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SWE_Train.c
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// 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.
//=============================================//
// Codes for Semantic Word Embedding //
// ACL-2015, Beijing //
//---------------------------------------------//
// Quan Liu, University of Science and //
// Technology of China. 2015 //
// http://home.ustc.edu.cn/~quanliu/ //
//=============================================//
// Semantic Word Embedding Main Code
// Modified from Google Word2Vec toolkit.
// For demo running, we use text8 from http://mattmahoney.net/dc/text8.zip for experiments.
// It may take just several minutes to finish the model training process.
//=== Here is the orginal word2vec license ===//
// 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.
#define ON_LINUX
//#define MKL_YES
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <time.h>
#ifdef MKL_YES
#include <mkl.h>
#endif
#ifdef ON_LINUX
#include <pthread.h>
#endif
#ifdef ON_WINDOWS
#include <process.h>
#include <windows.h>
#endif
#define FILE_LENGTH 1048
#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;
int *point;
char *word, *code, codelen;
};
char train_file[FILE_LENGTH], output_file[FILE_LENGTH];
char save_vocab_file[FILE_LENGTH], read_vocab_file[FILE_LENGTH];
struct vocab_word *vocab;
int binary = 0, cbow = 0, 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;
// Quan Liu
#define SEM_KDBNUM 1
#define CVTEST_TIME 1000000
struct TypeInEquation {
// sim(word_A, word_B) > sim(word_C, word_D)
int index_A;
int index_B;
int index_C;
int index_D;
// distance(word_C, word_D) - distance(word_A, word_B)
};
struct TypeTermKDB {
long long *KDB_IDset;
int KDB_nums;
};
// Type Qsem track
struct TypeQsemVal
{
real Qsem_total;
long long SatisfyNum;
real SatisfyRate;
long long totalKDBNum;
real Qsem_total_prev;
real Dgap_total;
real Dgap_total_prev;
};
struct TypeQbaseVal
{
double log10_prob;
long long token_num;
real QbaseVal;
};
real *zero_vector;
time_t runTimer, endTimer;
struct tm *begRunTime;
struct tm *endRunTime;
// Semantic Constraint
char semwe_inequation_file[FILE_LENGTH];
char semwe_inequation_fileCV[FILE_LENGTH];
real semwe_add_time = 0.0;
real semwe_inter_coeff = 0.1; // 0.1*sema1c + 0.9*baseline
real semwe_weight_decay = 0.0; // weight decay to prevent overfitting
real semwe_hinge_margin = 0.0; // Hinge function low bound, f(x) = max(low_bound, x)
int delta_left = 1;
int delta_right= 1;
struct TypeInEquation *KnowDB_InEquation;
struct TypeTermKDB *KnowDB_TermKDB;
struct TypeInEquation *KnowDB_InEquation_CV;
struct TypeTermKDB *KnowDB_TermKDB_CV;
struct TypeQsemVal KnowDB_QsemVal; // running curve trends
struct TypeQsemVal KnowDB_QsemVal_CV; // running curve trends
struct TypeQbaseVal SemWE_Qbase;
///////////////////////////////////////
