/// <summary>
/// Generate the input to the neural network to predict. It will look at the current
/// date as well as the number of days leading up to it specified by EvalWindow.
/// This method is used both internally and externally.
/// </summary>
/// <param name="marketData">The market data to use.</param>
/// <param name="marketDataIndex">The point that we want to predict from.</param>
/// <returns></returns>
public INeuralData CreateData(
List<LoadedMarketData> marketData,
int marketDataIndex)
{
INeuralData neuralData = new BasicNeuralData(14);
int totalPatterns = 0;
var patternCount = new int[14];
for (var i = 0; i < EvalWindow; i++)
{
var data = marketData[(marketDataIndex-EvalWindow) + i];
var candle = new IdentifyCandleStick();
candle.SetStats(data);
var pattern = candle.DeterminePattern();
if (pattern == IdentifyCandleStick.UNKNOWN) continue;
totalPatterns++;
patternCount[pattern]++;
}
if (totalPatterns == 0)
return null;
for (var i = 0; i < 14; i++)
{
neuralData[i] = patternCount[i] / ((double)totalPatterns);
}
return neuralData;
}
/// <summary>
/// Construct a data set from an input and idea array.
/// </summary>
/// <param name="input">The input into the neural network for training.</param>
/// <param name="ideal">The idea into the neural network for training.</param>
public BasicNeuralDataSet(double[][] input, double[][] ideal)
{
for (int i = 0; i < input.Length; i++)
{
double[] tempInput = new double[input[0].Length];
double[] tempIdeal = null;
for (int j = 0; j < tempInput.Length; j++)
{
tempInput[j] = input[i][j];
}
BasicNeuralData idealData = null;
if (ideal != null)
{
tempIdeal = new double[ideal[0].Length];
for (int j = 0; j < tempIdeal.Length; j++)
{
tempIdeal[j] = ideal[i][j];
}
idealData = new BasicNeuralData(tempIdeal);
}
BasicNeuralData inputData = new BasicNeuralData(tempInput);
this.Add(inputData, idealData);
}
}
/// <summary>
/// Generate a random training set.
/// </summary>
/// <param name="seed">The seed value to use, the same seed value will always produce
/// the same results.</param>
/// <param name="count">How many training items to generate.</param>
/// <param name="inputCount">How many input numbers.</param>
/// <param name="idealCount">How many ideal numbers.</param>
/// <param name="min">The minimum random number.</param>
/// <param name="max">The maximum random number.</param>
/// <returns>The random training set.</returns>
public static BasicNeuralDataSet Generate(long seed,
int count, int inputCount,
int idealCount, double min, double max)
{
LinearCongruentialGenerator rand =
new LinearCongruentialGenerator(seed);
BasicNeuralDataSet result = new BasicNeuralDataSet();
for (int i = 0; i < count; i++)
{
INeuralData inputData = new BasicNeuralData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData.Data[j] = rand.Range(min, max);
}
INeuralData idealData = new BasicNeuralData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
BasicNeuralDataPair pair = new BasicNeuralDataPair(inputData,
idealData);
result.Add(pair);
}
return result;
}
/// <summary>
/// Handle reading an item tag.
/// </summary>
/// <param name="xmlIn">The XML reader.</param>
private void HandleItem(ReadXML xmlIn)
{
IDictionary<String, String> properties = xmlIn.ReadPropertyBlock();
INeuralDataPair pair = null;
INeuralData input = new BasicNeuralData(NumberList
.FromList(CSVFormat.EG_FORMAT, properties
[BasicNeuralDataSetPersistor.TAG_INPUT]));
if (properties.ContainsKey(BasicNeuralDataSetPersistor.TAG_IDEAL))
{
// supervised
INeuralData ideal = new BasicNeuralData(NumberList
.FromList(CSVFormat.EG_FORMAT, properties
[BasicNeuralDataSetPersistor.TAG_IDEAL]));
pair = new BasicNeuralDataPair(input, ideal);
}
else
{
// unsupervised
pair = new BasicNeuralDataPair(input);
}
this.currentDataSet.Add(pair);
}
/// <inheritdoc/>
public void Compute(double[] input, double[] output)
{
BasicNeuralData input2 = new BasicNeuralData(input);
INeuralData output2 = this.Compute(input2);
EngineArray.ArrayCopy(output2.Data, output);
}
/// <summary>
/// Compute the output for the given input.
