static void Main()
{
weights = new double[3];
network = new ActivationNetwork(new SignumActivationFunction(), 2, 1);
weights[0] = ((ActivationNeuron)network.Layers[0].Neurons[0]).Threshold = 0;
weights[1] = network.Layers[0].Neurons[0].Weights[0] = 0.9;
weights[2] = network.Layers[0].Neurons[0].Weights[1] = 0.2;
learning = new PerceptronLearning(network);
learning.LearningRate = 0.005;
form = new MyForm()
{
WindowState = FormWindowState.Maximized
};
form.Paint += (s, a) => Redraw(a.Graphics);
var timer = new System.Windows.Forms.Timer();
timer.Interval = 10;
timer.Tick += NextSample;
timer.Start();
Application.Run(form);
}
/// <summary>
/// Initializes the member network and the member teacher.
/// </summary>
/// <param name="network"></param>
/// <param name="teacher"></param>
private void SetUpNetwork(ActivationNetwork network, NeuralNetworkInfo info)
{
string teacher = info.Teacher;
m_Network = network;
if (teacher == "BackPropagation")
{
BackPropagationLearning learner = new BackPropagationLearning(m_Network);
learner.LearningRate = info.LearningRate;
learner.Momentum = info.Momentum;
m_Teacher = learner;
}
else if (teacher == "Perceptron")
{
PerceptronLearning learner = new PerceptronLearning(m_Network);
learner.LearningRate = info.LearningRate;
m_Teacher = learner;
}
else if (teacher == "DeltaRule")
{
DeltaRuleLearning learner = new DeltaRuleLearning(m_Network);
learner.LearningRate = info.LearningRate;
m_Teacher = learner;
}
else
{
BackPropagationLearning learner = new BackPropagationLearning(m_Network);
learner.LearningRate = info.LearningRate;
learner.Momentum = info.Momentum;
m_Teacher = learner;
}
}
protected override ISupervisedLearning CreateTeacher()
{
var teacher = new PerceptronLearning(_network)
{
LearningRate = _learningRate,
};
return(teacher);
}
public static void cc()
{
//整理输入输出数据
double[][] input = new double[4][];
double[][] output = new double[4][];
input[0] = new double[] { 0, 0 }; output[0] = new double[] { 0 };
input[1] = new double[] { 0, 1 }; output[1] = new double[] { 0 };
input[2] = new double[] { 1, 0 }; output[2] = new double[] { 0 };
input[3] = new double[] { 1, 1 }; output[3] = new double[] { 1 };
for (int i = 0; i < 4; i++)
{
Console.WriteLine("input{0}: ===> {1},{2} output{0}: ===> {3}", i, input[i][0], input[i][1], output[i][0]);
}
//建立网络，层数1，输入2，输出1，激励函数阈函数
ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, 1);
//学习方法为感知器学习算法
PerceptronLearning teacher = new PerceptronLearning(network);
//定义绝对误差
double error = 1.0;
Console.WriteLine();
Console.WriteLine("learning error ===> {0}", error);
//输出学习速率
Console.WriteLine();
Console.WriteLine("learning rate ===> {0}", teacher.LearningRate);
//迭代次数
int iterations = 0;
Console.WriteLine();
while (error > 0.001)
{
error = teacher.RunEpoch(input, output);
Console.WriteLine("learning error ===> {0}", error);
iterations++;
}
Console.WriteLine("iterations ===> {0}", iterations);
Console.WriteLine();
Console.WriteLine("sim:");
//模拟
for (int i = 0; i < 4; i++)
{
Console.WriteLine("input{0}: ===> {1},{2} sim{0}: ===> {3}", i, input[i][0], input[i][1], network.Compute(input[i])[0]);
}
}
private static void TrainNetwork(LearningData learningData, String networkPath)
{
ActivationNetwork network = new ActivationNetwork(
UseBipolar ? (IActivationFunction) new BipolarSigmoidFunction(1) : (IActivationFunction) new SigmoidFunction(),
784,
