public void test1()
{
Console.WriteLine("Running test 1...");
NeuronSettings nSettings = new NeuronSettings();
NeuronLayerSettings layerSettings = new NeuronLayerSettings(nSettings);
layerSettings.mNumNeuronsPerLayerMin = 2;
layerSettings.mNumNeuronsPerLayerMax = 2;
NeuronNetSettings nnSettings = new NeuronNetSettings(layerSettings);
nnSettings.mNumInputs = 1;
nnSettings.mNumOutputs = 1;
nnSettings.mNumLayersMin = 0;
nnSettings.mNumLayersMax = 0;
GeneticAlgorithmSettings gaSettings = new GeneticAlgorithmSettings(nnSettings.mFactory);
gaSettings.mMaxPopulation = 100;
GeneticAlgorithm ga = new GeneticAlgorithm(gaSettings);
List<double> inputs = new List<double>() { 0 };
List<double> expected = new List<double>() { 0 };
int mNumEpochs = 100;
Console.WriteLine("Learning...");
for (int iEpoch = 0; iEpoch < mNumEpochs; iEpoch++)
{
Console.Write(string.Format("Epoch: {0} / {1}\t", iEpoch + 1, mNumEpochs));
for (int x = 0; x <= 100; x++)
{
inputs.Clear();
expected.Clear();
inputs.Add((double)x / 100.0);
expected.Add((double)x / 100.0);
learnPopulation(ga, inputs, expected);
}
Console.Write(string.Format("Average: {0}\t", ga.AverageFitness));
Console.Write(string.Format("Best: {0}\t", ga.BestFitness));
Console.Write(string.Format("Worst: {0}\n", ga.WorstFitness));
ga.epoch();
}
Console.WriteLine("Predicting...");
for (int x = 0; x <= 100; x++)
{
inputs[0] = (double)x / 100.0;
var outputs = predict(ga, inputs);
Console.WriteLine(string.Format("Input:\t{0}\t\tOutput:\t{1}", inputs[0], outputs[0]));
}
}
public NeuronNet(ObjectPool pool, NeuronNetSettings settings)
: base(pool, settings)
{
mPool = new ObjectPool(settings.mLayerSettings.mFactory, settings.mNumLayersMax);
mOutputLayer = new NeuronLayer(mPool, settings.mLayerSettings);
}
public NeuronNetFactory(NeuronNetSettings nnSettings)
: base(nnSettings)
{
}
public NeuronNet(ObjectPool pool, NeuronNetSettings settings)
: base(pool, settings)
{
mPool = new ObjectPool(settings.mLayerSettings.mFactory, settings.mNumLayersMax);
mOutputLayer = new NeuronLayer(mPool, settings.mLayerSettings);
}
public void test5Thread()
{
Console.WriteLine("Running test 5, min...");
DateTime timeStart = DateTime.Now;
NeuronSettings nSettings = new NeuronSettings();
NeuronLayerSettings layerSettings = new NeuronLayerSettings(nSettings);
layerSettings.mNumNeuronsPerLayerMin = 2;
layerSettings.mNumNeuronsPerLayerMax = 2;
NeuronNetSettings nnSettings = new NeuronNetSettings(layerSettings);
nnSettings.mNumInputs = 2;
nnSettings.mNumOutputs = 1;
nnSettings.mNumLayersMin = 2;
nnSettings.mNumLayersMax = 2;
GeneticAlgorithmSettings gaSettings = new GeneticAlgorithmSettings(nnSettings.mFactory);
gaSettings.mMaxPopulation = 100;
GeneticAlgorithm ga = new GeneticAlgorithm(gaSettings);
List<double> inputs = new List<double>() { 0, 0 };
List<double> expected = new List<double>() { 0 };
int iEpoch = 0;
Console.WriteLine("Learning...");
while (!mLearningStopped && iEpoch < 100)
{
Console.Write(string.Format("Epoch: {0}\t", iEpoch++));
for (double x = 0; x <= 1; x += 0.01)
{
for (double y = 0; y <= 1; y += 0.01)
{
inputs[0] = x;
inputs[1] = y;
expected[0] = x*y;
learnPopulation(ga, inputs, expected);
}
}
Console.Write(string.Format("Average: {0:0.00}\t", ga.AverageFitness));
Console.Write(string.Format("Best: {0:0.00}\t", ga.BestFitness));
Console.Write(string.Format("Worst: {0:0.00}\n", ga.WorstFitness));
ga.epoch();
}
DateTime timeEnd = DateTime.Now;
TimeSpan ts = timeEnd - timeStart;
Console.WriteLine("Learning took: " + ts.ToString());
Console.WriteLine("Predicting...");
for (double x = 0; x < 1; x += 0.1)
{
for (double y = 0; y < 1; y += 0.1)
{
inputs[0] = x;
inputs[1] = y;
var outputs = predict(ga, inputs);
Console.WriteLine(string.Format("Input:\t{0}, {1}\t\tOutput:\t{2:0.000}", inputs[0], inputs[1], outputs[0]));
}
}
}
public void test4()
{
Console.WriteLine("Running test 4, cos...");
NeuronSettings nSettings = new NeuronSettings();
NeuronLayerSettings layerSettings = new NeuronLayerSettings(nSettings);
layerSettings.mNumNeuronsPerLayerMin = 1;
layerSettings.mNumNeuronsPerLayerMax = 1;
NeuronNetSettings nnSettings = new NeuronNetSettings(layerSettings);
nnSettings.mNumInputs = 1;
nnSettings.mNumOutputs = 1;
nnSettings.mNumLayersMin = 0;
nnSettings.mNumLayersMax = 0;
GeneticAlgorithmSettings gaSettings = new GeneticAlgorithmSettings(nnSettings.mFactory);
gaSettings.mMaxPopulation = 1000;
GeneticAlgorithm ga = new GeneticAlgorithm(gaSettings);
List<double> inputs = new List<double>() { 0 };
List<double> expected = new List<double>() { 0 };
int mNumEpochs = 100;
Console.WriteLine("Learning...");
for (int iEpoch = 0; iEpoch < mNumEpochs; iEpoch++)
{
Console.Write(string.Format("Epoch: {0} / {1}\t", iEpoch + 1, mNumEpochs));
for (double x = -1.5; x <= 1.5; x += 0.001)
{
inputs[0] = x;
expected[0] = Math.Sin(x);
learnPopulation(ga, inputs, expected);
}
Console.Write(string.Format("Average: {0}\t", ga.AverageFitness));
Console.Write(string.Format("Best: {0}\t", ga.BestFitness));
Console.Write(string.Format("Worst: {0}\n", ga.WorstFitness));
ga.epoch();
}
Console.Write(ga.SortedPopulation[0].ToString());
}
public NeuronNetFactory(NeuronNetSettings nnSettings)
: base(nnSettings)
{
}