// function definition
void ReadWord(char *word, FILE *fin);
int GetWordHash(char *word);
int SearchVocab(char *word);
int ReadWordIndex(FILE *fin);
void SemWE_AddToKnowDB_InSet(int word_index, long long eleID);
void SemWE_AddToKnowDB_CVSet(int word_index, long long eleID);
void SemWE_LoadInEquation_InSet(char *inequation_file);
void SemWE_LoadInEquation_CVSet(char *inequation_file);
real SemWE_VectorDot(real *vec_a, real *vec_b);
void SemWE_VectorLinear(real *main_vector, real *scale_vector, real scale_coeff);
void SemWE_VectorCopy(real *vec_a, real *vec_b);
void SemWE_VectorScale(real *input_vector, real scale_coeff);
real SemWE_VectorNorm(real *input_vector);
void SemWE_VectorMinus(real *minus_res, real *vec_a, real *vec_b);
real SemWE_CalcCosine(int index_A, int index_B);
real SemWE_DeriveHinge(real key_value);
real SemWE_CalcuHinge(real key_value);
void SemWE_QsemDerive_Cosine(int word_index, real* derive_distEu);
struct TypeQsemVal SemWE_Qsem_Cosine_InSet();
struct TypeQsemVal SemWE_Qsem_Cosine_CVSet();
//////////////////////////////////////////////
// Partial derivative
void SemWE_QsemDerive_Cosine(int word_index, real* derive_distEu)
{
int i = 0;
int k = 0;
int kdb_num;
long long eleID = -1;
struct TypeInEquation tmp_InEquation;
real dist_AB = 0;
real dist_CD = 0;
int index_A = -1;
int index_B = -1;
int index_C = -1;
int index_D = -1;
real norm_A = 0.0;
real norm_B = 0.0;
real norm_C = 0.0;
real norm_D = 0.0;
real CD_minus_AB;
real ABCD_Sigmoid;
real derive_Sigmoid;
real* derive_wordCD = (real*)malloc(layer1_size*sizeof(real));
real* derive_wordAB = (real*)malloc(layer1_size*sizeof(real));
SemWE_VectorCopy(derive_distEu, zero_vector);
SemWE_VectorCopy(derive_wordAB, zero_vector);
SemWE_VectorCopy(derive_wordCD, zero_vector);
kdb_num = KnowDB_TermKDB[word_index].KDB_nums;
for (i = 0; i < kdb_num; ++i)
{
eleID = KnowDB_TermKDB[word_index].KDB_IDset[i];
tmp_InEquation = KnowDB_InEquation[eleID];
index_A = tmp_InEquation.index_A;
index_B = tmp_InEquation.index_B;
index_C = tmp_InEquation.index_C;
index_D = tmp_InEquation.index_D;
///
norm_A = SemWE_VectorNorm(&syn0[index_A*layer1_size]); // |A|
norm_B = SemWE_VectorNorm(&syn0[index_B*layer1_size]);
norm_C = SemWE_VectorNorm(&syn0[index_C*layer1_size]);
norm_D = SemWE_VectorNorm(&syn0[index_D*layer1_size]);
dist_AB = SemWE_VectorDot(&syn0[index_A*layer1_size], &syn0[index_B*layer1_size]); //A*B
dist_CD = SemWE_VectorDot(&syn0[index_C*layer1_size], &syn0[index_D*layer1_size]);
dist_AB /= (norm_A*norm_B); // consine distance A*B/(|A||B|)
dist_CD /= (norm_C*norm_D);
// Distance minus Sigmoid
// distance(word_C, word_D) - distance(word_A, word_B)
CD_minus_AB = semwe_hinge_margin - (dist_AB - dist_CD);
ABCD_Sigmoid = SemWE_CalcuHinge(CD_minus_AB); // hinge loss
derive_Sigmoid = SemWE_DeriveHinge(ABCD_Sigmoid); // f'
if (derive_Sigmoid != 0.0)
{
// Zero 归零了
