/// <summary>
/// Clone into the flat network passed in.
/// </summary>
///
/// <param name="result">The network to copy into.</param>
public void CloneFlatNetwork(FlatNetwork result)
{
result._inputCount = _inputCount;
result._layerCounts = EngineArray.ArrayCopy(_layerCounts);
result._layerIndex = EngineArray.ArrayCopy(_layerIndex);
result._layerOutput = EngineArray.ArrayCopy(_layerOutput);
result._layerSums = EngineArray.ArrayCopy(_layerSums);
result._layerFeedCounts = EngineArray.ArrayCopy(_layerFeedCounts);
result._contextTargetOffset = EngineArray
.ArrayCopy(_contextTargetOffset);
result._contextTargetSize = EngineArray
.ArrayCopy(_contextTargetSize);
result._layerContextCount = EngineArray
.ArrayCopy(_layerContextCount);
result._biasActivation = EngineArray.ArrayCopy(_biasActivation);
result._outputCount = _outputCount;
result._weightIndex = _weightIndex;
result._weights = _weights;
result._activationFunctions = new IActivationFunction[_activationFunctions.Length];
for (int i = 0; i < result._activationFunctions.Length; i++)
{
result._activationFunctions[i] = (IActivationFunction)_activationFunctions[i].Clone();
}
result._beginTraining = _beginTraining;
result._endTraining = _endTraining;
}
/// <summary>
/// Clone the network.
/// </summary>
///
/// <returns>A clone of the network.</returns>
public virtual Object Clone()
{
var result = new FlatNetwork();
CloneFlatNetwork(result);
return(result);
}
public TrainFlatNetworkBackPropagation(FlatNetwork network, IMLDataSet training, double theLearningRate, double theMomentum)
: base(network, training)
{
this._xef52c16be8e501c9 = theMomentum;
this._x9b481c22b6706459 = theLearningRate;
this._xe4def4d471bbc130 = new double[network.Weights.Length];
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
var network = new FlatNetwork(2, 4, 0, 1, false);
network.Randomize();
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
var train = new TrainFlatNetworkResilient(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.01);
var output = new double[1];
// test the neural network
Console.WriteLine(@"Neural Network Results:");
foreach (IMLDataPair pair in trainingSet)
{
double[] input = pair.Input.Data;
network.Compute(input, output);
Console.WriteLine(input[0] + @"," + input[1] + @":" + output[0]);
}
}
public TrainFlatNetworkQPROP(FlatNetwork network, IMLDataSet training, double theLearningRate)
: base(network, training)
{
this.LearningRate = theLearningRate;
this.LastDelta = new double[base.Network.Weights.Length];
this.Decay = 0.0001;
this.OutputEpsilon = 0.35;
}
public TrainFlatNetworkSCG(FlatNetwork network, IMLDataSet training)
: base(network, training)
{
int num;
if ((((uint) num) - ((uint) num)) <= uint.MaxValue)
{
this._xd938fd32778a1c95 = true;
if (((uint) num) < 0)
{
goto Label_0069;
}
goto Label_0134;
}
if ((((uint) num) & 0) == 0)
{
goto Label_00BD;
}
goto Label_0094;
Label_0069:
this._xb55b340ae3a3e4e0 = new double[num];
this._x268cb8b20222b0dc = true;
if ((((uint) num) + ((uint) num)) <= uint.MaxValue)
{
if (((uint) num) >= 0)
