public void CalculateOutputStepTest()
{
// 0 0 1 => 0
// 1 1 1 => 1
// 1 0 1 => 1
// 0 1 1 => 0
// test
// 1 0 0 => ?
var weightAdjust = new Func <TrainingCalcModel, double>((m) =>
{
return(m.Error * m.Input * m.ActualOutput * (1 - m.ActualOutput));
});
var inputNodes1 = new InputLayer()
{
new InputNode(0), // { InValue = 0 });
new InputNode(1), // { InValue = 0 });
new InputNode(2) // { InValue = 1 });
};
var outnode = new OutputNode(1, FunctionFactory.GetActivationFunction(eActivationFunc.Step), weightAdjust);
outnode.AddInputNodes(inputNodes1);
outnode.Bias = 0.0;
for (int x = 0; x < 100000; x++)
{
inputNodes1.SetInput(new double[] { 0.0, 0.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(0);
inputNodes1.SetInput(new double[] { 1.0, 1.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(1);
inputNodes1.SetInput(new double[] { 1.0, 0.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(1);
inputNodes1.SetInput(new double[] { 0.0, 1.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(0);
}
outnode.SetInput(new double[] { 1.0, 0.0, 0.0 });
outnode.CalculateOutput();
var outp = outnode.Output;
Assert.IsTrue(outp == 1);
outnode.SetInput(new double[] { 0.0, 0.0, 0.0 });
outnode.CalculateOutput();
outp = outnode.Output;
Assert.IsTrue(outp == 0.0);
}
public void CalculateOutputSigmoidMultiLayerTest()
{
// 0 0 1 => 0
// 1 1 1 => 1
// 1 0 1 => 1
// 0 1 1 => 0
// test
// 1 0 0 => ?
Random r = new Random();
Func <double> getRandom = (() => { return(r.NextDouble()); });
var weightAdjust = new Func <TrainingCalcModel, double>((m) =>
{
return(m.Error * m.Input * m.ActualOutput * (1 - m.ActualOutput));
});
var sigFunction = FunctionFactory.GetActivationFunction(eActivationFunc.Sigmoid);
var step = new Func <double, double>((z) =>
{
return(z > 0.0 ? 1 : 0);
});
var inputNodes1 = new InputLayer();
inputNodes1.Add(new InputNode(0)); // { InValue = 0 });
inputNodes1.Add(new InputNode(1)); // { InValue = 0 });
inputNodes1.Add(new InputNode(2)); // { InValue = 1 });
var bias = -2.0;
var hiddenNodes = new List <HiddenNode>()
{
new HiddenNode(0, sigFunction, weightAdjust, getRandom)
{
Bias = bias
},
new HiddenNode(1, sigFunction, weightAdjust, getRandom)
{
Bias = bias
},
new HiddenNode(2, sigFunction, weightAdjust, getRandom)
{
Bias = bias
}
};
foreach (var hidden in hiddenNodes)
{
hidden.AddInputNodes(inputNodes1);
}
var outnode = new OutputNode(0, step, weightAdjust, getRandom);
outnode.AddInputNodes(hiddenNodes);
//outnode.Bias = -2.0;
Debug.WriteLine("Node {0} weight:{1}", 0, inputNodes1[0].Weights[0]);
Debug.WriteLine("Node {0} weight:{1}", 1, inputNodes1[0].Weights[1]);
Debug.WriteLine("Node {0} weight:{1}", 0, hiddenNodes[0].Weights[0]);
Debug.WriteLine("Node {0} weight:{1}", 0, hiddenNodes[1].Weights[0]);
for (int x = 0; x < 100000; x++)
{
hiddenNodes.ForEach((h) =>
{
h.CalculateOutput();
});
outnode.SetInput(new double[] { 0.0, 0.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(0);
outnode.SetInput(new double[] { 1.0, 1.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(1);
outnode.SetInput(new double[] { 1.0, 0.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(1);
outnode.SetInput(new double[] { 0.0, 1.0, 1.0 });
outnode.CalculateOutput();
outnode.AdjustWeights(0);
}
Debug.WriteLine("Node {0} weight:{1}", 0, inputNodes1[0].Weights[0]);
Debug.WriteLine("Node {0} weight:{1}", 1, inputNodes1[0].Weights[1]);
Debug.WriteLine("Node {0} weight:{1}", 0, hiddenNodes[0].Weights[0]);
Debug.WriteLine("Node {0} weight:{1}", 0, hiddenNodes[1].Weights[0]);
outnode.SetInput(new double[] { 1.0, 0.0, 0.0 });
outnode.CalculateOutput();
var outp = outnode.Output;
Assert.IsTrue(outp == 1);
outnode.SetInput(new double[] { 0.0, 0.0, 0.0 });
outnode.CalculateOutput();
outp = outnode.Output;
Assert.IsTrue(outp == 0);
}