/// </summary>
/// <param name="input">The input to the SVM.</param>
/// <returns>The results from the SVM.</returns>
public override INeuralData Compute(INeuralData input)
{
INeuralData result = new BasicNeuralData(this.outputCount);
svm_node[] formattedInput = MakeSparse(input);
for (int i = 0; i < this.outputCount; i++)
{
double d = svm.svm_predict(this.models[i], formattedInput);
result[i] = d;
}
return result;
}
public virtual BasicNeuralData GenerateInputNeuralData(int index)
{
BasicNeuralData data;
int num;
int num2;
if ((index + this._xc278386c02fd6e51) <= this._x6fa2570084b2ad39.Count)
{
data = new BasicNeuralData(this._x57202a8751db8895);
num = 0;
num2 = 0;
goto Label_007B;
}
if ((((uint) index) & 0) == 0)
{
if ((((uint) num) & 0) == 0)
{
throw new TemporalError("Can't generate input temporal data beyond the end of provided data.");
}
goto Label_0077;
}
Label_002C:
foreach (TemporalDataDescription description in this._x6849a3dfb0329317)
{
if (description.IsInput)
{
data[num++] = this.x8c7fc30b887213d1(description, index + num2);
}
}
Label_0077:
num2++;
Label_007B:
if (num2 < this._xc278386c02fd6e51)
{
goto Label_002C;
}
return data;
}
/// <summary>
/// Mencari solusi model neural network
/// </summary>
private void searchSolution()
{
// Normalize Data
switch (this.selectedActivationFunction)
{
case ActivationFunctionEnumeration.SemiLinearFunction:
this.activationFunction = new SemiLinearFunction();
this.normalizeData(0.1, 0.9);
break;
case ActivationFunctionEnumeration.SigmoidFunction:
this.activationFunction = new SigmoidFunction();
this.normalizeData(0.1, 0.9);
break;
case ActivationFunctionEnumeration.BipolarSigmoidFunction:
this.activationFunction = new BipolarSigmoidFunction();
this.normalizeData(-0.9, 0.9);
break;
case ActivationFunctionEnumeration.HyperbolicTangentFunction:
this.activationFunction = new HyperbolicTangentFunction();
this.normalizeData(-0.9, 0.9);
break;
default:
this.activationFunction = new BipolarSigmoidFunction();
this.normalizeData(-0.9, 0.9);
break;
}
//create network
this.network = new BasicNetwork();
this.network.AddLayer(new FeedforwardLayer(this.activationFunction, this.inputLayerNeurons));
this.network.AddLayer(new FeedforwardLayer(this.activationFunction, this.hiddenLayerNeurons));
this.network.AddLayer(new FeedforwardLayer(this.activationFunction, this.outputLayerNeurons));
this.network.Reset();
//variable for looping
//needToStop = false;
double mse = 0.0, error = 0.0, mae=0.0;
int iteration = 1;
// parameters
double msle = 0.0, mspe = 0.0, generalizationLoss = 0.0, pq = 0.0;
double[] trainingErrors = new double[this.strip];
for (int i = 0; i < this.strip; i++) trainingErrors[i] = double.MaxValue / strip;
double lastMSE = double.MaxValue;
// advanced early stopping
int n = this.data.Length - this.network.InputLayer.NeuronCount;
int validationSet = (int)Math.Round(this.validationSetRatio * n);
int trainingSet = n - validationSet;
double[][] networkTrainingInput = new double[trainingSet][];
double[][] networkTrainingOutput = new double[trainingSet][];
for (int i = 0; i < trainingSet; i++)
{
networkTrainingInput[i] = new double[this.network.InputLayer.NeuronCount];
networkTrainingOutput[i] = new double[1];
}
for (int i = 0; i < trainingSet; i++)
{
for (int j = 0; j < this.network.InputLayer.NeuronCount; j++)
{
networkTrainingInput[i][j] = this.networkInput[i][j];
}
networkTrainingOutput[i][0] = this.networkOutput[i][0];
}
// validation set
double[] solutionValidation = new double[validationSet];
double[] inputForValidation = new double[this.network.InputLayer.NeuronCount];
double[] inputForValidationNetwork = new double[this.network.InputLayer.NeuronCount];
