public FlatLayer(IActivationFunction activation, int count, double biasActivation)
{
this.Activation = activation;
this._x10f4d88af727adbc = count;
this._x25922738b86264c8 = biasActivation;
this._x4d51c0aa16352a14 = null;
}
/// <summary>
/// Construct a flat layer.
/// </summary>
///
/// <param name="activation">The activation function.</param>
/// <param name="count">The neuron count.</param>
/// <param name="biasActivation">The bias activation.</param>
public FlatLayer(IActivationFunction activation, int count,
double biasActivation)
{
Activation = activation;
_count = count;
_biasActivation = biasActivation;
_contextFedBy = null;
}
/// <summary>
/// Construct a flat layer.
/// </summary>
///
/// <param name="activation">The activation function.</param>
/// <param name="count">The neuron count.</param>
/// <param name="biasActivation">The bias activation.</param>
public FlatLayer(IActivationFunction activation, int count,
double biasActivation)
{
Activation = activation;
_count = count;
_biasActivation = biasActivation;
_contextFedBy = null;
}
/// <summary>
/// Construct an RBF flat network.
/// </summary>
///
/// <param name="inputCount">The number of input neurons. (also the number of dimensions)</param>
/// <param name="hiddenCount">The number of hidden neurons.</param>
/// <param name="outputCount">The number of output neurons.</param>
/// <param name="rbf"></param>
public FlatNetworkRBF(int inputCount, int hiddenCount,
int outputCount, IRadialBasisFunction[] rbf)
{
var layers = new FlatLayer[3];
_rbf = rbf;
layers[0] = new FlatLayer(new ActivationLinear(), inputCount, 0.0d);
layers[1] = new FlatLayer(new ActivationLinear(), hiddenCount, 0.0d);
layers[2] = new FlatLayer(new ActivationLinear(), outputCount, 0.0d);
Init(layers);
}
/// <summary>
/// Construct an RBF flat network.
/// </summary>
///
/// <param name="inputCount">The number of input neurons. (also the number of dimensions)</param>
/// <param name="hiddenCount">The number of hidden neurons.</param>
/// <param name="outputCount">The number of output neurons.</param>
/// <param name="rbf"></param>
public FlatNetworkRBF(int inputCount, int hiddenCount,
int outputCount, IRadialBasisFunction[] rbf)
{
var layers = new FlatLayer[3];
_rbf = rbf;
layers[0] = new FlatLayer(new ActivationLinear(), inputCount, 0.0d);
layers[1] = new FlatLayer(new ActivationLinear(), hiddenCount, 0.0d);
layers[2] = new FlatLayer(new ActivationLinear(), outputCount, 0.0d);
Init(layers);
}
public FlatNetworkRBF(int inputCount, int hiddenCount, int outputCount, IRadialBasisFunction[] rbf)
{
FlatLayer[] layers = new FlatLayer[3];
this._rbf = rbf;
layers[0] = new FlatLayer(new ActivationLinear(), inputCount, 0.0);
do
{
layers[1] = new FlatLayer(new ActivationLinear(), hiddenCount, 0.0);
}
while ((((uint) hiddenCount) & 0) != 0);
if (2 != 0)
{
layers[2] = new FlatLayer(new ActivationLinear(), outputCount, 0.0);
base.Init(layers);
}
}
/// <summary>
/// Construct a flat neural network.