//784,
10);
network.Randomize();
Int32 epochIndex = 0;
//// create teacher
PerceptronLearning teacher = new PerceptronLearning(network); // new BackPropagationLearning(network);
//PerceptronLearning teacher = new PerceptronLearning(network);// new BackPropagationLearning(network);
teacher.LearningRate = 0.1f;
////teacher.Momentum = 0.5f;
Double error = Double.MaxValue;
Double previousError = Double.MaxValue;
Int32 counter = 100;
// loop
while (counter > 0)
{
// run epoch of learning procedure
error = teacher.RunEpoch(learningData.Input, learningData.Output);
if (error > previousError)
{
teacher.LearningRate = teacher.LearningRate * 0.5f;
}
Console.WriteLine(String.Format("{0} {1}", epochIndex, error));
epochIndex++;
previousError = error;
counter--;
}
network.Save(networkPath);
//Double[] output = network.Compute(learningData.Input[0]);
}
public void TestName()
{
IActivationFunction function = new BipolarSigmoidFunction();
var network = new ActivationNetwork(function,
inputsCount: 2, neuronsCount: new[] { 2, 1 });
var layer = network.Layers[0] as ActivationLayer;
var teacher = new PerceptronLearning(network);
teacher.LearningRate = 0.1;
var input = new double[4][];
input[0] = new[] { 0d, 0d };
input[1] = new[] { 0d, 1d };
input[2] = new[] { 1d, 0d };
input[3] = new[] { 1d, 1d };
var output = new double[4][];
output[0] = new[] { 0d };
output[1] = new[] { 0d };
output[2] = new[] { 0d };
output[3] = new[] { 1d };
//// Iterate until stop criteria is met
double error = double.PositiveInfinity;
double previous;
var needToStop = false;
while (!needToStop)
{
}
do
{
previous = error;
// // Compute one learning iteration
error = teacher.RunEpoch(input, output);
} while (Math.Abs(previous - error) > 0.01);
int[] answers = input.Apply(network.Compute).GetColumn(0).Apply(System.Math.Sign);
//https://github.com/accord-net/framework/blob/development/Samples/Neuro/Perceptron/Applications/OneLayerPerceptron.cs
}
public void Treinar(double[][] dados)
{
double[][] output = new double[dados.Length][];
double[][] input = new double[dados.Length][];
for (int i = 0; i < dados.Length; i++)
{
input[i] = new double[Variaveis];
output[i] = new double[TotalNeuronios];
for (int j = 0; j < Variaveis; j++)
{
input[i][j] = dados[i][j];
}
int classe = Convert.ToInt32(dados[i][4]) - 1;
output[i][classe] = 1;
}
ActivationNetwork network = new ActivationNetwork(new SigmoidFunction(ValorAlpha), Variaveis, TotalNeuronios);
ActivationLayer layer = network[0];
PerceptronLearning teacher = new PerceptronLearning(network);
teacher.LearningRate = CamadaRate;
int interacao = 1000;
int count = 0;
List <double> errolist = new List <double>();
while (count <= interacao)
{
double erro = teacher.RunEpoch(input, output) / dados.Length;
errolist.Add(erro);
count++;
}
CriaColunaFuncao();
for (int i = 0; i < TotalNeuronios; i++)
{
this.FuncaoNeuronio.Rows.Add("Neuronio [" + (i + 1) + "]", Convert.ToInt32(layer[i].Threshold), layer[i][0]);
}
}
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);
}
public void Teach()
{
// Teacher method.
// Perceptron was experimentally found to give best results.
var teacher = new PerceptronLearning(Network);
// Value to track network's effectivness.
// If value increases over time, network is overfitted - terminate learning process.
var previousError = double.MaxValue;
// Value tracks the amount of times error value increased.
