// main.c

// word2vec

//

// Created by suhui on 15/3/20.

// Copyright (c) 2015年 suhui. All rights reserved.

//

#include <stdio.h>

#include <stdlib.h>

#include <string.h>

#include <math.h>

#include <pthread.h>

// 一个word的最大长度

#define MAX_STRING 100

// 对f的运算结果进行缓存，存储1000个，需要用的时候查表

#define EXP_TABLE_SIZE 1000

// 最大计算到6 (exp^6 / (exp^6 + 1))，最小计算到-6 (exp^-6 / (exp^-6 + 1))

#define MAX_EXP 6

// 定义最大的句子长度

#define MAX_SENTENCE_LENGTH 1000

// 定义最长的霍夫曼编码长度

#define MAX_CODE_LENGTH 40

// 哈希，线性探测，开放定址法，装填系数0.7

const int vocab_hash_size = 30000000;

//重命名浮点数

typedef float real;

struct vocab_word {

};

// 训练文件、输出文件名称定义

char train_file[MAX_STRING], output_file[MAX_STRING];

// 词汇表输出文件和词汇表读入文件名称定义

char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];

// 声明词汇表结构体

struct vocab_word *vocab;

// binary 0则vectors.bin输出为二进制（默认），1则为文本形式

// cbow 1使用cbow框架，0使用skip-gram框架

// debug_mode 大于0，加载完毕后输出汇总信息，大于1，加载训练词汇的时候输出信息，训练过程中输出信息

// window 窗口大小，在cbow中表示了word vector的最大的sum范围，在skip-gram中表示了max space between words（w1,w2,p(w1 | w2)）

// min_count 删除长尾词的词频标准

// num_threads 线程数

// min_reduce ReduceVocab删除词频小于这个值的词，因为哈希表总共可以装填的词汇数是有限的

int binary = 0, cbow = 0, debug_mode = 2, window = 5, min_count = 5, num_threads = 1, min_reduce = 1;

int *vocab_hash; // 词汇表的hash存储，下标是词的hash，内容是词在vocab中的位置，a[word_hash] = word index in vocab

// vocab_max_size 词汇表的最大长度，可以扩增，每次扩1000

// vocab_size 词汇表的现有长度，接近vocab_max_size的时候会扩容

// layer1_size 隐层的节点数

long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100;

// train_words 训练的单词总数（词频累加）

// word_count_actual 已经训练完的word个数

// file_size 训练文件大小，ftell得到

// classes 输出word clusters的类别数

long long train_words = 0, word_count_actual = 0, file_size = 0, classes = 0;

// alpha BP算法的学习速率，过程中自动调整

// starting_alpha 初始alpha值

// sample 亚采样概率的参数，亚采样的目的是以一定概率拒绝高频词，使得低频词有更多出镜率，默认为0，即不进行亚采样

real alpha = 0.025, starting_alpha, sample = 0;

// syn0 单词的向量输入 concatenate word vectors

// syn1 hs(hierarchical softmax)算法中隐层节点到霍夫曼编码树非叶结点的映射权重

// syn1neg ns(negative sampling)中隐层节点到分类问题的映射权重

// expTable 预先存储f函数结果，算法执行中查表

real *syn0, *syn1, *syn1neg, *expTable;

// start 算法运行的起始时间，会用于计算平均每秒钟处理多少词

clock_t start;

// hs 采用hs还是ns的标志位，默认采用hs

int hs = 1, negative = 0;

// table_size 静态采样表的规模

// table 采样表

const int table_size = 1e8;

int *table;

// 根据词频生成采样表

void InitUnigramTable() {

int a, i;

long long train_words_pow = 0;

real d1, power = 0.75; // 概率与词频的power次方成正比

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; // 第一个词出现的概率

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; // 处理最后一段概率，防止越界

}

}

//从文件file中每次读取一个单词

void ReadWord(char * word, FILE * file){

int a = 0, ch;

while (!feof(file)) {

ch = fgetc(file);

if (ch == 13) continue; //读下一行 \r

if ((ch == ' ') || (ch == '\t') || (ch == '\n') ) {

if(a > 0){

if (ch == '\n') ungetc(ch, file);// 把一个字符回退到输入流中

break;

}

if (ch == '\n') {

strcpy(word, (char *)"</s>");

return;

}else continue;

