void InitNeuralNet()
{
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(inputVectorSize, outVectorSize);
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);
}
public static void Treinar(string papel, string nomeRedeNeural, List<DadosBE> dadosBE, int numeroNeuronios, double taxaAprendizado, int ciclos, int numeroDivisoesCrossValidation, int shift, double versao)
{
if (dadosBE.Count == 0)
return;
List<Treinamento> treinamentos = DataBaseUtils.DataBaseUtils.SelecionarTreinamentos_V3(dadosBE, versao);
treinamentos = treinamentos.Where(trein => trein.DivisaoCrossValidation != shift).ToList();
int inputLayerCount = treinamentos.First().Input.Count();
int outputLayerCount = treinamentos.First().Output.Count();
BackpropagationNetwork network;
//int numeroNeuronios = 4;
//double taxaAprendizado = 0.25d;
ActivationLayer inputLayer = new LinearLayer(inputLayerCount);
ActivationLayer hiddenLayer = new SigmoidLayer(numeroNeuronios);
ActivationLayer outputLayer = new SigmoidLayer(outputLayerCount);
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(taxaAprendizado);
TrainingSet trainingSet = new TrainingSet(inputLayerCount, outputLayerCount);
//foreach (KeyValuePair<double[], double[]> kvp in dadosPorJanelamento)
//{
// trainingSet.Add(new TrainingSample(kvp.Key, kvp.Value));
//}
foreach (Treinamento treinamento in treinamentos)
{
trainingSet.Add(new TrainingSample(treinamento.Input.ToArray(), treinamento.Output.ToArray()));
}
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs argsNw)
{
//trainingProgressBar.Value = (int)(argsNw.TrainingIteration * 100d / cycles);
//Application.DoEvents();
});
network.Learn(trainingSet, ciclos);
double erroGeralRede = 0;
foreach (Treinamento treinamento in treinamentos)
{
double[] previsao = network.Run(treinamento.Input.ToArray());
double erroRede = 1 - Math.Min(previsao.First(), treinamento.Output.First()) / Math.Max(previsao.First(), treinamento.Output.First());
erroGeralRede += erroRede;
}
erroGeralRede = erroGeralRede / treinamentos.Count;
using (Stream stream = File.Open(diretorioRedes + "\\RedesPrevisaoFinanceira\\" + nomeRedeNeural + ".ndn", FileMode.Create))
{
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, network);
}
}
public async Task<double[]> Start(int generations, string trainingFileLocation)
{
var results = new int[10000];
var trainingData = CreateTrainingDataFromImage(trainingFileLocation, @"C:\Users\pruss\Documents\Visual Studio 2015\Projects\WrittenTextRecognition\Training Data\train-labels.idx1-ubyte", out results);
var resultsConverted = NumbersToBitRow(results);
// TODO: Trainingsdaten aus Datei laden
var trainingSet = new TrainingSet(28 * 28, 10);
for(int i = 0; i < results.Length; i++)
{
trainingSet.Add(new TrainingSample(trainingData[i], resultsConverted[i]));
}
//trainingSet.Add(new TrainingSample(new double[2] { 0d, 0d }, new double[1] { 0d }));
//trainingSet.Add(new TrainingSample(new double[2] { 0d, 1d }, new double[1] { 1d }));
//trainingSet.Add(new TrainingSample(new double[2] { 1d, 0d }, new double[1] { 1d }));
//trainingSet.Add(new TrainingSample(new double[2] { 1d, 1d }, new double[1] { 0d }));
var errorList = new double[generations];
int j = 1;
MainWindow.Network.EndEpochEvent +=
delegate (object network, TrainingEpochEventArgs args)
{
errorList[args.TrainingIteration] = MainWindow.Network.MeanSquaredError;
UpdateProgressBar((int)(j / (generations / 100.0)));
j++;
};
var t1 = Task.Factory.StartNew(() =>
{
MainWindow.Network.Learn(trainingSet, generations);
});
await Task.WhenAll(t1);
UpdateProgressBar(0);
return errorList;
}
/// <summary>
/// Trains the neural network for the given training set (Batch Training)
/// </summary>
/// <param name="trainingSet">
/// The training set to use
/// </param>
/// <param name="trainingEpochs">
/// Number of training epochs. (All samples are trained in some random order, in every
/// training epoch)
/// </param>
/// <exception cref="ArgumentNullException">
/// if <c>trainingSet</c> is <c>null</c>
/// </exception>
/// <exception cref="ArgumentException">
/// if <c>trainingEpochs</c> is zero or negative
/// </exception>
public virtual void Learn(TrainingSet trainingSet, int trainingEpochs)
{
// Validate
Helper.ValidateNotNull(trainingSet, "trainingSet");
Helper.ValidatePositive(trainingEpochs, "trainingEpochs");
if ((trainingSet.InputVectorLength != inputLayer.NeuronCount)
|| (trainingMethod == TrainingMethod.Supervised && trainingSet.OutputVectorLength != outputLayer.NeuronCount)
