/**
* \fn void trainBdd(std::string bddName, int dim, int maxPts, int k)
* \brief Trains the specified BDD.
*
* \param[in] bddName The name of the BDD.
* \param[in] dim The dimension of the STIPs.
* \param[in] maxPts The maximum number of points we want to compute.
* \param[in] k The number of cluster (means).
*/
void im_train_bdd(std::string bddName, int k){
std::string path2bdd("bdd/" + bddName);
std::string KMeansFile(path2bdd + "/" + "training.means");
//std::cout << path2bdd << std::endl;
//int desc = getDescID(path2bdd);
//int dim = getDim(desc);
// Loading the DenseTrack settings
// Loading BDD
IMbdd bdd(bddName,path2bdd);
bdd.load_bdd_configuration(path2bdd.c_str(),"imconfig.xml");
// In im_train_bdd all the people are the training people !!
std::vector<std::string> trainingPeople = bdd.getPeople();
// Loading activities
std::vector<std::string> activities = bdd.getActivities();
int nrActivities = activities.size();
int labels[nrActivities];
int index = 0;
for(std::vector<std::string>::iterator activity = activities.begin();
activity != activities.end();
++activity){
labels[index] = index + 1;
index++;
}
if(k%nrActivities != 0){
std::cerr << "k is not divisible by nrActivities !" << std::endl;
exit(EXIT_FAILURE);
}
// Saving KMeans settings
bdd.changeKMSettings("specifical",
k,
"training.means");
im_create_specifics_training_means(bdd, trainingPeople);
// SVM train
MatrixC trainMC = MatrixC(nrActivities,labels);
std::vector<std::string> testingPeople;
MatrixC testMC = MatrixC(nrActivities, labels);
double crossValidationAccuracy = im_svm_train(bdd,
trainingPeople, trainMC,
testingPeople, testMC);
trainMC.calculFrequence();
trainMC.exportMC(path2bdd,"training_confusion_matrix.txt");
bdd.write_bdd_configuration(path2bdd.c_str(),"imconfig.xml");
std::cout << "Resume of the training phase:" << std::endl;
bdd.show_bdd_configuration();
std::cout << "Train recognition rate:" << std::endl;
std::cout << "\t cross validation accuracy=" << crossValidationAccuracy << std::endl;
std::cout << "\t tau_train=" << trainMC.recognitionRate*100 << "%" << std::endl;
std::cout << "\t total number of train BOWs=" << trainMC.nrTest << std::endl;
}
//claimed function for calling the program, only due to the assignment tasks
static void matrixmult(const int M, const int N, const int K, double * A, const int lda, double * B, int ldb, double * C, int ldc)
{
Matrix MatrixA(M,K,A,lda);
Matrix MatrixB(K,N,B,ldb);
Matrix MatrixC(M,N);
//for matrices with dimensions to the power of two strassen is used
if(Matrix::checkIfPowerOfTwo(M,K))
{
if(Matrix::checkIfPowerOfTwo(K,N))
{
Matrix::matmult(MatrixA,MatrixB,MatrixC);
}
}
else
{
//otherwise native matrix multiplication
MatrixC = MatrixA*MatrixB;
}
for(size_t i = 0; i< MatrixC.getMdim() ; ++i)
{
for(size_t j = 0; j<MatrixC.getNdim() ; ++j)
{
C[i*ldc + j] = MatrixC.getValueAt(i,j);
}
}
}
int main()
{
long start_time; //Used for execution time (Start of execution)
long stop_time; //Used for execution end time (End of execution)
Matrix MatrixA("DataMatrixA.txt");//Input File data goes to Matrix A
Matrix MatrixB("DataMatrixB.txt");// Input File data goes to Matrix B
Matrix MatrixC(MatrixA.getRow(),MatrixB.getColumn());//Matrix C result gets rows of A and columns of B
start_time=clock();//Starts timer
HorizontalMultiply(MatrixA, MatrixB,MatrixC);//Computing Multiplication
stop_time=clock();//End timer
double Horizontal_Time= (stop_time-start_time)/(double)(CLOCKS_PER_SEC); //Execution time
MatrixC.print(); //Print result of Matrix C
cout << endl;
cout << "________________________________________________________________ " << endl;
cout << "Divide Horizontal Time: (Core Count in System): " << Horizontal_Time << " seconds" << endl;
cout << "________________________________________________________________ " << endl;
