * 257 + word[a];//采取257进制
hash = hash % vocab_hash_size;
return hash;
}
// Returns position of a word in the vocabulary; if the word is not found, returns -1
// 返回一个词在词汇表中的位置，如果不存在则返回-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
// 从文件流中读取一个词，并返回这个词在词汇表中的位置
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 将一个词添加到一个词汇中
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)//如果hash值冲突了
hash = (hash + 1) % vocab_hash_size;//使用开放地址法解决冲突
vocab_hash[hash] = vocab_size - 1;//由词的hash值找到她所在词汇表的排序位置
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
// 根据词频排序
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
//出现太少的词直接丢弃
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
// 重新计算hash查找。vocab_hash是由hash值找到该词所在位置
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
// 再次移除词频过小的词，缩减词汇表
void ReduceVocab()
{
int a, b = 0;
unsigned int hash;
for (a = 0; a < vocab_size; a++)//我草，这很容易看错啊
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根据词频创建huffman树
// Frequent words will have short uniqe binary codes词频越大的单词有越短的huffman编码
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' 找出目前权值最小的两个节点
if (pos1 >= 0)//第一个权值最小的节点
{
if (count[pos1] < count[pos2])
{
min1i = pos1;
pos1--;
}
else
{
min1i = pos2;
pos2++;
}
}
else
{
min1i = pos2;
pos2++;
}
if (pos1 >= 0)//第二个权值最小的节点
{
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;//节点编码为1，之前默认是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];
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);
}
//从分词文件中统计每个单词的词频
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);//返回该词在词汇表中的位置
if (i == -1)//该词之前不存在
{
a = AddWordToVocab(word);//把该词添加到词汇表中
vocab[a].cn = 1;
}
else vocab[i].cn++;//更新词频
if (vocab_size > vocab_hash_size * 0.7)//如果词汇表太庞大，就缩减
ReduceVocab();
}
SortVocab();//根据词频排序词汇表
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);
}
//从文件读取词汇，该文件已经统计好了每个词汇的词频
void ReadVocab()
{
long long a, i = 0;
char c;
char word[MAX_STRING];
FILE *fin = fopen(read_vocab_file, "rb");//打开词汇文件
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);//从fin进入一个词到word中
if (feof(fin)) break;
a = AddWordToVocab(word);//把该词添加到词汇中，并返回该词的位置
fscanf(fin, "%lld%c", &vocab[a].cn, &c);//读取词频？c是干啥的吗，读取空格吗
i++;
}
SortVocab();//根据词频排序
if (debug_mode > 0)
{
printf("Vocab size: %lld\n", vocab_size);
printf("Words in train file: %lld\n", train_words);
}
//读取训练数据
fin = fopen(train_file, "rb");
if (fin == NULL)
{
printf("ERROR: training data file not found!\n");
exit(1);
}
fseek(fin, 0, SEEK_END);
file_size = ftell(fin);
fclose(fin);
}
void InitNet()
{
long long a, b;
a = posix_memalign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));
//先知道这个也是申请动态数组，对齐还有128这个参数以后再了解
if (syn0 == NULL)
{
printf("Memory allocation failed\n"); exit(1);
}
if (hs)//采用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)//还有负样本
{
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;
CreateBinaryTree();//建立huffman树，对每个单词进行编码
}
//这个线程函数执行之前，已经做好了一些工作：根据词频排序的词汇表，每个单词的huffman编码
void *TrainModelThread(void *id)
{
long long a, b, d, 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;
unsigned long long next_random = (long long)id;
real f, g;
clock_t now;
real *neu1 = (real *)calloc(layer1_size, sizeof(real));
real *neu1e = (real *)calloc(layer1_size, sizeof(real));
FILE *fi = fopen(train_file, "rb");
//每个线程对应一段文本。根据线程id找到自己负责的文本的初始位置
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)(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)
{
while (1)
{
word = ReadWordIndex(fi);//从文件流中读取一个词，并返回这个词在词汇表中的位置
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)//对高频词进行下采样，不过要保持排序不变。
{
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++;
//1000个单词视作一个句子？
if (sentence_length >= MAX_SENTENCE_LENGTH) break;
}
