public void MatrixCalc()
{
// W*I+B test calculattin Input*Weights + Biases
int nbInput = 6;
int nbOutput = 3;
NNArray I = NNArray.Random(nbInput);
NNMatrix W = NNMatrix.Random(nbInput, nbOutput);
NNArray B = NNArray.Random(nbOutput);
var tmp = W * I;
var O = tmp + B;
Assert.AreEqual(O.Length, nbOutput);
}
public void MatrixMult()
{
NNMatrix M1 = NNMatrix.Random(2, 2); // 3 row * 4 Columns
NNMatrix M2 = NNMatrix.Identity(2, 2);
var M3 = M1 * M2;
Assert.IsTrue(M1.Equals(M3));
M1 = NNMatrix.Random(3, 4); // 3 row * 4 Columns
M2 = NNMatrix.Random(4, 2);
M3 = M1 * M2;
Assert.AreEqual(M3.NbRows, 3);
Assert.AreEqual(M3.NbCols, 2);
}
public void MatrixPerformance()
{
// W*I+B test calculattin Input*Weights + Biases
int nbInput = 300;
int nbOutput = 200;
NNArray I = NNArray.Random(nbInput);
NNMatrix W = NNMatrix.Random(nbInput, nbOutput);
NNArray B = NNArray.Random(nbOutput);
int count = 200;
DateTime start;
DateTime end;
double duration;
// Not optimized
start = DateTime.Now;
for (int i = 0; i < count; i++)
{
NNArray res = W * I + B;
}
end = DateTime.Now;
duration = (end - start).TotalMilliseconds / 1000;
Console.WriteLine("Duration Non Optimized: "+duration);
// Optimized
NNArray O = NNArray.Random(nbOutput);
start = DateTime.Now;
for (int i = 0; i < count; i++)
{
W.Multiply(I,O);
O.Add(B,O);
}
end = DateTime.Now;
duration = (end - start).TotalMilliseconds / 1000;
Console.WriteLine("Duration Optimized: " + duration);
}