// Once it reaches a treshold, stop trainig - network is overfitted.
var errorIncreaseCount = 0;
// Epochs tracks the amount of cycles neural network took.
// Used to prevent infinite training.
var epochs = 0;
Console.WriteLine("Teaching network.");
// Train the neural network.
// Training lasts until EITHER network starts to get overfitted with data,
// or trainig has reached maximum epoch count.
while (errorIncreaseCount < OverfittedErrorTreshold && epochs < EpochsMax)
{
var error = teacher.RunEpoch(_inputData, _outputData);
if (error > previousError)
{
errorIncreaseCount++;
}
else
{
errorIncreaseCount = 0;
}
previousError = error;
epochs += 1;
if (epochs % 20 == 0)
{
Console.WriteLine($"{(double)epochs / EpochsMax * 100}%");
}
}
}
void OnSceneGUI()
{
PerceptronLearning editorObj = target as PerceptronLearning;
if (editorObj == null)
{
return;
}
float newW0 = EditorGUILayout.FloatField("W0", editorObj.W0);
float newW1 = EditorGUILayout.FloatField("W1", editorObj.W1);
if (newW0 != editorObj.W0 || newW1 != editorObj.W1)
{
editorObj.W0 = newW0;
editorObj.W1 = newW1;
Vector3 localPosition = editorObj.w.transform.localPosition;
editorObj.w.transform.localPosition = new Vector3(newW0, newW1, localPosition.z);
}
}
/// <summary>
/// Machine Learning
/// </summary>
private void Training()
{
// Creating Network
network = new DeepBeliefNetwork(
new GaussianFunction(), // Activation function
inputsCount: 4, // Input degree
hiddenNeurons: new int[] { 1 }); // Output degree
// Initialize the network weight with gaussian distribution
new GaussianWeights(network).Randomize();
network.UpdateVisibleWeights();
// Creating DBN Learning Algorithm
var teacher = new PerceptronLearning(network);
// Start learning. Do it for 1000 times.
for (int i = 0; i < 1000; i++)
{
teacher.RunEpoch(inputs, outputs);
}
// 重みの更新
network.UpdateVisibleWeights();
}
static void Main()
{
Console.WriteLine("Создание нейронной сети...");
DataItem data = DataItemFactory.GetNumericData();
int inputCount = data.Input.First().Length;
var neuronCount = new[] { 10 };
IActivationFunction function = new BipolarSigmoidFunction();
double learningRate = 0.1;
double momentum = 0;
int maxEpochNumber = 1000000;
double minErrorChange = 0.000001;
double minError = 0.001;
var network = MultilayerNeuralNetwork.CreateNetwork(neuronCount, inputCount, function);
network.Randomize();
Console.WriteLine("Создание нейронной сети завершено.");
var teacher = new PerceptronLearning(network)
{
LearningRate = learningRate
};
int epochNumber = 1;
double lastError = double.MaxValue;
double error;
double errorChange;
Console.WriteLine("Start learning...");
do
{
DateTime dtStart = DateTime.Now;
error = teacher.RunEpoch(data.Input, data.Output) / data.Input.Length;
Console.WriteLine(
$"Epoch #{epochNumber} finished; " +
$"current error is {error}; " +
$"it takes: {(DateTime.Now - dtStart).Duration()}");
errorChange = Math.Abs(lastError - error);
lastError = error;
epochNumber++;
}while (epochNumber < maxEpochNumber &&
error > minError &&
errorChange > minErrorChange);
for (int i = 0; i < data.Input.Length; i++)
{
double[] outputs = network.Compute(data.Input[i]);
string strIn = "";
foreach (var input in data.Output[i])
{
strIn += $"{input},";
}
string strOut = "";
foreach (var output in outputs)
{
strOut += $"{Math.Abs(output):0.00},";
}
Console.WriteLine($"{i}. Expected {strIn} Actual {strOut}");
}
}
public BackPropogation()