sentence_position = 0;
}
if (feof(fi)) break;
if (word_count > train_words / num_threads) break;//如果当前线程已处理的单词超过了 阈值，则退出。
word = sen[sentence_position];
if (word == -1) continue;
for (c = 0; c < layer1_size; c++) neu1[c] = 0;
for (c = 0; c < layer1_size; c++) neu1e[c] = 0;
next_random = next_random * (unsigned long long)25214903917 + 11;
b = next_random % window;
if (cbow)
{ //train the cbow architecture
// in -> hidden
for (a = b; a < window * 2 + 1 - b; a++) if (a != window)//扫描目标单词的左右几个单词
{
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++)//layer1_size词向量的维度，默认值是100
neu1[c] += syn0[c + last_word * layer1_size];//传说中的向量和？
}
if (hs) for (d = 0; d < vocab[word].codelen; d++)//开始遍历huffman树，每次一个节点
{
f = 0;
l2 = vocab[word].point[d] * layer1_size;//point应该记录的是huffman的路径。找到当前节点，并算出偏移
// Propagate hidden -> output
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];//计算内积
if (f <= -MAX_EXP) continue;//内积不在范围内直接丢弃
else if (f >= MAX_EXP) continue;
else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];//内积之后sigmoid函数
// 'g' is the gradient multiplied by the learning rate
g = (1 - vocab[word].code[d] - f) * alpha;//偏导数的一部分
//layer1_size是向量的维度
// Propagate errors output -> hidden 反向传播误差，从huffman树传到隐藏层。下面就是把当前内节点的误差传播给隐藏层，syn1[c + l2]是偏导数的一部分。
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
// Learn weights hidden -> output 更新当前内节点的向量，后面的neu1[c]其实是偏导数的一部分
for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
}
// NEGATIVE SAMPLING
if (negative > 0)
for (d = 0; d < negative + 1; d++)
{
if (d == 0)
{
target = word;//目标单词
label = 1;//正样本
}
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;//负样本
}
l2 = target * layer1_size;
f = 0;
for (c = 0; c < layer1_size; c++)
f += neu1[c] * syn1neg[c + l2];//内积
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];//隐藏层的误差
for (c = 0; c < layer1_size; c++)
syn1neg[c + l2] += g * neu1[c];//更新负样本向量
}
// hidden -> in
for (a = b; a < window * 2 + 1 - b; a++)
if (a != window)//cbow模型 更新的不是中间词语的向量，而是周围几个词语的向量。
{
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];//更新词向量
}
}
else
{ //train skip-gram
for (a = b; a < window * 2 + 1 - b; a++)
if (a != window)//扫描周围几个词语
{
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++)//遍历叶子节点
{
f = 0;
l2 = vocab[word].point[d] * layer1_size;//point记录的是huffman的路径
// Propagate hidden -> output 感觉源代码这个英语注释有点误导人，这里的隐藏层就是输入层，就是词向量。
for (c = 0; c < layer1_size; c++)
f += syn0[c + l1] * syn1[c + l2];//计算两个词向量的内积
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;//偏导数的一部分
// Propagate errors output -> hidden
for (c = 0; c < layer1_size; c++)
neu1e[c] += g * syn1[c + l2];//隐藏层的误差
// Learn weights hidden -> output
for (c = 0; c < layer1_size; c++)
syn1[c + l2] += g * syn0[c + l1];//更新叶子节点向量
}
// NEGATIVE SAMPLING
if (negative > 0)//这个同cobow差不多
for (d = 0; d < negative + 1; d++)
{
if (d == 0)
{
target = word;
label = 1;
}
else
{
next_random = next_random * (unsigned long long)25214903917 + 11;
target = table[(next_random >> 16) % table_size];
if (target == 0) target = next_random % (vocab_size - 1) + 1;
if (target == word) continue;
label = 0;
}
l2 = target * layer1_size;
f = 0;
for (c = 0; c < layer1_size; c++)
f += syn0[c + l1] * syn1neg[c + l2];
if (f > MAX_EXP) g = (label - 1) * alpha;
else if (f < -MAX_EXP)
g = (label - 0) * alpha;
else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
for (c = 0; c < layer1_size; c++)
neu1e[c] += g * syn1neg[c + l2];
for (c = 0; c < layer1_size; c++)
syn1neg[c + l2] += g * syn0[c + l1];
}
// Learn weights input -> hidden
for (c = 0; c < layer1_size; c++)
syn0[c + l1] += neu1e[c];//更新周围几个词语的向量
}
}
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();//从文件读入词汇
else
LearnVocabFromTrainFile();//从训练文件学习词汇