|| (trainingMethod == TrainingMethod.Unsupervised && trainingSet.OutputVectorLength != 0))
{
throw new ArgumentException("Invalid training set");
}
// Reset isStopping
isStopping = false;
// Re-Initialize the network
Initialize();
for (int currentIteration = 0; currentIteration < trainingEpochs; currentIteration++)
{
int[] randomOrder = Helper.GetRandomOrder(trainingSet.TrainingSampleCount);
// Beginning a new training epoch
OnBeginEpoch(currentIteration, trainingSet);
// Check for Jitter Epoch
if (jitterEpoch > 0 && currentIteration % jitterEpoch == 0)
{
for (int i = 0; i < connectors.Count; i++)
{
connectors[i].Jitter(jitterNoiseLimit);
}
}
for (int index = 0; index < trainingSet.TrainingSampleCount; index++)
{
TrainingSample randomSample = trainingSet[randomOrder[index]];
// Learn a random training sample
OnBeginSample(currentIteration, randomSample);
LearnSample(trainingSet[randomOrder[index]], currentIteration, trainingEpochs);
OnEndSample(currentIteration, randomSample);
// Check if we need to stop
if (isStopping) { isStopping = false; return; }
}
// Training Epoch successfully complete
OnEndEpoch(currentIteration, trainingSet);
// Check if we need to stop
if (isStopping) { isStopping = false; return; }
}
}
public Optimiser(string filename)
{
Utils.Logger.Log("Loading stopwatch... ");
stopWatch = new Stopwatch();
ResultCounter = new Stopwatch();
this.filename = filename;
Utils.Logger.Log("Loading preprocessor parameters from " + filename);
dataAccess = new DataAccess(filename);
preprocessor = new Preprocessor();
preprocessor.ImageSize = new Size(Convert.ToInt32(dataAccess.GetParameter("Master_Width")), Convert.ToInt32(dataAccess.GetParameter("Master_Height")));
preprocessor.KeepAspectRatio = Convert.ToBoolean(dataAccess.GetParameter("Master_Aspect"));
preprocessor.ScalingMethod = (ScalingMethods)Convert.ToInt32(dataAccess.GetParameter("Master_Resize"));
preprocessor.ContrastStretch = Convert.ToBoolean(dataAccess.GetParameter("Filter_Stretch"));
preprocessor.Histogram = Convert.ToBoolean(dataAccess.GetParameter("Filter_Histo"));
preprocessor.Gaussian = Convert.ToBoolean(dataAccess.GetParameter("Filter_Gaussian"));
preprocessor.GaussianStrength = Convert.ToInt32(dataAccess.GetParameter("Filter_BlurStr"));
preprocessor.ContrastAdjustment = Convert.ToBoolean(dataAccess.GetParameter("Filter_Contrast"));
preprocessor.ContrastStrength = Convert.ToDecimal(dataAccess.GetParameter("Filter_ContrastStr"));
preprocessor.Greyscale = Convert.ToBoolean(dataAccess.GetParameter("Filter_Greyscale"));
preprocessor.Bradley = Convert.ToBoolean(dataAccess.GetParameter("Filter_Bradley"));
preprocessor.Threshold = Convert.ToBoolean(dataAccess.GetParameter("Filter_Threshold"));
preprocessor.ThresholdStrength = Convert.ToDecimal(dataAccess.GetParameter("Filter_ThresholdStr"));
/*
dataAccess.SetParameter("Opt_Bp_LearningType", cmbLearningRateType.SelectedItem.ToString());
dataAccess.SetParameter("Opt_Bp_InitialLearnRate", txtInitialRate.Text);
dataAccess.SetParameter("Opt_Bp_FinalLearnRate", txtFinalRate.Text);
dataAccess.SetParameter("Opt_Bp_JitterEpoch", txtJitterEpoch.Text);
dataAccess.SetParameter("Opt_Bp_JitterNoiseLimit", txtJitterNoiseLimit.Text);
dataAccess.SetParameter("Opt_Bp_MaxIterations", txtMaxIterations.Text);
dataAccess.SetParameter("Opt_Bp_MinError", txtMinimumError.Text);
*/
bool usePSO = false;
bool useBP = false;
try
{
useBP = Convert.ToBoolean(dataAccess.GetParameter("Opt_Bp_Enabled"));
}
catch (Exception)
{
Utils.Logger.Log("Warning unable to read BP params");
}
try
{
usePSO = Convert.ToBoolean(dataAccess.GetParameter("Opt_Pso_Enabled"));
}
catch (Exception)
{
Utils.Logger.Log("Warning unable to read PSO params");
}
if (usePSO && useBP)
{
throw new NotImplementedException("At this current time you cannot use both BP and PSO");
}
InputGroup[] inputGroups = dataAccess.GetInputGroups();
SourceItem[] sourceItems = dataAccess.GetSourceItems();
/*
Utils.Logger.Log("Preprocessing images...");
foreach (SourceItem item in sourceItems)
{
Utils.Logger.Log("Preprocessing item {0} ", item.Filename);
item.InternalImage = preprocessor.Process((Bitmap)item.InternalImage);
}
*/
int total = 0;
foreach (InputGroup inputGroup in inputGroups)
{
if (inputGroup.InputGroupType == InputGroupType.Grid)
{
total += (inputGroup.Segments) * (inputGroup.Segments);
}
else
{
total += inputGroup.Segments;
}
}
maxIterations = Convert.ToInt32(dataAccess.GetParameter("Opt_Global_MaxIterations"));
