tree.c

//  Copyright 2013 Google Inc. All Rights Reserved.
//
//  Licensed under the Apache License, Version 2.0 (the "License");
//  you may not use this file except in compliance with the License.
//  You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
//  Unless required by applicable law or agreed to in writing, software
//  distributed under the License is distributed on an "AS IS" BASIS,
//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//  See the License for the specific language governing permissions and
//  limitations under the License.

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <pthread.h>

#define MAX_STRING 100
#define EXP_TABLE_SIZE 1000
#define MAX_EXP 6
#define MAX_SENTENCE_LENGTH 1000
#define MAX_CODE_LENGTH 200
#define TEST_LENGTH 1000000

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;
	int *point;
	char *word, *code, codelen;
	int vector_index;
};

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 = 300000, vocab_size = 0, layer1_size = 100;
long long train_words = 0, word_count_actual = 0, iter = 2, file_size = 0, classes = 0;
real alpha = 0.025, starting_alpha, sample = 1e-3;
real *syn0, *syn1, *expTable;
clock_t start;

int rec[TEST_LENGTH], recrear = 0;

int hs = 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 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 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[vocab_size].vector_index = -1;
	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) {
	return ((struct vocab_word *)b)->cn - ((struct vocab_word *)a)->cn;
}

// Sorts the vocabulary by frequency using word counts
void SortVocab() {
	int a, size;
	unsigned int hash;
	// Sort the vocabulary and keep </s> at the first position
	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);
}

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);
		if (i == -1) {
			a = AddWordToVocab(word);
			vocab[a].cn = 1;
		} else vocab[i].cn++;
		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);
}

void ReadVocab() {
	long long a, i = 0;
	char c;
	char word[MAX_STRING];
	FILE *fin = fopen(read_vocab_file, "rb");
	if (fin == NULL) {
		printf("Vocabulary file not found\n");
		exit(1);
	}
	for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
	vocab_size = 0;
	while (1) {
		ReadWord(word, fin);
		if (feof(fin)) break;
		a = AddWordToVocab(word);
		fscanf(fin, "%lld%c", &vocab[a].cn, &c);
		i++;
	}
	SortVocab();
	if (debug_mode > 0) {
		printf("Vocab size: %lld\n", vocab_size);
		printf("Words in train file: %lld\n", train_words);
	}
	fin = fopen(train_file, "rb");
	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;
	}
	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;
	}
	printf("Create binary tree\n");
	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, local_iter = iter;
	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");
	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);
				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) {
					real ran = (sqrt(vocab[word].cn / (sample * train_words)) + 1) * (sample * train_words) / vocab[word].cn;
					next_random = next_random * (unsigned long long)25214903917 + 11;
					if (ran < (next_random & 0xFFFF) / (real)65536) continue;
				}
				sen[sentence_length] = word;
				sentence_length++;
				if (sentence_length >= MAX_SENTENCE_LENGTH) break;
			}
			sentence_position = 0;
		}
		if (feof(fi) || (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 > 0 && vocab[word].code[d] == 0) || (f < 0 && vocab[word].code[d] == 1)){
						rec[recrear] = 0;
					} else {
						rec[recrear] = 2;
					}
					if(f <= -MAX_EXP || f >= MAX_EXP) rec[recrear]++;
					recrear = recrear + 1; if(recrear == TEST_LENGTH) recrear = 0;

					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];
				}
				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 > 0 && vocab[word].code[d] == 0) || (f < 0 && vocab[word].code[d] == 1)){
						rec[recrear] = 0;
					} else {
						rec[recrear] = 2;
					}
					if(f <= -MAX_EXP || f >= MAX_EXP) rec[recrear]++;
					recrear = recrear + 1; if(recrear == TEST_LENGTH) recrear = 0;

