public override ConfusionMatrix Execute()
{
//Create an network with one layer and one neuron in that layer
var network = new ActivationNetwork(new ThresholdFunction(), 3, 1);
//Bind the reference of the neuron
var neuron = network.Layers[0].Neurons[0] as ActivationNeuron;
//Create the Perceptron learning algorithm
//Library perceptron implements a single layer linear classifier
var teacher = new PerceptronLearning(network);
teacher.LearningRate = 0.1;
//Enrich the dimensions of the vectors, padding 1 to the end
var richTraining = AlgorithmHelpers.PaddDimension(trainingSet);
var richTesting = AlgorithmHelpers.PaddDimension(testSet);
//Training the network until the error is small enough
//or 500 hundred iterations have been computed
int epochs = 0;
while (true)
{
double error = teacher.RunEpoch(richTraining, trainingOutput);/// trainingSet.Length;
++epochs;
if (error < 0.025 * trainingSet.Length || epochs == 500)
{
break;
}
}
var predicted = richTesting
.Select(x => neuron.Compute(x))
.Select(x => Convert.ToInt32(x))
.ToArray();
//Create a confusion matrix with the calculated parameters
ConfusionMatrix cmatrix = new ConfusionMatrix(predicted, expected, POSITIVE, NEGATIVE);
OnAlgorithmEnded(Enumerable.Repeat(neuron, 1), cmatrix);
return(cmatrix);
}