minError = Convert.ToDouble(dataAccess.GetParameter("Opt_Global_MinError"));
maxTime = Convert.ToInt32(dataAccess.GetParameter("Opt_Global_MaxTime"));
results = new float[Convert.ToInt32(dataAccess.GetParameter("Opt_Global_BufferSize"))];
if (useBP)
{
int learningRateFunction = Convert.ToInt32(dataAccess.GetParameter("Opt_Bp_LearningType"));
double initialLR = Convert.ToDouble(dataAccess.GetParameter("Opt_Bp_InitialLearnRate"));
double finalLR = Convert.ToDouble(dataAccess.GetParameter("Opt_Bp_FinalLearnRate"));
int jitterEpoch = Convert.ToInt32(dataAccess.GetParameter("Opt_Bp_JitterEpoch"));
double jitterNoiseLimit = Convert.ToDouble(dataAccess.GetParameter("Opt_Bp_JitterNoiseLimit"));
NeuronDotNet.Core.Backpropagation.LinearLayer inputLayer = new NeuronDotNet.Core.Backpropagation.LinearLayer(preprocessor.ImageSize.Width * preprocessor.ImageSize.Height);
NeuronDotNet.Core.Backpropagation.SigmoidLayer hiddenLayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(total);
hiddenLayer.InputGroups = inputGroups.Length;
NeuronDotNet.Core.Backpropagation.SigmoidLayer outputLayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(1);
hiddenLayer.Initializer = new NguyenWidrowFunction();
new BackpropagationConnector(
inputLayer,
hiddenLayer,
inputGroups,
preprocessor.ImageSize.Width,
preprocessor.ImageSize.Height
);
new BackpropagationConnector(hiddenLayer, outputLayer);
network = new BackpropagationNetwork(inputLayer, outputLayer);
switch (learningRateFunction)
{
case 0:
network.SetLearningRate(initialLR);
break;
case 1:
network.SetLearningRate(new NeuronDotNet.Core.LearningRateFunctions.ExponentialFunction(initialLR, finalLR));//exp
break;
case 2:
network.SetLearningRate(new NeuronDotNet.Core.LearningRateFunctions.HyperbolicFunction(initialLR, finalLR));//hyp
break;
case 3:
network.SetLearningRate(new NeuronDotNet.Core.LearningRateFunctions.LinearFunction(initialLR, finalLR));//lin
break;
default:
throw new ArgumentOutOfRangeException("The learning rate index is out of range.\n");
}
network.JitterEpoch = jitterEpoch;
network.JitterNoiseLimit = jitterNoiseLimit;
}
if (usePSO)
{
double minP = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MinP"));
double maxP = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MaxP"));
double minI = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MinI"));
double maxI = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MaxI"));
double quant = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_Quant"));
double vMax = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_vMax"));
int clamping = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_Clamping"));
int initLinks = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_InitLinks"));
int randomness = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_Randomness"));
int randOrder = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_ParticleOrder"));
int rotation = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_Rotation"));
int dimensions = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_Dimensions"));
int swarmSize = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_Particles"));
double k = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_k"));
double p = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_p"));
double w = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_w"));
double c = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_c"));
Parameters param = new Parameters();
param.vMax = vMax;
param.clamping = clamping;
// 0 => no clamping AND no evaluation. WARNING: the program
// may NEVER stop (in particular with option move 20 (jumps)) 1
// *1 => classical. Set to bounds, and velocity to zero
param.initLink = initLinks; // 0 => re-init links after each unsuccessful iteration
// 1 => re-init links after each successful iteration
param.rand = randomness; // 0 => Use KISS as random number generator.
// Any other value => use the system one
param.randOrder = randOrder; // 0 => at each iteration, particles are modified
// always according to the same order 0..S-1
//*1 => at each iteration, particles numbers are
// randomly permutated
param.rotation = rotation;
// WARNING. Experimental code, completely valid only for dimension 2
// 0 => sensitive to rotation of the system of coordinates
// 1 => non sensitive (except side effects),
// by using a rotated hypercube for the probability distribution
// WARNING. Quite time consuming!
