bool SVM::predict_(VectorDouble &inputVector){
if( !trained ){
errorLog << "predict_(VectorDouble &inputVector) - The SVM model has not been trained!" << endl;
return false;
}
if( inputVector.size() != numInputDimensions ){
errorLog << "predict_(VectorDouble &inputVector) - The size of the input vector (" << inputVector.size() << ") does not match the number of features of the model (" << numInputDimensions << ")" << endl;
return false;
}
if( param.probability == 1 ){
if( !predictSVM( inputVector, maxLikelihood, classLikelihoods ) ){
errorLog << "predict(VectorDouble inputVector) - Prediction Failed!" << endl;
return false;
}
}else{
if( !predictSVM( inputVector ) ){
errorLog << "predict(VectorDouble inputVector) - Prediction Failed!" << endl;
return false;
}
}
return true;
}
int main(int argc, char** argv)
{
/////////////////////////////////////////////////////
///usage:
///AnormalyDetection -t [0 1] [ [-f] or [-d [0 1 2 3...] [-l lable] location] ]
//AnormalyDetection -t [0 1] -f
//AnormalyDetection -t [0 1] -d [0 1 2 3...] [-l lable] location
///////////////////////////////////////////////////////
/*printf("argc = %d\n", argc);
for(int i = 1; i < argc; i ++)
printf("%d %s\n", i, argv[i]);*/
char **output = new char*[4];
for(int i = 0; i < 4; i ++)
{
output[i] = new char[MaxStringLength];
memcpy(output[i], "\0", MaxStringLength);
}
int n = 0;
if(!options(argc, argv, output, n))
{
printf("error usage!\n");
printf("usage:program options location\n");
printf("options:-t, -f, -d , -l\n");
return 1;
}
/*for(int i = 0; i < 4; i ++)
{
printf("%d: %s\n", i+1, output[i]);
}*/
int type = atoi(output[0]);
if(n == 1)
{
if(type == 0)
{
trainSVMModel();
}
if(type == 1)
{
//printf("entering detection...\n");
predictSVM();
}
return 0;
}
int dataset = atoi(output[1]);
switch(dataset)
{
case 1://UMN
frameWidth = 320;
frameHeight = 240;
break;
case 2://BEHAVE
frameWidth = 640;
frameHeight = 480;
break;
case 3://UCSD_PED1
frameWidth = 238;
frameHeight = 158;
break;
case 4://UCSD_PED2
frameWidth = 238;
frameHeight = 158;
break;
default:
break;
}
char* location = output[2];
vector<string> files;
readDataset(location, files);
if(type == 0)
{
trainSVMModel(files);
}
if(type == 1)
{
//printf("entering detection...\n");
vector<int> labels;
readLabels(output[3], labels);
predictSVM(files, labels);
}
return 0;
}
