public void TestMethod1()
{
PerceptronLayer layer = new PerceptronLayer(4, 2);
var error = new Array(4, true)
{
[0] = 0.2,
[1] = 0.3,
[2] = 0.4,
[3] = 0.5
};
var input = new Array(2, true)
{
[0] = 0.5,
[1] = 2
};
var actual = layer.FormOutput(input);
var result = layer.Train(error, input, actual);
}
public void Calculate()
{
const int layer0 = 3, layer1 = 2, layer2 = 3;
#region 1 Iteration
double[] input = { 0.8, 0.76, 0.54 };
double[] weight1 = { 0.35, 0.46, 0.51 };
double[] weight2 = { 0.4, 0.87, 0.36 };
double[] sum1 = { 0, 0 };
for (var i = 0; i < input.Length; i++)
{
sum1[0] += input[i] * weight1[i];
sum1[1] += input[i] * weight2[i];
}
double[] output1 = { Expanent(sum1[0]), Expanent(sum1[1]) };
#endregion
#region 2 Iteration
double[] weight3 = { 0.5, 0.14 };
double[] weight4 = { 0.76, 0.86 };
double[] weight5 = { 0.95, 0.12 };
double[] sum2 = { 0, 0, 0 };
for (var i = 0; i < output1.Length; i++)
{
sum2[0] += output1[i] * weight3[i];
sum2[1] += output1[i] * weight4[i];
sum2[2] += output1[i] * weight5[i];
}
double[] output2 = { Expanent(sum2[0]), Expanent(sum2[1]), Expanent(sum2[2]) };
#endregion
#region Train
double[] expected = { 0.87, 1, 0.32 };
double[] error = new double[layer2];
for (var i = 0; i < error.Length; i++)
{
error[i] = output2[i] - expected[i];
}
double[] weightDelta = new double[layer2];
for (var i = 0; i < layer2; i++)
{
weightDelta[i] = error[i] * output2[i] * (1 - output2[i]);
}
var newWeight3 = new double[layer1];
var newWeight4 = new double[layer1];
var newWeight5 = new double[layer1];
const double learningRate = 0.1;
for (var i = 0; i < layer1; i++)
{
newWeight3[i] = weight3[i] - output1[i] * weightDelta[0] * learningRate;
newWeight4[i] = weight4[i] - output1[i] * weightDelta[1] * learningRate;
newWeight5[i] = weight5[i] - output1[i] * weightDelta[2] * learningRate;
}
#region 2 Iteration
double[] error2 = new double[layer1];
for (var i = 0; i < layer1; i++)
{
error2[i] += weight3[i] * weightDelta[0];
error2[i] += weight4[i] * weightDelta[1];
error2[i] += weight5[i] * weightDelta[2];
}
double[] weightDelta2 = new double[layer1];
for (var i = 0; i < layer1; i++)
{
weightDelta2[i] = error2[i] * output1[i] * (1 - output1[i]);
}
var newWeight1 = new double[layer0];
var newWeight2 = new double[layer0];
for (var i = 0; i < layer0; i++)
{
newWeight1[i] = weight1[i] - input[i] * weightDelta2[0] * learningRate;
newWeight2[i] = weight2[i] - input[i] * weightDelta2[1] * learningRate;
}
#endregion
#endregion
#region Compare
var weightList1 = new List <List <double> > {
new List <double>(weight1), new List <double>(weight2)
};
var perceptronLayer1 = new PerceptronLayer(weightList1);
var weightList2 = new List <List <double> >
{
new List <double>(weight3),
new List <double>(weight4),
new List <double>(weight5)
};
var perceptronLayer2 = new PerceptronLayer(weightList2);
var inputArray = new Array(input);
var result1 = (Array)perceptronLayer1.FormOutput(inputArray);
var result2 = (Array)perceptronLayer2.FormOutput(result1);
var errorResult1 = new Array(error);
var errorResult2 = (Array)perceptronLayer2.Train(errorResult1, result1, result2);
var errorResult3 = (Array)perceptronLayer1.Train(errorResult2, inputArray, result1);
for (var i = 0; i < 3; i++)
{
Assert.AreEqual(perceptronLayer1.Neurals[0].Weights[i], newWeight1[i], 0.001);
Assert.AreEqual(perceptronLayer1.Neurals[1].Weights[i], newWeight2[i], 0.001);
}
#endregion
}