param.stop = 0; // Stop criterion
// 0 => error < pb.epsilon
// 1 => eval >= pb.evalMax
// 2 => ||x-solution|| < pb.epsilon
// ===========================================================
// RUNs
// Initialize some objects
//pb = new Problem(function);
// You may "manipulate" S, p, w and c
// but here are the suggested values
param.S = swarmSize;
if (param.S > 910) param.S = 910;
param.K = (int)k;
param.p = p;
// (to simulate the global best PSO, set param.p=1)
//param.p=1;
param.w = w;
param.c = c;
NeuronDotNet.Core.PSO.LinearLayer inputLayer = new NeuronDotNet.Core.PSO.LinearLayer(preprocessor.ImageSize.Width * preprocessor.ImageSize.Height);
NeuronDotNet.Core.PSO.SigmoidLayer hiddenLayer = new NeuronDotNet.Core.PSO.SigmoidLayer(total);
hiddenLayer.InputGroups = inputGroups.Length;
NeuronDotNet.Core.PSO.SigmoidLayer outputLayer = new NeuronDotNet.Core.PSO.SigmoidLayer(1);
hiddenLayer.Initializer = new NguyenWidrowFunction();
new PSOConnector(
inputLayer,
hiddenLayer,
inputGroups,
preprocessor.ImageSize.Width,
preprocessor.ImageSize.Height
);
new PSOConnector(hiddenLayer, outputLayer);
PSONetwork n = new PSONetwork(inputLayer, outputLayer);
n.PsoParameters = param;
n.PsoProblem.MaxI = maxI;
n.PsoProblem.MinI = minI;
n.PsoProblem.MaxP = maxP;
n.PsoProblem.MinP = minP;
n.PsoProblem.Quantisation = quant;
network = n;
}
set = new TrainingSet(preprocessor.ImageSize.Width * preprocessor.ImageSize.Height, 1);
foreach (SourceItem item in sourceItems)
{
double[] weights = Utils.getImageWeights(item.InternalImage, inputGroups);
set.Add(new TrainingSample(weights, new double[] { (double)item.SampleType }));
}
network.EndEpochEvent += new TrainingEpochEventHandler(network_EndEpochEvent);
network.Initialize();
}
/// <summary>
/// Creates a new instance of training epoch event arguments
/// </summary>
/// <param name="trainingIteration">
/// Current training iteration
/// </param>
/// <param name="trainingSet">
/// The training set associated with the event
/// </param>
public TrainingEpochEventArgs(int trainingIteration, TrainingSet trainingSet)
{
this.trainingSet = trainingSet;
this.trainingIteration = trainingIteration;
}
private void Train(object sender, EventArgs e)
{
EnableControls(false);
if (!int.TryParse(txtCycles.Text.Trim(), out cycles)) { cycles = 5000; }
if (!double.TryParse(txtLearningRate.Text.Trim(), out learningRate)) { learningRate = 0.25d; }
if (!int.TryParse(txtNeuronCount.Text.Trim(), out neuronCount)) { neuronCount = 3; }
if (cycles < 1) { cycles = 1; }
if (learningRate < 0.01) { learningRate = 0.01; }
if (neuronCount < 1) { neuronCount = 1; }
txtNeuronCount.Text = neuronCount.ToString();
txtCycles.Text = cycles.ToString();
txtLearningRate.Text = learningRate.ToString();
errorList = new double[cycles];
InitGraph();
LinearLayer inputLayer = new LinearLayer(2);
SigmoidLayer hiddenLayer = new SigmoidLayer(neuronCount);
SigmoidLayer outputLayer = new SigmoidLayer(1);
new BackpropagationConnector(inputLayer, hiddenLayer);
new BackpropagationConnector(hiddenLayer, outputLayer);
xorNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
xorNetwork.SetLearningRate(learningRate);
TrainingSet trainingSet = new TrainingSet(2, 1);
trainingSet.Add(new TrainingSample(new double[2] { 0d, 0d }, new double[1] { 0d }));
trainingSet.Add(new TrainingSample(new double[2] { 0d, 1d }, new double[1] { 1d }));
trainingSet.Add(new TrainingSample(new double[2] { 1d, 0d }, new double[1] { 1d }));
trainingSet.Add(new TrainingSample(new double[2] { 1d, 1d }, new double[1] { 0d }));
double max = 0d;
xorNetwork.EndEpochEvent +=
delegate(object network, TrainingEpochEventArgs args)
{
errorList[args.TrainingIteration] = xorNetwork.MeanSquaredError;
max = Math.Max(max, xorNetwork.MeanSquaredError);
progressBar.Value = (int)(args.TrainingIteration * 100d / cycles);
};
xorNetwork.Learn(trainingSet, cycles);
double[] indices = new double[cycles];
for (int i = 0; i < cycles; i++) { indices[i] = i; }
lblTrainErrorVal.Text = xorNetwork.MeanSquaredError.ToString("0.000000");
LineItem errorCurve = new LineItem("Error Dynamics", indices, errorList, Color.Tomato, SymbolType.None, 1.5f);
errorGraph.GraphPane.YAxis.Scale.Max = max;
errorGraph.GraphPane.CurveList.Add(errorCurve);
errorGraph.Invalidate();
EnableControls(true);
}
/// <summary>
/// Creates a new instance of training epoch event arguments
/// </summary>
/// <param name="trainingIteration">
/// Current training iteration
/// </param>
/// <param name="trainingSet">
/// The training set associated with the event
/// </param>
public TrainingEpochEventArgs(int trainingIteration, TrainingSet trainingSet)
{
this.trainingSet = trainingSet;
this.trainingIteration = trainingIteration;
}
//public void TreinarRedeNeural(List<DadosBE> dadosBE, string papel = "PETR4")
//{
//}
public static void Treinar(string papel, string nomeRedeNeural, List<DadosBE> dadosBE, int janelaEntrada, int numeroNeuronios, double taxaAprendizado, int ciclos, int numeroDivisoesCrossValidation, int shift)
{
if (dadosBE.Count < janelaEntrada)
return;
/*//inputLayerCount será a soma de 2+n ao valor da janela de entrada, onde n é o (TOE(tamanho original da entrada) - 5) / 5(arredondado para baixo). Ou seja, para cada 5 dias informados além de 5 iniciais, +1 cotação do dolar será informada
//EX: passaremos a cotação do dia 1 ao dia 9 (9 dias) para prever o decimo dia. Portanto:
//-TOE - 5 = 4.
