public static double TrainNetwork(String what,
FreeformNetwork network, IMLDataSet trainingSet) {
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(
network, score, 10, 2, 100);
IMLTrain trainMain = new FreeformBackPropagation(network, trainingSet,0.00001, 0.0);
StopTrainingStrategy stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
EncogUtility.TrainToError(trainMain, 0.01);
return trainMain.Error;
}
public void Train(BasicNetwork network, IMLDataSet training)
{
IMLTrain trainMain = new LevenbergMarquardtTraining(network, training);
// train the neural network
var stop = new StopTrainingStrategy();
ICalculateScore score = new TrainingSetScore(trainMain.Training);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop() && trainMain.IterationNumber < 1500)
{
trainMain.Iteration();
Console.WriteLine("Training " + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
}
private void trainNetworkBackprop()
{
// IMLTrain train = new Backpropagation(this.network, this.input,this.ideal, 0.000001, 0.1);
IMLDataSet aset = new BasicMLDataSet(input, ideal);
int epoch = 1;
// train the neural network
ICalculateScore score = new TrainingSetScore(aset);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, aset, 0.001, 0.0);
StopTrainingStrategy stop = new StopTrainingStrategy();
var pop = new NEATPopulation(INPUT_SIZE, OUTPUT_SIZE, 1000);
// train the neural network
var step = new ActivationStep();
step.Center = 0.5;
pop.OutputActivationFunction = step;
var train = new NEATTraining(score, pop);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
trainMain.AddStrategy(new HybridStrategy(train));
network.ClearContext();
while (!stop.ShouldStop())
{
trainMain.Iteration();
train.Iteration();
Console.WriteLine(@"Training " + @"Epoch #" + epoch + @" Error:" + trainMain.Error+ @" Genetic iteration:"+trainAlt.IterationNumber+ @"neat iteration:"+train.IterationNumber );
epoch++;
}
}
private double TrainNetwork(String what, BasicNetwork network, IMLDataSet trainingSet)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(
network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, trainingSet, 0.00001, 0.0);
var stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop())
{
trainMain.Iteration();
app.WriteLine("Training " + what + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
return trainMain.Error;
}
public static double TrainNetworks(SupportVectorMachine network, MarketMLDataSet training)
{
// train the neural network
SVMTrain trainMain = new SVMTrain(network, training);
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
trainMain.AddStrategy(stop);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
trainMain.PreIteration();
trainMain.Iteration();
trainMain.PostIteration();
Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error);
}
sw.Stop();
Console.WriteLine("SVM Trained in :" + sw.ElapsedMilliseconds + "For error:" + trainMain.Error + " Iterated:" + trainMain.IterationNumber);
return trainMain.Error;
}
private double TrainNetwork(String what, BasicNetwork network, IMLDataSet trainingSet, string Method)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain;
if (Method.Equals("Leven"))
{
Console.WriteLine("Using LevenbergMarquardtTraining");
trainMain = new LevenbergMarquardtTraining(network, trainingSet);
}
else
trainMain = new Backpropagation(network, trainingSet);
var stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop())
{
trainMain.Iteration();
app.WriteLine("Training " + what + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
return trainMain.Error;
}
/// <summary>
/// Perform an individual job unit, which is a single network to train and
/// evaluate.
