private void UnitTest1()
{
OutputText.Text = "";
NetworkDocument doc = new NetworkDocument();
InputNeuron i1 = new InputNeuron() {OutputValue = 0.7};
InputNeuron i2 = new InputNeuron() {OutputValue = 0.1};
InputNeuron i3 = new InputNeuron() {OutputValue = 0.3};
doc.Inputs.Add(i1);
doc.Inputs.Add(i2);
doc.Inputs.Add(i3);
OutputNeuron o = new OutputNeuron();
doc.Outputs.Add(new OutputNeuron());
NeuronHelper.ConnectNodes(i1, o, -0.3);
NeuronHelper.ConnectNodes(i2, o, 3.1);
NeuronHelper.ConnectNodes(i3, o, 0.5);
double expected = 0.25;
double answer = o.SumInputs(true);
OutputText.Text += "Summation test 3 inputs values 0.7, 0.1,0.3 respective weights 0.3,3.1,0.5" +
Environment.NewLine;
OutputText.Text += "Expected = " + expected + Environment.NewLine;
OutputText.Text += "Actual = " + answer + Environment.NewLine;
}
private void UnitTest3()
{
double output1;
double output2;
OutputText.Text = "";
NetworkDocument doc = new NetworkDocument() {Bias = -2.0,Momentum = 0.05,LearningRate = 0.5};
doc.ActivationFunction = Enumerations.ActivationFunctions.ActivationSigmoidal;
doc.UsesBias = true;
InputNeuron i0 = new InputNeuron() { OutputValue = 0.1 };
InputNeuron i1 = new InputNeuron() { OutputValue = 0.9 };
doc.Inputs.Add(i0);
doc.Inputs.Add(i1);
doc.NumLayers = 2;//this should build the layers
HiddenNeuron l00 = new HiddenNeuron(){BiasValue = 2.0};
HiddenNeuron l01 = new HiddenNeuron() { BiasValue = 2.0 };
HiddenNeuron l10 = new HiddenNeuron() { BiasValue = 3.0 };
HiddenNeuron l11 = new HiddenNeuron() { BiasValue = -2.0 };
doc.Hidden[0].Add(l00);
doc.Hidden[0].Add(l01);
doc.Hidden[1].Add(l10);
doc.Hidden[1].Add(l11);
OutputNeuron o = new OutputNeuron() {ExpectedValue = 0.9,BiasValue = -2};
doc.Outputs.Add(o);
//connect layer 1
NeuronHelper.ConnectNodes(i0, l00, -2);
NeuronHelper.ConnectNodes(i0, l01, 3);
NeuronHelper.ConnectNodes(i1, l00, -2);
NeuronHelper.ConnectNodes(i1, l01, 3);
//connect layer 2
NeuronHelper.ConnectNodes(l00, l10, -2);
NeuronHelper.ConnectNodes(l00, l11, 2);
NeuronHelper.ConnectNodes(l01, l10, -4);
NeuronHelper.ConnectNodes(l01, l11, 2);
//output layer
NeuronHelper.ConnectNodes(l10, o, 3);
NeuronHelper.ConnectNodes(l11, o, 1);
output1 = l00.SumInputs(true);
output2 = l01.SumInputs(true);
WriteOutput(0, output1);
WriteOutput(5, output2);
output1 = l00.CalcOutput(doc);
output2 = l01.CalcOutput(doc);
WriteOutput(0.5, output1);
WriteOutput(0.99, output2);
output1 = l10.SumInputs(doc.UsesBias);
output2 = l11.SumInputs(doc.UsesBias);
WriteOutput(-1.972, output1);
WriteOutput(0.98, output2);
output1 = l10.CalcOutput(doc);
output2 = l11.CalcOutput(doc);
WriteOutput(0.122, output1);
WriteOutput(0.728, output2);
output1 = doc.Outputs[0].SumInputs(doc.UsesBias);
output2 = doc.Outputs[0].CalcOutput(doc);
WriteOutput(-0.906, output1);
WriteOutput(0.288, output2);
//backward pass
output1 = doc.Outputs[0].CalcError();
WriteOutput(0.125, output1);
output1 = l10.CalcError();
WriteOutput(0.040, output1);
output1 = l11.CalcError();
WriteOutput(0.025, output1);
output1 = l00.CalcError();
WriteOutput(0, output1);
output1 = l01.CalcError();
WriteOutput(0, output1);
o.AdjustFromWeights(doc);
l10.AdjustFromWeights(doc);
l11.AdjustFromWeights(doc);
l00.AdjustFromWeights(doc);
l01.AdjustFromWeights(doc);
}
