public void TestAddCUDA()
{
//Alloc device memory
int count_rows_a = 2;
int count_shared = 2;
int count_cols_b = 2;
double alfa = 1.0;
double beta = 1.0;
CudaDeviceVariable <double> A = new double[] { 0, 0, 0, 0 };
CudaDeviceVariable <double> B = new double[] { 0, 0, 0, 0 };
CudaDeviceVariable <double> C = new double[] { 0, 0, 0, 0 };
//Clean up
CudaBlas blas = new CudaBlas();
blas.Gemm(Operation.NonTranspose, Operation.NonTranspose,
count_rows_a, count_shared, count_cols_b,
alfa,
A, count_rows_a,
B, count_shared,
beta,
C, count_rows_a);
//Copy data back to host
double[] result = C;
ToolsArray.print(result);
Assert.AreEqual(0, result[0], 0.001);
// Um right this should be moved
blas.Dispose();
}
public void LearnXORTest()
{
float [][] inputs = new float [4][];
float[][] targets = new float [4][];
inputs[0] = new float[] { 0, 0 };
inputs[1] = new float[] { 0, 1 };
inputs[2] = new float[] { 1, 0 };
inputs[3] = new float[] { 1, 1 };
targets[0] = new float[] { 0 };
targets[1] = new float[] { 1 };
targets[2] = new float[] { 1 };
targets[3] = new float[] { 0 };
MultiLayerPerceptron mlp = new MultiLayerPerceptron(new int [] { 2, 2, 1 });
mlp.set_learning_rate(0.2f);
mlp.set_eligibility(0.1f);
for (int i = 0; i < 10000; i++)
{
mlp.train_batch(inputs, targets);
}
ToolsArray.print(mlp.estimate(inputs[0]));
ToolsArray.print(mlp.estimate(inputs[1]));
ToolsArray.print(mlp.estimate(inputs[2]));
ToolsArray.print(mlp.estimate(inputs[3]));
}
public BigInteger[,] KeyArray()
{
return(ToolsArray.Copy(d_public_key));
}