}

word[a] = ch;

a++;

if (a >= MAX_STRING - 1) a--;

}

word[a] = 0;

}

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;

}

int AddWordToVocab(char * word){

unsigned int hash, lengh = strlen(word) + 1;

if (lengh > MAX_STRING) lengh = MAX_STRING;

vocab[vocab_size].word = (char *) calloc(lengh, sizeof(char));

strcpy(vocab[vocab_size].word, word);

vocab[vocab_size].cn = 0;

vocab_size++;

if (vocab_size + 2 >= vocab_max_size ) {

vocab_max_size += 1000;// 每次增加1000个词位

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; // 线性探索hash

vocab_hash[hash] = vocab_size - 1;// 记录在词汇表中的存储位置

return vocab_size - 1;// 返回添加的单词在词汇表中的存储位置

}

//// 比较函数，词汇表需使用词频进行排序(qsort)

int VocabCompare(const *a, const *b){

return ((struct vocab_word *)b)->cn - ((struct vocab_word *)a)->cn;

}

//根据单词词频排序

void SortVocab() {

int a, size;

unsigned int hash;

// 排序

// 并且保证</s>在第一位

qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);//词汇表快排

for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;//词汇重排了，哈希记录的index也乱了，所有的hash记录清除，下面会重建

size = vocab_size;

train_words = 0;// 用于训练的词汇总数（词频累加）

for (a = 0; a < size; a++) {

// 删除特别低频的词

if (vocab[a].cn < min_count) {

vocab_size--;

free(vocab[vocab_size].word);

} else {

//原来的hash失效需要重新计算

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));

// 给霍夫曼编码和路径的词汇表索引分配空间

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));

}

}

// 读入词汇表文件到词汇表数据结构

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;

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);

}

// 线性探索，开放定址法

int SearchVocab(char * word) {

unsigned int hash = GetWordHash(word);

while (1) {

// 没有这个词

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; // 额，这个代码永远运行不到......

}

void ReduceVocab() {

// reduces the vocabulary by removing infrequent tokens.

int a, b = 0;

unsigned int hash;

// 最后剩下b个词，词频均大于min_reduce

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;

// 重新分配hash索引

for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;

for (a = 0; a < vocab_size; a++) {

hash = GetWordHash(vocab[a].word);

while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;

vocab_hash[hash] = a;

}

fflush(stdout);

min_reduce++;

}

// 装载训练文件到词汇表数据结构

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;

// 首先添加的是回车

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) {// 如果这个单词不存在，我们将其加入hash表

a = AddWordToVocab(word);

vocab[a].cn = 1;

} else vocab[i].cn++;// 否则词频加一

// 如果超出装填系数，将词汇表扩容

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);//文件大小

fclose(fin);

}

// 输出单词和词频到文件

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);

}

// 根据词频生成霍夫曼树

void CreateBinaryTree() {

long long a, b, i;

long long min1i, min2i;

long long pos1, pos2;

long long point[MAX_CODE_LENGTH];// 最长的编码值

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++) {

// 每次寻找两个最小的点做合并，最小的点为0，词小的点为1

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;

}

// 顺着父子关系找回编码

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; // 逆序，把第一个赋值为root（即2*vocab_size - 2 - vocab_size）

for (b = 0; b < i; b++) {

vocab[a].code[i - b - 1] = code[b];// 编码逆序，没有根节点，左子树0，右子树1

vocab[a].point[i - b] = point[b] - vocab_size;

}

}

free(count);

free(binary);

free(parent_node);

}

int ReadWordIndex(FILE * fin) {

char word[MAX_STRING];

ReadWord(word, fin);

if (feof(fin)) return -1;

return SearchVocab(word);

}

// 网络结构初始化

void InitNet() {

// intialize the neural network structure

long long a, b;

// posix_memalign() 成功时会返回size字节的动态内存，并且这块内存的地址是alignment(这里是128)的倍数

// syn0 存储的是word vectors

a = posix_memlign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));

if (syn0 == NULL) {

printf("Memory allocation failed\n"); exit(1);

}

// Hierarchical Softmax

if (hs) {

// hs中，用syn1

a = posix_memlign((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;

}

// Negative Sampling

if (negative > 0) {

// ns中，用syn1neg

a = posix_memlign((void **)&syn1neg, 128, (long long)vocab_size * layer1_size * sizeof(real));

if (syn1neg == NULL) {printf("Memory allocaiton 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;//随机初始化word vectors