//4 / 5 = 0.8, arredondando para baixo, 0.
//portanto apenas 2 cotações do dolar serão informadas: a do dia 1 e a do dia 9
//de 10 a 14 dias de entrada, 3 cotaçoes do dolar. de 15 a 19, 4 cotações do dolar e assim por diante.
int inputLayerCount = janelaEntrada + 2;
//Somamos 2 a janela de entrada pois informaremos também a estação do ano e o valor de bollinger
inputLayerCount += 2;
//O primeiro valor será a cotação do ativo para o dia seguinte e o segundo valor a cotação do dolar para o dia seguinte
int outputLayerCount = 2;
*/
List<Treinamento> treinamentos = DataBaseUtils.DataBaseUtils.SelecionarTreinamentos_V2(dadosBE, janelaEntrada, 1);
treinamentos = treinamentos.Where(trein => trein.DivisaoCrossValidation != shift).ToList();
int inputLayerCount = treinamentos.First().Input.Count();
int outputLayerCount = treinamentos.First().Output.Count();
BackpropagationNetwork network;
//int numeroNeuronios = 4;
//double taxaAprendizado = 0.25d;
ActivationLayer inputLayer = new LinearLayer(inputLayerCount);
ActivationLayer hiddenLayer = new SigmoidLayer(numeroNeuronios);
ActivationLayer outputLayer = new SigmoidLayer(outputLayerCount);
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(taxaAprendizado);
TrainingSet trainingSet = new TrainingSet(inputLayerCount, outputLayerCount);
//foreach (KeyValuePair<double[], double[]> kvp in dadosPorJanelamento)
//{
// trainingSet.Add(new TrainingSample(kvp.Key, kvp.Value));
//}
foreach (Treinamento treinamento in treinamentos)
{
trainingSet.Add(new TrainingSample(treinamento.Input.ToArray(), treinamento.Output.ToArray()));
}
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs argsNw)
{
//trainingProgressBar.Value = (int)(argsNw.TrainingIteration * 100d / cycles);
//Application.DoEvents();
});
bool erroAceito = false;
int cicloAtual = ciclos / 2;
while (erroAceito == false && cicloAtual <= ciclos)
{
erroAceito = true;
network.Learn(trainingSet, cicloAtual);
double erroGeralRede = 0;
foreach (Treinamento treinamento in treinamentos)
{
double[] previsao = network.Run(treinamento.Input.ToArray());
double erroRede = 1 - Math.Min(previsao.First(), treinamento.Output.First()) / Math.Max(previsao.First(), treinamento.Output.First());
erroGeralRede += erroRede;
if (erroRede > 0.01)//Verifica se houve mais de 1% de erro
{
trainingSet.Add(new TrainingSample(treinamento.Input.ToArray(), treinamento.Output.ToArray()));
}
}
erroGeralRede = erroGeralRede / treinamentos.Count;
if (erroGeralRede > 0.01)
erroAceito = false;
cicloAtual += ciclos / 2;
}
using (Stream stream = File.Open(diretorioRedes + "\\RedesPrevisaoFinanceira\\" + nomeRedeNeural + ".ndn", FileMode.Create))
{
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, network);
}
}
public void Sine()
{
LinearLayer entrada = new LinearLayer(4);
SineLayer oculta = new SineLayer(3);
//NeuronDotNet.Core.Backpropagation.
SineLayer salida = new SineLayer(3);
new BackpropagationConnector(entrada, oculta);
new BackpropagationConnector(oculta, salida);
red = new BackpropagationNetwork(entrada, salida);
red.SetLearningRate(0.3);
datos = new TrainingSet(4, 3);
}
public void cargarredneuronal()
{
try
{
pBar.Value = 0;
//C:\ARCHIVOS ARFF
Stream stream = File.Open(@"C:\ARCHIVOSARFF\red\red.ndn", FileMode.Open);
//Stream stream = File.Open(@"C:\ARCHIVOS ARFF\iris-datos.txt", FileMode.Open);
IFormatter formatter = new BinaryFormatter();
red = (NeuronDotNet.Core.Backpropagation.BackpropagationNetwork)formatter.Deserialize(stream);
stream = File.Open(@"C:\ARCHIVOSARFF\red\data.ndn", FileMode.Open);
//stream = File.Open(@"C:\ARCHIVOS ARFF\iris-datos-n.txt", FileMode.Open);
formatter = new BinaryFormatter();
datos = (TrainingSet)formatter.Deserialize(stream);
for (int i = 1; i <= 100; i++)
{
pBar.Value = i;
}
MessageBox.Show("La red se ha cargado!");
}
catch (Exception)
{
MessageBox.Show("Error al cargar la red neuronal", "Error Crítico", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
static void Training(List<double> dadosTreinamento, int janela, int ciclos)
{
if (dadosTreinamento.Count < janela)
return;
/*Cria um mapeamento de entradas para saida com o janelamento informado*/
//List<KeyValuePair<double[], double>> dadosPorJanelamento = new List<KeyValuePair<double[], double>>();
//for (int i = 0; i < dadosTreinamento.Count - janela - 1; i += janela + 1)
//{
// dadosPorJanelamento.Add(new KeyValuePair<double[], double>(dadosTreinamento.Skip(i).Take(janela).ToArray(), dadosTreinamento.Skip(i).Take(janela + 1).First()));