SemWE_VectorCopy(derive_wordAB, zero_vector);
SemWE_VectorCopy(derive_wordCD, zero_vector);
//计算Sij对Wi的梯度
if (word_index == index_A){
SemWE_VectorCopy(derive_wordAB, &syn0[index_B*layer1_size]);
//derive_wordAB += Wj / (|Wi|*|Wj|)
SemWE_VectorScale(derive_wordAB, (1.0/(norm_A*norm_B)));
//derive_wordAB += -Sij / |Wi|^2 * Wi
SemWE_VectorLinear(derive_wordAB, &syn0[index_A*layer1_size], (-1*dist_AB/(norm_A*norm_A)));
}
else if (word_index == index_B){
SemWE_VectorCopy(derive_wordAB, &syn0[index_A*layer1_size]);
SemWE_VectorScale(derive_wordAB, (1.0/(norm_A*norm_B)));
SemWE_VectorLinear(derive_wordAB, &syn0[index_B*layer1_size], (-1*dist_AB/(norm_B*norm_B)));
}
if (word_index == index_C){
SemWE_VectorCopy(derive_wordCD, &syn0[index_D*layer1_size]);
SemWE_VectorScale(derive_wordCD, (1.0/(norm_C*norm_D)));
SemWE_VectorLinear(derive_wordCD, &syn0[index_C*layer1_size], (-1*dist_CD/(norm_C*norm_C)));
}
else if (word_index == index_D){
SemWE_VectorCopy(derive_wordCD, &syn0[index_C*layer1_size]);
SemWE_VectorScale(derive_wordCD, (1.0/(norm_C*norm_D)));
SemWE_VectorLinear(derive_wordCD, &syn0[index_D*layer1_size], (-1*dist_CD/(norm_D*norm_D)));
}
//Sik对Wi的梯度 - Sij对Wi的梯度
SemWE_VectorLinear(derive_wordCD, derive_wordAB, -1.0);
//D对Wt的梯度= E [ f' * (Sik对Wi的梯度 - Sij对Wi的梯度 ) ]
SemWE_VectorLinear(derive_distEu, derive_wordCD, derive_Sigmoid);
}
}
free(derive_wordAB); derive_wordAB = NULL;
free(derive_wordCD); derive_wordCD = NULL;
}
// All the Know DB Qsem
struct TypeQsemVal SemWE_Qsem_Cosine_InSet()
{
int i = 0;
int kdb_num = 0;
struct TypeQsemVal Qsem_Value;
struct TypeInEquation tmp_InEquation;
long long elemID = 0;
real dist_AB = 0;
real dist_CD = 0;
int index_A = -1;
int index_B = -1;
int index_C = -1;
int index_D = -1;
real CD_minus_AB;
real ABCD_Sigmoid;
Qsem_Value.SatisfyNum = 0;
Qsem_Value.Qsem_total = 0.0;
Qsem_Value.Dgap_total = 0.0;
kdb_num = KnowDB_QsemVal.totalKDBNum;
for (elemID = 0; elemID < kdb_num; ++elemID)
{
tmp_InEquation = KnowDB_InEquation[elemID];
index_A = tmp_InEquation.index_A;
index_B = tmp_InEquation.index_B;
index_C = tmp_InEquation.index_C;
index_D = tmp_InEquation.index_D;
dist_AB = SemWE_CalcCosine(index_A, index_B);
dist_CD = SemWE_CalcCosine(index_C, index_D);
// Distance minus Sigmoid
// distance(word_C, word_D) - distance(word_A, word_B)
CD_minus_AB = semwe_hinge_margin-(dist_AB -dist_CD);
ABCD_Sigmoid = SemWE_CalcuHinge(CD_minus_AB);
Qsem_Value.Qsem_total += ABCD_Sigmoid;
Qsem_Value.Dgap_total += CD_minus_AB;
if (dist_AB > dist_CD)
{
Qsem_Value.SatisfyNum++;
}
}
Qsem_Value.SatisfyRate = 1.0*Qsem_Value.SatisfyNum / KnowDB_QsemVal.totalKDBNum;
//printf("Qsem_Value.Qsem_total=%f, Qsem_Value.SatisfyRate=%f\n", Qsem_Value.Qsem_total, Qsem_Value.SatisfyRate);
return Qsem_Value;
}
struct TypeQsemVal SemWE_Qsem_Cosine_CVSet()
{
int i = 0;
int kdb_num = 0;
struct TypeQsemVal Qsem_Value;
