AI-PSO

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META.yml  view on Meta::CPAN

# http://module-build.sourceforge.net/META-spec.html
name:         AI-PSO
version:      0.86
version_from: lib/AI/PSO.pm
installdirs:  site
requires:
    Callback:                      0
    Math::Random:                  0

distribution_type: module
generated_by: ExtUtils::MakeMaker version 6.30

README  view on Meta::CPAN

PSO version 0.81
================

INSTALLATION

To install this module type the following:

   perl Makefile.PL
   make
   make test
   make install

DEPENDENCIES

This module requires these other modules and libraries:

examples/NeuralNet/NeuralNet.h  view on Meta::CPAN

/// \class Neuron NeuralNet.h NeuralNet
/// \brief exported class which simulates a neruon within a Neural Net
///
class NEURALNET_API Neuron
{
    public:

        ///
        /// \fn Neuron()
        /// \brief constructor
        /// \note, add flag in constructor to choose what type of TransferFunction to use
        ///
        Neuron()
        {
            m_capacity = 1;
            m_numConnections = 0;
            m_neurons = new Neuron*[m_capacity];
            m_weights = new double[m_capacity];
            m_value = 0;
            xfer = new UnityGain();
        }

examples/NeuralNet/NeuralNet.h  view on Meta::CPAN

            delete [] m_neurons;
            delete [] m_weights;
            delete xfer;
        }

        
        ///
        /// \fn virtual double value()
        /// \brief calculates the value of the neuron.  It is virtual
        ///            because the value is calculated differently for
        ///            different types of Neurons.
        ///
        virtual double value()
        {
            for(int i = 0; i < m_numConnections; i++)
                m_value += m_neurons[i]->value() * m_weights[i];
            return m_value = xfer->compute(m_value);
        }


        ///

examples/NeuralNet/NeuralNet.h  view on Meta::CPAN

                m_output.addConnection(&m_hidden[k]);
        }


        int        m_numInputs;    /// number of input Neurons    in network
        int        m_numHidden;    /// number of hidden Neurons in network
        Input  *m_inputs;        /// array of Input Neurons
//        Neuron *m_hidden;        /// array of hidden Neurons
        Hidden *m_hidden;        /// array of hidden Neurons
        Neuron    m_output;        /// the single output Neuron (it is more efficient to have a separate network for each output)
        string  m_xferFunc;        /// type of transfer function for hidden neurons
};

#endif