//}
List<KeyValuePair<double[], double>> dadosPorJanelamento = GetCotacoesPorJanelamento(dadosTreinamento, janela);
/*Cria um mapeamento de entradas para saida com o janelamento informado*/
BackpropagationNetwork network;
int neuronCount = 4;
double learningRate = 0.25d;
ActivationLayer inputLayer = new LinearLayer(janela);
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(janela, 1);
foreach (KeyValuePair<double[], double> kvp in dadosPorJanelamento)
{
trainingSet.Add(new TrainingSample(kvp.Key, new double[] { kvp.Value }));
}
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs argsNw)
{
//trainingProgressBar.Value = (int)(argsNw.TrainingIteration * 100d / cycles);
//Application.DoEvents();
});
bool correct = false;
int currentCycles = ciclos / 5;
while (correct == false && currentCycles <= ciclos)
{
network.Learn(trainingSet, currentCycles);
foreach (KeyValuePair<double[], double> kvp in dadosPorJanelamento)
{
double previsao = network.Run(kvp.Key)[0];
if (Math.Abs(kvp.Value - previsao) > (kvp.Value / 100 * 0.5))//Verifica se houve mais de 5% de erro
{
correct = false;
trainingSet.Add(new TrainingSample(kvp.Key, new double[] { kvp.Value }));
}
else
correct = true;
}
currentCycles += ciclos / 5;
}
using (Stream stream = File.Open(System.IO.Directory.GetCurrentDirectory() + @"\network.ndn", FileMode.Create))
{
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, network);
}
}
public static double Treinar(string nomeRedeNeural, List<Treinamento> treinamentos, int numeroNeuronios, double taxaAprendizado, int ciclos)
{
if (treinamentos.Count == 0)
return -1;
int inputLayerCount = treinamentos.First().Input.Count();
int outputLayerCount = treinamentos.First().Output.Count();
BackpropagationNetwork network;
//int numeroNeuronios = 4;
//double taxaAprendizado = 0.25d;
ActivationLayer inputLayer = new LinearLayer(inputLayerCount);
//ActivationLayer hiddenLayer = new SigmoidLayer(numeroNeuronios);
ActivationLayer outputLayer = new LinearLayer(outputLayerCount);
outputLayer.UseFixedBiasValues = true;
new BackpropagationConnector(inputLayer, outputLayer).Initializer = new RandomFunction(0, 0.3d);
//new BackpropagationConnector(hiddenLayer, outputLayer).Initializer = new RandomFunction(0, 0.3d);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(taxaAprendizado);
TrainingSet trainingSet = new TrainingSet(inputLayerCount, outputLayerCount);
foreach (Treinamento treinamento in treinamentos)
{
trainingSet.Add(new TrainingSample(treinamento.Input.ToArray(), treinamento.Output.ToArray()));
}
double lastMeanSquareError = 1;
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs argsNw)
{
if (argsNw.TrainingIteration > 0 && argsNw.TrainingIteration % 100 == 0)
{
double erroAt = Convert.ToDouble(senderNetwork.GetType().GetProperty("MeanSquaredError").GetValue(senderNetwork, null));
//Se o erro cresceu ou se o erro melhorou menos do que 0.05%, parar o aprendizado
if (erroAt > lastMeanSquareError || Math.Abs(lastMeanSquareError - erroAt) < (lastMeanSquareError / 100))//0.1% de melhora..
{
network.StopLearning();
}
else
lastMeanSquareError = erroAt;
}
});
network.Learn(trainingSet, ciclos);
int numeroAcertos = 0;
foreach (Treinamento treinamento in treinamentos)
{
double[] previsao = network.Run(treinamento.Input.ToArray());
//double erroRede = 1 - Math.Min(previsao.First(), treinamento.Output.First()) / Math.Max(previsao.First(), treinamento.Output.First());
if (ValoresMaximosNoMesmoIndice(previsao, treinamento.Output.ToArray()))
{
numeroAcertos++;
}
}
double acertoRede = numeroAcertos / treinamentos.Count * 100;
using (Stream stream = File.Open(diretorioRedes + nomeRedeNeural + ".ndn", FileMode.Create))
{
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, network);
}
return acertoRede;
}
/// <summary>
/// Treina a rede que diz qual é a melhor configuração de rede para um papel em um dia
/// </summary>
/// <param name="nomeRede"></param>
/// <param name="trainingSet"></param>
private static void TreinarRedeDiaria(string nomeRede, TrainingSet trainingSet)
{
BackpropagationNetwork network;
int numeroNeuronios = 4;
double taxaAprendizado = 0.25d;
ActivationLayer inputLayer = new LinearLayer(trainingSet.InputVectorLength);
ActivationLayer hiddenLayer = new SigmoidLayer(numeroNeuronios);
ActivationLayer outputLayer = new SigmoidLayer(trainingSet.OutputVectorLength);
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(taxaAprendizado);