/// </summary>
///
/// <param name="input">Neurons in the input layer.</param>
/// <param name="hidden1"></param>
/// <param name="hidden2"></param>
/// <param name="output">Neurons in the output layer.</param>
/// <param name="tanh">True if this is a tanh activation, false for sigmoid.</param>
public FlatNetwork(int input, int hidden1, int hidden2,
int output, bool tanh)
{
IActivationFunction linearAct = new ActivationLinear();
FlatLayer[] layers;
IActivationFunction act = (tanh)
? (new ActivationTANH())
: (IActivationFunction)(new ActivationSigmoid());
if ((hidden1 == 0) && (hidden2 == 0))
{
layers = new FlatLayer[2];
layers[0] = new FlatLayer(linearAct, input,
DefaultBiasActivation);
layers[1] = new FlatLayer(act, output,
NoBiasActivation);
}
else if ((hidden1 == 0) || (hidden2 == 0))
{
int count = Math.Max(hidden1, hidden2);
layers = new FlatLayer[3];
layers[0] = new FlatLayer(linearAct, input,
DefaultBiasActivation);
layers[1] = new FlatLayer(act, count,
DefaultBiasActivation);
layers[2] = new FlatLayer(act, output,
NoBiasActivation);
}
else
{
layers = new FlatLayer[4];
layers[0] = new FlatLayer(linearAct, input,
DefaultBiasActivation);
layers[1] = new FlatLayer(act, hidden1,
DefaultBiasActivation);
layers[2] = new FlatLayer(act, hidden2,
DefaultBiasActivation);
layers[3] = new FlatLayer(act, output,
NoBiasActivation);
}
_isLimited = false;
_connectionLimit = 0.0d;
Init(layers);
}
public void Init(FlatLayer[] layers)
{
int num2;
int num3;
int num4;
int num5;
FlatLayer layer;
FlatLayer layer2;
int num6;
int num7;
int length = layers.Length;
if (((uint) num4) <= uint.MaxValue)
{
this._inputCount = layers[0].Count;
goto Label_0452;
}
if (((uint) num5) <= uint.MaxValue)
{
goto Label_03A5;
}
goto Label_01EF;
Label_00AA:
if (num5 >= 0)
{
goto Label_0357;
}
Label_00B2:
this._beginTraining = 0;
this._endTraining = this._layerCounts.Length - 1;
this._weights = new double[num4];
this._layerOutput = new double[num3];
this._layerSums = new double[num3];
this.ClearContext();
return;
Label_00CE:
num6 += layers[num7].TotalCount;
Label_00DC:
num7--;
Label_00E2:
if (num7 >= 0)
{
while (layers[num7].ContextFedBy == layer)
{
this._hasContext = true;
this._contextTargetSize[num2] = layers[num7].ContextCount;
if (((uint) num5) >= 0)
{
goto Label_011B;
}
}
goto Label_00CE;
}
num2++;
if ((((uint) num4) - ((uint) num4)) <= uint.MaxValue)
{
if ((((uint) num7) - ((uint) num4)) > uint.MaxValue)
{
goto Label_0357;
}
if ((((uint) num4) & 0) != 0)
{
goto Label_0452;
}
num5--;
goto Label_00AA;
}
if ((((uint) num3) + ((uint) num3)) >= 0)
{
goto Label_013B;
}
Label_011B:
this._contextTargetOffset[num2] = num6 + (layers[num7].TotalCount - layers[num7].ContextCount);
goto Label_00CE;
Label_013B:
num7 = layers.Length - 1;
goto Label_00E2;
Label_0182:
num6 = 0;
if ((((uint) num4) + ((uint) num2)) <= uint.MaxValue)
{
goto Label_013B;
}
return;
Label_018A:
if (num2 == 0)
{
this._weightIndex[num2] = 0;
this._layerIndex[num2] = 0;
goto Label_0182;
}
this._weightIndex[num2] = this._weightIndex[num2 - 1] + (this._layerCounts[num2] * this._layerFeedCounts[num2 - 1]);