{
return;
}
goto Label_00BD;
}
Label_0094:
num = this._x2f33d779e5a20b28.Length;
this._xb9f048818eee629c = new double[num];
this._x9af996963d03fd21 = new double[num];
if (((uint) num) > uint.MaxValue)
{
goto Label_0134;
}
this._x9c79b5ad7b769b12 = new double[num];
goto Label_0069;
Label_00BD:
this._x31add3b0010a0a52 = 0.0;
this._x3271cefb1a159639 = 1E-06;
this._xd4d57078d70c1d3d = 0.0;
this._x20753e64c7ee4faf = 0.0;
this._xac19ed778412b7a3 = false;
this._x2f33d779e5a20b28 = EngineArray.ArrayCopy(network.Weights);
goto Label_0094;
Label_0134:
this._xd938fd32778a1c95 = true;
this._xf7845a6fecd5afc3 = 0.0;
if ((((uint) num) + ((uint) num)) > uint.MaxValue)
{
return;
}
goto Label_00BD;
}
/// <inheritdoc/>
public virtual void Init(BasicNetwork theNetwork, IMLDataSet theTraining)
{
int weightCount = theNetwork.Structure.Flat.Weights.Length;
_flat = theNetwork.Flat;
_training = theTraining;
_network = theNetwork;
_gradients = new double[weightCount];
_hessianMatrix = new Matrix(weightCount, weightCount);
_hessian = _hessianMatrix.Data;
}
/// <inheritdoc/>
public virtual void Init(BasicNetwork theNetwork, IMLDataSet theTraining)
{
int weightCount = theNetwork.Structure.Flat.Weights.Length;
flat = theNetwork.Flat;
training = theTraining;
network = theNetwork;
gradients = new double[weightCount];
hessianMatrix = new Matrix(weightCount, weightCount);
hessian = hessianMatrix.Data;
derivative = new double[weightCount];
}
public static void Evaluate(FlatNetwork network, IMLDataSet trainingSet)
{
double[] output = new double[1];
foreach (IMLDataPair pair in trainingSet)
{
network.Compute(pair.Input.Data, output);
Console.WriteLine(pair.Input.Data[0] + @"," + pair.Input[1]
+ @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
}
}
/// <summary>
/// Construct a cross validation trainer.
/// </summary>
///
/// <param name="train">The training</param>
/// <param name="k">The number of folds.</param>
public CrossValidationKFold(IMLTrain train, int k) : base(train.Method, (FoldedDataSet) train.Training)
{
_train = train;
Folded.Fold(k);
_flatNetwork = ((BasicNetwork) train.Method).Structure.Flat;
_networks = new NetworkFold[k];
for (int i = 0; i < _networks.Length; i++)
{
_networks[i] = new NetworkFold(_flatNetwork);
}
}
public GradientWorker(FlatNetwork theNetwork, TrainFlatNetworkProp theOwner, IMLDataSet theTraining, int theLow, int theHigh, double[] theFlatSpots, IErrorFunction ef)
{
goto Label_0155;
Label_0114:
this._x071bde1041617fce = theOwner;
this._x0ba854627e1326f9 = theFlatSpots;
this._x58c3d5da5c5c72db = new double[this._x87a7fc6a72741c2e.LayerOutput.Length];
this._xe05127febf8b7904 = new double[this._x87a7fc6a72741c2e.Weights.Length];
this._xd505507cf33ae543 = new double[this._x87a7fc6a72741c2e.OutputCount];
if (0 == 0)
{
this._x2f33d779e5a20b28 = this._x87a7fc6a72741c2e.Weights;
if ((((uint) theHigh) + ((uint) theLow)) <= uint.MaxValue)
{
this._xb25095f37f20a1c1 = this._x87a7fc6a72741c2e.LayerIndex;
if (((uint) theLow) <= uint.MaxValue)
{
this._xe05f7b8f952f0ba4 = this._x87a7fc6a72741c2e.LayerCounts;