struct TypeInEquation tmp_InEquation;
long long elemID = 0;
real dist_AB = 0;
real dist_CD = 0;
int index_A = -1;
int index_B = -1;
int index_C = -1;
int index_D = -1;
real CD_minus_AB;
real ABCD_Sigmoid;
Qsem_Value.SatisfyNum = 0;
Qsem_Value.Qsem_total = 0.0;
Qsem_Value.Dgap_total = 0.0;
kdb_num = KnowDB_QsemVal_CV.totalKDBNum;
for (elemID = 0; elemID < kdb_num; ++elemID)
{
tmp_InEquation = KnowDB_InEquation_CV[elemID];
index_A = tmp_InEquation.index_A;
index_B = tmp_InEquation.index_B;
index_C = tmp_InEquation.index_C;
index_D = tmp_InEquation.index_D;
dist_AB = SemWE_CalcCosine(index_A, index_B);
dist_CD = SemWE_CalcCosine(index_C, index_D);
// Distance minus Sigmoid
// distance(word_C, word_D) - distance(word_A, word_B)
CD_minus_AB = semwe_hinge_margin-(dist_AB -dist_CD);
ABCD_Sigmoid = SemWE_CalcuHinge(CD_minus_AB);
Qsem_Value.Qsem_total += ABCD_Sigmoid;
Qsem_Value.Dgap_total += CD_minus_AB;
if (dist_AB > dist_CD)
{
Qsem_Value.SatisfyNum++;
}
}
Qsem_Value.SatisfyRate = 1.0*Qsem_Value.SatisfyNum / KnowDB_QsemVal_CV.totalKDBNum;
//printf("VALID: Qsem_Value.Qsem_total=%f, Qsem_Value.SatisfyRate=%f\n", Qsem_Value.Qsem_total, Qsem_Value.SatisfyRate);
return Qsem_Value;
}
///////////////////////////////////////////////////
/// Math function
real SemWE_CalcuHinge(real key_value)
{
// October 31, 2014
if (key_value > 0){
return key_value;
}
else{
return 0;
}
}
real SemWE_DeriveHinge(real key_value)
{
real core_derive = 0.0;
if (key_value > 0){
core_derive = 1;
}
else{
core_derive = 0;
}
return core_derive;
}
//
real SemWE_VectorDot(real *vec_a, real *vec_b)
{
/*int incx = 1;
int incy = 1;
int size = layer1_size;
real fastres = 0.0;
fastres = sdot(&size, vec_a, &incx, vec_b, &incy);
return fastres;
*/
int i = 0;
int incx = 1;
int incy = 1;
int vec_size = layer1_size;
real fastres = 0.0;
#ifdef MKL_YES
fastres = sdot(&vec_size, vec_a, &incx, vec_b, &incy);
#else
for (i = 0; i < vec_size; ++i){
fastres += vec_a[i]*vec_b[i];
}
#endif
return fastres;
}
void SemWE_VectorCopy(real *vec_a, real *vec_b)
{
// vec_a = vec_b
/*int incx = 1;
int incy = 1;
int size = layer1_size;
//int i = 0;
scopy(&size, vec_b, &incx, vec_a, &incy);
*/
int i = 0;
int incx = 1;
int incy = 1;
int vec_size = layer1_size;
#ifdef MKL_YES
scopy(&vec_size, vec_b, &incx, vec_a, &incy);
#else
for (i = 0; i < vec_size; ++i){
vec_a[i] = vec_b[i];
}
#endif
}
void SemWE_VectorMinus(real *minus_res, real *vec_a, real *vec_b)
{
int i = 0;
for (i = 0; i < layer1_size; ++i){
minus_res[i] = vec_a[i] - vec_b[i];
}
}
void SemWE_VectorLinear(real *main_vector, real *scale_vector, real scale_coeff)
{
// main_vector = main_vector + scale_coeff * scale_vector
/*int incx = 1;
int incy = 1;
int size = layer1_size;
saxpy(&size, &scale_coeff, scale_vector, &incx, main_vector, &incy);
*/
int i = 0;
int incx = 1;
int incy = 1;
int vec_size = layer1_size;
