//Methods
/// <summary>
/// Resets the predictors provider to its initial state.
/// </summary>
public void Reset()
{
_activationMDW?.Reset();
_firingMDW?.Reset();
_activationStat?.Reset();
_activationDiffStat?.Reset();
_lastActivation = 0d;
_lastNormalizedActivation = 0d;
_lastSpike = false;
//Predictors
foreach (IPredictor predictor in _predictorCollection)
{
predictor.Reset();
}
return;
}
//Methods
public void Run()
{
//Filter test
RealFeatureFilter rff = new RealFeatureFilter(new Interval(-1, 1));
for (int i = 1; i <= 1500; i++)
{
rff.Update(_rand.NextDouble() * _rand.Next(0, 10000));
}
double featureValue = 0.5;
double filterValue = rff.ApplyFilter(featureValue);
double reverseValue = rff.ApplyReverse(filterValue);
Console.WriteLine($"Feature: {featureValue} Filter: {filterValue} Reverse: {reverseValue}");
//Pulse generator test
BasicStat sampleStat = new BasicStat();
sampleStat.Reset();
PulseGeneratorSettings modSettings = new PulseGeneratorSettings(1, 1.5, PulseGeneratorSettings.TimingMode.Poisson);
IGenerator generator = new PulseGenerator(modSettings);
int steps = 10000;
double period = 0;
for (int i = 0; i < steps; i++)
{
++period;
double sample = generator.Next();
//Console.WriteLine(sample);
if (sample != 0)
{
sampleStat.AddSampleValue(period);
period = 0;
}
}
Console.WriteLine($"Mean: {sampleStat.ArithAvg} StdDev: {sampleStat.StdDev} Min: {sampleStat.Min} Max: {sampleStat.Max}");
Console.ReadLine();
//Random distributions test
BasicStat rStat = new BasicStat();
for (int i = 0; i < 200; i++)
{
double r = _rand.NextFilterredGaussianDouble(0.5, 1, -0.5, 1);
rStat.AddSampleValue(r);
Console.WriteLine(r);
}
Console.WriteLine($"Mean: {rStat.ArithAvg} StdDev: {rStat.StdDev} Min: {rStat.Min} Max: {rStat.Max}");
Console.ReadLine();
//Activation tests
double fadingSum = 0;
for (int i = 0; i < 1000; i++)
{
fadingSum *= (1d - 0.1);
fadingSum += 1d;
Console.WriteLine(fadingSum);
}
Console.ReadLine();
IActivationFunction testAF = ActivationFactory.Create(new SimpleIFSettings(refractoryPeriods: 0), new Random(0));
TestActivation(testAF, 100, 3.5, 10, 70);
SimpleIFSettings setup = new SimpleIFSettings();
FindAFInputBorders(ActivationFactory.Create(setup, new Random(0)),
-0.1,
20
);
//Linear algebra test
double[] flatData =
{
0.2, 5, 17.3, 1.01, 54, 7,
2.2, 5.5, 12.13, 11.57, 5.71, -85,
-70.1, 15, -18.3, 0.3, 42, -6.25,
0.042, 1, 7.75, -81.01, -21.29, 5.44,
0.1, 4, -4.3, 18.01, 7.12, -3.14,
-80.1, 24.4, 4.3, 12.03, 2.789, -13
};
Matrix testM = new Matrix(6, 6, flatData);
/*
* //Inversion test
* Matrix resultM = new Matrix(testM);
* resultM.SingleThreadInverse();
*/
/*
* //Transpose test
* Matrix resultM = testM.Transpose();
*/
/*
* //Multiply test
* Matrix resultM = Matrix.Multiply(testM, testM);
* for (int i = 0; i < resultM.NumOfRows; i++)
* {
* Console.WriteLine($"{resultM.Data[i][0]}; {resultM.Data[i][1]}; {resultM.Data[i][2]}; {resultM.Data[i][3]}; {resultM.Data[i][4]}; {resultM.Data[i][5]}");
* }
*/
;
int numOfweights = 3;
int xIdx, dIdx = 0;
double[][] data = new double[3][];
data[dIdx] = new double[numOfweights];
xIdx = -1;
data[dIdx][++xIdx] = 2;
data[dIdx][++xIdx] = 1;
data[dIdx][++xIdx] = 3;
++dIdx;
data[dIdx] = new double[numOfweights];
xIdx = -1;
data[dIdx][++xIdx] = 1;
data[dIdx][++xIdx] = 3;
data[dIdx][++xIdx] = -3;
++dIdx;
data[dIdx] = new double[numOfweights];
xIdx = -1;
data[dIdx][++xIdx] = -2;
data[dIdx][++xIdx] = 4;
data[dIdx][++xIdx] = 4;
//Matrix M = new Matrix(data, true);
//Matrix I = M.Inverse(false);
//Matrix identity = M * I; //Must lead to identity matrix
Matrix I = new Matrix(3, 3);
I.AddScalarToDiagonal(1);
Matrix X = new Matrix(I);
X.Multiply(0.1);
Matrix XT = X.Transpose();
Matrix R = XT * X;
Console.ReadLine();
///*
SimpleIFSettings settings = new SimpleIFSettings(new RandomValueSettings(15, 15),
new RandomValueSettings(0.05, 0.05),
new RandomValueSettings(5, 5),
new RandomValueSettings(20, 20),
0
);
IActivationFunction af = ActivationFactory.Create(settings, new Random(0));
//*/
TestActivation(af, 800, 0.15, 10, 600);
return;
}