//TrainingSet trainingSet = new TrainingSet(janelaEntrada, janelaSaida);
//foreach (KeyValuePair<double[], double[]> kvp in dadosPorJanelamento)
//{
// trainingSet.Add(new TrainingSample(kvp.Key, kvp.Value));
//}
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs argsNw)
{
//trainingProgressBar.Value = (int)(argsNw.TrainingIteration * 100d / cycles);
//Application.DoEvents();
});
bool erroAceito = false;
int cicloAtual = ciclos / 5;
while (erroAceito == false && cicloAtual <= ciclos)
{
network.Learn(trainingSet, cicloAtual);
foreach (TrainingSample treinamento in trainingSet.TrainingSamples.Distinct())
{
double[] previsao = network.Run(treinamento.InputVector);
double erroAcumulado = 0;
for (int indRede = 0; indRede < trainingSet.OutputVectorLength; indRede++)
{
erroAcumulado += 1 - Math.Min(previsao[indRede], treinamento.OutputVector[indRede]) / Math.Max(previsao[indRede], treinamento.OutputVector[indRede]);
}
double erroMedio = erroAcumulado / trainingSet.TrainingSampleCount;
if (erroMedio > 0.3)//Verifica se houve mais de 3% de erro
{
erroAceito = false;
trainingSet.Add(treinamento);
}
else
erroAceito = erroAceito && true;
}
cicloAtual += ciclos / 5;
}
using (Stream stream = File.Open(diretorioRedesCaptacao + nomeRede + ".ndn", FileMode.Create))
{
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, network);
}
}
/// <summary>
/// Invokes EndEpochEvent
/// </summary>
/// <param name="currentIteration">
/// Current training iteration
/// </param>
/// <param name="trainingSet">
/// Training set which got trained successfully this epoch
/// </param>
protected virtual void OnEndEpoch(int currentIteration, TrainingSet trainingSet)
{
if (EndEpochEvent != null)
{
EndEpochEvent(this, new TrainingEpochEventArgs(currentIteration, trainingSet));
}
}
public void Tangensial()
{
LinearLayer entrada = new LinearLayer(4);
TanhLayer oculta = new TanhLayer(3);
TanhLayer salida = new TanhLayer(3);
new BackpropagationConnector(entrada, oculta);
new BackpropagationConnector(oculta, salida);
red = new BackpropagationNetwork(entrada, salida);
red.SetLearningRate(0.3);
datos = new TrainingSet(4, 3);
}
private BackpropagationNetwork Treinar(int numeroNeuronios, double taxaAprendizado, int ciclos)
{
List<Agencia> agencias = Agencia.PegarTodas();
List<KeyValuePair<double[], double[]>> dadosEntrada = new List<KeyValuePair<double[], double[]>>();
foreach (Agencia agencia in agencias)
{
KeyValuePair<double[], double[]> kvp = new KeyValuePair<double[], double[]>(new double[] { NormalizarLatitude(agencia.Latitude), NormalizarLongitude(agencia.Longitude) }, ConverterZonaParaArrayDouble(agencia.Zona));
dadosEntrada.Add(kvp);
}
BackpropagationNetwork network;
//int numeroNeuronios = 4;
//double taxaAprendizado = 0.25d;
ActivationLayer inputLayer = new LinearLayer(2);
ActivationLayer hiddenLayer = new SigmoidLayer(numeroNeuronios);
ActivationLayer outputLayer = new SigmoidLayer(4);
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(taxaAprendizado);
TrainingSet trainingSet = new TrainingSet(2, 4);
foreach (KeyValuePair<double[], double[]> kvp in dadosEntrada)
{
trainingSet.Add(new TrainingSample(kvp.Key, kvp.Value));
}
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs argsNw)
{
//trainingProgressBar.Value = (int)(argsNw.TrainingIteration * 100d / cycles);
//Application.DoEvents();
});
//network.Learn(trainingSet, ciclos);
int cicloAtual = ciclos / 5;
int acertos = 0;
while (cicloAtual <= ciclos && acertos / agencias.Count < 0.99)
{
network.Learn(trainingSet, cicloAtual);
acertos = 0;
foreach (Agencia agencia in agencias)
{
double[] input = new double[] { NormalizarLatitude(agencia.Latitude), NormalizarLongitude(agencia.Longitude) };
double[] resultado = network.Run(input);
if (ConverterArrayDoubleParaZona(resultado) != agencia.Zona)
trainingSet.Add(new TrainingSample(input, ConverterZonaParaArrayDouble(agencia.Zona)));
else
acertos++;
}
cicloAtual += ciclos / 5;
}
//using (Stream stream = File.Open(System.IO.Directory.GetCurrentDirectory() + "\\" + nomeRedeNeural + ".ndn", FileMode.Create))
using (Stream stream = File.Open("..\\Pasta\\" + nomeRede + ".ndn", FileMode.Create))
{
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, network);
}
return network;
}
/// <summary>
/// train and save as the spcified path
/// </summary>
/// <param name="eigen">
/// A <see cref="EigenValueTags"/>
/// </param>
private static void TrainNetwork(EigenValueTags eigen)
