void RadialBasisFunction::Train(HostMatrix<float> &Input, HostMatrix<float> &Target){
//std::cout << "Training" << std::endl;
// c_width = (float*) malloc(sizeof(float)*network_size);
// memset(c_width,0,sizeof(float)*network_size);
DeviceMatrix<float> device_X(Input);
//std::cout << "KMeans" << std::endl;
clock_t initialTime = clock();
KMeans KM;
KM.SetSeed(seed);
dCenters = KM.Execute(device_X,network_size);
cudaThreadSynchronize();
times[0] = (clock() - initialTime);
//std::cout << "Adjust Widths" << std::endl;
/*Adjust width using mean of distance to neighbours*/
initialTime = clock();
AdjustWidths(number_neighbours);
cudaThreadSynchronize();
times[1] = (clock() - initialTime);
/*Training weights and scaling factor*/
HostMatrix<float> TargetArr(Target.Rows(),NumClasses);
memset(TargetArr.Pointer(),0,sizeof(float)*TargetArr.Elements());
for(int i = 0; i < Target.Rows(); i++){
TargetArr(i,((int)Target(i,0)-1)) = 1;
}
DeviceMatrix<float> d_Target(TargetArr);
//std::cout << "Calculating Weights" << std::endl;
initialTime = clock();
DeviceMatrix<float> device_activ_matrix(device_X.Rows(),dCenters.Rows(),ColumnMajor);
KernelActivationMatrix(device_activ_matrix.Pointer(),device_X.Pointer(),dCenters.Pointer(),device_X.Columns(),dCenters.Columns(),device_activ_matrix.Columns(),device_activ_matrix.Rows(),scaling_factor,device_c_width.Pointer());
DeviceMatrix<float> d_Aplus = UTILS::pseudoinverse(device_activ_matrix);
dWeights = DeviceMatrix<float>(d_Aplus.Rows(),d_Target.Columns());
d_Aplus.Multiply(d_Aplus,d_Target,dWeights);
/*Return Weights and Centers*/
cudaThreadSynchronize();
times[2] = (clock() - initialTime);
// cudaMemcpy(c_width,device_c_width.Pointer(),sizeof(float)*device_c_width.Length(),cudaMemcpyDeviceToHost);
// this->Weights = HostMatrix<float>(dWeights);
// this->Centers = HostMatrix<float>(dCenters);
}
/*Calculate the root mean square error of outputs against targets*/
double rmse_error(HostMatrix<float> &Target,HostMatrix<float> &Output){
//check global error
float sum = 0;
int i;
for(i = 0; i < Target.Rows(); i++){
sum = sum + pow(Target(i,0) - Output(i,0),2);
}
return sqrt(sum/Target.Rows());
}
void writeHeader(HostMatrix<float> &Input, int number_classes, int seed){
cout << "\n=== Run information ===\n\n";
cout << std::left << setw(50) << "Random Seed" << std::left << setw(20) << seed << endl;
cout << std::left << setw(50) << "Number of Basis Functions" << std::left << setw(20) << NETWORK_SIZE << endl;
cout << std::left << setw(50) << "Number of Neighbours for Width Estimation" << std::left << setw(20) << RNEIGHBOURS << endl;
cout << std::left << setw(50) << "Number of Folds" << std::left << setw(20) << KFOLDS << endl;
cout << std::left << setw(50) << "Number of Attributes" << std::left << setw(20) << Input.Columns() << endl;
cout << std::left << setw(50) << "Number of Classes" << std::left << setw(20) << number_classes << endl;
cout << std::left << setw(50) << "Number of Instances" << std::left << setw(20) << Input.Rows() << endl;
}
/*Count number of miscalculated outputs against targets*/
int error_calc(HostMatrix<float> &Target,HostMatrix<float> &Output){
//check global error
int i;
int error = 0;
for(i = 0; i < Target.Rows(); i++){
if(Target(i,0) - Output(i,0) != 0){
error += 1;
}
}
return error;
}