CreateBinaryTree();// 创建霍夫曼树

}

// learning: hs (hierarchical softmax) v.s. negative sampling

// model: cbow v.s. skip gram

void *TrainModelThread(void *id) {

// word 向sen中添加单词用，句子完成后表示句子中的当前单词

// last_word 上一个单词，辅助扫描窗口

// sentence_length 当前句子的长度（单词数）

// sentence_position 当前单词在当前句子中的index

long long a, b, d, word, last_word, sentence_length = 0, sentence_position = 0;

// word_count 已训练语料总长度

// last_word_count 保存值，以便在新训练语料长度超过某个值时输出信息

// sen 单词数组，表示句子

long long word_count = 0, last_word_count = 0, sen[MAX_SENTENCE_LENGTH + 1];

// l1 ns中表示word在concatenated word vectors中的起始位置，之后layer1_size是对应的word vector，

//因为把矩阵拉成长向量了

// l2 cbow或ns中权重向量的起始位置，之后layer1_size是对应的syn1或syn1neg，因为把矩阵拉成长向量了

// c 循环中的计数作用

// target ns中当前的sample

// label ns中当前sample的label

long long l1, l2, c, target, label;

// id 线程创建的时候传入，辅助随机数生成

unsigned long long next_random = (long long) id;

// f e^x / (1/e^x)，fs中指当前编码为是0（父亲的左子节点为0，右为1）的概率，

// ns中指label是1的概率

// g 误差(f与真实值的偏离)与学习速率的乘积

real f, g; // function and gradient

clock_t now;

// 隐层节点

real * neu1 = (real *)calloc(layer1_size, sizeof(real));

// 误差累计项，其实对应的是Gneu1

real * neu1e = (real *)calloc(layer1_size, sizeof(real));

// 将文件内容分配给各个线程

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("%cAlpah: %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)); // 自动调整学习速率

if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;// 学习速率有下限

}

if (sentence_length == 0) {// 如果当前句子长度为0

while(1) {

word = ReadWordIndex(fi);

if (feof(fi)) break;// 读到文件末尾

if (word == -1) continue;// 没有这个单词

word_count++;// 单词计数增加

if (word == 0) break;// 是个回车

// 这里的亚采样是指 Sub-Sampling，Mikolov 在论文指出这种亚采样能够带来 2 到 10 倍的性能提升，并能够提升低频词的表示精度。

// 低频词被丢弃概率低，高频词被丢弃概率高

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;// 当前单词在当前句中的index，起始值为0

}

// 照应while中的break，如果读到末尾，退出

if (feof(fi)) break;

// 已经做到了一个thread应尽的工作量，就退出

if (word_count > train_words / num_threads) break;

// 取句子中的第一个单词，开始运行BP算法

word = sen[sentence_position];

if (word == -1) continue;

// 隐层节点值和隐层节点误差累计项清零

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是个随机数，0到window-1，指定了本次算法操作实际的窗口大小

b = next_random % window;

if (cbow) { // train the cbow architecture - HS or NS

// IN -> HIDDEN

// 将窗口内的word vectors累加到隐层节点上

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;// 这个单词没有

for (c = 0; c < layer1_size; c++)

neu1[c] += syn0[c + last_word * layer1_size];

}

// HIERARCHICAL SOFTMAX

//当前中心词在哈弗曼数中从根节点到该节点的路径上的每一个点和neu1结合做sigmoid二分类

//label为1-code[j]

if (hs) for (d = 0; d < vocab[word].codelen; d++) {

// 这里的codelen其实是少一个的，所以不会触及point里面最后一个负数

f = 0;

l2 = vocab[word].point[d] * layer1_size;

// propagate hidden -> output

// 准备计算f

for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];

// 不在expTable内的舍弃掉，比较暴力

if (f <= -MAX_EXP) continue;

else if (f >= MAX_EXP) continue;

// 从expTable中查找，快速计算

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;

// propogate errors output -> hidden

// 记录累积误差项

for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];

// learning weights hidden -> output

// 更新隐层到霍夫曼树非叶节点的权重

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) { // 当前词的分类器应当输出1

target = word;

label = 1;

} else { // 采样使得与target不同，不然continue，label为0，也即最多采样negative个negative sample

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];