this._layerIndex[num2] = this._layerIndex[num2 - 1] + this._layerCounts[num2 - 1];
if ((((uint) num7) + ((uint) num2)) <= uint.MaxValue)
{
goto Label_0182;
}
Label_01EF:
if ((((uint) num6) | 8) == 0)
{
goto Label_00B2;
}
num4 += layer.Count * layer2.TotalCount;
if (0 == 0)
{
goto Label_018A;
}
Label_026C:
this._layerFeedCounts[num2] = layer.Count;
this._layerContextCount[num2] = layer.ContextCount;
this._activationFunctions[num2] = layer.Activation;
Label_0299:
num3 += layer.TotalCount;
if (layer2 == null)
{
goto Label_018A;
}
goto Label_01EF;
Label_02AC:
if (num5 > 0)
{
goto Label_02EB;
}
Label_02B1:
this._biasActivation[num2] = layer.BiasActivation;
this._layerCounts[num2] = layer.TotalCount;
goto Label_026C;
Label_02EB:
layer2 = layers[num5 - 1];
if ((((uint) num6) - ((uint) num4)) >= 0)
{
if ((((uint) length) - ((uint) num7)) >= 0)
{
goto Label_02B1;
}
goto Label_02AC;
}
goto Label_03A5;
Label_0357:
layer = layers[num5];
layer2 = null;
goto Label_03CB;
Label_03A5:
this._layerFeedCounts = new int[length];
this._contextTargetOffset = new int[length];
this._contextTargetSize = new int[length];
if ((((uint) length) - ((uint) num3)) < 0)
{
goto Label_00DC;
}
this._biasActivation = new double[length];
num2 = 0;
num3 = 0;
num4 = 0;
if ((((uint) length) & 0) == 0)
{
num5 = layers.Length - 1;
goto Label_00AA;
}
Label_03CB:
if ((((uint) num6) - ((uint) num7)) >= 0)
{
if ((((uint) num3) - ((uint) num5)) > uint.MaxValue)
{
goto Label_0299;
}
if ((((uint) num2) - ((uint) num4)) < 0)
{
goto Label_03A5;
}
}
if (2 != 0)
{
goto Label_02AC;
}
if ((((uint) num4) | 0x80000000) == 0)
{
goto Label_0182;
}
goto Label_02EB;
Label_0452:
this._outputCount = layers[length - 1].Count;
this._layerCounts = new int[length];
this._layerContextCount = new int[length];
this._weightIndex = new int[length];
this._layerIndex = new int[length];
this._activationFunctions = new IActivationFunction[length];
if (((uint) length) < 0)
{
return;
}
goto Label_03A5;
}
public FlatNetwork(int input, int hidden1, int hidden2, int output, bool tanh)
{
// This item is obfuscated and can not be translated.
FlatLayer[] layerArray;
IActivationFunction function2;
int num;
IActivationFunction function3;
IActivationFunction activation = new ActivationLinear();
if (tanh)
{
goto Label_01EA;
}
goto Label_0219;
Label_000B:
this._connectionLimit = 0.0;
this.Init(layerArray);
if (0 != 0)
{
if (((uint) hidden1) > uint.MaxValue)
{
goto Label_0069;
}
goto Label_0219;
}
if (((uint) num) < 0)
{
goto Label_0189;
}
return;
Label_0069:
layerArray[3] = new FlatLayer(function2, output, 0.0);
if ((((uint) num) + ((uint) output)) >= 0)
{
}
Label_0098:
this._isLimited = false;
goto Label_000B;
Label_00A1:
if (hidden2 != 0)
{
if ((((uint) tanh) - ((uint) hidden2)) > uint.MaxValue)
{
goto Label_01AA;
}
layerArray = new FlatLayer[4];
layerArray[0] = new FlatLayer(activation, input, 1.0);
layerArray[1] = new FlatLayer(function2, hidden1, 1.0);
if ((((uint) tanh) + ((uint) hidden2)) <= uint.MaxValue)
{
layerArray[2] = new FlatLayer(function2, hidden2, 1.0);
if (((uint) hidden2) > uint.MaxValue)
{
goto Label_015F;
}
goto Label_0069;