/// </summary>
///
/// <param name="context">Contains information about the job unit.</param>
public override sealed void PerformJobUnit(JobUnitContext context)
{
var network = (BasicNetwork) context.JobUnit;
BufferedMLDataSet buffer = null;
IMLDataSet useTraining = _training;
if (_training is BufferedMLDataSet)
{
buffer = (BufferedMLDataSet) _training;
useTraining = (buffer.OpenAdditional());
}
// train the neural network
double error = Double.PositiveInfinity;
for (int z = 0; z < _weightTries; z++)
{
network.Reset();
Propagation train = new ResilientPropagation(network,
useTraining);
var strat = new StopTrainingStrategy(0.001d,
5);
train.AddStrategy(strat);
train.ThreadCount = 1; // force single thread mode
for (int i = 0;
(i < _iterations) && !ShouldStop
&& !strat.ShouldStop();
i++)
{
train.Iteration();
}
error = Math.Min(error, train.Error);
}
if (buffer != null)
{
buffer.Close();
}
if (!ShouldStop)
{
// update min and max
_high = Math.Max(_high, error);
_low = Math.Min(_low, error);
if (_hidden1Size > 0)
{
int networkHidden1Count;
int networkHidden2Count;
if (network.LayerCount > 3)
{
networkHidden2Count = network.GetLayerNeuronCount(2);
networkHidden1Count = network.GetLayerNeuronCount(1);
}
else
{
networkHidden2Count = 0;
networkHidden1Count = network.GetLayerNeuronCount(1);
}
int row, col;
if (_hidden2Size == 0)
{
row = networkHidden1Count - _hidden[0].Min;
col = 0;
}
else
{
row = networkHidden1Count - _hidden[0].Min;
col = networkHidden2Count - _hidden[1].Min;
}
if ((row < 0) || (col < 0))
{
Console.Out.WriteLine("STOP");
}
_results[row][col] = error;
}
// report status
_currentTry++;
UpdateBest(network, error);
ReportStatus(
context,
"Current: "
+ NetworkToString(network)
+ "; Best: "
+ NetworkToString(_bestNetwork));
}
}
public static SupportVectorMachine SVMSearch(SupportVectorMachine anetwork, IMLDataSet training)
{
SVMSearchTrain bestsearch = new SVMSearchTrain(anetwork, training);
StopTrainingStrategy stop = new StopTrainingStrategy(0.00000000001, 1);
bestsearch.AddStrategy(stop);
while (bestsearch.IterationNumber < 30 && !stop.ShouldStop())
{
bestsearch.Iteration();
Console.WriteLine("Iteration #" + bestsearch.IterationNumber + " Error :" + bestsearch.Error);
}
bestsearch.FinishTraining();
return anetwork;
}
public static double TrainSVM(SVMTrain train, SupportVectorMachine machine)
{
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
train.AddStrategy(stop);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
train.PreIteration();
train.Iteration();
train.PostIteration();
Console.WriteLine(@"Iteration #:" + train.IterationNumber + @" Error:" + train.Error +" Gamma:"+train.Gamma);
}
sw.Stop();
Console.WriteLine(@"SVM Trained in :" + sw.ElapsedMilliseconds);
return train.Error;
}
public static double TrainNetworks(BasicNetwork network, IMLDataSet minis)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(minis);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, minis, 0.0001, 0.01);
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
trainMain.Iteration();
Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error + @" Genetic Iteration:" + trainAlt.IterationNumber);
}
sw.Stop();
return trainMain.Error;
}
public static double TrainNetworks(BasicNetwork network, IMLDataSet minis)
{
Backpropagation trainMain = new Backpropagation(network, minis,0.0001,0.6);
//set the number of threads below.
trainMain.ThreadCount = 0;
// train the neural network
ICalculateScore score = new TrainingSetScore(minis);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
// IMLTrain trainMain = new Backpropagation(network, minis, 0.0001, 0.01);
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
//prune strategy not in GIT!...Removing it.
