//
// PRIVATE METHODS
//
private Matrix createDerivativeMatrix(Vector lastInducedField) {
List<Double> lst = new List<Double>();
for (int i = 0; i < lastInducedField.size(); i++) {
lst.Add(layer.getActivationFunction().deriv(
lastInducedField.getValue(i)));
}
return Matrix.createDiagonalMatrix(lst);
}
public Vector plus(Vector v)
{
Vector result = new Vector(size());
for (int i = 0; i < size(); i++)
{
result.setValue(i, getValue(i) + v.getValue(i));
}
return result;
}
public void testFeedForward()
{
// example 11.14 of Neural Network Design by Hagan, Demuth and Beale
// lots of tedious tests necessary to ensure nn is fundamentally correct
Matrix weightMatrix1 = new Matrix(2, 1);
weightMatrix1.set(0, 0, -0.27);
weightMatrix1.set(1, 0, -0.41);
Vector biasVector1 = new Vector(2);
biasVector1.setValue(0, -0.48);
biasVector1.setValue(1, -0.13);
Layer layer1 = new Layer(weightMatrix1, biasVector1,
new LogSigActivationFunction());
Vector inputVector1 = new Vector(1);
inputVector1.setValue(0, 1);
Vector expected = new Vector(2);
expected.setValue(0, 0.321);
expected.setValue(1, 0.368);
Vector result1 = layer1.feedForward(inputVector1);
Assert.AreEqual(expected.getValue(0), result1.getValue(0), 0.001);
Assert.AreEqual(expected.getValue(1), result1.getValue(1), 0.001);
Matrix weightMatrix2 = new Matrix(1, 2);
weightMatrix2.set(0, 0, 0.09);
weightMatrix2.set(0, 1, -0.17);
Vector biasVector2 = new Vector(1);
biasVector2.setValue(0, 0.48);
Layer layer2 = new Layer(weightMatrix2, biasVector2,
new PureLinearActivationFunction());
Vector inputVector2 = layer1.getLastActivationValues();
Vector result2 = layer2.feedForward(inputVector2);
Assert.AreEqual(0.446, result2.getValue(0), 0.001);
}
public Vector feedForward(Vector inputVector) {
lastInput = inputVector;
Matrix inducedField = weightMatrix.times(inputVector).plus(biasVector);
Vector inducedFieldVector = new Vector(numberOfNeurons());
for (int i = 0; i < numberOfNeurons(); i++) {
inducedFieldVector.setValue(i, inducedField.get(i, 0));
}
lastInducedField = inducedFieldVector.copyVector();
Vector resultVector = new Vector(numberOfNeurons());
for (int i = 0; i < numberOfNeurons(); i++) {
resultVector.setValue(i, activationFunction
.activation(inducedFieldVector.getValue(i)));
}
// set the result as the last activation value
lastActivationValues = resultVector.copyVector();
return resultVector;
}
