コード例 #1
0
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);

}
コード例 #2
0
ファイル: Dataset.cpp プロジェクト: kallaballa/Neurocid
/*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());
}
コード例 #3
0
ファイル: RBF.cpp プロジェクト: kallaballa/Neurocid
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;

}
コード例 #4
0
ファイル: Dataset.cpp プロジェクト: kallaballa/Neurocid
/*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;
}