/// <summary>
/// 点击计算按钮
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void tsmiCalculate_Click(object sender, EventArgs e)
{
// 创建输入层、隐层和输出层
ActivationLayer inputLayer = GetLayer(cboInputLayerType.SelectedItem.ToString(), 2);
ActivationLayer hiddenLayer = GetLayer(cboHiddenLayerType.SelectedItem.ToString(), int.Parse(txtHiddenLayerCount.Text));
ActivationLayer outputLayer = GetLayer(cboOutputLayerType.SelectedItem.ToString(), 1);
// 创建层之间的关联
new BackpropagationConnector(inputLayer, hiddenLayer, ConnectionMode.Complete).Initializer = new RandomFunction(0, 0.3);
new BackpropagationConnector(hiddenLayer, outputLayer, ConnectionMode.Complete).Initializer = new RandomFunction(0, 0.3);
// 创建神经网络
var network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(double.Parse(txtInitialLearningRate.Text), double.Parse(txtFinalLearningRate.Text));
// 进行训练
var trainingSet = new TrainingSet(2, 1);
for (var i = 0; i < 17; i++)
{
var x1 = data[i, 0];
var x2 = data[i, 1];
var y = data[i, 2];
var inputVector = new double[] { x1, x2 };
var outputVector = new double[] { y };
var trainingSample = new TrainingSample(inputVector, outputVector);
trainingSet.Add(trainingSample);
}
network.SetLearningRate(0.3, 0.1);
network.Learn(trainingSet, int.Parse(txtTrainingEpochs.Text));
network.StopLearning();
// 进行预测
for (var i = 0; i < 17; i++)
{
var x1 = data[i, 0];
var x2 = data[i, 1];
var y = data[i, 2];
var testInput = new double[] { x1, x2 };
var testOutput = network.Run(testInput)[0];
var absolute = testOutput - y;
var relative = Math.Abs((testOutput - y) / testOutput);
dgvData.Rows[i].Cells[3].Value = testOutput.ToString("f3");
dgvData.Rows[i].Cells[4].Value = absolute.ToString("f3");
dgvData.Rows[i].Cells[5].Value = (relative * 100).ToString("f1") + "%";
}
}
public override void Train(IForecastingDataSets datasets)
{
OnStartRunning(new ComponentRunEventArgs(datasets));
AnnModelParameter para = mParameter as AnnModelParameter;
LinearLayer inputLayer = new LinearLayer(datasets.InputData[0].Length);
SigmoidLayer hiddenLayer = new SigmoidLayer(para.HiddenNeuronsCount[0]);
SigmoidLayer outputLayer = new SigmoidLayer(1);
new BackpropagationConnector(inputLayer, hiddenLayer).Initializer = new RandomFunction(0d, 0.3d);
new BackpropagationConnector(hiddenLayer, outputLayer).Initializer = new RandomFunction(0d, 0.3d);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(para.LearningRate);
network.JitterEpoch = para.JitterEpoch;
network.JitterNoiseLimit = para.JitterNoiseLimit;
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs args)
{
// TODO: trainning error needs to be calculated
OnRunningEpoch(new AnnModelRunEpochEventArgs(args.TrainingIteration + 1, 0));
});
network.Learn(ForecastingDataSets.ConvertToTrainingSet(datasets), para.Iterations);
datasets.ForecastedData = new double[datasets.InputData.Length][];
for (int i = 0; i < datasets.InputData.Length; i++)
{
datasets.ForecastedData[i] = new double[1];
datasets.ForecastedData[i][0] = Forecast(datasets.InputData[i]);
}
OnFinishRunning(new ComponentRunEventArgs(datasets));
}
public void LabTest1()
{
var inputLayer = new LinearLayer(5);
var hiddenLayer = new TanhLayer(neuronCount);
var outputLayer = new TanhLayer(2);
new BackpropagationConnector(inputLayer, hiddenLayer);
new BackpropagationConnector(hiddenLayer, outputLayer);
_xorNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
_xorNetwork.SetLearningRate(learningRate);
var trainingSet = new TrainingSet(5, 2);
trainingSet.Add(new TrainingSample(new double[] { 0, 0, 0, 0, 0 }, new double[] { 0, 0 }));
trainingSet.Add(new TrainingSample(new double[] { 0, 0, 0, 1, 0 }, new double[] { 3, 3 }));
trainingSet.Add(new TrainingSample(new double[] { 0, 0, 1, 0, 0 }, new double[] { 2, 2 }));
