/**
* Calculates the AUC measurment.
*
* @param algorithm The optimisation algorithm with a NNTrainingProblem.
* @return A Vector with the AUC for each NN output.
*/
@Override
public Vector getValue(Algorithm algorithm) {
Vector solution = (Vector) algorithm.getBestSolution().getPosition();
NNTrainingProblem problem = (NNTrainingProblem) algorithm.getOptimisationProblem();
StandardPatternDataTable generalisationSet = problem.getGeneralisationSet();
NeuralNetwork neuralNetwork = problem.getNeuralNetwork();
neuralNetwork.setWeights(solution);
// Arrange outputs and target values into ArrayLists.
ArrayList<ArrayList<Real>> targets = new ArrayList<ArrayList<Real>>();
ArrayList<ArrayList<Real>> outputs = new ArrayList<ArrayList<Real>>();
// case of multiple outputs
if (generalisationSet.getRow(0).getTarget() instanceof Vector) {
int size = ((Vector) generalisationSet.getRow(0).getTarget()).size();
for (int i = 0; i < size; ++i) {
targets.add(new ArrayList<Real>());
outputs.add(new ArrayList<Real>());
}
for (StandardPattern pattern : generalisationSet) {
Vector target = (Vector) pattern.getTarget();
Vector output = neuralNetwork.evaluatePattern(pattern);
for (int curOutput = 0; curOutput < target.size(); ++curOutput) {
targets.get(curOutput).add((Real) target.get(curOutput));
outputs.get(curOutput).add((Real) output.get(curOutput));
}
}
}
// case of single output
else {
targets.add(new ArrayList<Real>());
outputs.add(new ArrayList<Real>());
for (StandardPattern pattern : generalisationSet) {
Real target = (Real) pattern.getTarget();
Vector output = neuralNetwork.evaluatePattern(pattern);
targets.get(0).add(target);
outputs.get(0).add((Real) output.get(0));
}
}
// Calculate the Vector of AUC values
Vector results = Vector.of();
for (int curOutput = 0; curOutput < outputs.size(); ++curOutput) {
results.add(Real.valueOf(areaUnderCurve(targets.get(curOutput), outputs.get(curOutput))));
}
return results;
}
/** Test the get row method. */
@Test
public void testGetRow() {
StandardPattern pattern = stringTargetPatterns.getRow(0);
List<Type> row = typedData.getRow(0);
for (int j = 0; j < row.size() - 1; j++) {
Assert.assertEquals(row.get(j), pattern.getVector().get(j));
}
Assert.assertEquals(row.get(row.size() - 1), pattern.getTarget());
pattern = vectorTargetPatterns.getRow(0);
row = typedData.getRow(2);
for (int j = 0; j < row.size() - 3; j++) {
Assert.assertEquals(row.get(j), pattern.getVector().get(j));
}
for (int j = row.size() - 3; j < row.size(); j++) {
Assert.assertEquals(row.get(j), ((Vector) pattern.getTarget()).get(j - (row.size() - 3)));
}
}
@Test
public void testRemoveRow() {
StandardPattern removed = stringTargetPatterns.removeRow(0);
List<Type> row = typedData.getRow(0);
for (int j = 0; j < row.size() - 1; j++) {
Assert.assertEquals(row.get(j), removed.getVector().get(j));
}
Assert.assertEquals(row.get(row.size() - 1), removed.getTarget());
removed = vectorTargetPatterns.removeRow(1);
row = typedData.getRow(3);
for (int j = 0; j < row.size() - 3; j++) {
Assert.assertEquals(row.get(j), removed.getVector().get(j));
}
for (int j = row.size() - 3; j < row.size(); j++) {
Assert.assertEquals(row.get(j), ((Vector) removed.getTarget()).get(j - (row.size() - 3)));
}
Assert.assertEquals(1, stringTargetPatterns.size());
Assert.assertEquals(1, vectorTargetPatterns.size());
}
/**
* Performs a gradient decent backpropagation given the previous {@link StandardPattern} as input
* as well as the weight updates after the previous execution of a backpropagation. If the
* previous weight updates do not exist, the visitor will create them and initialize them to zero.
