// 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 {

};

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 = 1, debug_mode = 2, window = 5, min_count = 5, num_threads = 12, min_reduce = 1;

int *vocab_hash;

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

long long train_words = 0, word_count_actual = 0, iter = 5, file_size = 0, classes = 0;

real alpha = 0.025, starting_alpha, sample = 1e-3;

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

clock_t start;

int hs = 0, negative = 5;

const int table_size = 1e8;

int *table;

void InitUnigramTable() {

int a, i;

double train_words_pow = 0;

double d1, power = 0.75;

table = (int *)malloc(table_size * sizeof(int));

for (a = 0; a < vocab_size; a++) train_words_pow += pow(vocab[a].cn, power);

i = 0;

d1 = pow(vocab[i].cn, power) / train_words_pow;

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

table[a] = i;

if (a / (double)table_size > d1) {

i++;

d1 += pow(vocab[i].cn, power) / 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, char *eof) {

int a = 0, ch;

while (1) {

ch = fgetc_unlocked(fin);

if (ch == EOF) {

*eof = 1;

break;

}

if (ch == 13) continue;

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

if (a > 0) {

if (ch == '\n') ungetc(ch, fin);

break;

}

if (ch == '\n') {

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

return;

} else continue;

}

word[a] = ch;

a++;

if (a >= MAX_STRING - 1) a--; // Truncate too long words

}

word[a] = 0;

}

// Returns hash value of a word

int GetWordHash(char *word) {

unsigned long long a, hash = 0;

for (a = 0; a < strlen(word); a++) hash = hash * 257 + word[a];

hash = hash % vocab_hash_size;

return hash;

}

// Returns position of a word in the vocabulary; if the word is not found, returns -1

int SearchVocab(char *word) {

unsigned int hash = GetWordHash(word);

while (1) {

if (vocab_hash[hash] == -1) return -1;

if (!strcmp(word, vocab[vocab_hash[hash]].word)) return vocab_hash[hash];

hash = (hash + 1) % vocab_hash_size;

}

return -1;

}

// Reads a word and returns its index in the vocabulary

int ReadWordIndex(FILE *fin, char *eof) {

char word[MAX_STRING], eof_l = 0;

ReadWord(word, fin, &eof_l);

if (eof_l) {

*eof = 1;

return -1;

}

return SearchVocab(word);

}

// Adds a word to the vocabulary

int AddWordToVocab(char *word) {

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

if (length > MAX_STRING) length = MAX_STRING;

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

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

vocab[vocab_size].cn = 0;

vocab_size++;

// Reallocate memory if needed

if (vocab_size + 2 >= vocab_max_size) {

vocab_max_size += 1000;

vocab = (struct vocab_word *)realloc(vocab, vocab_max_size * sizeof(struct vocab_word));

}

hash = GetWordHash(word);

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

vocab_hash[hash] = vocab_size - 1;

return vocab_size - 1;

}

// Used later for sorting by word counts

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

long long l = ((struct vocab_word *)b)->cn - ((struct vocab_word *)a)->cn;

if (l > 0) return 1;

if (l < 0) return -1;

return 0;

}

// Sorts the vocabulary by frequency using word counts

void SortVocab() {

int a, size;

unsigned int hash;

// Sort the vocabulary and keep </s> at the first position

qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);

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

size = vocab_size;

train_words = 0;

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

// Words occuring less than min_count times will be discarded from the vocab

if ((vocab[a].cn < min_count) && (a != 0)) {

vocab_size--;

free(vocab[a].word);

} else {

// Hash will be re-computed, as after the sorting it is not actual

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

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

vocab_hash[hash] = a;

train_words += vocab[a].cn;

}

}

vocab = (struct vocab_word *)realloc(vocab, (vocab_size + 1) * sizeof(struct vocab_word));

// Allocate memory for the binary tree construction

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

vocab[a].code = (char *)calloc(MAX_CODE_LENGTH, sizeof(char));

vocab[a].point = (int *)calloc(MAX_CODE_LENGTH, sizeof(int));

