float Statistics::calcEntropy()
{
//calculate entropy
float result = 0;
for (int direction = 0; direction < 4; direction++)
{
float** com = 0;
switch(direction)
{
case 0:
com = this->m_cooc0;
break;
case 1:
com = this->m_cooc1;
break;
case 2:
com = this->m_cooc2;
break;
case 3:
com = this->m_cooc3;
break;
}
result += calcEntropy(com);
}
return result / 4;
}
int main(int argc, char** argv){
if(argc <3){
cerr<<"Usage: ./main <path/to/target/config/file> <path/to/target/src/dir>"<<endl;
cerr<<"or"<<endl;
cerr<<"Usage: ./main -f <path/to/predefined/config-name/file> <path/to/target/src/dir>"<<endl;
return 0;
}
std::vector<std::string> vec_options;
vec_options.clear();
if( argc == 4 /*&& argv[1] == "-f"*/){
readOptions( vec_options, argv[2]); //从option-name.txt中读入配置名
}
else{ //通过augeaus程序分析配置文件得到配置名
std::string conf_file_path = string(argv[1]);
if( confilter(conf_file_path) == -1){
cerr<<"filter the option name in Config file failed"<<endl;
return -1;
}
readOptions(vec_options, "./conf_list.dat");
}
//先对每个配置项生成字典，计算熵，确定权重，具体每个词单独的字典匹配使用时再生成
std::map< std::string, double > entropy_dict;
entropy_dict.clear();
calcEntropy(vec_options, entropy_dict);
printf("options size : %d\n", vec_options.size());
// for(int i =0; i< vec_options.size(); i++){
// printf("%s\n", vec_options[i].c_str());
// }
// for( std::map<string, double>::iterator ite=entropy_dict.begin(); ite!=entropy_dict.end(); ite++){
// printf("%s %lf\n", ite->first.c_str(), ite->second);
// }
vector<string> file_v;
if(argc == 3)
traverseDir(argv[2], file_v);
else if( argc == 4)
traverseDir(argv[3], file_v);
printf("file_v size : %d\n", file_v.size());
string targetfile = findTargetFile(file_v, vec_options); //如果stack太小会出现cannot access to variable ...的错误
if( targetfile == ""){
cerr<<"Don't find any targetfile to analyze!"<<endl;
return -1;
}
analyzeTargetFile(targetfile, vec_options, entropy_dict);
}
float Statistics::calcInformationMeasures1()
{
float result = 0;
for (int direction = 0; direction < 4; direction++)
{
float** com = 0;
switch(direction)
{
case 0:
com = this->m_cooc0;
break;
case 1:
com = this->m_cooc1;
break;
case 2:
com = this->m_cooc2;
break;
case 3:
com = this->m_cooc3;
break;
}
//calculate HXY1
float HXY1 = 0;
for (int i = 0; i < this->m_numColors; i++)
{
for (int j = 0; j < this->m_numColors; j++)
{
HXY1 += com[i][j] * log(px(com, i) * py(com, j) + Statistics::epsilon);
}
}
HXY1 *= -1;
//calculate HX and HY
float HX = 0, HY = 0;
for (int i = 0; i < this->m_numColors; i++)
{
HX += px(com, i) * log(px(com, i) + Statistics::epsilon);
HY += py(com, i) * log(py(com, i) + Statistics::epsilon);
}
HX *= -1;
HY *= -1;
//calculate HXY
float HXY = calcEntropy(com);
//calculate information measures 1
result += (HXY - HXY1) / max(HX, HY);
}
return result / 4;
}
float Statistics::calcInformationMeasures2()
{
float result = 0;
for (int direction = 0; direction < 4; direction++)
{
float** com = 0;
switch(direction)
{
case 0:
com = this->m_cooc0;
break;
case 1:
com = this->m_cooc1;
break;
case 2:
com = this->m_cooc2;
break;
case 3:
com = this->m_cooc3;
break;
}
//calculate HXY2
float HXY2 = 0;
for (int i = 0; i < this->m_numColors; i++)
{
for (int j = 0; j < this->m_numColors; j++)
{
HXY2 += px(com, i) * py(com, j) * log(px(com, i) * py(com, j) + Statistics::epsilon);
}
}
HXY2 *= -1;
//calculate HXY
float HXY = calcEntropy(com);
//calculate information measures 1
result += sqrt(1 - exp(-2.0 * (HXY2 - HXY)));
}
return result / 4;
}