*
* @param architecture
*/
@Override
public void visit(Architecture architecture) {
List<Layer> layers = architecture.getLayers();
int numLayers = layers.size();
int currentLayerIdx = numLayers - 1; // skip input layer
Layer currentLayer = layers.get(currentLayerIdx);
int layerSize = currentLayer.size();
Layer nextLayer = null;
int nextLayerSize = -1;
Neuron currentNeuron;
// setup delta storage
layerWeightsDelta = new double[numLayers - 1][]; // not necessary for input layer
// calculate output layer deltas
layerWeightsDelta[currentLayerIdx - 1] = new double[layerSize];
for (int k = 0; k < layerSize; k++) {
currentNeuron = currentLayer.get(k);
double t_k =
layerSize > 1
? ((Vector) previousPattern.getTarget()).doubleValueOf(k)
: ((Real) previousPattern.getTarget()).doubleValue();
double o_k = currentNeuron.getActivation();
layerWeightsDelta[currentLayerIdx - 1][k] =
-1.0 * (t_k - o_k) * currentNeuron.getActivationFunction().getGradient(o_k);
}
// calculate deltas for all hidden layers
for (currentLayerIdx = numLayers - 2; currentLayerIdx > 0; currentLayerIdx--) {
currentLayer = layers.get(currentLayerIdx);
layerSize = currentLayer.size();
layerSize = currentLayer.isBias() ? layerSize - 1 : layerSize;
layerWeightsDelta[currentLayerIdx - 1] = new double[layerSize];
for (int j = 0; j < layerSize; j++) {
layerWeightsDelta[currentLayerIdx - 1][j] = 0.0;
nextLayer = layers.get(currentLayerIdx + 1);
nextLayerSize = nextLayer.size();
nextLayerSize = nextLayer.isBias() ? nextLayerSize - 1 : nextLayerSize;
for (int k = 0; k < nextLayerSize; k++) {
double w_kj = nextLayer.get(k).getWeights().doubleValueOf(j);
layerWeightsDelta[currentLayerIdx - 1][j] += w_kj * layerWeightsDelta[currentLayerIdx][k];
}
currentNeuron = currentLayer.get(j);
layerWeightsDelta[currentLayerIdx - 1][j] *=
currentNeuron.getActivationFunction().getGradient(currentNeuron.getActivation());
}
}
// storage for the weight updates
if (previousWeightUpdates == null) {
previousWeightUpdates = new double[numLayers - 1][];
for (currentLayerIdx = numLayers - 1; currentLayerIdx > 0; currentLayerIdx--) {
for (int k = 0; k < layerSize; k++) {
currentLayer = layers.get(currentLayerIdx);
layerSize = currentLayer.isBias() ? currentLayer.size() - 1 : currentLayer.size();
int previousLayerSize = layers.get(currentLayerIdx - 1).size();
previousWeightUpdates[currentLayerIdx - 1] =
new double[layerSize * previousLayerSize + previousLayerSize + 1];
}
}
}
((ForwardingLayer) layers.get(0)).setSource(new PatternInputSource(previousPattern));
// updates output and all hidden layer weights
for (currentLayerIdx = numLayers - 1;
currentLayerIdx > 0;
currentLayerIdx--) { // loop excludes input layer
currentLayer = layers.get(currentLayerIdx);
layerSize = currentLayer.isBias() ? currentLayer.size() - 1 : currentLayer.size();
int previousLayerSize = -1;
Layer previousLayer = null;
for (int k = 0; k < layerSize; k++) {
currentNeuron = currentLayer.get(k);
previousLayer = layers.get(currentLayerIdx - 1);
previousLayerSize = previousLayer.size();
double tmp = (-1.0 * learningRate) * layerWeightsDelta[currentLayerIdx - 1][k];
for (int j = 0; j < previousLayerSize; j++) {
double weight = currentNeuron.getWeights().doubleValueOf(j);
double newWeightUpdate = tmp * previousLayer.getNeuralInput(j);
double update =
newWeightUpdate
+ momentum
* previousWeightUpdates[currentLayerIdx - 1][k * previousLayerSize + j];
currentNeuron.getWeights().setReal(j, weight + update);
previousWeightUpdates[currentLayerIdx - 1][k * previousLayerSize + j] = newWeightUpdate;
}
}
}
}