{
InitializeComponent();
activation_nework = new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 10, 1);
watch1 = new Stopwatch();
watch2 = new Stopwatch();
watch3 = new Stopwatch();
backgroundWorkerTrainer.Disposed += backgroundWorkerTrainer_Disposed;
backgroundWorkerTrainer.DoWork += backgroundWorkerTrainer_DoWork;
backgroundWorkerTrainer.ProgressChanged += backgroundWorkerTrainer_ProgressChanged;
backgroundWorkerTrainer.WorkerSupportsCancellation = true;
backgroundWorkerTrainer.WorkerReportsProgress = true;
saveFileDialog1.Filter = "feed forward network files (*.ffn)|*.ffn";
saveFileDialog1.Title = "Save neural networkfile";
saveFileDialog1.InitialDirectory = null;
saveFileDialog1.FileName = null;
openFileDialog1.Filter = "feed forward network files (*.ffn)|*.ffn";
openFileDialog1.Title = "Load neural network file";
openFileDialog1.InitialDirectory = null;
openFileDialog1.FileName = null;
backgroundWorkerSignal.Disposed += backgroundWorkerSignal_Disposed;
backgroundWorkerSignal.WorkerSupportsCancellation = true;
backgroundWorkerSignal.WorkerReportsProgress = true;
backgroundWorkerSignal.DoWork += backgroundWorkerSignal_DoWork;
backgroundWorkerSignal.RunWorkerCompleted += backgroundWorkerSignal_RunWorkerCompleted;//80, 70, 60, 50, 40,
network1 = activation_nework;
network2 = activation_nework; // new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 40, 1);
network3 = activation_nework; // new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 40, 1);
network4 = activation_nework; // new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 40, 1);
network5 = new ActivationNetwork(new BipolarSigmoidFunction(), 50,1);
network6 = new ActivationNetwork(new BipolarSigmoidFunction(), 50, 1);
teacher = new BackPropagationLearning(network1);
evteacher = new EvolutionaryLearning(network2, 100);
reprop = new ResilientBackpropagationLearning(network3);
lbteacher = new LevenbergMarquardtLearning(network4);
delta = new DeltaRuleLearning(network5);
perceptron = new PerceptronLearning(network6);
delta.LearningRate = 1;
perceptron.LearningRate = 0.1;
myPane = new GraphPane();
listPointsOne = new PointPairList();
myPane = zedGraphControl1.GraphPane;
// set a title
myPane.Title.Text = "Error VS Time";
// set X and Y axis titles
myPane.XAxis.Title.Text = "Time in Milliseconds";
myPane.YAxis.Title.Text = "Error";
myCurveOne = myPane.AddCurve("Learning curve", listPointsOne, Color.Red, SymbolType.None);
// myCurveOne = myPane.AddCurve("Resilient Back Propagation", listPointstwo, Color.Green, SymbolType.None);
// myCurveOne = myPane.AddCurve("Genetic Learning", listPointsthree, Color.Blue, SymbolType.None);
}
// Worker thread
void SearchSolution()
{
// prepare learning data
var input = new double[this.samples][];
var output = new double[this.samples][];
for (var i = 0; i < this.samples; i++)
{
input[i] = new double[this.variables];
output[i] = new double[1];
// copy input
for (var j = 0; j < this.variables; j++)
{
input[i][j] = this.data[i, j];
}
// copy output
output[i][0] = this.classes[i];
}
// create perceptron
var network = new ActivationNetwork(new ThresholdFunction(), this.variables, 1);
var neuron = network[0][0];
// create teacher
var teacher = new PerceptronLearning(network);
// set learning rate
teacher.LearningRate = this.learningRate;
// iterations
var iteration = 1;
// statistic files
StreamWriter errorsFile = null;
StreamWriter weightsFile = null;