}
goto Label_000B;
}
Label_010F:
num = Math.Max(hidden1, hidden2);
layerArray = new FlatLayer[] { new FlatLayer(activation, input, 1.0), new FlatLayer(function2, num, 1.0), new FlatLayer(function2, output, 0.0) };
goto Label_0098;
Label_015F:
if (((uint) output) > uint.MaxValue)
{
goto Label_01AA;
}
if ((((uint) tanh) + ((uint) num)) <= uint.MaxValue)
{
goto Label_0198;
}
Label_0189:
if (-1 != 0)
{
goto Label_00A1;
}
goto Label_010F;
Label_0198:
if (hidden1 != 0)
{
goto Label_00A1;
}
goto Label_010F;
Label_01AA:
if (hidden2 != 0)
{
goto Label_0198;
}
layerArray = new FlatLayer[] { new FlatLayer(activation, input, 1.0), new FlatLayer(function2, output, 0.0) };
goto Label_0098;
Label_01EA:
function2 = new ActivationTANH();
if (hidden1 != 0)
{
goto Label_015F;
}
goto Label_01AA;
Label_0219:
function3 = new ActivationSigmoid();
if ((((uint) num) | 2) != 0)
{
goto Label_01EA;
}
goto Label_010F;
}
public FlatNetwork(FlatLayer[] layers)
{
this.Init(layers);
}
/// <summary>
/// Construct a flat network.
/// </summary>
///
/// <param name="layers">The layers of the network to create.</param>
public void Init(FlatLayer[] layers)
{
int layerCount = layers.Length;
_inputCount = layers[0].Count;
_outputCount = layers[layerCount - 1].Count;
_layerCounts = new int[layerCount];
_layerContextCount = new int[layerCount];
_weightIndex = new int[layerCount];
_layerIndex = new int[layerCount];
_activationFunctions = new IActivationFunction[layerCount];
_layerFeedCounts = new int[layerCount];
_contextTargetOffset = new int[layerCount];
_contextTargetSize = new int[layerCount];
_biasActivation = new double[layerCount];
int index = 0;
int neuronCount = 0;
int weightCount = 0;
for (int i = layers.Length - 1; i >= 0; i--)
{
FlatLayer layer = layers[i];
FlatLayer nextLayer = null;
if (i > 0)
{
nextLayer = layers[i - 1];
}
_biasActivation[index] = layer.BiasActivation;
_layerCounts[index] = layer.TotalCount;
_layerFeedCounts[index] = layer.Count;
_layerContextCount[index] = layer.ContextCount;
_activationFunctions[index] = layer.Activation;
neuronCount += layer.TotalCount;
if (nextLayer != null)
{
weightCount += layer.Count * nextLayer.TotalCount;
}
if (index == 0)
{
_weightIndex[index] = 0;
_layerIndex[index] = 0;
}
else
{
_weightIndex[index] = _weightIndex[index - 1]
+ (_layerCounts[index] * _layerFeedCounts[index - 1]);
_layerIndex[index] = _layerIndex[index - 1]
+ _layerCounts[index - 1];
}
int neuronIndex = 0;
for (int j = layers.Length - 1; j >= 0; j--)
{
if (layers[j].ContextFedBy == layer)
{
_hasContext = true;
_contextTargetSize[index] = layers[j].ContextCount;
_contextTargetOffset[index] = neuronIndex
+ (layers[j].TotalCount - layers[j].ContextCount);
}
neuronIndex += layers[j].TotalCount;
}
index++;
}
_beginTraining = 0;
_endTraining = _layerCounts.Length - 1;
_weights = new double[weightCount];
_layerOutput = new double[neuronCount];
_layerSums = new double[neuronCount];
ClearContext();
}
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>
/// 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();
}
/// <summary>
/// Construct a flat network.