#ifdef MKL_YES
saxpy(&vec_size, &scale_coeff, scale_vector, &incx, main_vector, &incy);
#else
for (i = 0; i < vec_size; ++i){
main_vector[i] += scale_coeff * scale_vector[i];
}
#endif
}
void SemWE_VectorScale(real *input_vector, real scale_coeff)
{
/*int i = 0;
int incx = 1;
int size = layer1_size;
sscal(&size, &scale_coeff, input_vector, &incx);
*/
int i = 0;
int incx = 1;
int vec_size = layer1_size;
#ifdef MKL_YES
sscal(&vec_size, &scale_coeff, input_vector, &incx);
#else
for (i = 0; i < vec_size; ++i){
input_vector[i] = scale_coeff * input_vector[i];
}
#endif
}
real SemWE_VectorNorm(real *input_vector)
{
/*int i = 0;
real norm_value = 0.0;
int incx = 1;
int size = layer1_size;
norm_value = snrm2(&size, input_vector, &incx);
return norm_value;
*/
int i = 0;
real norm_value = 0.0;
int incx = 1;
int vec_size = layer1_size;
#ifdef MKL_YES
norm_value = snrm2(&vec_size, input_vector, &incx);
#else
for (i = 0; i < vec_size; ++i){
norm_value += (input_vector[i]*input_vector[i]);
}
norm_value = sqrt(norm_value);
#endif
return norm_value;
}
real SemWE_CalcCosine(int index_A, int index_B)
{
real cosine_value = 0.0;
real norm_vec_a = 0.0;
real norm_vec_b = 0.0;
cosine_value = SemWE_VectorDot(&syn0[index_A*layer1_size], &syn0[index_B*layer1_size]);
norm_vec_a = SemWE_VectorNorm(&syn0[index_A*layer1_size]);
norm_vec_b = SemWE_VectorNorm(&syn0[index_B*layer1_size]);
cosine_value = cosine_value/(norm_vec_a*norm_vec_b);
//printf("cosine<%d %d>: %f\n", index_A, index_B, cosine_value);
return cosine_value;
}
//////////////////////////////////////////////
// Load Knowledge InEquation
void SemWE_LoadInEquation_InSet(char *inequation_file)
{
int i = 0;
long long eleNum = 0;
long long eleID = 0;
FILE *fKNOW = NULL;
KnowDB_TermKDB = (struct TypeTermKDB*)malloc(vocab_size*sizeof(struct TypeTermKDB));
for (i = 0; i < vocab_size; ++i)
{
KnowDB_TermKDB[i].KDB_IDset = (long long*)calloc(SEM_KDBNUM, sizeof(long long));
KnowDB_TermKDB[i].KDB_nums = 0;
}
fKNOW = fopen(inequation_file, "rb");
if (fKNOW == NULL){
printf("ERROR, can not open file %s\n", inequation_file);
exit(1);
}
/// read inequation num
/// file format: sim(word_A, word_B) > sim(word_C, word_D)
while (1) {
char word_A[MAX_STRING];
char word_B[MAX_STRING];
char word_C[MAX_STRING];
char word_D[MAX_STRING];
char word_T[MAX_STRING];
ReadWord(word_A, fKNOW);
ReadWord(word_B, fKNOW);
ReadWord(word_C, fKNOW);
ReadWord(word_D, fKNOW);
ReadWord(word_T, fKNOW);
if (feof(fKNOW)) break;
if (strcmp(word_T, "</s>") != 0)
{
printf("please ensure 4 words at every line\n");
exit(1);
}
eleNum++;
}
fclose(fKNOW);
printf("--- InEquation Nums: %lld\n", eleNum);
/// main read
KnowDB_QsemVal.totalKDBNum = eleNum;
KnowDB_InEquation = (struct TypeInEquation *)malloc(eleNum * sizeof(struct TypeInEquation));
fKNOW = fopen(inequation_file, "r");
/// file format: sim(word_A, word_B) > sim(word_C, word_D)