trainingSet.Add(new TrainingSample(new double[] { 0, 0, 1, 1, 0 }, new double[] { 2, 3 }));
trainingSet.Add(new TrainingSample(new double[] { 0, 1, 0, 0, 0 }, new double[] { 1, 1 }));
trainingSet.Add(new TrainingSample(new double[] { 0, 1, 0, 1, 0 }, new double[] { 1, 3 }));
trainingSet.Add(new TrainingSample(new double[] { 0, 1, 1, 0, 0 }, new double[] { 1, 2 }));
trainingSet.Add(new TrainingSample(new double[] { 0, 1, 1, 1, 0 }, new double[] { 1, 3 }));
trainingSet.Add(new TrainingSample(new double[] { 22, 1, 1, 1, 22 }, new double[] { 1, 3 }));
_errorList = new double[cycles];
//_xorNetwork.EndEpochEvent += EndEpochEvent;
_xorNetwork.Learn(trainingSet, cycles);
var result = _xorNetwork.Run(new double[] { 0, 0, 1, 1, 0 });
}
public BackpropagationNetwork network(int trainInVectorDimension, int trainOutVectorDimension)
{
this.hiddenLayerList = HiddenLayerList();
ActivationLayer inputLayer = new LinearLayer(trainInVectorDimension);
ActivationLayer outputLayer = new SigmoidLayer(trainOutVectorDimension);
BackpropagationConnector bpc0 = new BackpropagationConnector(inputLayer, this.hiddenLayerList[0]);
for (int i = 1; i < this.hiddenLayerList.Count; i++)
{
bpc0 = new BackpropagationConnector(this.hiddenLayerList[i - 1], this.hiddenLayerList[i]);
}
bpc0 = new BackpropagationConnector(this.hiddenLayerList[this.hiddenLayerList.Count - 1], outputLayer);
BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);
/*ActivationLayer inputLayer = hiddenLayerList[0];
* ActivationLayer outputLayer = hiddenLayerList[hiddenLayerList.Count - 1];
*
* if(hiddenLayerList.Count != 2)
* {
* BackpropagationConnector bpc0 = new BackpropagationConnector(inputLayer, this.hiddenLayerList[1]);
* for (int i = 2; i < this.hiddenLayerList.Count - 1; i++)
* {
* bpc0 = new BackpropagationConnector(this.hiddenLayerList[i - 1], this.hiddenLayerList[i]);
* }
* bpc0 = new BackpropagationConnector(this.hiddenLayerList[this.hiddenLayerList.Count - 2], outputLayer);
* }
*
* BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);*/
network.SetLearningRate(this.learningRate);
return(network);
}
private void button37_Click(object sender, EventArgs e)
{
TrainingSet egitimseti = new TrainingSet(35, 5);
egitimseti.Add(new TrainingSample(VeriSeti.A, new double[5] {
1, 0, 0, 0, 0
}));
egitimseti.Add(new TrainingSample(VeriSeti.A1, new double[5] {
1, 0, 0, 0, 0
}));
egitimseti.Add(new TrainingSample(VeriSeti.B, new double[5] {
0, 1, 0, 0, 0
}));
egitimseti.Add(new TrainingSample(VeriSeti.C, new double[5] {
0, 0, 1, 0, 0
}));
egitimseti.Add(new TrainingSample(VeriSeti.D, new double[5] {
0, 0, 0, 1, 0
}));
egitimseti.Add(new TrainingSample(VeriSeti.E, new double[5] {
0, 0, 0, 0, 1
}));
ag.SetLearningRate(Convert.ToDouble(txt_ogrenmekatsayisi.Text));
ag.Learn(egitimseti, Convert.ToInt32(txt_ogrenmehizi.Text));
txt_ogrenmekatsayisi.Enabled = false;
txt_ogrenmehizi.Enabled = false;
lbl_hata.Text = ag.MeanSquaredError.ToString();
button37.Enabled = false;
MessageBox.Show("Yapay Sinir Ağı Eğitildi.", "Bilgi");
}
void CreateNewNetwork()
{
LinearLayer inputLayer = new LinearLayer(neurons);
SigmoidLayer hiddenLayer = new SigmoidLayer(hidden1Neurons);
SigmoidLayer hiddenLayer2 = new SigmoidLayer(hidden2Neurons);
LinearLayer outputLayer = new LinearLayer(outputNum);
BackpropagationConnector conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
conn1.Initializer = new RandomFunction(0d, 0.001d);
BackpropagationConnector conn3 = new BackpropagationConnector(hiddenLayer, hiddenLayer2);
conn3.Initializer = new RandomFunction(0d, 0.001d);
BackpropagationConnector conn2 = new BackpropagationConnector(hiddenLayer2, outputLayer);
conn2.Initializer = new RandomFunction(0d, 0.001d);
conn1.Initialize();
