public void testFeedForwardAndBAckLoopWorksWithMomentum()
{
// example 11.14 of Neural Network Design by Hagan, Demuth and Beale
Matrix hiddenLayerWeightMatrix = new Matrix(2, 1);
hiddenLayerWeightMatrix.Set(0, 0, -0.27);
hiddenLayerWeightMatrix.Set(1, 0, -0.41);
Vector hiddenLayerBiasVector = new Vector(2);
hiddenLayerBiasVector.SetValue(0, -0.48);
hiddenLayerBiasVector.SetValue(1, -0.13);
Vector input = new Vector(1);
input.SetValue(0, 1);
Matrix outputLayerWeightMatrix = new Matrix(1, 2);
outputLayerWeightMatrix.Set(0, 0, 0.09);
outputLayerWeightMatrix.Set(0, 1, -0.17);
Vector outputLayerBiasVector = new Vector(1);
outputLayerBiasVector.SetValue(0, 0.48);
Vector error = new Vector(1);
error.SetValue(0, 1.261);
double learningRate = 0.1;
double momentumFactor = 0.5;
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(
hiddenLayerWeightMatrix, hiddenLayerBiasVector,
outputLayerWeightMatrix, outputLayerBiasVector);
ffnn.SetTrainingScheme(new BackPropagationLearning(learningRate,
momentumFactor));
ffnn.ProcessInput(input);
ffnn.ProcessError(error);
Matrix finalHiddenLayerWeights = ffnn.GetHiddenLayerWeights();
Assert.AreEqual(-0.2675, finalHiddenLayerWeights.Get(0, 0), 0.001);
Assert.AreEqual(-0.4149, finalHiddenLayerWeights.Get(1, 0), 0.001);
Vector hiddenLayerBias = ffnn.GetHiddenLayerBias();
Assert.AreEqual(-0.4775, hiddenLayerBias.GetValue(0), 0.001);
Assert.AreEqual(-0.1349, hiddenLayerBias.GetValue(1), 0.001);
Matrix finalOutputLayerWeights = ffnn.GetOutputLayerWeights();
Assert.AreEqual(0.1304, finalOutputLayerWeights.Get(0, 0), 0.001);
Assert.AreEqual(-0.1235, finalOutputLayerWeights.Get(0, 1), 0.001);
Vector outputLayerBias = ffnn.GetOutputLayerBias();
Assert.AreEqual(0.6061, outputLayerBias.GetValue(0), 0.001);
}