public static Layer createLayer(int neuronsCount, Type transferFunctionClass)
{
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.setProperty("transferFunction", transferFunctionClass.Name);
Layer layer = new Layer(neuronsCount, neuronProperties);
return(layer);
}
public static Layer createLayer(int neuronsCount, TransferFunctionType transferFunctionType)
{
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.setProperty("transferFunction", transferFunctionType.ToString());
Layer layer = new Layer(neuronsCount, neuronProperties);
return(layer);
}
/// <summary>
/// Creates and returns a new instance of Multi Layer Perceptron </summary>
/// <param name="layersStr"> space separated number of neurons in layers </param>
/// <param name="transferFunctionType"> transfer function type for neurons </param>
/// <returns> instance of Multi Layer Perceptron </returns>
public static MultiLayerPerceptron createMLPerceptron(string layersStr, TransferFunctionType transferFunctionType, Type learningRule, bool useBias, bool connectIO)
{
List <int> layerSizes = VectorParser.parseInteger(layersStr);
NeuronProperties neuronProperties = new NeuronProperties(transferFunctionType, useBias);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(layerSizes, neuronProperties);
// set learning rule - TODO: use reflection here
//JAVA TO C# CONVERTER WARNING: The .NET Type.FullName property will not always yield results identical to the Java Class.getName method:
if (learningRule.FullName.Equals(typeof(BackPropagation).FullName))
{
nnet.LearningRule = new BackPropagation();
}
//JAVA TO C# CONVERTER WARNING: The .NET Type.FullName property will not always yield results identical to the Java Class.getName method:
else if (learningRule.FullName.Equals(typeof(MomentumBackpropagation).FullName))
{
nnet.LearningRule = new MomentumBackpropagation();
}
//JAVA TO C# CONVERTER WARNING: The .NET Type.FullName property will not always yield results identical to the Java Class.getName method:
else if (learningRule.FullName.Equals(typeof(DynamicBackPropagation).FullName))
{
nnet.LearningRule = new DynamicBackPropagation();
}
//JAVA TO C# CONVERTER WARNING: The .NET Type.FullName property will not always yield results identical to the Java Class.getName method:
else if (learningRule.FullName.Equals(typeof(ResilientPropagation).FullName))
{
nnet.LearningRule = new ResilientPropagation();
}
// connect io
if (connectIO)
{
nnet.connectInputsToOutputs();
}
return(nnet);
}
/// <summary>
/// Creates and returns neuron instance according to the given specification in neuronProperties.
/// </summary>
/// <param name="neuronProperties">
/// specification of neuron properties </param>
/// <returns> returns instance of neuron with specified properties </returns>
public static Neuron createNeuron(NeuronProperties neuronProperties)
{
InputFunction inputFunction = null;
System.Type inputFunctionClass = neuronProperties.InputFunction;
if (inputFunctionClass != null)
{
inputFunction = createInputFunction(inputFunctionClass);
}
TransferFunction transferFunction = createTransferFunction(neuronProperties.TransferFunctionProperties);
Neuron neuron = null;
System.Type neuronClass = neuronProperties.NeuronType;
// use two param System.Reflection.ConstructorInfo to create neuron
try
{
object[] paramList = new object[2];
paramList[0] = inputFunction;
paramList[1] = transferFunction;
neuron = (Neuron)Activator.CreateInstance(neuronClass, paramList);
}
catch (java.lang.NoSuchMethodException)
{
// throw new NeurophException("NoSuchMethod while creating Neuron!", e);
}
catch (java.lang.InstantiationException e)
{
throw new NeurophException("InstantiationException while creating Neuron!", e);
}
catch (java.lang.IllegalAccessException e)
{
throw new NeurophException("IllegalAccessException while creating Neuron!", e);
}
catch (InvocationTargetException e)
{
throw new NeurophException("InvocationTargetException while creating Neuron!", e);
}
if (neuron == null)
{
// use System.Reflection.ConstructorInfo without params to create neuron
try
{
System.Reflection.ConstructorInfo con = neuronClass.GetConstructor(new System.Type[] { });
neuron = (Neuron)con.Invoke(new object[] { });
}
catch (java.lang.IllegalAccessException e)
{
Console.Error.WriteLine("InstantiationException while creating Neuron!");
System.Console.WriteLine(e.ToString());
Console.Write(e.StackTrace);
}
catch (java.lang.InstantiationException e)
{
Console.Error.WriteLine("InstantiationException while creating Neuron!");
System.Console.WriteLine(e.ToString());
Console.Write(e.StackTrace);
}
}
if (neuronProperties.hasProperty("thresh"))
{
((ThresholdNeuron)neuron).Thresh = (double)neuronProperties.getProperty("thresh");
}
else if (neuronProperties.hasProperty("bias"))
{
((InputOutputNeuron)neuron).Bias = (double)neuronProperties.getProperty("bias");
}
return(neuron);
}
/// <summary>
/// Creates and returns instance of Layer with specified number of neurons with specified properties </summary>
/// <param name="neuronsCount"> </param>
/// <param name="neuronProperties"> </param>
/// <returns> </returns>
public static Layer createLayer(int neuronsCount, NeuronProperties neuronProperties)
{
Layer layer = new Layer(neuronsCount, neuronProperties);
return(layer);
}
