public function testInverse()
{
$matrixData1 = [[1, 2, 3, 4]];
$matrixData2 = [[1], [2], [3], [4]];
$matrix1 = Matrix::matrixFromDoubles($matrixData1);
$checkMatrix = Matrix::matrixFromDoubles($matrixData2);
$matrix2 = MatrixMath\transpose($matrix1);
$this->assertTrue($matrix2->equals($checkMatrix));
}
/**
* Train the neural network for the specified pattern.
* The neural network
* can be trained for more than one pattern. To do this simply call the
* train method more than once.
*
* @param
* MLData pattern
* The pattern to train for.
*/
public function addPattern(MLData $pattern)
{
if ($pattern->size() != $this->getNeuronCount()) {
throw new NeuralNetworkError("Network with " + $this->getNeuronCount() + " neurons, cannot learn a pattern of size " + $pattern->size());
}
// Create a row matrix from the input, convert boolean to bipolar
$m2 = Matrix::createRowMatrix($pattern->getData());
// Transpose the matrix and multiply by the original input matrix
$m1 = MatrixMath\transpose($m2);
$m3 = MatrixMath\multiply($m1, $m2);
// matrix 3 should be square by now, so create an identity
// matrix of the same size.
$identity = MatrixMath\identity($m3->getRows());
// subtract the identity matrix
$m4 = MatrixMath\subtract($m3, $identity);
// now add the calculated matrix, for this pattern, to the
// existing weight matrix.
$this->convertHopfieldMatrix($m4);
}