while (1) {
char word_A[MAX_STRING];
char word_B[MAX_STRING];
char word_C[MAX_STRING];
char word_D[MAX_STRING];
char word_T[MAX_STRING];
int index_A = -1;
int index_B = -1;
int index_C = -1;
int index_D = -1;
int index_T = -1;
ReadWord(word_A, fKNOW);
ReadWord(word_B, fKNOW);
ReadWord(word_C, fKNOW);
ReadWord(word_D, fKNOW);
ReadWord(word_T, fKNOW);
if (feof(fKNOW)) break;
index_A = SearchVocab(word_A);
index_B = SearchVocab(word_B);
index_C = SearchVocab(word_C);
index_D = SearchVocab(word_D);
KnowDB_InEquation[eleID].index_A = index_A;
KnowDB_InEquation[eleID].index_B = index_B;
KnowDB_InEquation[eleID].index_C = index_C;
KnowDB_InEquation[eleID].index_D = index_D;
//KnowDB_InEquation[eleID].AB_minus_CD = 0.0;
if (delta_left == 1)
{
SemWE_AddToKnowDB_InSet(index_A, eleID);
if (index_B != index_A) SemWE_AddToKnowDB_InSet(index_B, eleID);
}
if (delta_right == 1)
{
if (index_C != index_B && index_C != index_A) SemWE_AddToKnowDB_InSet(index_C, eleID);
if (index_D != index_C && (index_D != index_B && index_D != index_A)) SemWE_AddToKnowDB_InSet(index_D, eleID);
}
eleID++;
}
fclose(fKNOW);
printf("--- Finish reading the Knowledge Database\n");
//SemWE_SaveReadKDB();
}
void SemWE_AddToKnowDB_InSet(int word_index, long long eleID)
{
int kdb_num = 0;
KnowDB_TermKDB[word_index].KDB_nums++;
kdb_num = KnowDB_TermKDB[word_index].KDB_nums;
if (kdb_num > SEM_KDBNUM)
{
KnowDB_TermKDB[word_index].KDB_IDset = (long long*)realloc(KnowDB_TermKDB[word_index].KDB_IDset, kdb_num*sizeof(long long));
}
KnowDB_TermKDB[word_index].KDB_IDset[kdb_num-1] = eleID;
}
void SemWE_LoadInEquation_CVSet(char *inequation_file)
{
int i = 0;
long long eleNum = 0;
long long eleID = 0;
FILE *fKNOW = NULL;
KnowDB_TermKDB_CV = (struct TypeTermKDB*)malloc(vocab_size*sizeof(struct TypeTermKDB));
for (i = 0; i < vocab_size; ++i)
{
KnowDB_TermKDB_CV[i].KDB_IDset = (long long*)calloc(SEM_KDBNUM, sizeof(long long));
KnowDB_TermKDB_CV[i].KDB_nums = 0;
}
fKNOW = fopen(inequation_file, "rb");
if (fKNOW == NULL){
printf("ERROR, can not open file %s\n", inequation_file);
exit(1);
}
/// read inequation num
/// file format: sim(word_A, word_B) > sim(word_C, word_D)
while (1) {
char word_A[MAX_STRING];
char word_B[MAX_STRING];
char word_C[MAX_STRING];
char word_D[MAX_STRING];
char word_T[MAX_STRING];
ReadWord(word_A, fKNOW);
ReadWord(word_B, fKNOW);
ReadWord(word_C, fKNOW);
ReadWord(word_D, fKNOW);
ReadWord(word_T, fKNOW);
if (feof(fKNOW)) break;
if (strcmp(word_T, "</s>") != 0)
{
printf("please ensure 4 words at every line\n");
exit(1);
}
eleNum++;
}
fclose(fKNOW);
printf("--- CV set InEquation Nums: %lld\n", eleNum);
/// main read
KnowDB_QsemVal_CV.totalKDBNum = eleNum;
KnowDB_InEquation_CV = (struct TypeInEquation *)malloc(eleNum * sizeof(struct TypeInEquation));
fKNOW = fopen(inequation_file, "r");
/// file format: sim(word_A, word_B) > sim(word_C, word_D)