try
{
// check if we need to save statistics to files
if (this.saveStatisticsToFiles)
{
// open files
errorsFile = File.CreateText("errors.csv");
weightsFile = File.CreateText("weights.csv");
}
// erros list
var errorsList = new ArrayList();
// loop
while (!this.needToStop)
{
// save current weights
if (weightsFile != null)
{
for (var i = 0; i < this.variables; i++)
{
weightsFile.Write(neuron[i] + ";");
}
weightsFile.WriteLine(neuron.Threshold);
}
// run epoch of learning procedure
var error = teacher.RunEpoch(input, output);
errorsList.Add(error);
// show current iteration
this.iterationsBox.Text = iteration.ToString();
// save current error
if (errorsFile != null)
{
errorsFile.WriteLine(error);
}
// show classifier in the case of 2 dimensional data
if ((neuron.InputsCount == 2) && (neuron[1] != 0))
{
var k = -neuron[0] / neuron[1];
var b = -neuron.Threshold / neuron[1];
var classifier = new double[2, 2]
{
{ this.chart.RangeX.Min, this.chart.RangeX.Min * k + b },
{ this.chart.RangeX.Max, this.chart.RangeX.Max * k + b }
};
// update chart
this.chart.UpdateDataSeries("classifier", classifier);
}
// stop if no error
if (error == 0)
{
break;
}
iteration++;
}
// show perceptron's weights
this.weightsList.Items.Clear();
for (var i = 0; i < this.variables; i++)
{
this.weightsList.Items.Add(string.Format("Weight {0}", i + 1));
this.weightsList.Items[i].SubItems.Add(neuron[i].ToString("F6"));
}
this.weightsList.Items.Add("Threshold");
this.weightsList.Items[this.variables].SubItems.Add(neuron.Threshold.ToString("F6"));
// show error's dynamics
var errors = new double[errorsList.Count, 2];
for (int i = 0, n = errorsList.Count; i < n; i++)
{
errors[i, 0] = i;
errors[i, 1] = (double)errorsList[i];
}
this.errorChart.RangeX = new DoubleRange(0, errorsList.Count - 1);
this.errorChart.RangeY = new DoubleRange(0, this.samples);
this.errorChart.UpdateDataSeries("error", errors);
}
catch (IOException)
{
MessageBox.Show("Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
finally
{
// close files
if (errorsFile != null)
{
errorsFile.Close();
}
if (weightsFile != null)
{
weightsFile.Close();
}
}
// enable settings controls
EnableControls(true);
}
private void button1_Click(object sender, EventArgs e)
{
// НЕЙРОСЕТЬ
ActivationNeuron neuron = network[0][0];
PerceptronLearning teacher = new PerceptronLearning(network);
teacher.LearningRate = 1;
StreamWriter errorsFile = null;
StreamWriter weightsFile = null;
errorsFile = File.CreateText("errors.csv");
weightsFile = File.CreateText("weights.csv");
// iterations
int iteration = 1;
ArrayList errorsList = new ArrayList();
double[][] input = new double[samplesNum][];
double[][] output = new double[samplesNum][];
for (int i = 0; i < samplesNum; i++)
{
input[i] = new double[2];
output[i] = new double[1];
// copy input
input[i][0] = inputs[i].X;
input[i][1] = inputs[i].Y;
// copy output
output[i][0] = inputs[i].type;
}
while (true)
{
// save current weights
if (weightsFile != null)
{
for (int i = 0; i < 2; i++)
{
weightsFile.Write(neuron[i] + ";");
}
weightsFile.WriteLine(neuron.Threshold);
}
// run epoch of learning procedure
double error = teacher.RunEpoch(input, output);
errorsList.Add(error);
// show current iteration
iterationsBox.Text = iteration.ToString();
// save current error
if (errorsFile != null)
{
errorsFile.WriteLine(error);
}
// show classifier in the case of 2 dimensional data
if ((neuron.InputsCount == 2) && (neuron[1] != 0))
