public void OnAlgorithmEnded(IEnumerable<ActivationNeuron> neurons, ConfusionMatrix matrix)
{
var handler = Finished;
if (handler != null)
{
var e = new AlgorithmFinishedEventArgs()
{
Neurons = neurons,
Matrix = matrix
};
handler(this, e);
}
}
public void OnAlgorithmEnded(IEnumerable <ActivationNeuron> neurons, ConfusionMatrix matrix)
{
var handler = Finished;
if (handler != null)
{
var e = new AlgorithmFinishedEventArgs()
{
Neurons = neurons,
Matrix = matrix
};
handler(this, e);
}
}
private void neuralNetwork_Finished(object sender, AlgorithmFinishedEventArgs e)
{
var classifiers = new List<Double[,]>();
foreach (var neuron in e.Neurons)
{
double k = (neuron.Weights[1] != 0) ? (-neuron.Weights[0] / neuron.Weights[1]) : 0;
double b = (neuron.Weights[1] != 0) ? (-((ActivationNeuron)neuron).Threshold / neuron.Weights[1]) : 0;
double[,] classifier = new double[2, 2]
{
{ (float)m_minX, (float)m_minX * k + b },
{ (float)m_maxX, (float)m_maxX * k + b }
};
classifiers.Add(classifier);
}
var bestCM = e.Matrix;
if (MostAccurateNN == null || bestCM.Accuracy > MostAccurateNN.Item2.Accuracy)
MostAccurateNN = Tuple.Create(classifiers, bestCM);
m_view.ChartUpdate("nnChart", "classifier1", MostAccurateNN.Item1);
}
private void perceptron_Finished(object sender, AlgorithmFinishedEventArgs e)
{
var neuron = e.Neurons.First();
// Calculate the coordinates of the classifier
double k = (neuron.Weights[1] != 0) ? (-neuron.Weights[0] / neuron.Weights[1]) : 0;
double b = (neuron.Weights[1] != 0) ? (-((ActivationNeuron)neuron).Threshold / neuron.Weights[1]) : 0;
// Create the line and feed it to the data series
double[,] classifier = new double[2, 2]
{
{ (float)m_minX, (float)m_minX * k + b },
{ (float)m_maxX, (float)m_maxX * k + b }
};
var cmatrix = e.Matrix;
//Update the most accurate classification
if (MostAccuratePerceptron == null || cmatrix.Accuracy > MostAccuratePerceptron.Item2.Accuracy)
{
MostAccuratePerceptron = Tuple.Create(classifier, cmatrix);
}
m_view.ChartUpdate("perceChart", "classifier", MostAccuratePerceptron.Item1);
}