while (1) {
char word_A[MAX_STRING];
char word_B[MAX_STRING];
char word_C[MAX_STRING];
char word_D[MAX_STRING];
char word_T[MAX_STRING];
int index_A = -1;
int index_B = -1;
int index_C = -1;
int index_D = -1;
int index_T = -1;
ReadWord(word_A, fKNOW);
ReadWord(word_B, fKNOW);
ReadWord(word_C, fKNOW);
ReadWord(word_D, fKNOW);
ReadWord(word_T, fKNOW);
if (feof(fKNOW)) break;
index_A = SearchVocab(word_A);
index_B = SearchVocab(word_B);
index_C = SearchVocab(word_C);
index_D = SearchVocab(word_D);
KnowDB_InEquation_CV[eleID].index_A = index_A;
KnowDB_InEquation_CV[eleID].index_B = index_B;
KnowDB_InEquation_CV[eleID].index_C = index_C;
KnowDB_InEquation_CV[eleID].index_D = index_D;
//KnowDB_InEquation[eleID].AB_minus_CD = 0.0;
SemWE_AddToKnowDB_CVSet(index_A, eleID);
if (index_B != index_A) SemWE_AddToKnowDB_CVSet(index_B, eleID);
if (index_C != index_B && index_C != index_A) SemWE_AddToKnowDB_CVSet(index_C, eleID);
if (index_D != index_C && (index_D != index_B && index_D != index_A)) SemWE_AddToKnowDB_CVSet(index_D, eleID);
eleID++;
}
fclose(fKNOW);
printf("--- Finish reading the CV Knowledge Database\n");
}
////
void SemWE_AddToKnowDB_CVSet(int word_index, long long eleID)
{
int kdb_num = 0;
KnowDB_TermKDB_CV[word_index].KDB_nums++;
kdb_num = KnowDB_TermKDB_CV[word_index].KDB_nums;
if (kdb_num > SEM_KDBNUM)
{
KnowDB_TermKDB_CV[word_index].KDB_IDset = (long long*)realloc(KnowDB_TermKDB_CV[word_index].KDB_IDset, kdb_num*sizeof(long long));
}
KnowDB_TermKDB_CV[word_index].KDB_IDset[kdb_num-1] = eleID;
}
//////////////////////////////////////////////
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++) train_words_pow += pow(vocab[a].cn, power);
i = 0;
d1 = pow(vocab[i].cn, power) / (real)train_words_pow;
for (a = 0; a < table_size; a++) {
table[a] = i;
if (a / (real)table_size > d1) {
i++;
d1 += pow(vocab[i].cn, power) / (real)train_words_pow;
}
if (i >= vocab_size) i = vocab_size - 1;
}
}
// Reads a single word from a file, assuming space + tab + EOL to be word boundaries
void ReadWord(char *word, FILE *fin) {
int a = 0, ch;
while (!feof(fin)) {
ch = fgetc(fin);
if (ch == 13) continue;
if ((ch == ' ') || (ch == '\t') || (ch == '\n')) {
if (a > 0) {
if (ch == '\n') ungetc(ch, fin);
break;
}
if (ch == '\n') {
strcpy(word, (char *)"</s>");
return;
} else continue;
}
word[a] = ch;
a++;
if (a >= MAX_STRING - 1) a--; // Truncate too long words
}
word[a] = 0;
}
// Returns hash value of a word
int GetWordHash(char *word) {
unsigned long long a, hash = 0;
for (a = 0; a < strlen(word); a++) hash = hash * 257 + word[a];
hash = hash % vocab_hash_size;
return hash;
}
// Returns 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_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];
long long wordcn = 0;
FILE *fin = fopen(read_vocab_file, "rb");
if (fin == NULL) {
printf("Vocabulary file not found\n");