					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];
				}
				// 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);
}

long long *pos;
int used = 0;
int record[MAX_CODE_LENGTH];
char point[MAX_CODE_LENGTH];
int record_count;

void newTree(long long l, long long r){
	if(l > r) return;
	long long a, b, c, d;
	if(l == r) {
		a = pos[l];
		vocab[a].codelen = record_count;
		for(b = 0; b < record_count; b++){
			vocab[a].point[b] = record[b];
			vocab[a].code[b] = point[b];
		}
		return;
	}
	record[record_count++] = used;
	used++;
	long long *que1 = (long long *)calloc(r - l + 2, sizeof(long long));
	long long *que2 = (long long *)calloc(r - l + 2, sizeof(long long));
	long long top1 = 0, top2 = 0;
	real *cent = (real *)calloc(2 * layer1_size + 2, sizeof(real));
	real closev, x0, x1;

	for(a = l; a <= r; a++) {
		if(a % 2 == 0) {
			que1[top1++] = pos[a];
		} else {
			que2[top2++] = pos[a];
		}
	}

	for(a = 0; a < 10; a++){
		for(b = 0; b < layer1_size; b++){
			cent[b] = 0;
			cent[b + layer1_size] = 0;
		}
		for(b = 0; b < top1; b++){
			for(c = 0; c < layer1_size; c++){
				cent[c] += syn0[que1[b] * layer1_size + c];
			}
		}
		for(b = 0; b < top2; b++){
			for(c = 0; c < layer1_size; c++){
				cent[c + layer1_size] += syn0[que2[b] * layer1_size + c];
			}
		}
		for(b = 0; b < 2; b++){
			closev = 0;
			for(c = 0; c < layer1_size; c++){
				closev += cent[b * layer1_size + c] * cent[b * layer1_size + c];
			}
			closev = sqrt(closev);
			for(c = 0; c < layer1_size; c++){
				cent[layer1_size * b + c] /= closev;
			}
		}
		top1 = 0; top2 = 0;
		for(b = l; b <= r; b++){
			closev = -10;
			x0 = x1 = 0;
			c = pos[b];
			for(d = 0; d < layer1_size; d++){
				x0 += syn0[c * layer1_size + d] * cent[d];
				x1 += syn0[c * layer1_size + d] * cent[layer1_size + d];
			}
			if(x0 < x1){
				que1[top1++] = c;
			} else{
				que2[top2++] = c;
			}
		}
	}

	if(top1 == 0) que1[top1++] = que2[--top2];
	if(top2 == 0) que2[top2++] = que1[--top1];

	for(a = 0; a < top1; a++) {
		pos[l + a] = que1[a];
	}
	for(a = 0; a < top2; a++) {
		pos[r - a] = que2[a];
	}
	point[record_count - 1] = 0;
	newTree(l, l + top1 - 1);
	point[record_count - 1] = 1;
	newTree(l + top1, r);
	record_count--;
}

void PrintRecord() {
	int cnt[10] = {0, 0, 0, 0, 0}, a;
	for(a = 0; a < TEST_LENGTH; a++){
		cnt[rec[a]]++;
	}
	printf("\n\nRight & Sure  %d  Right  & Not Sure %d\nWrong & Sure %d Wrong & Not Sure %d \n\n", cnt[1], cnt[0], cnt[3], cnt[2]);
}

void Output() {
	long a, b, c, d;
	FILE *fo;
	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 %lld ", vocab[a].word, vocab[a].cn);
			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");
		}
		for(a = 0; a < vocab_size; a++) {
			int l = vocab[a].codelen;
			fprintf(fo, "%d", l);
			for(b = 0; b < l; b++){
				fprintf(fo, " %d %d", vocab[a].point[b], vocab[a].code[b]);
			}
			fprintf(fo, "\n");
		}
		for (a = 0; a < vocab_size; a++) {
			for(b = 0; b < layer1_size; b++){
				fprintf(fo, "%lf ", hs ? syn1[a * layer1_size + b]: 0);
			}
			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);
}

void TrainModel() {
	long a, b;
	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;
	printf("Starting InitNet\n");
	InitNet();
	printf("Starting training\n");
	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);
	PrintRecord();
	int l = strlen(output_file);
	output_file[l] = '1';
	output_file[l + 1] = 0;
	Output();

	if(hs) {
		start = clock();
		word_count_actual = 0;
		if(hs) {
			for(a = 0; a <= vocab_size; a++) for(b = 0; b < layer1_size; b++) syn1[a * layer1_size + b] = 0;
		}

		pos = (long long *) calloc(vocab_size * 2 + 1, sizeof(long long));
		for(a = 0; a < vocab_size; a++) pos[a] = a;
		newTree(0, vocab_size - 1);
		unsigned long long next_random = 1;
		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;
		}
		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);
		PrintRecord();
		output_file[l] = '2';
		output_file[l + 1] = 0;
		Output();
	}
}

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-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 -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 *)"-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));
	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;
}