this._x7d5bf19d36074a85 = this._x87a7fc6a72741c2e.WeightIndex;
this._x5e72e5e601f79c78 = this._x87a7fc6a72741c2e.LayerOutput;
this._x59e01312f2f4aa96 = this._x87a7fc6a72741c2e.LayerSums;
this._xc99b49dd213196ca = this._x87a7fc6a72741c2e.LayerFeedCounts;
this._x2cb049236d33bbda = ef;
}
}
}
this._x61830ac74d65acc3 = BasicMLDataPair.CreatePair(this._x87a7fc6a72741c2e.InputCount, this._x87a7fc6a72741c2e.OutputCount);
if (0 == 0)
{
return;
}
Label_0155:
this._x84e81691256999b2 = new ErrorCalculation();
this._x87a7fc6a72741c2e = theNetwork;
this._x823a2b9c8bf459c5 = theTraining;
if (0xff == 0)
{
return;
}
do
{
if ((((uint) theHigh) + ((uint) theLow)) > uint.MaxValue)
{
goto Label_0114;
}
this._xd12d1dba8a023d95 = theLow;
}
while (0 != 0);
this._x628ea9b89457a2a9 = theHigh;
goto Label_0114;
}
public TrainFlatNetworkResilient(FlatNetwork network, IMLDataSet training, double zeroTolerance, double initialUpdate, double maxStep)
: base(network, training)
{
if ((((uint) initialUpdate) + ((uint) initialUpdate)) <= uint.MaxValue)
{
this._x3cb1c2ba1a55e11e = new double[network.Weights.Length];
this._x04273e480202ea1d = zeroTolerance;
this._xc63f21bb42b68cf5 = maxStep;
if ((((uint) zeroTolerance) + ((uint) initialUpdate)) >= 0)
{
this._xa3001a0faa4f9279 = new double[base.Network.Weights.Length];
this._xe4def4d471bbc130 = new double[base.Network.Weights.Length];
}
}
for (int i = 0; i < this._x3cb1c2ba1a55e11e.Length; i++)
{
this._x3cb1c2ba1a55e11e[i] = initialUpdate;
}
}
protected TrainFlatNetworkProp(FlatNetwork network, IMLDataSet training)
{
Label_0036:
this._x823a2b9c8bf459c5 = training;
if (0x7fffffff != 0)
{
this._x87a7fc6a72741c2e = network;
this.Gradients = new double[this._x87a7fc6a72741c2e.Weights.Length];
}
this._x135f4ca6b0437cfc = new double[this._x87a7fc6a72741c2e.Weights.Length];
if (4 != 0)
{
this._x9d091b0a73271a88 = training;
this._xc2715388adc0d1f8 = 0;
this._xb8f73f2bca8b57ea = null;
this.FixFlatSpot = true;
this.ErrorFunction = new LinearErrorFunction();
if (0xff == 0)
{
goto Label_0036;
}
}
}
public CrossValidationKFold(IMLTrain train, int k)
: base(train.Method, (FoldedDataSet) train.Training)
{
int num;
if ((((uint) k) | 1) != 0)
{
this._xd87f6a9c53c2ed9f = train;
base.Folded.Fold(k);
goto Label_0083;
}
if (0xff != 0)
{
goto Label_0083;
}
Label_0039:
num = 0;
while (num < this._x5f6ed0047d99f4b6.Length)
{
this._x5f6ed0047d99f4b6[num] = new NetworkFold(this._xef94864849922d07);
if (((uint) k) >= 0)
{
}
num++;
}
if (((uint) num) <= uint.MaxValue)
{
return;
}
Label_0083:
this._xef94864849922d07 = ((BasicNetwork) train.Method).Structure.Flat;
this._x5f6ed0047d99f4b6 = new NetworkFold[k];
if (8 == 0)
{
return;
}
goto Label_0039;
}
/// <summary>
/// Construct a fold from the specified flat network.
/// </summary>
///
/// <param name="flat">THe flat network.</param>
public NetworkFold(FlatNetwork flat)
{
_weights = EngineArray.ArrayCopy(flat.Weights);
_output = EngineArray.ArrayCopy(flat.LayerOutput);
}
/// <summary>
/// Copy weights and output to the network.