}

}

// Reduces the vocabulary by removing infrequent tokens

void ReduceVocab() {

int a, b = 0;

unsigned int hash;

for (a = 0; a < vocab_size; a++) if (vocab[a].cn > min_reduce) {

vocab[b].cn = vocab[a].cn;

vocab[b].word = vocab[a].word;

b++;

} else free(vocab[a].word);

vocab_size = b;

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

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

// Hash will be re-computed, as it is not actual

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

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

vocab_hash[hash] = a;

}

fflush(stdout);

min_reduce++;

}

// Create binary Huffman tree using the word counts

// Frequent words will have short uniqe binary codes

void CreateBinaryTree() {

long long a, b, i, min1i, min2i, pos1, pos2, point[MAX_CODE_LENGTH];

char code[MAX_CODE_LENGTH];

long long *count = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));

long long *binary = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));

long long *parent_node = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));

for (a = 0; a < vocab_size; a++) count[a] = vocab[a].cn;

for (a = vocab_size; a < vocab_size * 2; a++) count[a] = 1e15;

pos1 = vocab_size - 1;

pos2 = vocab_size;

// Following algorithm constructs the Huffman tree by adding one node at a time

for (a = 0; a < vocab_size - 1; a++) {

// First, find two smallest nodes 'min1, min2'

if (pos1 >= 0) {

if (count[pos1] < count[pos2]) {

min1i = pos1;

pos1--;

} else {

min1i = pos2;

pos2++;

}

} else {

min1i = pos2;

pos2++;

}

if (pos1 >= 0) {

if (count[pos1] < count[pos2]) {

min2i = pos1;

pos1--;

} else {

min2i = pos2;

pos2++;

}

} else {

min2i = pos2;

pos2++;

}

count[vocab_size + a] = count[min1i] + count[min2i];

parent_node[min1i] = vocab_size + a;

parent_node[min2i] = vocab_size + a;

binary[min2i] = 1;

}

// Now assign binary code to each vocabulary word

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

b = a;

i = 0;

while (1) {

code[i] = binary[b];

point[i] = b;

i++;

b = parent_node[b];

if (b == vocab_size * 2 - 2) break;

}

vocab[a].codelen = i;

vocab[a].point[0] = vocab_size - 2;

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

vocab[a].code[i - b - 1] = code[b];

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

}

}

free(count);

free(binary);

free(parent_node);

}

//从词的文本构建词库

void LearnVocabFromTrainFile() {

char word[MAX_STRING], eof = 0;

FILE *fin;

long long a, i, wc = 0;

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

fin = fopen(train_file, "rb");

if (fin == NULL) {

printf("ERROR: training data file not found!\n");

exit(1);

}

vocab_size = 0;

AddWordToVocab((char *)"</s>");//把分隔符加入词库

while (1) {

ReadWord(word, fin, &eof);

if (eof) break;

train_words++;

wc++;

if ((debug_mode > 1) && (wc >= 1000000)) {

printf("%lldM%c", train_words / 1000000, 13);

fflush(stdout);

wc = 0;

}

i = SearchVocab(word);//如果存在，返回该词的索引值，不存在，则返回-1

if (i == -1) {

a = AddWordToVocab(word);

vocab[a].cn = 1;

} else vocab[i].cn++;

/*

当出现“vocab_size > vocab_hash_size * 0.7”时，需要对低频词进行处理。

其中，vocab_size表示的是目前词库中词的个数，vocab_hash_size表示的是初始设定的hash表的大小。

在处理低频词的过程中，通过参数“min_reduce”来控制，若词出现的次数小于等于该值时，则从词库中删除该词。

在删除了低频词后，需要重新对词库中的词进行hash值的计算。

*/

if (vocab_size > vocab_hash_size * 0.7) ReduceVocab();

}

SortVocab();

if (debug_mode > 0) {

printf("Vocab size: %lld\n", vocab_size);

printf("Words in train file: %lld\n", train_words);

}

file_size = ftell(fin);

fclose(fin);

}

void SaveVocab() {

long long i;

FILE *fo = fopen(save_vocab_file, "wb");

for (i = 0; i < vocab_size; i++) fprintf(fo, "%s %lld\n", vocab[i].word, vocab[i].cn);

fclose(fo);