// array for saving neural weights and parameters
this.bestValidationError = double.MaxValue;
this.bestWeightMatrix = new double[this.network.Layers.Count -1][,];
this.bestSolution = new double[n];
for (int i = 0; i < this.network.Layers.Count - 1; i++)
{
this.bestWeightMatrix[i] = new double[this.network.Layers[i].WeightMatrix.Rows, this.network.Layers[i].WeightMatrix.Cols];
}
//best network criterion
double bestNetworkError = double.MaxValue, bestNetworkMSE = double.MaxValue, bestNetworkMAE = double.MaxValue;
// build array for graph
this.solutionData = new double[n];
this.predictedPoint = new cPoint[n];
this.validationPoint = new cPoint[validationSet];
//initialize point for graph
predictedDS.Samples = predictedPoint;
validationDS.Samples = validationPoint;
this.predictedDS.Active = true;
// prepare training data
INeuralDataSet dataset;
if (this.useAdvanceEarlyStopping)
dataset = new BasicNeuralDataSet(networkTrainingInput, networkTrainingOutput);
else
dataset = new BasicNeuralDataSet(this.networkInput, this.networkOutput);
// initialize trainer
this.learning = new Backpropagation(this.network, dataset, this.learningRate, this.momentum);
//training
while (!needToStop)
{
double sse = 0.0;
double sae = 0.0;
double ssle = 0.0;
double sspe = 0.0;
this.learning.Iteration();
error = learning.Error;
if (this.useAdvanceEarlyStopping)
{
this.validationDS.Active = true;
}
else
{
this.validationDS.Active = false;
}
for (int i = 0; i < n; i++)
{
INeuralData neuraldata = new BasicNeuralData(this.networkInput[i]);
this.solutionData[i] = (this.network.Compute(neuraldata)[0]
- this.minNormalizedData) / this.factor + this.minData;
this.predictedPoint[i].x = i + this.network.InputLayer.NeuronCount;
this.predictedPoint[i].y = (float)this.solutionData[i];
sse += Math.Pow(this.solutionData[i] - this.data[i + this.network.InputLayer.NeuronCount], 2);
sae += Math.Abs(this.solutionData[i] - this.data[i + this.network.InputLayer.NeuronCount]);
//calculate advance early stopping
if (this.useAdvanceEarlyStopping)
{
if (i < n - validationSet)
{
ssle += Math.Pow(this.solutionData[i] - this.data[i + this.network.InputLayer.NeuronCount], 2);
}
else
{
// initialize the first validation set input
if (i == n - validationSet)
{
for (int j = 0; j < this.network.InputLayer.NeuronCount; j++)
{
inputForValidation[this.network.InputLayer.NeuronCount - 1 - j] = this.data[this.data.Length - (n - i) - 1 - j];
}
}
for (int j = 0; j < this.network.InputLayer.NeuronCount; j++)
{
inputForValidationNetwork[j] = (inputForValidation[j] - this.minData) * this.factor + this.minNormalizedData;
}
INeuralData neuraldataval = new BasicNeuralData(inputForValidationNetwork);
solutionValidation[i - n + validationSet] = (this.network.Compute(neuraldataval)[0] - this.minNormalizedData) / this.factor + this.minData;
this.validationPoint[i - n + validationSet].x = i + this.network.InputLayer.NeuronCount;
this.validationPoint[i - n + validationSet].y = (float)solutionValidation[i - n + validationSet];
sspe += Math.Pow(this.data[i + this.network.InputLayer.NeuronCount] - solutionValidation[i - n + validationSet], 2);
// initialize the next validation set input from the current validation set input
for (int j = 0; j < this.network.InputLayer.NeuronCount - 1; j++)
{
inputForValidation[j] = inputForValidation[j + 1];
}
inputForValidation[this.network.InputLayer.NeuronCount - 1] = solutionValidation[i - n + validationSet];
}
}
}
mse = sse / this.solutionData.Length;
mae = sae / this.solutionData.Length;
//Console.WriteLine(error.ToString());
//Display it
this.iterationBox.Text = iteration.ToString();
this.maeBox.Text = mae.ToString("F5");
this.mseBox.Text = mse.ToString("F5");