/// </summary>
///
/// <param name="target">The network to copy to.</param>
public void CopyToNetwork(FlatNetwork target)
{
EngineArray.ArrayCopy(_weights, target.Weights);
EngineArray.ArrayCopy(_output, target.LayerOutput);
}
/// <summary>
/// Create an array of activations based on a flat network.
/// </summary>
/// <param name="flat">The flat network.</param>
/// <returns></returns>
public int[] CreateActivations(FlatNetwork flat)
{
var result = new int[flat.ActivationFunctions.Length];
for (int i = 0; i < flat.ActivationFunctions.Length; i++)
{
IActivationFunction af = flat.ActivationFunctions[i];
if (af is ActivationLinear)
{
result[i] = 0;
}
else if (af is ActivationTANH)
{
result[i] = 1;
}
if (af is ActivationSigmoid)
{
result[i] = 2;
}
if (af is ActivationElliottSymmetric)
{
result[i] = 3;
}
if (af is ActivationElliott)
{
result[i] = 4;
}
}
return result;
}
/// <summary>
/// Create an array of doubles to hold the specified flat network.
/// </summary>
/// <param name="flat">The flat network to use as a model.</param>
/// <returns>The new array.</returns>
public double[] CreateParams(FlatNetwork flat)
{
var result = new double[flat.ActivationFunctions.Length];
EngineArray.Fill(result, 1);
return result;
}
/// <summary>
/// Construct a propagation object.
/// </summary>
///
/// <param name="network">The network.</param>
/// <param name="training">The training set.</param>
protected Propagation(IContainsFlat network, IMLDataSet training)
: base(TrainingImplementationType.Iterative)
{
_network = network;
_flat = network.Flat;
_training = training;
Gradients = new double[_flat.Weights.Length];
_lastGradient = new double[_flat.Weights.Length];
_indexable = training;
_numThreads = 0;
_reportedException = null;
FixFlatSpot = true;
ErrorFunction = new LinearErrorFunction();
}
public void FinalizeStructure()
{
FlatLayer[] layerArray;
int num;
BasicLayer layer;
if (this._layers.Count >= 2)
{
goto Label_00D4;
}
throw new NeuralNetworkError("There must be at least two layers before the structure is finalized.");
Label_001A:
if (num < this._layers.Count)
{
goto Label_009A;
}
this._flat = new FlatNetwork(layerArray);
this.FinalizeLimit();
Label_003D:
this._layers.Clear();
this.EnforceLimit();
return;
Label_0062:
layerArray[num] = layer;
num++;
goto Label_001A;
Label_006F:
if (2 != 0)
{
goto Label_0062;
}
Label_0088:
while (layer.Activation == null)
{
layer.Activation = new ActivationLinear();
if (0 == 0)
{
goto Label_006F;
}
}
if (((uint) num) > uint.MaxValue)
{
goto Label_006F;
}
goto Label_0062;
Label_009A:
layer = (BasicLayer) this._layers[num];
goto Label_0088;
if ((((uint) num) - ((uint) num)) < 0)
{
goto Label_003D;
}
Label_00D4:
layerArray = new FlatLayer[this._layers.Count];
num = 0;
if (((uint) num) < 0)
{
goto Label_009A;
}
if (0 != 0)
{
goto Label_006F;
}
goto Label_001A;
}
/// <summary>
/// Clone into the flat network passed in.