}

//直接读取词库

void ReadVocab() {

long long a, i = 0;

char c, eof = 0;

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, &eof);

if (eof) break;

a = AddWordToVocab(word);

fscanf(fin, "%lld%c", &vocab[a].cn, &c);

i++;

}

SortVocab();

if (debug_mode > 0) {

printf("Vocab size: %lld\n", vocab_size);

printf("Words in train file: %lld\n", train_words);

}

fin = fopen(train_file, "rb");

if (fin == NULL) {

printf("ERROR: training data file not found!\n");

exit(1);

}

fseek(fin, 0, SEEK_END);

file_size = ftell(fin);

fclose(fin);

}

void InitNet() {

long long a, b;

unsigned long long next_random = 1;

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 (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)

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 (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)

syn1neg[a * layer1_size + b] = 0;

}

for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++) {

next_random = next_random * (unsigned long long)25214903917 + 11;

syn0[a * layer1_size + b] = (((next_random & 0xFFFF) / (real)65536) - 0.5) / layer1_size;

}

CreateBinaryTree();

}

void *TrainModelThread(void *id) {

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

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

long long l1, l2, c, target, label, local_iter = iter;

unsigned long long next_random = (long long)id;

char eof = 0;

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");

fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);

while (1) {

if (word_count - last_word_count > 10000) {

word_count_actual += word_count - last_word_count;

last_word_count = word_count;

if ((debug_mode > 1)) {

now=clock();

printf("%cAlpha: %f Progress: %.2f%% Words/thread/sec: %.2fk ", 13, alpha,

word_count_actual / (real)(iter * train_words + 1) * 100,

word_count_actual / ((real)(now - start + 1) / (real)CLOCKS_PER_SEC * 1000));

fflush(stdout);

}

alpha = starting_alpha * (1 - word_count_actual / (real)(iter * train_words + 1));

if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;

}

if (sentence_length == 0) {

while (1) {

word = ReadWordIndex(fi, &eof);

if (eof) break;

if (word == -1) continue;

word_count++;

if (word == 0) break;

// The subsampling randomly discards frequent words while keeping the ranking same

if (sample > 0) {

real ran = (sqrt(vocab[word].cn / (sample * train_words)) + 1) * (sample * train_words) / vocab[word].cn;

next_random = next_random * (unsigned long long)25214903917 + 11;

if (ran < (next_random & 0xFFFF) / (real)65536) continue;

}

sen[sentence_length] = word;

sentence_length++;

if (sentence_length >= MAX_SENTENCE_LENGTH) break;

}

sentence_position = 0;

}

if (eof || (word_count > train_words / num_threads)) {

word_count_actual += word_count - last_word_count;

local_iter--;

if (local_iter == 0) break;

word_count = 0;

last_word_count = 0;

sentence_length = 0;

fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);

continue;

}

word = sen[sentence_position];

if (word == -1) continue;

for (c = 0; c < 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

cw = 0;

for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {

c = sentence_position - window + a;

if (c < 0) continue;

if (c >= sentence_length) continue;

last_word = sen[c];

if (last_word == -1) continue;

for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size];

cw++;

}

if (cw) {

for (c = 0; c < layer1_size; c++) neu1[c] /= cw;

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

f = 0;

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

// Propagate hidden -> output

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

if (f <= -MAX_EXP) continue;

else if (f >= MAX_EXP) continue;

else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];

// 'g' is the gradient multiplied by the learning rate

g = (1 - vocab[word].code[d] - f) * alpha;

// Propagate errors output -> hidden

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

// Learn weights hidden -> output

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

}

// 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 += 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

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) {

c = sentence_position - window + a;

if (c < 0) continue;

if (c >= sentence_length) continue;

last_word = sen[c];

if (last_word == -1) continue;

l1 = last_word * layer1_size;

for (c = 0; c < layer1_size; c++) neu1e[c] = 0;

// HIERARCHICAL SOFTMAX

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

f = 0;

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

// Propagate hidden -> output

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

if (f <= -MAX_EXP) continue;

else if (f >= MAX_EXP) continue;

else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];

// 'g' is the gradient multiplied by the learning rate

g = (1 - vocab[word].code[d] - f) * alpha;