this.errorBox.Text = error.ToString("F5");
seriesGraph.Refresh();
if (this.useAdvanceEarlyStopping)
{
//calculate advance early stopping 2
mspe = sspe / validationSet;
msle = ssle / (this.solutionData.Length - validationSet);
//save best weight
if (this.bestValidationError > mspe)
{
this.bestValidationError = mspe;
this.bestSolution = this.solutionData;
// weight matrix
for (int i = 0; i < this.network.Layers.Count - 1; i++)
for (int j = 0; j < this.network.Layers[i].WeightMatrix.Rows; j++)
for (int k = 0; k < this.network.Layers[i].WeightMatrix.Cols; k++)
this.bestWeightMatrix[i][j,k] = this.network.Layers[i].WeightMatrix[j, k];
bestNetworkError = error;
bestNetworkMAE = mae;
bestNetworkMSE = mse;
}
//calculate generalization loss &pq
generalizationLoss = 100 * (mspe / this.bestValidationError - 1);
trainingErrors[(iteration - 1) % this.strip] = msle;
double minStripTrainingError = double.MaxValue, sumStripTrainingError = 0.0;
for (int i = 0; i < this.strip; i++)
{
sumStripTrainingError += trainingErrors[i];
if (trainingErrors[i] < minStripTrainingError) minStripTrainingError = trainingErrors[i];
}
double trainingProgress = 1000 * ((sumStripTrainingError / (this.strip * minStripTrainingError)) - 1);
pq = generalizationLoss / trainingProgress;
//display advance early stopping
this.learningErrorBox.Text = msle.ToString("F5");
this.validationErrorBox.Text = mspe.ToString("F5");
this.generalizationLossBox.Text = generalizationLoss.ToString("F5");
this.pqBox.Text = pq.ToString("F5");
this.seriesGraph.Refresh();
//stopping
switch (this.advanceStoppingMethod)
{
case AdvanceStoppingMethodEnumeration.GeneralizationLoss:
if (generalizationLoss > this.generalizationLossTreshold) needToStop = true;
break;
case AdvanceStoppingMethodEnumeration.ProgressQuotient:
if (pq > this.pqTreshold) needToStop = true;
break;
}
}
if (this.withCheckingCycle && iteration % this.checkingCycle == 0)
{
switch (this.checkingMethod)
{
case CheckingMethodEnumeration.byMSEValue:
if (mse <= this.byMSEValueStopping) needToStop = true;
break;
case CheckingMethodEnumeration.byMSEChange:
if (lastMSE - mse <= this.byMSEChangeStopping) needToStop = true;
break;
}
lastMSE = mse;
}
if (iteration >= this.maxIteration)
{
needToStop = true;
}
iteration++;
}
//restore weight
if (this.useAdvanceEarlyStopping)
{
this.solutionData = this.bestSolution;
// weight matrix
for (int i = 0; i < this.network.Layers.Count - 1; i++)
for (int j = 0; j < this.network.Layers[i].WeightMatrix.Rows; j++)
for (int k = 0; k < this.network.Layers[i].WeightMatrix.Cols; k++)
this.network.Layers[i].WeightMatrix[j, k] = this.bestWeightMatrix[i][j, k];
//best network criterion
this.error = bestNetworkError;
this.mse = bestNetworkMSE;
this.mae = bestNetworkMAE;
}
else
{
this.error = error;
this.mse = mse;
this.mae = mae;
}
this.enableControls(true);
}
/// <summary>
/// Construct the chain rule calculation.
/// </summary>
/// <param name="network">The network to use.</param>
/// <param name="indexableTraining">The training set to use.</param>
public JacobianChainRule(BasicNetwork network,
IIndexable indexableTraining)
{
this.indexableTraining = indexableTraining;
this.network = network;
this.parameterSize = network.Structure.CalculateSize();
this.inputLength = (int)this.indexableTraining.Count;
this.jacobian = EngineArray.AllocateDouble2D(this.inputLength, this.parameterSize);
this.rowErrors = new double[this.inputLength];
BasicNeuralData input = new BasicNeuralData(
this.indexableTraining.InputSize);
BasicNeuralData ideal = new BasicNeuralData(
this.indexableTraining.IdealSize);
this.pair = new BasicNeuralDataPair(input, ideal);
}
/// <summary>
/// Generate neural ideal data for the specified index.
