private int[] mXVector; // input (X) vector
#endregion Fields
#region Methods
// Public Functions //
// Initialise //
// Initialises ART1 vectors at required length //
// Clears lists from previous runs //
// Adds single output node to recognition field //
public void Initialise()
{
// Create ART vector arrays
mXVector = new int[ mNumberOfInputNodes ];
mCVector = new int[ mNumberOfInputNodes ];
mTVector = new int[ mNumberOfInputNodes ];
// Tidy lists
mInputPatterns.Clear();
mRecognitionField.Clear();
// Create and initialise one outputnode and add to recognition field
BinaryOutputNode blankNode = new BinaryOutputNode();
blankNode.Initialise( mNumberOfInputNodes );
mRecognitionField.Add ( blankNode );
}
// Train //
// Trains the best match node that passes a vigilance test //
// Required to calculate the number of active bits in the comparison (Z) vector //
// Sets the nodes feed forward weights using this number //
// Sets the feed back weights to the comparison (Z) vector as new exemplar //
private void Train()
{
// Get number of active bits in comparison (Z) vector to rescale winning nodes feedforward weights
int numberOfActiveBits = 0; // counter for number of active 'bits' in comparison vector
// Looping through number of bits in input (X) vector
for( int i = 0; i < mNumberOfInputNodes; i++ ) {
numberOfActiveBits += mCVector[ i ]; // sum of number of active bits
}
// Train winning output node
mRecognitionField[ mBestNeuron ].Train( mCVector, numberOfActiveBits );
// Create and initialise one outputnode and add to recognition field
BinaryOutputNode blankNode = new BinaryOutputNode();
blankNode.Initialise( mNumberOfInputNodes );
mRecognitionField.Add( blankNode );
}