{
double k = -neuron[0] / neuron[1];
double b = -neuron.Threshold / neuron[1];
}
// stop if no error
if (error == 0)
{
break;
}
iteration++;
}
// show error's dynamics
double[,] errors = new double[errorsList.Count, 2];
for (int i = 0, n = errorsList.Count; i < n; i++)
{
errors[i, 0] = i;
errors[i, 1] = (double)errorsList[i];
}
errorChart.AddDataSeries("error", Color.Red, AForge.Controls.Chart.SeriesType.ConnectedDots, 3, false);
errorChart.RangeX = new DoubleRange(0, errorsList.Count - 1);
errorChart.RangeY = new DoubleRange(0, samplesNum);
errorChart.UpdateDataSeries("error", errors);
if (errorsFile != null)
{
errorsFile.Close();
}
if (weightsFile != null)
{
weightsFile.Close();
}
}
public BackPropogation()
{
InitializeComponent();
activation_nework = new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 10, 1);
watch1 = new Stopwatch();
watch2 = new Stopwatch();
watch3 = new Stopwatch();
backgroundWorkerTrainer.Disposed += backgroundWorkerTrainer_Disposed;
backgroundWorkerTrainer.DoWork += backgroundWorkerTrainer_DoWork;
backgroundWorkerTrainer.ProgressChanged += backgroundWorkerTrainer_ProgressChanged;
backgroundWorkerTrainer.WorkerSupportsCancellation = true;
backgroundWorkerTrainer.WorkerReportsProgress = true;
saveFileDialog1.Filter = "feed forward network files (*.ffn)|*.ffn";
saveFileDialog1.Title = "Save neural networkfile";
saveFileDialog1.InitialDirectory = null;
saveFileDialog1.FileName = null;
openFileDialog1.Filter = "feed forward network files (*.ffn)|*.ffn";
openFileDialog1.Title = "Load neural network file";
openFileDialog1.InitialDirectory = null;
openFileDialog1.FileName = null;
backgroundWorkerSignal.Disposed += backgroundWorkerSignal_Disposed;
backgroundWorkerSignal.WorkerSupportsCancellation = true;
backgroundWorkerSignal.WorkerReportsProgress = true;
backgroundWorkerSignal.DoWork += backgroundWorkerSignal_DoWork;
backgroundWorkerSignal.RunWorkerCompleted += backgroundWorkerSignal_RunWorkerCompleted; //80, 70, 60, 50, 40,
network1 = activation_nework;
network2 = activation_nework; // new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 40, 1);
network3 = activation_nework; // new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 40, 1);
network4 = activation_nework; // new ActivationNetwork(new BipolarSigmoidFunction(), 50, 50, 40, 1);
network5 = new ActivationNetwork(new BipolarSigmoidFunction(), 50, 1);
network6 = new ActivationNetwork(new BipolarSigmoidFunction(), 50, 1);
teacher = new BackPropagationLearning(network1);
evteacher = new EvolutionaryLearning(network2, 100);
reprop = new ResilientBackpropagationLearning(network3);
lbteacher = new LevenbergMarquardtLearning(network4);
delta = new DeltaRuleLearning(network5);
perceptron = new PerceptronLearning(network6);
delta.LearningRate = 1;
perceptron.LearningRate = 0.1;
myPane = new GraphPane();
listPointsOne = new PointPairList();
myPane = zedGraphControl1.GraphPane;
// set a title
myPane.Title.Text = "Error VS Time";
// set X and Y axis titles
myPane.XAxis.Title.Text = "Time in Milliseconds";
myPane.YAxis.Title.Text = "Error";
myCurveOne = myPane.AddCurve("Learning curve", listPointsOne, Color.Red, SymbolType.None);
// myCurveOne = myPane.AddCurve("Resilient Back Propagation", listPointstwo, Color.Green, SymbolType.None);
// myCurveOne = myPane.AddCurve("Genetic Learning", listPointsthree, Color.Blue, SymbolType.None);
}
// Worker thread