long start_time2; //Timer for twice the core count
long stop_time2;//End timer for twice the core count
Matrix MatrixC2(MatrixA.getRow(),MatrixB.getColumn()); //MatrixC2 gets A rows and B cols
start_time2= clock();//Start timer for function for twice core count
HorizontalMultiply2(MatrixA, MatrixB, MatrixC2);//Twice core count multipling
stop_time2 = clock();//End timer for function for twice core count
double Horizontal_Time2 = (stop_time2-start_time2)/(double)(CLOCKS_PER_SEC); //Total execution time of twice core count Multiply
MatrixC2.print();//Print Matrix C
cout << endl;
cout << "_______________________________________________________________"<< endl;
cout << "Divide Horizontal Time: (Twice amount of Cores in a system): " << Horizontal_Time2 << " seconds " << endl;
cout << "_______________________________________________________________" << endl;
return 0;
}
void im_leave_one_out(std::string bddName,
int k){
std::string path2bdd("bdd/" + bddName);
std::string KMeansFile(path2bdd + "/" + "training.means");
// std::cout << path2bdd << std::endl;
// Loading BDD
IMbdd bdd(bddName,path2bdd);
bdd.load_bdd_configuration(path2bdd.c_str(),"imconfig.xml");
// Saving KMeans settings
bdd.changeKMSettings("specifical", k, "training.means");
// Loading feature points settings
std::string descriptor = bdd.getDescriptor();
// Loading activities
std::vector<std::string> activities = bdd.getActivities();
int nrActivities = activities.size();
int labels[nrActivities];
int index = 0;
for(std::vector<std::string>::iterator activity = activities.begin();
activity != activities.end();
++activity){
labels[index] = index + 1;
index++;
}
if(k%nrActivities != 0){
std::cerr << "k is not divisible by nrActivities !" << std::endl;
exit(EXIT_FAILURE);
}
MatrixC trainMC = MatrixC(nrActivities,labels);
MatrixC testMC = MatrixC(nrActivities,labels);
double crossValidationAccuracy = 0;
std::vector<std::string> people = bdd.getPeople();
// Leave One Person Out
for(std::vector<std::string>::iterator person = people.begin() ;
person != people.end();
++person){
std::vector<std::string> testingPeople;
std::vector<std::string> trainingPeople;
testingPeople.push_back(*person);
// Filling trainingPeople
for(std::vector<std::string>::iterator trainingPerson = people.begin() ;
trainingPerson != people.end();
++trainingPerson){
if((*trainingPerson).compare(*person) != 0)
trainingPeople.push_back(*trainingPerson);
}
std::cout << "Testing" << *person << std::endl;
im_create_specifics_training_means(bdd, trainingPeople);
crossValidationAccuracy += im_svm_train(bdd,
trainingPeople, trainMC,
testingPeople, testMC);
std::cout << "Test " << *person << " ok!" << std::endl;
}
crossValidationAccuracy /= people.size();
trainMC.calculFrequence();
trainMC.exportMC(path2bdd,"training_confusion_matrix.txt");
trainMC.output();
testMC.calculFrequence();
testMC.exportMC(path2bdd,"testing_confusion_matrix.txt");
testMC.output();
std::cout << "#######################################" << std::endl;
std::cout << "######## RESUME OF THE TEST ###########" << std::endl;
std::cout << "#######################################" << std::endl;
std::cout << "Number of people: " << people.size() << std::endl;
std::cout << "Descriptor ID: " << descriptor << std::endl;
std::cout << "Number of means: " << k << std::endl;
std::cout << "Train recognition rate:" << std::endl;
std::cout << "\t cross validation accuracy=" << crossValidationAccuracy << std::endl;
std::cout << "\t tau_train=" << trainMC.recognitionRate*100 << "%" << std::endl;
std::cout << "\t total number of train BOWs=" << trainMC.nrTest << std::endl;
std::cout << "Test recognition rate:" << std::endl;
std::cout << "\t tau_test=" << testMC.recognitionRate*100 << "%" << std::endl;
std::cout << "\t total number of test BOWs=" << testMC.nrTest << std::endl;
}
