static void AccuracyTestPerc()
{
Console.WriteLine("Accuracy test perc:");
int[] neurons = new int[] { 3, 4, 2 };
bool[] delays = new bool[] { false, false };
string[] afs = new string[] { "Binary Step", "Identity" };
float[][] w = new float[][]
{
new float[]
{
1.0f / 2, 1.0f / 3, -1.0f / 5,
1, 1.0f / 2, -1.0f / 4,
1.0f / 2, -1, 1.0f / 2,
-1, 1.0f / 4, 1.0f / 3
},
new float[]
{
1.0f / 2, -1.0f / 3, 1.0f / 4, -1,
1, -1.0f / 2, -1.0f / 3, 1.0f / 4
}
};
PerceptronManaged p = new PerceptronManaged(new PerceptronTopology(3, neurons, delays, afs));
p.SetWeights(w);
var p2 = p.Copy();
float[] y = p2.Solve(new float[] { 1, 0, 1 });
float[] answer = { 5.0f / 12, 1.0f / 6 };
float EPS = 1e-5f;
if ((Math.Abs(y[0] - answer[0]) > EPS) || (Math.Abs(y[1] - answer[1]) > EPS))
{
Console.WriteLine("FAIL");
}
else
{
Console.WriteLine("PASS");
}
}
static void PerformanceTest()
{
Console.WriteLine("Performance test");
Stopwatch sw = new Stopwatch();
int N = 1000000;
int layers = 7;
int[] neurons = new int[] { 5, 55, 70, 100, 60, 15, 2 };
bool[] delays = new bool[] { true, true, true, true, true, false };
string af = "Logistic";
string[] afs = new string[]
{
af, af, af, af, af, af
};
sw.Start();
PerceptronTopology topology = new PerceptronTopology(layers, neurons, delays, afs);
sw.Stop();
Console.WriteLine("topology creation = " + sw.ElapsedMilliseconds);
sw.Start();
PerceptronManaged perc = new PerceptronManaged(topology);
perc.SetWeights(GenerateWeights(neurons, delays));
sw.Stop();
Console.WriteLine("perc creation = " + sw.ElapsedMilliseconds);
float[] x = { 1.0f, 0.0f, 0.0f, 1.0f, 0.0f };
sw.Start();
for (int i = 0; i < N; i++)
{
float[] y = perc.Solve(x);
}
sw.Stop();
Console.WriteLine("calc = " + sw.ElapsedMilliseconds);
}