/// </summary>
///
/// <param name="result">The network to copy into.</param>
public void CloneFlatNetwork(FlatNetwork result)
{
result._inputCount = _inputCount;
result._layerCounts = EngineArray.ArrayCopy(_layerCounts);
result._layerIndex = EngineArray.ArrayCopy(_layerIndex);
result._layerOutput = EngineArray.ArrayCopy(_layerOutput);
result._layerSums = EngineArray.ArrayCopy(_layerSums);
result._layerFeedCounts = EngineArray.ArrayCopy(_layerFeedCounts);
result._contextTargetOffset = EngineArray
.ArrayCopy(_contextTargetOffset);
result._contextTargetSize = EngineArray
.ArrayCopy(_contextTargetSize);
result._layerContextCount = EngineArray
.ArrayCopy(_layerContextCount);
result._biasActivation = EngineArray.ArrayCopy(_biasActivation);
result._outputCount = _outputCount;
result._weightIndex = _weightIndex;
result._weights = _weights;
result._activationFunctions = new IActivationFunction[_activationFunctions.Length];
for (int i = 0; i < result._activationFunctions.Length; i++)
{
result._activationFunctions[i] = (IActivationFunction) _activationFunctions[i].Clone();
}
result._beginTraining = _beginTraining;
result._endTraining = _endTraining;
}
/// <summary>
/// Build the synapse and layer structure. This method should be called after
/// you are done adding layers to a network, or change the network's logic
/// property.
/// </summary>
///
public void FinalizeStructure()
{
if (_layers.Count < 2)
{
throw new NeuralNetworkError(
"There must be at least two layers before the structure is finalized.");
}
var flatLayers = new FlatLayer[_layers.Count];
for (int i = 0; i < _layers.Count; i++)
{
var layer = (BasicLayer) _layers[i];
if (layer.Activation == null)
{
layer.Activation = new ActivationLinear();
}
flatLayers[i] = layer;
}
_flat = new FlatNetwork(flatLayers);
FinalizeLimit();
_layers.Clear();
EnforceLimit();
}
private void CheckWithModel(FlatNetwork model, FlatNetwork pruned)
{
Assert.AreEqual(model.Weights.Length, pruned.Weights.Length);
Assert.AreEqual(model.ContextTargetOffset, pruned.ContextTargetOffset);
Assert.AreEqual(model.ContextTargetSize, pruned.ContextTargetSize);
Assert.AreEqual(model.LayerCounts, pruned.LayerCounts);
Assert.AreEqual(model.LayerFeedCounts, pruned.LayerFeedCounts);
Assert.AreEqual(model.LayerIndex, pruned.LayerIndex);
Assert.AreEqual(model.LayerOutput.Length, pruned.LayerOutput.Length);
Assert.AreEqual(model.WeightIndex, pruned.WeightIndex);
}
public static long BenchmarkEncogFlat(double[][] input, double[][] output)
{
var network = new FlatNetwork(input[0].Length, HIDDEN_COUNT, 0,
output[0].Length, false);
network.Randomize();
var trainingSet = new BasicMLDataSet(input, output);
var train = new TrainFlatNetworkBackPropagation(
network, trainingSet, 0.7, 0.7);
var a = new double[2];
var b = new double[1];
var sw = new Stopwatch();
sw.Start();
// run epoch of learning procedure
for (int i = 0; i < ITERATIONS; i++)
{
train.Iteration();
}
sw.Stop();
return sw.ElapsedMilliseconds;
}
public void CloneFlatNetwork(FlatNetwork result)
{
int num;
result._inputCount = this._inputCount;
goto Label_0172;
Label_001F:
if (num < result._activationFunctions.Length)
{
result._activationFunctions[num] = (IActivationFunction) this._activationFunctions[num].Clone();
if ((((uint) num) & 0) == 0)
{
if (-2 == 0)
{
goto Label_0133;
}
num++;
goto Label_001F;
}
if ((((uint) num) | 4) != 0)
{
goto Label_0172;
}
goto Label_0122;
}
result._beginTraining = this._beginTraining;
result._endTraining = this._endTraining;
return;
Label_00BF:
result._weights = this._weights;
result._activationFunctions = new IActivationFunction[this._activationFunctions.Length];
num = 0;
goto Label_001F;
Label_0122:
result._layerIndex = EngineArray.ArrayCopy(this._layerIndex);
Label_0133:
result._layerOutput = EngineArray.ArrayCopy(this._layerOutput);
result._layerSums = EngineArray.ArrayCopy(this._layerSums);
result._layerFeedCounts = EngineArray.ArrayCopy(this._layerFeedCounts);
result._contextTargetOffset = EngineArray.ArrayCopy(this._contextTargetOffset);
result._contextTargetSize = EngineArray.ArrayCopy(this._contextTargetSize);
result._layerContextCount = EngineArray.ArrayCopy(this._layerContextCount);
result._biasActivation = EngineArray.ArrayCopy(this._biasActivation);
result._outputCount = this._outputCount;
result._weightIndex = this._weightIndex;
if (3 == 0)
{
goto Label_001F;
}
goto Label_00BF;
Label_0172:
result._layerCounts = EngineArray.ArrayCopy(this._layerCounts);
if ((((uint) num) - ((uint) num)) > uint.MaxValue)
{
goto Label_00BF;
}
goto Label_0122;
}
/// <summary>
/// Construct the chain rule worker.
