public function testBipolar2double()
{
// test a 1x4
$booleanData1 = [true, false, true, false];
$checkData1 = [1, -1, 1, -1];
$matrix1 = Matrix::createRowMatrix(BiPolarUtil\bipolar2double($booleanData1));
$checkMatrix1 = Matrix::createRowMatrix($checkData1);
$this->assertTrue($matrix1->equals($checkMatrix1));
// test a 2x2
$booleanData2 = [[true, false], [false, true]];
$checkData2 = [[1, -1], [-1, 1]];
$matrix2 = Matrix::matrixFromDoubles(BiPolarUtil\bipolar2double($booleanData2));
$checkMatrix2 = Matrix::matrixFromDoubles($checkData2);
$this->assertTrue($matrix2->equals($checkMatrix2));
}
/**
* 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);
}
public function testVectorLength()
{
$vectorData = [1.0, 2.0, 3.0, 4.0];
$vector = Matrix::createRowMatrix($vectorData);
$this->assertEquals(5, intval(MatrixMath\vectorLength($vector)));
$nonVector = new Matrix(2, 2);
try {
MatrixMath\vectorLength($nonVector);
$this->assertTrue(false);
} catch (MatrixError $e) {
}
}