/// </summary>
/// <param name="index">The index to generate for.</param>
/// <returns>The neural data generated.</returns>
public virtual BasicNeuralData GenerateOutputNeuralData(int index)
{
if (index + _predictWindowSize > _points.Count)
{
throw new TemporalError("Can't generate prediction temporal data "
+ "beyond the end of provided data.");
}
var result = new BasicNeuralData(_outputNeuronCount);
int resultIndex = 0;
for (int i = 0; i < _predictWindowSize; i++)
{
foreach (TemporalDataDescription desc in _descriptions)
{
if (desc.IsPredict)
{
result[resultIndex++] = FormatData(desc, index
+ i);
}
}
}
return result;
}
/// <summary>
/// Write an array.
/// </summary>
/// <param name="data">The data to write.</param>
/// <param name="inputCount">How much of the data is input.</param>
public void Write(double[] data, int inputCount)
{
if (this.idealCount == 0)
{
BasicNeuralData inputData = new BasicNeuralData(data);
this.dataset.Add(inputData);
}
else
{
BasicNeuralData inputData = new BasicNeuralData(
this.inputCount);
BasicNeuralData idealData = new BasicNeuralData(
this.idealCount);
int index = 0;
for (int i = 0; i < this.inputCount; i++)
{
inputData[i] = data[index++];
}
for (int i = 0; i < this.idealCount; i++)
{
idealData[i] = data[index++];
}
this.dataset.Add(inputData, idealData);
}
}
/// <summary>
/// Generate random training into a training set.
/// </summary>
/// <param name="training">The training set to generate into.</param>
/// <param name="seed">The seed to use.</param>
/// <param name="count">How much data to generate.</param>
/// <param name="min">The low random value.</param>
/// <param name="max">The high random value.</param>
public static void Generate(INeuralDataSet training,
long seed,
int count,
double min, double max)
{
LinearCongruentialGenerator rand
= new LinearCongruentialGenerator(seed);
int inputCount = training.InputSize;
int idealCount = training.IdealSize;
for (int i = 0; i < count; i++)
{
INeuralData inputData = new BasicNeuralData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData[j] = rand.Range(min, max);
}
INeuralData idealData = new BasicNeuralData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
BasicNeuralDataPair pair = new BasicNeuralDataPair(inputData,
idealData);
training.Add(pair);
}
}
public IMLData BuildForNetworkInput(double[] data)
{
int num2;
int num5;
int num = 0;
goto Label_0148;
Label_00A8:
this.InitForOutput();
IMLData data2 = new BasicNeuralData(num2);
int num4 = 0;
using (IEnumerator<IOutputField> enumerator3 = this._outputFields.GetEnumerator())
{
IOutputField field3;
goto Label_00F6;
Label_00C8:
num5 = 0;
while (num5 < field3.SubfieldCount)
{
data2.Data[num4++] = field3.Calculate(num5);
num5++;
}
Label_00F6:
if (enumerator3.MoveNext())
{
goto Label_0119;
}
return data2;
Label_0101:
if ((((uint) num) & 0) != 0)
{
goto Label_0122;
}
goto Label_00C8;
Label_0119:
field3 = enumerator3.Current;
Label_0122:
if (field3.Ideal)
{
goto Label_00F6;
}
goto Label_0101;
}
if (2 != 0)
{
return data2;
}
Label_0148:
using (IEnumerator<IInputField> enumerator = this._inputFields.GetEnumerator())
{
IInputField field;
Label_0157:
if (enumerator.MoveNext())
{
goto Label_01D2;
}
goto Label_01EF;
Label_0168:
if (!field.UsedForNetworkInput)
{
goto Label_0157;
if (((uint) num) >= 0)
{
goto Label_01A2;
}
}
if (num >= data.Length)
{
throw new NormalizationError("Can't build data, input fields used for neural input, must match provided data(" + data.Length + ").");
}
if ((((uint) num4) - ((uint) num2)) < 0)
{
goto Label_0157;
}
Label_01A2:
field.CurrentValue = data[num++];
goto Label_0157;
Label_01D2:
field = enumerator.Current;
goto Label_0168;
}
Label_01EF:
num2 = 0;
if ((((uint) num5) - ((uint) num)) <= uint.MaxValue)
{
using (IEnumerator<IOutputField> enumerator2 = this._outputFields.GetEnumerator())
{
IOutputField field2;
Label_0031:
if (enumerator2.MoveNext())
{
goto Label_0077;
}
goto Label_00A8;
Label_003E:
while (field2.Ideal)
{
if (((uint) num2) <= uint.MaxValue)
{
goto Label_0031;
}
}
int num3 = 0;
while (num3 < field2.SubfieldCount)
{
num2++;
num3++;
}
goto Label_0031;
Label_0077:
field2 = enumerator2.Current;
if ((((uint) num3) & 0) == 0)
{
}
goto Label_003E;
}
}
goto Label_00A8;
}
/// <summary>
/// Construct the LMA object.