/// </summary>
///
/// <param name="layers">The layers of the network to create.</param>
public void Init(FlatLayer[] layers)
{
int layerCount = layers.Length;
_inputCount = layers[0].Count;
_outputCount = layers[layerCount - 1].Count;
_layerCounts = new int[layerCount];
_layerContextCount = new int[layerCount];
_weightIndex = new int[layerCount];
_layerIndex = new int[layerCount];
_activationFunctions = new IActivationFunction[layerCount];
_layerFeedCounts = new int[layerCount];
_contextTargetOffset = new int[layerCount];
_contextTargetSize = new int[layerCount];
_biasActivation = new double[layerCount];
int index = 0;
int neuronCount = 0;
int weightCount = 0;
for (int i = layers.Length - 1; i >= 0; i--)
{
FlatLayer layer = layers[i];
FlatLayer nextLayer = null;
if (i > 0)
{
nextLayer = layers[i - 1];
}
_biasActivation[index] = layer.BiasActivation;
_layerCounts[index] = layer.TotalCount;
_layerFeedCounts[index] = layer.Count;
_layerContextCount[index] = layer.ContextCount;
_activationFunctions[index] = layer.Activation;
neuronCount += layer.TotalCount;
if (nextLayer != null)
{
weightCount += layer.Count*nextLayer.TotalCount;
}
if (index == 0)
{
_weightIndex[index] = 0;
_layerIndex[index] = 0;
}
else
{
_weightIndex[index] = _weightIndex[index - 1]
+ (_layerCounts[index]*_layerFeedCounts[index - 1]);
_layerIndex[index] = _layerIndex[index - 1]
+ _layerCounts[index - 1];
}
int neuronIndex = 0;
for (int j = layers.Length - 1; j >= 0; j--)
{
if (layers[j].ContextFedBy == layer)
{
_hasContext = true;
_contextTargetSize[index] = layers[j].ContextCount;
_contextTargetOffset[index] = neuronIndex
+ (layers[j].TotalCount - layers[j].ContextCount);
}
neuronIndex += layers[j].TotalCount;
}
index++;
}
_beginTraining = 0;
_endTraining = _layerCounts.Length - 1;
_weights = new double[weightCount];
_layerOutput = new double[neuronCount];
_layerSums = new double[neuronCount];
ClearContext();
}
/// <summary>
/// Construct a flat neural network.
/// </summary>
///
/// <param name="input">Neurons in the input layer.</param>
/// <param name="hidden1"></param>
/// <param name="hidden2"></param>
/// <param name="output">Neurons in the output layer.</param>
/// <param name="tanh">True if this is a tanh activation, false for sigmoid.</param>
public FlatNetwork(int input, int hidden1, int hidden2,
int output, bool tanh)
{
IActivationFunction linearAct = new ActivationLinear();
FlatLayer[] layers;
IActivationFunction act = (tanh)
? (new ActivationTANH())
: (IActivationFunction) (new ActivationSigmoid());
if ((hidden1 == 0) && (hidden2 == 0))
{
layers = new FlatLayer[2];
layers[0] = new FlatLayer(linearAct, input,
DefaultBiasActivation);
layers[1] = new FlatLayer(act, output,
NoBiasActivation);
}
else if ((hidden1 == 0) || (hidden2 == 0))
{
int count = Math.Max(hidden1, hidden2);
layers = new FlatLayer[3];
layers[0] = new FlatLayer(linearAct, input,
DefaultBiasActivation);
layers[1] = new FlatLayer(act, count,
DefaultBiasActivation);
layers[2] = new FlatLayer(act, output,
NoBiasActivation);
}
else
{
layers = new FlatLayer[4];
layers[0] = new FlatLayer(linearAct, input,
DefaultBiasActivation);
layers[1] = new FlatLayer(act, hidden1,
DefaultBiasActivation);
layers[2] = new FlatLayer(act, hidden2,
DefaultBiasActivation);
layers[3] = new FlatLayer(act, output,
NoBiasActivation);
}
_isLimited = false;
_connectionLimit = 0.0d;
Init(layers);
}