// Propagate errors output -> hidden

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

// Learn weights hidden -> output

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

}

// 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];

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;

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

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

starting_alpha = alpha;

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

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

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

InitNet();

if (negative > 0) 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) {

// Save the word vectors

fprintf(fo, "%lld %lld\n", vocab_size, layer1_size);

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

fprintf(fo, "%s ", vocab[a].word);

if (binary) for (b = 0; b < layer1_size; b++) fwrite(&syn0[a * layer1_size + b], sizeof(real), 1, fo);

else for (b = 0; b < layer1_size; b++) fprintf(fo, "%lf ", syn0[a * layer1_size + b]);

fprintf(fo, "\n");

}

} else {

// Run K-means on the word vectors

int clcn = classes, iter = 10, closeid;

int *centcn = (int *)malloc(classes * sizeof(int));

int *cl = (int *)calloc(vocab_size, sizeof(int));

real closev, x;

real *cent = (real *)calloc(classes * layer1_size, sizeof(real));

for (a = 0; a < vocab_size; a++) cl[a] = a % clcn;

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

for (b = 0; b < clcn * layer1_size; b++) cent[b] = 0;

for (b = 0; b < clcn; b++) centcn[b] = 1;

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

for (d = 0; d < layer1_size; d++) cent[layer1_size * cl[c] + d] += syn0[c * layer1_size + d];

centcn[cl[c]]++;

}

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

closev = 0;

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

cent[layer1_size * b + c] /= centcn[b];

closev += cent[layer1_size * b + c] * cent[layer1_size * b + c];

}

closev = sqrt(closev);

for (c = 0; c < layer1_size; c++) cent[layer1_size * b + c] /= closev;

}

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

closev = -10;

closeid = 0;

for (d = 0; d < clcn; d++) {

x = 0;

for (b = 0; b < layer1_size; b++) x += cent[layer1_size * d + b] * syn0[c * layer1_size + b];

if (x > closev) {

closev = x;

closeid = d;

}

}

cl[c] = closeid;

}

}

// Save the K-means classes

for (a = 0; a < vocab_size; a++) fprintf(fo, "%s %d\n", vocab[a].word, cl[a]);

free(centcn);

free(cent);

free(cl);

}

fclose(fo);

}

int ArgPos(char *str, int argc, char **argv) {

int a;

for (a = 1; a < argc; a++) if (!strcmp(str, argv[a])) {

if (a == argc - 1) {

printf("Argument missing for %s\n", str);

exit(1);

}

return a;

}

return -1;

}

int main(int argc, char **argv) {

int i;

if (argc == 1) {

printf("WORD VECTOR estimation toolkit v 0.1c\n\n");

printf("Options:\n");

printf("Parameters for training:\n");

printf("\t-train <file>\n");

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

printf("\t-output <file>\n");

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

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

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

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

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

printf("\t-sample <float>\n");

printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency in the training data\n");

printf("\t\twill be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)\n");

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

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

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

printf("\t\tNumber of negative examples; default is 5, common values are 3 - 10 (0 = not used)\n");

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

printf("\t\tUse <int> threads (default 12)\n");

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

printf("\t\tRun more training iterations (default 5)\n");

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

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

printf("\t-alpha <float>\n");

printf("\t\tSet the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW\n");

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

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

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

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

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

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

printf("\t-save-vocab <file>\n");

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

printf("\t-read-vocab <file>\n");

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

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

printf("\t\tUse the continuous bag of words model; default is 1 (use 0 for skip-gram model)\n");

printf("\nExamples:\n");

printf("./word2vec -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -cbow 1 -iter 3\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 (cbow) alpha = 0.05;

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 *)"-iter", argc, argv)) > 0) iter = atoi(argv[i + 1]);

if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);

if ((i = ArgPos((char *)"-classes", argc, argv)) > 0) classes = atoi(argv[i + 1]);

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));//使用近似的方式去计算sigmod函数，将区间[−6,6]（设置的参数MAX_EXP为6）

//等距离划分成EXP_TABLE_SIZE等份，并将每个区间中的sigmoid值计算好

//存入到数组expTable中，需要使用时，直接从数组中查找。

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;

}