void SearchSolution()
{
// prepare learning data
double[][] input = new double[samples][];
double[][] output = new double[samples][];
for (int i = 0; i < samples; i++)
{
input[i] = new double[2];
output[i] = new double[classesCount];
// set input
input[i][0] = data[i, 0];
input[i][1] = data[i, 1];
// set output
output[i][classes[i]] = 1;
}
// create perceptron
ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, classesCount);
ActivationLayer layer = network.Layers[0] as ActivationLayer;
// create teacher
PerceptronLearning teacher = new PerceptronLearning(network);
// set learning rate
teacher.LearningRate = learningRate;
// iterations
int iteration = 1;
// statistic files
StreamWriter errorsFile = null;
StreamWriter weightsFile = null;
try
{
// check if we need to save statistics to files
if (saveStatisticsToFiles)
{
// open files
errorsFile = File.CreateText("errors.csv");
weightsFile = File.CreateText("weights.csv");
}
// erros list
ArrayList errorsList = new ArrayList();
// loop
while (!needToStop)
{
// save current weights
if (weightsFile != null)
{
for (int i = 0; i < classesCount; i++)
{
weightsFile.Write("neuron" + i + ",");
weightsFile.Write(layer.Neurons[i].Weights[0] + ",");
weightsFile.Write(layer.Neurons[i].Weights[1] + ",");
weightsFile.WriteLine(((ActivationNeuron)layer.Neurons[i]).Threshold);
}
}
// run epoch of learning procedure
double error = teacher.RunEpoch(input, output);
errorsList.Add(error);
// save current error
if (errorsFile != null)
{
errorsFile.WriteLine(error);
}
// show current iteration
SetText(iterationsBox, iteration.ToString());
// stop if no error
if (error == 0)
{
break;
}
// show classifiers
for (int j = 0; j < classesCount; j++)
{
double k = (layer.Neurons[j].Weights[1] != 0) ? (-layer.Neurons[j].Weights[0] / layer.Neurons[j].Weights[1]) : 0;
double b = (layer.Neurons[j].Weights[1] != 0) ? (-((ActivationNeuron)layer.Neurons[j]).Threshold / layer.Neurons[j].Weights[1]) : 0;
double[,] classifier = new double[2, 2] {
{ chart.RangeX.Min, chart.RangeX.Min *k + b },
{ chart.RangeX.Max, chart.RangeX.Max *k + b }
};
// update chart
chart.UpdateDataSeries(string.Format("classifier" + j), classifier);
}
iteration++;
}
// show perceptron's weights
ClearList(weightsList);
for (int i = 0; i < classesCount; i++)
{
string neuronName = string.Format("Neuron {0}", i + 1);
// weight 0
ListViewItem item = AddListItem(weightsList, neuronName);
AddListSubitem(item, "Weight 1");
AddListSubitem(item, layer.Neurons[i].Weights[0].ToString("F6"));
// weight 1
item = AddListItem(weightsList, neuronName);
AddListSubitem(item, "Weight 2");
AddListSubitem(item, layer.Neurons[i].Weights[1].ToString("F6"));
// threshold
item = AddListItem(weightsList, neuronName);
AddListSubitem(item, "Threshold");
AddListSubitem(item, ((ActivationNeuron)layer.Neurons[i]).Threshold.ToString("F6"));
}
// show error's dynamics
double[,] errors = new double[errorsList.Count, 2];
for (int i = 0, n = errorsList.Count; i < n; i++)
{
errors[i, 0] = i;
errors[i, 1] = (double)errorsList[i];
}
errorChart.RangeX = new Range(0, errorsList.Count - 1);
errorChart.UpdateDataSeries("error", errors);
}
catch (IOException)
{
MessageBox.Show("Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
finally
{
// close files
if (errorsFile != null)
{
errorsFile.Close();
}
if (weightsFile != null)
{
weightsFile.Close();
}
}
// enable settings controls
EnableControls(true);
}