if (save_vocab_file[0] != 0)
SaveVocab();//保存词汇
if (output_file[0] == 0)
return;
InitNet();
if (negative > 0) InitUnigramTable();
start = clock();
for (a = 0; a < num_threads; a++) pthread_create(&pt[a], NULL, TrainModelThread, (void *)a);
for (a = 0; a < num_threads; a++) pthread_join(pt[a], NULL);
fo = fopen(output_file, "wb");
if (classes == 0) //不需要聚类，只需要输出词向量
{
// 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 //使用k-means进行聚类
{
// Run K-means on the word vectors
int clcn = classes, iter = 10, closeid;
int *centcn = (int *)malloc(classes * sizeof(int));//该类别的数量
int *cl = (int *)calloc(vocab_size, sizeof(int));//词到类别的映射
real closev, x;
real *cent = (real *)calloc(classes * layer1_size, sizeof(real));//质心数组
for (a = 0; a < vocab_size; a++)
cl[a] = a % clcn;//任意分类？
for (a = 0; a < iter; a++)
{
for (b = 0; b < clcn * layer1_size; b++)
cent[b] = 0;//质心清零
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];//求和放到质心数组中
centcn[cl[c]]++;//类别数量加1
}
for (b = 0; b < clcn; b++)//遍历所有类别
{
closev = 0;
for (c = 0; c < layer1_size; c++)
{
cent[layer1_size * b + c] /= centcn[b];//均值，就是求新的质心
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;//对质心进行归一化？
}
for (c = 0; c < vocab_size; c++)//对所有词语重新分类
{
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];//内积
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");
//输入文件：已分词的语料
printf("\t-train <file>\n");
printf("\t\tUse text data from <file> to train the model\n");
//输出文件：词向量或者词聚类
printf("\t-output <file>\n");
printf("\t\tUse <file> to save the resulting word vectors / word clusters\n");
//词向量的维度，默认值是100
printf("\t-size <int>\n");
printf("\t\tSet size of word vectors; default is 100\n");
//窗口大小，默认是5
printf("\t-window <int>\n");
printf("\t\tSet max skip length between words; default is 5\n");
//设定词出现频率的阈值，对于常出现的词会被随机下采样
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");
//是否采用softmax体系
printf("\t-hs <int>\n");
printf("\t\tUse Hierarchical Softmax; default is 1 (0 = not used)\n");
//负样本的数量，默认是0，通常使用5-10。0表示不使用。
printf("\t-negative <int>\n");
printf("\t\tNumber of negative examples; default is 0, common values are 5 - 10 (0 = not used)\n");
//开启的线程数量
printf("\t-threads <int>\n");
printf("\t\tUse <int> threads (default 1)\n");
//最小阈值。对于出现次数少于该值的词，会被抛弃掉。
printf("\t-min-count <int>\n");
printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
//学习速率初始值，默认是0.025
printf("\t-alpha <float>\n");
printf("\t\tSet the starting learning rate; default is 0.025\n");
//输出词类别，而不是词向量
printf("\t-classes <int>\n");
printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
//debug模式，默认是2，表示在训练过程中会输出更多信息
printf("\t-debug <int>\n");
printf("\t\tSet the debug mode (default = 2 = more info during training)\n");
//是否用binary模式保存数据，默认是0，表示否。
printf("\t-binary <int>\n");
printf("\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");
//保存词汇到这个文件
printf("\t-save-vocab <file>\n");
printf("\t\tThe vocabulary will be saved to <file>\n");
//词汇从该文件读取，而不是由训练数据重组
printf("\t-read-vocab <file>\n");
printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
//是否采用continuous bag of words算法。默认是0，表示采用另一个叫skip-gram的算法。
printf("\t-cbow <int>\n");
printf("\t\tUse the continuous bag of words model; default is 0 (skip-gram model)\n");
//工具使用样例
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, 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 -500)/ 500 * 6) 即 e^-6 ~ e^6
expTable[i] = exp((i / (real)EXP_TABLE_SIZE * 2 - 1) * MAX_EXP); // Precompute the exp() table
//expTable[i] = 1/(1+e^6) ~ 1/(1+e^-6)即 0.01 ~ 1 的样子
expTable[i] = expTable[i] / (expTable[i] + 1); // Precompute f(x) = x / (x + 1)
}
TrainModel();
return 0;
}
本文作者：linger
本文链接：http://blog.csdn.net/lingerlanlan/article/details/38232755