/// </summary>
/// <param name="network">The network to train. Must have a single output neuron.</param>
/// <param name="training">The training data to use. Must be indexable.</param>
public SVDTraining(BasicNetwork network, INeuralDataSet training)
{
ILayer outputLayer = network.GetLayer(BasicNetwork.TAG_OUTPUT);
if (outputLayer == null)
{
throw new TrainingError("SVD requires an output layer.");
}
if (outputLayer.NeuronCount != 1)
{
throw new TrainingError("SVD requires an output layer with a single neuron.");
}
if (network.GetLayer(RadialBasisPattern.RBF_LAYER) == null)
throw new TrainingError("SVD is only tested to work on radial basis function networks.");
rbfLayer = (RadialBasisFunctionLayer)network.GetLayer(RadialBasisPattern.RBF_LAYER);
this.Training = training;
this.network = network;
this.trainingLength = (int)this.Training.InputSize;
BasicNeuralData input = new BasicNeuralData(this.Training.InputSize);
BasicNeuralData ideal = new BasicNeuralData(this.Training.IdealSize);
this.pair = new BasicNeuralDataPair(input, ideal);
}
public virtual BasicNeuralData GenerateOutputNeuralData(int index)
{
if ((index + this._xcfe8f2ba20a6a8e4) <= this._x6fa2570084b2ad39.Count)
{
BasicNeuralData data = new BasicNeuralData(this._x0c37ff3adde9bc44);
int num = 0;
int num2 = 0;
while (num2 < this._xcfe8f2ba20a6a8e4)
{
using (IEnumerator<TemporalDataDescription> enumerator = this._x6849a3dfb0329317.GetEnumerator())
{
TemporalDataDescription current;
goto Label_005F;
Label_002C:
data[num++] = this.x8c7fc30b887213d1(current, index + num2);
if ((((uint) num) & 0) == 0)
{
}
goto Label_005F;
Label_0057:
if (current.IsPredict)
{
goto Label_002C;
}
Label_005F:
if (enumerator.MoveNext())
{
current = enumerator.Current;
goto Label_0057;
}
}
num2++;
}
if ((((uint) num) - ((uint) num2)) >= 0)
{
return data;
}
}
throw new TemporalError("Can't generate prediction temporal data beyond the end of provided data.");
}
/// <summary>
/// Generate input neural data for the specified index.
/// </summary>
/// <param name="index">The index to generate neural data for.</param>
/// <returns>The input neural data generated.</returns>
public virtual BasicNeuralData GenerateInputNeuralData(int index)
{
if (index + this.inputWindowSize > this.points.Count)
{
throw new TemporalError("Can't generate input temporal data "
+ "beyond the end of provided data.");
}
BasicNeuralData result = new BasicNeuralData(this.inputNeuronCount);
int resultIndex = 0;
for (int i = 0; i < this.inputWindowSize; i++)
{
int descriptionIndex = 0;
foreach (TemporalDataDescription desc in this.descriptions)
{
if (desc.IsInput)
{
result[resultIndex++] = this.FormatData(desc, index
+ i);
}
descriptionIndex++;
}
}
return result;
}
/// <summary>
/// Compute the values before sending output to the next layer.
/// This function allows the activation functions to be called.
