本文整理汇总了C#中BasicNetwork.Reset方法的典型用法代码示例。如果您正苦于以下问题：C# BasicNetwork.Reset方法的具体用法？C# BasicNetwork.Reset怎么用？C# BasicNetwork.Reset使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类BasicNetwork的用法示例。
在下文中一共展示了BasicNetwork.Reset方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Generate
/// <summary>
/// Generate the Hopfield neural network.
/// </summary>
/// <returns>The generated network.</returns>
public BasicNetwork Generate()
{
ILayer layer = new BasicLayer(new ActivationBiPolar(), false,
this.neuronCount);
BasicNetwork result = new BasicNetwork(new HopfieldLogic());
result.AddLayer(layer);
layer.AddNext(layer);
layer.X = PatternConst.START_X;
layer.Y = PatternConst.START_Y;
result.Structure.FinalizeStructure();
result.Reset();
return result;
}示例2: Generate
/// <summary>
/// Generate a Jordan neural network.
/// </summary>
/// <returns>A Jordan neural network.</returns>
public BasicNetwork Generate()
{
// construct an Jordan type network
ILayer input = new BasicLayer(this.activation, false,
this.inputNeurons);
ILayer hidden = new BasicLayer(this.activation, true,
this.hiddenNeurons);
ILayer output = new BasicLayer(this.activation, true,
this.outputNeurons);
ILayer context = new ContextLayer(this.outputNeurons);
BasicNetwork network = new BasicNetwork();
network.AddLayer(input);
network.AddLayer(hidden);
network.AddLayer(output);
output.AddNext(context, SynapseType.OneToOne);
context.AddNext(hidden);
int y = PatternConst.START_Y;
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
hidden.X = PatternConst.START_X;
hidden.Y = y;
context.X = PatternConst.INDENT_X;
context.Y = y;
y += PatternConst.INC_Y;
output.X = PatternConst.START_X;
output.Y = y;
network.Structure.FinalizeStructure();
network.Reset();
return network;
}示例3: Generate
/// <summary>
/// Generate the network.
/// </summary>
/// <returns>The generated network.</returns>
public BasicNetwork Generate()
{
ILayer input, instar, outstar;
int y = PatternConst.START_Y;
BasicNetwork network = new BasicNetwork();
network.AddLayer(input = new BasicLayer(new ActivationLinear(), false, this.inputCount));
network.AddLayer(instar = new BasicLayer(new ActivationCompetitive(), false, this.instarCount));
network.AddLayer(outstar = new BasicLayer(new ActivationLinear(), false, this.outstarCount));
network.Structure.FinalizeStructure();
network.Reset();
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
instar.X = PatternConst.START_X;
instar.Y = y;
y += PatternConst.INC_Y;
outstar.X = PatternConst.START_X;
outstar.Y = y;
// tag as needed
network.TagLayer(BasicNetwork.TAG_INPUT, input);
network.TagLayer(BasicNetwork.TAG_OUTPUT, outstar);
network.TagLayer(CPNPattern.TAG_INSTAR, instar);
network.TagLayer(CPNPattern.TAG_OUTSTAR, outstar);
return network;
}示例4: Generate
/// <summary>
/// Generate the RSOM network.
/// </summary>
/// <returns>The neural network.</returns>
public BasicNetwork Generate()
{
ILayer input = new BasicLayer(new ActivationLinear(), false,
this.inputNeurons);
ILayer output = new BasicLayer(new ActivationLinear(), false,
this.outputNeurons);
int y = PatternConst.START_Y;
BasicNetwork network = new BasicNetwork();
network.AddLayer(input);
network.AddLayer(output);
input.X = PatternConst.START_X;
output.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
output.Y = y;
network.Logic = new SOMLogic();
network.Structure.FinalizeStructure();
network.Reset();
return network;
}示例5: Generate
/// <summary>
/// Generate the network.
/// </summary>
/// <returns>The generated network.</returns>
public BasicNetwork Generate()
{
ILayer layer = new BasicLayer(new ActivationBiPolar(), true,
this.neuronCount);
BasicNetwork result = new BasicNetwork(new BoltzmannLogic());
result.SetProperty(BoltzmannLogic.PROPERTY_ANNEAL_CYCLES, this.annealCycles);
result.SetProperty(BoltzmannLogic.PROPERTY_RUN_CYCLES, this.runCycles);
result.SetProperty(BoltzmannLogic.PROPERTY_TEMPERATURE, this.temperature);
result.AddLayer(layer);
layer.AddNext(layer);
layer.X = PatternConst.START_X;
layer.Y = PatternConst.START_Y;
result.Structure.FinalizeStructure();
result.Reset();
return result;
}示例6: Generate
/// <summary>
/// Generate the RBF network.
/// </summary>
/// <returns>The neural network.</returns>
public BasicNetwork Generate()
{
ILayer input = new BasicLayer(new ActivationLinear(), false,
this.inputNeurons);
ILayer output = new BasicLayer(new ActivationLinear(), false, this.outputNeurons);
BasicNetwork network = new BasicNetwork();
RadialBasisFunctionLayer rbfLayer = new RadialBasisFunctionLayer(
this.hiddenNeurons);
network.AddLayer(input);
network.AddLayer(rbfLayer, SynapseType.Direct);
network.AddLayer(output);
network.Structure.FinalizeStructure();
network.Reset();
network.TagLayer(RBF_LAYER, rbfLayer);
rbfLayer.RandomizeRBFCentersAndWidths(this.inputNeurons, -1, 1, RBFEnum.Gaussian);
int y = PatternConst.START_Y;
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
rbfLayer.X = PatternConst.START_X;
rbfLayer.Y = y;
y += PatternConst.INC_Y;
output.X = PatternConst.START_X;
output.Y = y;
return network;
}示例7: Generate
/// <summary>
/// Generate the Elman neural network.
/// </summary>
/// <returns>The Elman neural network.</returns>
public BasicNetwork Generate()
{
int y = PatternConst.START_Y;
ILayer input = new BasicLayer(this.activation, false,
this.inputNeurons);
BasicNetwork result = new BasicNetwork();
result.AddLayer(input);
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
foreach (int count in this.hidden)
{
ILayer hidden = new BasicLayer(
this.activation, true, count);
result.AddLayer(hidden);
hidden.X = PatternConst.START_X;
hidden.Y = y;
y += PatternConst.INC_Y;
}
ILayer output = new BasicLayer(this.activation, true,
this.outputNeurons);
result.AddLayer(output);
output.X = PatternConst.START_X;
output.Y = y;
y += PatternConst.INC_Y;
result.Structure.FinalizeStructure();
result.Reset();
return result;
}示例8: Generate
/// <summary>
/// Generate the RSOM network.
/// </summary>
/// <returns>The neural network.</returns>
public BasicNetwork Generate()
{
ILayer output = new BasicLayer(new ActivationLinear(), false,
this.outputNeurons);
ILayer input = new BasicLayer(new ActivationLinear(), false,
this.inputNeurons);
BasicNetwork network = new BasicNetwork();
ILayer context = new ContextLayer(this.outputNeurons);
network.AddLayer(input);
network.AddLayer(output);
output.AddNext(context, SynapseType.OneToOne);
context.AddNext(input);
int y = PatternConst.START_Y;
input.X = PatternConst.START_X;
input.Y = y;
context.X = PatternConst.INDENT_X;
context.Y = y;
y += PatternConst.INC_Y;
output.X = PatternConst.START_X;
output.Y = y;
network.Structure.FinalizeStructure();
network.Reset();
return network;
}