// 这里直接上0、1，没有考虑计算精度问题……

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

// 根据隐层节点累积误差项，更新word vectors

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) { // 预测非中心的单词（邻域内的单词）

c = sentence_position - window + a;

if (c < 0) continue;

if (c >= sentence_length) continue;

last_word = sen[c];

if (last_word == -1) continue;

l1 = last_word * layer1_size;

// 累计误差项清零

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

// 待预测单词的 word vecotr 和 隐层-霍夫曼树非叶节点权重 的内积

for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1[c + l2];

// 同cbow中hs的讨论

// if (f <= -MAX_EXP) continue;

// else if (f >= MAX_EXP) continue;

if (f <= -MAX_EXP) f = 0;

else if (f >= MAX_EXP) f = 1;

// 以下内容同之前的cbow

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; // 这里的code[d]其实是下一层的，code错位了，point和code是错位的！

// 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];

// 以下内容同之前的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;

// 创建多线程

pthread_t *pt = (pthread_t *)malloc(num_threads * sizeof(pthread_t));

printf("Starting training using file %s\n", train_file);

starting_alpha = alpha;

// 优先从词汇表文件中加载，否则从训练文件中加载

if (read_vocab_file[0] != 0) ReadVocab(); else LearnVocabFromTrainFile();

// 输出词汇表文件，词+词频

if (save_vocab_file[0] != 0) SaveVocab();

if (output_file[0] == 0) return;

InitNet(); // 网络结构初始化

if (negative > 0) InitUnigramTable(); // 根据词频生成采样映射

start = clock(); // 开始计时

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);

// 训练结束，准备输出

fo = fopen(output_file, "wb");

if (classes == 0) { // 保存 word vectors

// Save the word vectors

fprintf(fo, "%lld %lld\n", vocab_size, layer1_size); // 词汇量，vector维数

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

// 运行K-means算法

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"); // 指定训练文件

printf("\t\tUse text data from <file> to train the model\n");

printf("\t-output <file>\n"); // 指定输出文件，以存储word vectors，或者单词类

printf("\t\tUse <file> to save the resulting word vectors / word clusters\n");

printf("\t-size <int>\n"); // word vector的维数，对应 layer1_size，默认是100

printf("\t\tSet size of word vectors; default is 100\n");

// 窗口大小，在cbow中表示了word vector的最大的叠加范围，在skip-gram中表示了max space between words（w1,w2,p(w1 | w2)）

printf("\t-window <int>\n");

printf("\t\tSet max skip length between words; default is 5\n");

printf("\t-sample <float>\n"); // 亚采样拒绝概率的参数

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"); // 使用hs求解，默认为1

printf("\t\tUse Hierarchical Softmax; default is 1 (0 = not used)\n");

printf("\t-negative <int>\n"); // 使用ns的时候采样的样本数

printf("\t\tNumber of negative examples; default is 0, common values are 5 - 10 (0 = not used)\n");

printf("\t-threads <int>\n"); // 指定线程数

printf("\t\tUse <int> threads (default 1)\n");

printf("\t-min-count <int>\n"); // 长尾词的词频阈值

printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");

printf("\t-alpha <float>\n"); // 初始的学习速率，默认为0.025

printf("\t\tSet the starting learning rate; default is 0.025\n");

printf("\t-classes <int>\n"); // 输出单词类别数，默认为0，也即不输出单词类

printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");

printf("\t-debug <int>\n"); // 调试等级，默认为2

printf("\t\tSet the debug mode (default = 2 = more info during training)\n");

printf("\t-binary <int>\n"); // 是否将结果输出为二进制文件，默认为0，即不输出为二进制

printf("\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");

printf("\t-save-vocab <file>\n"); // 词汇表存储文件

printf("\t\tThe vocabulary will be saved to <file>\n");

printf("\t-read-vocab <file>\n"); // 词汇表加载文件，则可以不指定trainfile

printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");

printf("\t-cbow <int>\n"); // 使用cbow框架

printf("\t\tUse the continuous bag of words model; default is 0 (skip-gram model)\n");

printf("\nExamples:\n"); // 使用示例

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;

}

// 文件名均空

output_file[0] = 0;

save_vocab_file[0] = 0;

read_vocab_file[0] = 0;

// 参数与变量的对应关系

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));

// 产生e^-6 到 e^6 之间的f值

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;

}