/// </summary>
/// <param name="pattern">The incoming Project.</param>
/// <returns>The output from this layer.</returns>
public override INeuralData Compute(INeuralData pattern)
{
INeuralData result = new BasicNeuralData(NeuronCount);
for (int i = 0; i < NeuronCount; i++)
{
if (this.radialBasisFunction[i] == null)
{
String str =
"Error, must define radial functions for each neuron";
#if logging
if (RadialBasisFunctionLayer.logger.IsErrorEnabled)
{
RadialBasisFunctionLayer.logger.Error(str);
}
#endif
throw new NeuralNetworkError(str);
}
IRadialBasisFunction f = this.radialBasisFunction[i];
if (pattern.Data.Length != f.Dimensions)
throw new Exception("Inputs must equal the number of dimensions.");
result[i] = f.Calculate(pattern.Data);
}
return result;
}
/// <summary>
/// Clone this object.
/// </summary>
/// <returns>A clone of this object.</returns>
public object Clone()
{
BasicNeuralData result = new BasicNeuralData(this.data);
return(result);
}
/// <summary>
/// Compute the weighted output from this synapse. Each neuron
/// in the from layer has a weighted connection to each of the
/// neurons in the next layer.
/// </summary>
/// <param name="input">The input from the synapse.</param>
/// <returns>The output from this synapse.</returns>
public override INeuralData Compute(INeuralData input)
{
INeuralData result = new BasicNeuralData(this.ToNeuronCount);
double[] inputArray = input.Data;
double[][] matrixArray = this.WeightMatrix.Data;
double[] resultArray = result.Data;
for (int i = 0; i < this.ToNeuronCount; i++) {
double sum = 0;
for(int j = 0;j<inputArray.Length;j++ )
{
sum+=inputArray[j]*matrixArray[j][i];
}
resultArray[i] = sum;
}
return result;
}
/// <summary>
/// Clone this object.
/// </summary>
/// <returns>A clone of this object.</returns>
public object Clone()
{
BasicNeuralData result = new BasicNeuralData(this.data);
return result;
}
/// <summary>
/// Build "input data for a neural network" based on the input values
/// provided. This allows input for a neural network to be normalized.
/// This is typically used when data is to be presented to a trained
/// neural network.
/// </summary>
/// <param name="data">The input values to be normalized.</param>
/// <returns>The data to be sent to the neural network.</returns>
public IMLData BuildForNetworkInput(double[] data)
{
// feed the input fields
int index = 0;
foreach (IInputField field in _inputFields)
{
if (field.UsedForNetworkInput)
{
if (index >= data.Length)
{
throw new NormalizationError(
"Can't build data, input fields used for neural input, must match provided data("
+ data.Length + ").");
}
field.CurrentValue = data[index++];
}
}
// count the output fields
int outputCount = 0;
foreach (IOutputField ofield in _outputFields)
{
if (!ofield.Ideal)
{
for (int sub = 0; sub < ofield.SubfieldCount; sub++)
{
outputCount++;
}
}
}
// process the output fields
InitForOutput();
var result = new BasicNeuralData(outputCount);
// write the value
int outputIndex = 0;
foreach (IOutputField ofield in _outputFields)
{
if (!ofield.Ideal)
{
for (int sub = 0; sub < ofield.SubfieldCount; sub++)
{
result[outputIndex++] = ofield.Calculate(sub);
}
}
}
return result;
}
/// <summary>
/// Create one training pair, either good or bad.
/// </summary>
/// <param name="data">The data to create from.</param>
/// <param name="index">The index into the data to create from.</param>
/// <param name="good">True if this was a good(bearish) period.</param>
/// <returns></returns>
public IMLDataPair CreateData(List<LoadedMarketData> data, int index, bool good)
{
BasicNeuralData ideal = new BasicNeuralData(1);
INeuralData input = CreateData(data, index);
if (input == null)
return null;
// ideal
if (good)
ideal[0] = 0.9;
else
ideal[0] = 0.1;
return new BasicMLDataPair(input, ideal);
}
/// <summary>
/// Meramalkan data
/// </summary>
/// <param name="step">step peramalan</param>
/// <returns>hasil peramalan</returns>
public double[] Forecast(int step)
{
if (step < 1) step = 1;
int inputsCount = this.network.InputLayer.NeuronCount;
double[] result = new double[step];
double[] inputForOut = new double[inputsCount];
double[] inputForOutNetwork = new double[inputsCount];
//input for forecasting
for (int j = 0; j < inputsCount; j++)
{
inputForOut[inputsCount - 1 - j] = this.data[this.data.Length - 1 - j];
}
//forecast
for (int i = 0; i < step; i++)
{
for (int j = 0; j < inputsCount; j++)
{
inputForOutNetwork[j] = (inputForOut[j] - this.minData) * this.factor + this.minNormalizedData;
}
INeuralData neuraldata = new BasicNeuralData(inputForOutNetwork);
// evalue the function
result[i] = (this.network.Compute(neuraldata)[0] - this.minNormalizedData) / this.factor + this.minData;
for (int j = 0; j < inputsCount - 1; j++)
{
inputForOut[j] = inputForOut[j + 1];
}
inputForOut[inputsCount - 1] = result[i];
}
return result;
}
/// <summary>
/// Compute the output from this synapse.