conn2.Initialize();
conn3.Initialize();
neuralNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
neuralNetwork.SetLearningRate(learningRate);
neuralNetwork.Initialize();
}
public Imagine(int w, int h)
{
W = w;
H = h;
Inputs = W * H * 3;
iLay = new LinearLayer(Inputs);
hLay = new SigmoidLayer(neuronCount);
oLay = new SigmoidLayer(w * h * 3);
c1 = new BackpropagationConnector(iLay, hLay, ConnectionMode.Complete);
c2 = new BackpropagationConnector(hLay, oLay, ConnectionMode.Complete);
net = new BackpropagationNetwork(iLay, oLay);
net.SetLearningRate(learningRate);
}
public void initNet(int fLayer, int sLayer, int tLayer)
{
ActivationLayer inputLayer = createLayer(fLayer, inputNeurons);
ActivationLayer hiddenLayer = createLayer(sLayer, hiddenNeurons);
ActivationLayer outputLayer = createLayer(tLayer, outputNeurons);
//var hiddenLayer = new LinearLayer(hiddenNeurons);
//var outputLayer = new LinearLayer(outputNeurons);
new BackpropagationConnector(inputLayer, hiddenLayer).Initializer = new RandomFunction(0d, 0.3d);
new BackpropagationConnector(hiddenLayer, outputLayer).Initializer = new RandomFunction(0d, 0.3d);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(learningRate);
SampleInput = new double[inputNeurons + outputNeurons];
SampleOutput = new double[outputNeurons];
}
// train butonu
private void btnTrain_Click(object sender, EventArgs e)
{
TrainingSet trainingSet = new TrainingSet(35, 5);
trainingSet.Add(new TrainingSample(Dataset.Letters.A, new double[5] {
1, 0, 0, 0, 0
}));
trainingSet.Add(new TrainingSample(Dataset.Letters.B, new double[5] {
0, 1, 0, 0, 0
}));
trainingSet.Add(new TrainingSample(Dataset.Letters.C, new double[5] {
0, 0, 1, 0, 0
}));
trainingSet.Add(new TrainingSample(Dataset.Letters.D, new double[5] {
0, 0, 0, 1, 0
}));
trainingSet.Add(new TrainingSample(Dataset.Letters.E, new double[5] {
0, 0, 0, 0, 1
}));
neuralNetwork.SetLearningRate(Convert.ToDouble(0.3));
neuralNetwork.Learn(trainingSet, Convert.ToInt32(5000));
btnTrain.Enabled = false;
btnGetResults.Enabled = true;
}
private void Start(object sender, EventArgs e)
{
CleanseGraph();
EnableControls(false);
curve.Color = enabledColor;
if (!int.TryParse(txtCycles.Text, out cycles))
{
cycles = 10000;
}
if (!double.TryParse(txtLearningRate.Text, out learningRate))
{
learningRate = 0.25d;
}
if (!int.TryParse(txtNeuronCount.Text, out neuronCount))
{
neuronCount = 10;
}
if (cycles <= 0)
{
cycles = 10000;
}
if (learningRate < 0 || learningRate > 1)
{
learningRate = 0.25d;
}
if (neuronCount <= 0)
{
neuronCount = 10;
}
txtCycles.Text = cycles.ToString();
txtLearningRate.Text = learningRate.ToString();
txtNeuronCount.Text = neuronCount.ToString();
LinearLayer inputLayer = new LinearLayer(1);
SigmoidLayer hiddenLayer = new SigmoidLayer(neuronCount);
SigmoidLayer outputLayer = new SigmoidLayer(1);
new BackpropagationConnector(inputLayer, hiddenLayer).Initializer = new RandomFunction(0d, 0.3d);
new BackpropagationConnector(hiddenLayer, outputLayer).Initializer = new RandomFunction(0d, 0.3d);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(learningRate);
TrainingSet trainingSet = new TrainingSet(1, 1);
for (int i = 0; i < curve.Points.Count; i++)
{
double xVal = curve.Points[i].X;
for (double input = xVal - 0.05; input < xVal + 0.06; input += 0.01)
{
trainingSet.Add(new TrainingSample(new double[] { input }, new double[] { curve.Points[i].Y }));
}
}
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs args)
{
trainingProgressBar.Value = (int)(args.TrainingIteration * 100d / cycles);
Application.DoEvents();
});
network.Learn(trainingSet, cycles);
StopLearning(this, EventArgs.Empty);
}
/// <summary>
/// This constructs a training procedure for standard backpropagation techniques.