/// </summary>
/// <param name="theNetwork">The network to calculate a Hessian for.</param>
/// <param name="theTraining">The training data.</param>
/// <param name="theLow">The low range.</param>
/// <param name="theHigh">The high range.</param>
public ChainRuleWorker(FlatNetwork theNetwork, IMLDataSet theTraining, int theLow, int theHigh)
{
int weightCount = theNetwork.Weights.Length;
_training = theTraining;
_flat = theNetwork;
_layerDelta = new double[_flat.LayerOutput.Length];
_actual = new double[_flat.OutputCount];
_derivative = new double[weightCount];
_totDeriv = new double[weightCount];
_gradients = new double[weightCount];
_weights = _flat.Weights;
_layerIndex = _flat.LayerIndex;
_layerCounts = _flat.LayerCounts;
_weightIndex = _flat.WeightIndex;
_layerOutput = _flat.LayerOutput;
_layerSums = _flat.LayerSums;
_layerFeedCounts = _flat.LayerFeedCounts;
_low = theLow;
_high = theHigh;
}
/// <summary>
/// Copy the weights and output from the network.
/// </summary>
///
/// <param name="source">The network to copy from.</param>
public void CopyFromNetwork(FlatNetwork source)
{
EngineArray.ArrayCopy(source.Weights, _weights);
EngineArray.ArrayCopy(source.LayerOutput, _output);
}
/// <summary>
/// Read an object.
/// </summary>
///
public Object Read(Stream mask0)
{
var result = new BasicNetwork();
var flat = new FlatNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("PARAMS"))
{
IDictionary<String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("NETWORK"))
{
IDictionary<String, String> p = section.ParseParams();
flat.BeginTraining = EncogFileSection.ParseInt(p,
BasicNetwork.TagBeginTraining);
flat.ConnectionLimit = EncogFileSection.ParseDouble(p,
BasicNetwork.TagConnectionLimit);
flat.ContextTargetOffset = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagContextTargetOffset);
flat.ContextTargetSize = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagContextTargetSize);
flat.EndTraining = EncogFileSection.ParseInt(p,
BasicNetwork.TagEndTraining);
flat.HasContext = EncogFileSection.ParseBoolean(p,
BasicNetwork.TagHasContext);
flat.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
flat.LayerCounts = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerCounts);
flat.LayerFeedCounts = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerFeedCounts);
flat.LayerContextCount = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagLayerContextCount);
flat.LayerIndex = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerIndex);
flat.LayerOutput = section.ParseDoubleArray(p,PersistConst.Output);
flat.LayerSums = new double[flat.LayerOutput.Length];
flat.OutputCount = EncogFileSection.ParseInt(p,
PersistConst.OutputCount);
flat.WeightIndex = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagWeightIndex);
flat.Weights = section.ParseDoubleArray(p, PersistConst.Weights);
flat.BiasActivation = section.ParseDoubleArray(p, BasicNetwork.TagBiasActivation);
}
else if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("ACTIVATION"))
{
int index = 0;
flat.ActivationFunctions = new IActivationFunction[flat.LayerCounts.Length];
foreach (String line in section.Lines)
{
IActivationFunction af;
IList<String> cols = EncogFileSection
.SplitColumns(line);
String name = ReflectionUtil.AfPath
+ cols[0];
try
{
af = (IActivationFunction) ReflectionUtil.LoadObject(name);
}
catch (TypeLoadException e)
{
throw new PersistError(e);
}
catch (TargetException e)
{
throw new PersistError(e);
}
catch (MemberAccessException e)
{
throw new PersistError(e);
}
for (int i = 0; i < af.ParamNames.Length; i++)
{
af.Params[i] =
CSVFormat.EgFormat.Parse(cols[i + 1]);
}
flat.ActivationFunctions[index++] = af;
}
}
}
result.Structure.Flat = flat;
return result;
}
public virtual object Clone()
{
FlatNetwork result = new FlatNetwork();
this.CloneFlatNetwork(result);
return result;
}
/// <summary>
/// Clone the network.