/// </summary>
/// <param name="input">The input to this synapse.</param>
/// <returns>The output from this synapse.</returns>
public INeuralData Compute(INeuralData input)
{
INeuralData result = new BasicNeuralData(ToNeuronCount);
if (this.neurons.Count == 0)
{
throw new NeuralNetworkError("This network has not been evolved yet, it has no neurons in the NEAT synapse.");
}
int flushCount = 1;
if (snapshot)
{
flushCount = networkDepth;
}
// iterate through the network FlushCount times
for (int i = 0; i < flushCount; ++i)
{
int outputIndex = 0;
int index = 0;
result.Clear();
// populate the input neurons
while (neurons[index].NeuronType == NEATNeuronType.Input)
{
neurons[index].Output = input[index];
index++;
}
// set the bias neuron
neurons[index++].Output = 1;
while (index < neurons.Count)
{
NEATNeuron currentNeuron = neurons[index];
double sum = 0;
foreach (NEATLink link in currentNeuron.InboundLinks)
{
double weight = link.Weight;
double neuronOutput = link.FromNeuron.Output;
sum += weight * neuronOutput;
}
double[] d = new double[1];
d[0] = sum / currentNeuron.ActivationResponse;
activationFunction.ActivationFunction(d,0,1);
neurons[index].Output = d[0];
if (currentNeuron.NeuronType == NEATNeuronType.Output)
{
result.Data[outputIndex++] = currentNeuron.Output;
}
index++;
}
}
return result;
}
/// <summary>
/// Compute the output for a given input to the neural network. This method
/// provides a parameter to specify an output holder to use. This holder
/// allows propagation training to track the output from each layer.
/// If you do not need this holder pass null, or use the other
/// compare method.
/// </summary>
/// <param name="input">The input provide to the neural network.</param>
/// <param name="useHolder">Allows a holder to be specified, this allows
/// propagation training to check the output of each layer.</param>
/// <returns>The results from the output neurons.</returns>
public virtual INeuralData Compute(INeuralData input,
NeuralOutputHolder useHolder)
{
NeuralOutputHolder holder;
ILayer inputLayer = this.network.GetLayer(BasicNetwork.TAG_INPUT);
#if logging
if (FeedforwardLogic.logger.IsDebugEnabled)
{
FeedforwardLogic.logger.Debug("Pattern " + input.ToString()
+ " presented to neural network");
}
#endif
if (useHolder == null && this.network.Structure.Flat != null)
{
this.network.Structure.UpdateFlatNetwork();
INeuralData result = new BasicNeuralData(this.network.Structure.Flat.OutputCount);
this.network.Structure.Flat.Compute(input.Data, result.Data);
return result;
}
if (useHolder == null)
{
holder = new NeuralOutputHolder();
}
else
{
holder = useHolder;
}
Compute(holder, inputLayer, input, null);
return holder.Output;
}
/// <summary>
/// Compute the weightless output from this synapse. Each neuron
/// in the from layer has a weightless connection to each of the
/// neurons in the next layer.
/// </summary>
/// <param name="input">The input from the synapse.</param>
/// <returns>The output from this synapse.</returns>
public override INeuralData Compute(INeuralData input)
{
INeuralData result = new BasicNeuralData(this.ToNeuronCount);
// just sum the input
double sum = 0;
for (int i = 0; i < input.Count; i++)
{
sum += input[i];
}
for (int i = 0; i < this.ToNeuronCount; i++)
{
result[i] = sum;
}
return result;
}