//PruneStrategy strategypruning = new PruneStrategy(0.91d, 0.001d, 10, network,minis, 0, 20);
//trainMain.AddStrategy(strategypruning);
EncogUtility.TrainConsole(trainMain,network,minis, 15.2);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
trainMain.Iteration();
Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error + @" Genetic Iteration:" + trainAlt.IterationNumber);
}
sw.Stop();
Console.WriteLine(@"Total elapsed time in seconds:" + TimeSpan.FromMilliseconds(sw.ElapsedMilliseconds).Seconds);
return trainMain.Error;
}
public override sealed void PerformJobUnit(JobUnitContext context)
{
double num;
int num2;
int num3;
int num4;
int layerNeuronCount;
int num6;
int num7;
BasicNetwork jobUnit = (BasicNetwork) context.JobUnit;
BufferedMLDataSet set = null;
IMLDataSet training = this._x823a2b9c8bf459c5;
if (this._x823a2b9c8bf459c5 is BufferedMLDataSet)
{
set = (BufferedMLDataSet) this._x823a2b9c8bf459c5;
if ((((uint) num) + ((uint) num2)) > uint.MaxValue)
{
goto Label_02BB;
}
}
else
{
if (((uint) num4) > uint.MaxValue)
{
goto Label_0302;
}
goto Label_02BB;
}
training = set.OpenAdditional();
goto Label_0302;
Label_0011:
this._x7ca40c9a68f86359++;
this.x6aa420caefd31103(jobUnit, num);
base.ReportStatus(context, "Current: " + NetworkToString(jobUnit) + "; Best: " + NetworkToString(this._x61bb83c40eed7f47));
if (((uint) num) >= 0)
{
return;
}
if (((uint) layerNeuronCount) <= uint.MaxValue)
{
goto Label_0104;
}
if ((((uint) layerNeuronCount) | 3) == 0)
{
}
Label_0090:
if (num6 >= 0)
{
if (num7 >= 0)
{
goto Label_00A7;
}
}
else if (0 != 0)
{
goto Label_00B9;
}
Console.Out.WriteLine("STOP");
Label_00A7:
this._xd559aa34776631a5[num6][num7] = num;
goto Label_0011;
Label_00B9:
if (this._xe4e6a25eae13e4b3 > 0)
{
goto Label_0188;
}
if (0 == 0)
{
if ((((uint) num3) & 0) != 0)
{
return;
}
goto Label_0011;
}
Label_0104:
num7 = 0;
goto Label_0090;
Label_0125:
num4 = jobUnit.GetLayerNeuronCount(1);
Label_012E:
if (this._x5426aa354995e9e0 != 0)
{
num6 = num4 - this._xab3ddaff42dd298a[0].Min;
num7 = layerNeuronCount - this._xab3ddaff42dd298a[1].Min;
goto Label_0090;
}
num6 = num4 - this._xab3ddaff42dd298a[0].Min;
if ((((uint) num3) | 0xfffffffe) != 0)
{
goto Label_0104;
}
return;
Label_0188:
if (jobUnit.LayerCount <= 3)
{
layerNeuronCount = 0;
goto Label_0125;
}
layerNeuronCount = jobUnit.GetLayerNeuronCount(2);
num4 = jobUnit.GetLayerNeuronCount(1);
goto Label_012E;
Label_0195:
if (base.ShouldStop)
{
return;
}
this._x628ea9b89457a2a9 = Math.Max(this._x628ea9b89457a2a9, num);
this._xd12d1dba8a023d95 = Math.Min(this._xd12d1dba8a023d95, num);
if ((((uint) num2) + ((uint) num6)) <= uint.MaxValue)
{
goto Label_00B9;
}
goto Label_0188;
Label_02BB:
num = double.PositiveInfinity;
num2 = 0;
Label_0217:
if (num2 < this._xe009ad1bd0a8245a)
{
StopTrainingStrategy strategy;
jobUnit.Reset();
Encog.Neural.Networks.Training.Propagation.Propagation propagation = new ResilientPropagation(jobUnit, training);
if ((((uint) num) - ((uint) num3)) >= 0)
{
strategy = new StopTrainingStrategy(0.001, 5);
}
propagation.AddStrategy(strategy);
propagation.ThreadCount = 1;
num3 = 0;
while (true)
{
if ((num3 < this._xdbf51c857aeb8093) && (!base.ShouldStop && !strategy.ShouldStop()))
{
propagation.Iteration();
}
else
{
num = Math.Min(num, propagation.Error);
if (4 != 0)
{
num2++;
goto Label_0217;
}
goto Label_0195;
}
num3++;
}
}
while (set != null)
{
set.Close();
if (((uint) num4) <= uint.MaxValue)
{
break;
}
}
goto Label_0195;
Label_0302:
if ((((uint) num4) - ((uint) layerNeuronCount)) < 0)
{
goto Label_0125;
}
goto Label_02BB;
}