/// </summary>
///
/// <returns>A clone of the network.</returns>
public virtual Object Clone()
{
var result = new FlatNetwork();
CloneFlatNetwork(result);
return result;
}
/// <summary>
/// Construct the chain rule worker.
/// </summary>
/// <param name="theNetwork">The network to calculate a Hessian for.</param>
/// <param name="theTraining">The training data.</param>
/// <param name="theLow">The low range.</param>
/// <param name="theHigh">The high range.</param>
public ChainRuleWorker(FlatNetwork theNetwork, IMLDataSet theTraining, int theLow, int theHigh)
{
_weightCount = theNetwork.Weights.Length;
_hessian = EngineArray.AllocateDouble2D(_weightCount,_weightCount);
_training = theTraining;
_flat = theNetwork;
_layerDelta = new double[_flat.LayerOutput.Length];
_actual = new double[_flat.OutputCount];
_totDeriv = new double[_weightCount];
_gradients = new double[_weightCount];
_weights = _flat.Weights;
_layerIndex = _flat.LayerIndex;
_layerCounts = _flat.LayerCounts;
_weightIndex = _flat.WeightIndex;
_layerOutput = _flat.LayerOutput;
_layerSums = _flat.LayerSums;
_layerFeedCounts = _flat.LayerFeedCounts;
_low = theLow;
_high = theHigh;
}
/// <summary>
/// Construct a gradient worker.
/// </summary>
///
/// <param name="theNetwork">The network to train.</param>
/// <param name="theOwner">The owner that is doing the training.</param>
/// <param name="theTraining">The training data.</param>
/// <param name="theLow">The low index to use in the training data.</param>
/// <param name="theHigh">The high index to use in the training data.</param>
/// <param name="theFlatSpots">Holds an array of flat spot constants.</param>
public GradientWorker(FlatNetwork theNetwork,
Propagation theOwner, IMLDataSet theTraining,
int theLow, int theHigh, double[] theFlatSpots, IErrorFunction ef)
{
_errorCalculation = new ErrorCalculation();
_network = theNetwork;
_training = theTraining;
_low = theLow;
_high = theHigh;
_owner = theOwner;
_flatSpot = theFlatSpots;
_layerDelta = new double[_network.LayerOutput.Length];
_gradients = new double[_network.Weights.Length];
_actual = new double[_network.OutputCount];
_weights = _network.Weights;
_layerIndex = _network.LayerIndex;
_layerCounts = _network.LayerCounts;
_weightIndex = _network.WeightIndex;
_layerOutput = _network.LayerOutput;
_layerSums = _network.LayerSums;
_layerFeedCounts = _network.LayerFeedCounts;
_ef = ef;
_pair = BasicMLDataPair.CreatePair(_network.InputCount,
_network.OutputCount);
}