/// More advanced ones will be used as seen in the example.
/// </summary>
/// <param name="writer"></param>
public TestingNdn(StreamWriter writer)
{
TrainingSample sample = new TrainingSample(
new double[] { },
new double[] { });
//We might make a gui for this later.
int numberOfNeurons = 3;
double learningRate = 0.5;
int numberOfCycles = 10000;
double[] errorList = new double[numberOfCycles];
LinearLayer inputLayer = new LinearLayer(2);
SigmoidLayer hiddenLayer = new SigmoidLayer(numberOfNeurons);
SigmoidLayer outputLayer = new SigmoidLayer(1);
// This layer is a event handler that fires when the output is generated, hence backpropagation.
BackpropagationConnector conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
BackpropagationConnector conn2 = new BackpropagationConnector(hiddenLayer, outputLayer);
BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(learningRate);
TrainingSet trainingSet = new TrainingSet(2, 1);
trainingSet.Add(new TrainingSample(new double[2] {
0, 0
}, new double[1] {
0
}));
trainingSet.Add(new TrainingSample(new double[2] {
0, 1
}, new double[1] {
1
}));
trainingSet.Add(new TrainingSample(new double[2] {
1, 0
}, new double[1] {
1
}));
trainingSet.Add(new TrainingSample(new double[2] {
1, 1
}, new double[1] {
0
}));
double max = 0;
// create an anonymous function to capture the error value of each iteration, and report back the percent of completion.
network.EndEpochEvent +=
delegate(object networkInput, TrainingEpochEventArgs args)
{
errorList[args.TrainingIteration] = network.MeanSquaredError;
max = Math.Max(max, network.MeanSquaredError);
PercentComplete = args.TrainingIteration * 100 / numberOfCycles;
};
network.Learn(trainingSet, numberOfCycles);
double[] indices = new double[numberOfCycles];
// for (int i = 0; i < numberOfCycles; i++) { indices[i] = i; } .. oh nvm, its for graphing the learning curve
// what to do for error list?
// errorList => for plotting stuff.
for (int i = 0; i < numberOfCycles; i++)
{
//Console.WriteLine(errorList[i]);
}
double[] outputResult = network.OutputLayer.GetOutput();
Console.WriteLine("final output");
double[] r1 = new double[] { 0, 0 };
double[] r2 = new double[] { 0, 1 };
double[] r3 = new double[] { 1, 0 };
double[] r4 = new double[] { 1, 1 };
Console.WriteLine(" 0 0 => " + network.Run(r1)[0]);
Console.WriteLine(" 0 1 => " + network.Run(r2)[0]);
Console.WriteLine(" 1 0 => " + network.Run(r3)[0]);
Console.WriteLine(" 1 1 => " + network.Run(r4)[0]);
}
private void buttonOK_Click(object sender, EventArgs e)
{
this.Cursor = Cursors.WaitCursor;
try
{
//
// 解析数据文件
//
int inputCount = 0;
int outputCount = 1;
if (textInputCount.Text.Length > 0)
{
inputCount = int.Parse(textInputCount.Text);
}
if (textOutputCount.Text.Length > 0)
{
outputCount = int.Parse(textOutputCount.Text);
}
TrainingSet trainingSet = DataFile.CsvFileToTrainingSet(textTrainingSet.Text, ref inputCount, ref outputCount);
TrainingSet crossvalidSet = null;
if (textCvSet.Text.Length > 0)
{
crossvalidSet = DataFile.CsvFileToTrainingSet(textCvSet.Text, ref inputCount, ref outputCount);
}
//
// 创建新网络
//
// 输入层始终与输入计数是线性关系
LinearLayer inputLayer = new LinearLayer(inputCount);
// 创建隐层
ActivationLayer hiddenLayer1 = null;
ActivationLayer hiddenLayer2 = null;
ActivationLayer outputLayer = null;
if (comboActFunction1.SelectedIndex < 0)
{
MessageBox.Show("请选择激活函数！"); return;
}
switch ((HiddenLayerType)comboActFunction1.SelectedItem)
{
case HiddenLayerType.Linear: hiddenLayer1 = new LinearLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Logarithmic: hiddenLayer1 = new LogarithmLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Sigmoid: hiddenLayer1 = new SigmoidLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Sine: hiddenLayer1 = new SineLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Tanh: hiddenLayer1 = new TanhLayer(int.Parse(textNeuronCount1.Text)); break;
