/// <summary>
/// Creates and returns a new instance of Perceptron network
/// </summary>
/// <param name="inputNeuronsCount">number of neurons in input layer</param>
/// <param name="outputNeuronsCount">number of neurons in output layer</param>
/// <param name="transferFunctionType">type of transfer function to use</param>
/// <param name="learningRule">learning rule class</param>
/// <returns>instance of Perceptron network</returns>
public static Perceptron CreatePerceptron(int inputNeuronsCount, int outputNeuronsCount, TransferFunctionType transferFunctionType, Type learningRule)
{
Perceptron nnet = new Perceptron(inputNeuronsCount, outputNeuronsCount, transferFunctionType);
if (learningRule.Name.Equals(typeof(PerceptronLearning).Name))
{
nnet.LearningRule = new PerceptronLearning();
}
else if (learningRule.Name.Equals(typeof(BinaryDeltaRule).Name))
{
nnet.LearningRule = new BinaryDeltaRule();
}
return nnet;
}
/// <summary>
/// Creates and returns a new instance of Perceptron network
/// </summary>
/// <param name="inputNeuronsCount">number of neurons in input layer</param>
/// <param name="outputNeuronsCount">number of neurons in output layer</param>
/// <param name="transferFunctionType">type of transfer function to use</param>
/// <returns>instance of Perceptron network</returns>
public static Perceptron CreatePerceptron(int inputNeuronsCount, int outputNeuronsCount, TransferFunctionType transferFunctionType)
{
Perceptron nnet = new Perceptron(inputNeuronsCount, outputNeuronsCount, transferFunctionType);
return nnet;
}