{
Log.Debug("================ Train Started ================ ");
string[] dLabels = eigen.FacesLabel;
int numInstances = eigen.eigenTaglist.Count;
int inputNodes = eigen.eigenTaglist[0].val.Length;
int outputNodes = dLabels.Length;
int hiddenNodes = inputNodes+outputNodes;
float[][] trainInput = new float[numInstances][];
float[][] trainOutput = new float[numInstances][];
//Random r = new Random();
int numstrain = 0;
for(int i=0;i<numInstances;i++){
trainInput[numstrain] = new float[inputNodes];
trainOutput[numstrain] = new float[outputNodes];
for(int j=0;j<dLabels.Length;j++){
if(eigen.eigenTaglist[i].tag.Equals(dLabels[j]))
trainOutput[numstrain][j] = 0.9f;
else
trainOutput[numstrain][j] = 0.1f;
}
for(int j=0;j<inputNodes;j++){
trainInput[numstrain][j] = eigen.eigenTaglist[i].val[j];
}
numstrain++;
}
// convert to double
Log.Debug("nums train = "+ numstrain);
double[][] trainInputD = new double[numstrain][];
double[][] trainOutputD = new double[numstrain][];
for(int i=0;i<numstrain;i++){
trainInputD[i] = new double[inputNodes];
trainOutputD[i] = new double[outputNodes];
for(int j=0;j<outputNodes;j++){
trainOutputD[i][j] = trainOutput[i][j];
}
for(int j=0;j<inputNodes;j++){
trainInputD[i][j] = trainInput[i][j];
}
}
// TimeSpan tp = System.DateTime.Now.TimeOfDay;
Log.Debug("#in = {0}, #hid = {1}, #out = {2}",inputNodes,hiddenNodes,outputNodes);
NeuronDotNet.Core.Backpropagation.SigmoidLayer inputLayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(inputNodes);
NeuronDotNet.Core.Backpropagation.SigmoidLayer hiddenlayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(hiddenNodes);
NeuronDotNet.Core.Backpropagation.SigmoidLayer outputlayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(outputNodes);
Log.Debug("BackpropagationConnector input_hidden = new BackpropagationConnector(inputLayer, hiddenlayer);");
BackpropagationConnector input_hidden = new BackpropagationConnector(inputLayer, hiddenlayer);
BackpropagationConnector hidden_output = new BackpropagationConnector(hiddenlayer, outputlayer);
input_hidden.Momentum = 0.3;
hidden_output.Momentum = 0.3;
Log.Debug("bpnet = new BackpropagationNetwork(inputLayer,outputlayer);");
bpnet = new BackpropagationNetwork(inputLayer,outputlayer);
Log.Debug("TrainingSet tset = new TrainingSet(inputNodes, outputNodes);");
TrainingSet tset = new TrainingSet(inputNodes, outputNodes);
for(int i=0;i<numstrain;i++)
tset.Add(new TrainingSample(trainInputD[i], trainOutputD[i]));
// prevent getting stuck in local minima
bpnet.JitterNoiseLimit = 0.0001;
bpnet.Initialize();
int numEpoch = 200;
bpnet.SetLearningRate(0.2);
bpnet.Learn(tset, numEpoch);
// Log.Debug("error = {0}",bpnet.MeanSquaredError);
// string savepath = facedbPath + "object/";
// if(!Directory.Exists(savepath))
// Directory.CreateDirectory(savepath);
// Serialize
string path = Path.Combine (FSpot.Global.BaseDirectory, "ann.dat");
SerializeUtil.Serialize(path, bpnet);
// Deserialize
//BackpropagationNetwork testnet = (BackpropagationNetwork)SerializeUtil.DeSerialize("nn.dat");
// Log.Debug("error = {0}",bpnet.MeanSquaredError);
//bpnet = (BackpropagationNetwork)SerializeUtil.DeSerialize("/home/hyperjump/nn.dat");
//Log.Debug("error = {0}",bpnet.MeanSquaredError);
// test by using training data
// int correct = 0;
// for(int i=0;i<numInstances;i++){
//
// double[] v = new double[inputNodes];
// for(int j=0;j<v.Length;j++){
// v[j] = (double)eigen.eigenTaglist[i].val[j];
//Console.Write("{0},",v[j]);
// }
//Console.WriteLine();
// double[] netOutput = bpnet.Run(v);
//Console.WriteLine("net out:");
// for(int j=0;j<netOutput.Length;j++)
// Console.Write("{0},",netOutput[j]);
// string result = FaceClassifier.Instance.AnalyseNetworkOutput(eigen, netOutput);
// if(eigen.eigenTaglist[i].tag.Equals(result))
// correct++;
// }
// Log.Debug("% correct = " + (float)correct/(float)numInstances * 100);
//Save Train Status
Log.Debug("Saving Train Status...");
List<Tstate> tstateList = new List<Tstate>();
int[] num = new int[dLabels.Length];
Log.Debug("num length = {0}",num.Length);
foreach(VTag vt in eigen.eigenTaglist){
for(int k=0;k<num.Length;k++)
if(vt.tag.Equals(dLabels[k]))
num[k]++;
}
for(int k=0;k<dLabels.Length;k++){
tstateList.Add(new Tstate(dLabels[k], num[k]));
}
FaceSpotDb.Instance.TrainingData.Trainstat = tstateList;
// Log.Debug("time ="+ System.DateTime.Now.TimeOfDay.Subtract(tp));
Log.Debug("================ Train ended ================ ");
}