}
if (textNeuronCount2.Text.Length > 0 && int.Parse(textNeuronCount2.Text) > 0)
{
switch ((HiddenLayerType)comboActFunction2.SelectedItem)
{
case HiddenLayerType.Linear: hiddenLayer2 = new LinearLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Logarithmic: hiddenLayer2 = new LogarithmLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Sigmoid: hiddenLayer2 = new SigmoidLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Sine: hiddenLayer2 = new SineLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Tanh: hiddenLayer2 = new TanhLayer(int.Parse(textNeuronCount2.Text)); break;
}
}
if (comboOutputFunction.SelectedIndex < 0)
{
MessageBox.Show("请选择输出函数！"); return;
}
switch ((HiddenLayerType)comboOutputFunction.SelectedItem)
{
case HiddenLayerType.Linear: outputLayer = new LinearLayer(outputCount); break;
case HiddenLayerType.Logarithmic: outputLayer = new LogarithmLayer(outputCount); break;
case HiddenLayerType.Sigmoid: outputLayer = new SigmoidLayer(outputCount); break;
case HiddenLayerType.Sine: outputLayer = new SineLayer(outputCount); break;
case HiddenLayerType.Tanh: outputLayer = new TanhLayer(outputCount); break;
}
// 连接层, hidden2是可选的
new BackpropagationConnector(inputLayer, hiddenLayer1);
if (hiddenLayer2 != null)
{
new BackpropagationConnector(hiddenLayer1, hiddenLayer2);
new BackpropagationConnector(hiddenLayer2, outputLayer);
}
else
{
new BackpropagationConnector(hiddenLayer1, outputLayer);
}
BackpropagationNetwork backpropNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
//
// 设置学习和退出参数
//
double startLearningRate = double.Parse(textStartLearningRate.Text);
double?finalLearningRate = null;
if (textFinalLearningRate.Text.Length > 0)
{
finalLearningRate = double.Parse(textFinalLearningRate.Text);
}
// 如果选择了学习率函数则使用
LearningRateFunction?lrf = null;
if (comboLRFunction.SelectedIndex > 0)
{
lrf = (LearningRateFunction)comboLRFunction.SelectedItem;
backpropNetwork.SetLearningRate(
LearningRateFactory.GetLearningRateFunction(lrf.Value, startLearningRate, finalLearningRate.Value));
}
else
{
// 否则使用普通学习率，也许有起点和终点
if (finalLearningRate.HasValue)
{
backpropNetwork.SetLearningRate(startLearningRate, finalLearningRate.Value);
}
else
{
backpropNetwork.SetLearningRate(startLearningRate);
}
}
// 如果给定，在连接器中设置动量
double?momentum = null;
if (textMomentum.Text.Length > 0)
{
momentum = double.Parse(textMomentum.Text);
foreach (ILayer layer in backpropNetwork.Layers)
{
foreach (BackpropagationConnector conn in layer.SourceConnectors)
{
conn.Momentum = momentum.Value;
}
foreach (BackpropagationConnector conn in layer.TargetConnectors)
{
conn.Momentum = momentum.Value;
}
}
}
//
// 新建工程和保存工程
//
int tmpInt;
NewProject = new NnProject();
NewProject.Network = backpropNetwork;
// 确保为新网络初始化权重，默认情况下新的训练周期将不会初始化
NewProject.Network.Initialize();
NewProject.ProjectName = textProjectName.Text.Trim();
NewProject.SaveFolder = textSaveFolder.Text;
NewProject.TrainingSet = trainingSet;
NewProject.CrossValidationSet = crossvalidSet;
NewProject.LearningParameters = new NnProject.NnLearningParameters();
NewProject.LearningParameters.InitialLearningRate = startLearningRate;
NewProject.LearningParameters.FinalLearningRate = finalLearningRate;
NewProject.LearningParameters.LearningRateFunction = lrf;
NewProject.LearningParameters.Momentum = momentum;
if (int.TryParse(textTrainingCycles.Text, out tmpInt))
{
NewProject.LearningParameters.MaxTrainingCycles = tmpInt;
}
NnProject.Save(NewProject, textSaveFolder.Text);
this.Close();
}
catch (Exception ex)
{
this.Cursor = Cursors.Default;
MessageBox.Show("Error creating network - " + ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
finally
{ this.Cursor = Cursors.Default; }
}
/// <summary>
/// Created this to test the custom neuron network with binary inputs.
/// </summary>
/// <param name="writer"></param>
public static void Test(
string file,
int numberOfInputNeurons,
int numberOfHiddenNeurons,
int numberOfOutputNeurons,
int numberOfCycles = 50000,
double learningRate = 0.25)
{
TrainingSample sample = new TrainingSample(
new double[] { },
new double[] { });
//We might make a gui for this later.
double[] errorList = new double[numberOfCycles];
int totalNumberOfNeurons = numberOfInputNeurons + numberOfOutputNeurons;
LinearLayer inputLayer = new LinearLayer(numberOfInputNeurons);
SigmoidLayer hiddenLayer = new SigmoidLayer(numberOfHiddenNeurons);
SigmoidLayer outputLayer = new SigmoidLayer(numberOfOutputNeurons);
// This layer is a event handler that fires when the output is generated, hence backpropagation.
BackpropagationConnector conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
BackpropagationConnector conn2 = new BackpropagationConnector(hiddenLayer, outputLayer);
BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(learningRate);
TrainingSet trainingSet = new TrainingSet(10, 8);
// A file stream reader.
var inDefaule = Console.In;
using (StreamReader reader = new StreamReader(file))
{
Console.SetIn(reader);
String line = "";
//trainingSet.Add(new TrainingSample(new double[] { 0, 0, 0, 0, 1 }, new double[1] { 1 }));
while ((line = reader.ReadLine()) != null)
{
String[] array = line.Split(',');
double[] inputArray = new double[10];
double[] outputArray = new double[8];
for (int i = 0; i < 10; i++)
{
inputArray[i] = Convert.ToDouble(array[i]);
}
for (int i = 0; i < 8; i++)
{
outputArray[i] = Convert.ToDouble(array[i + 11]);
}
trainingSet.Add(new TrainingSample(inputArray, outputArray));
}
}
double max = 0;
// create an anonymous function to capture the error value of each iteration, and report back the percent of completion.
network.EndEpochEvent +=
delegate(object networkInput, TrainingEpochEventArgs args)
{
errorList[args.TrainingIteration] = network.MeanSquaredError;
max = Math.Max(max, network.MeanSquaredError);
// PercentComplete = args.TrainingIteration * 100 / numberOfCycles;
};
network.Learn(trainingSet, numberOfCycles);
double[] indices = new double[numberOfCycles];
// for (int i = 0; i < numberOfCycles; i++) { indices[i] = i; } .. oh nvm, its for graphing the learning curve
// what to do for error list?
// errorList => for plotting stuff.
// for (int i = 0; i < numberOfCycles; i++)
// {
//Console.WriteLine(errorList[i]);
// }
// print out the error list for scientific evaluation.
StreamUtilities.DumpData("dumpErrorValues.txt", errorList);
double[] outputResult = network.OutputLayer.GetOutput();
outputResult = network.Run(new double[] { 0.47, 0.41, 0.12, 0.05, 0.1, 0.5, 0.1, 0.1, 0.05, 0.1 });
foreach (var d in outputResult)
{
Console.WriteLine("output: " + d);
}
// Console.WriteLine("final output");
}
