/**
* Calculate the Euclidean distance for the specified output neuron and the input vector. This is
* the square root of the squares of the differences between the weight and input vectors.
*
* @param matrix The matrix to get the weights from.
* @param input The input vector.
* @param outputNeuron The neuron we are calculating the distance for.
* @return The Euclidean distance.
*/
public double calculateEuclideanDistance(
final Matrix matrix, final MLData input, final int outputNeuron) {
double result = 0;
// Loop over all input data.
for (int i = 0; i < input.size(); i++) {
final double diff = input.getData(i) - matrix.get(outputNeuron, i);
result += diff * diff;
}
return BoundMath.sqrt(result);
}
/** {@inheritDoc} */
@Override
public final void iteration() {
if (this.mustInit) {
initWeights();
}
double worstDistance = Double.NEGATIVE_INFINITY;
for (final MLDataPair pair : this.training) {
final MLData out = this.network.computeInstar(pair.getInput());
// determine winner
final int winner = EngineArray.indexOfLargest(out.getData());
// calculate the distance
double distance = 0;
for (int i = 0; i < pair.getInput().size(); i++) {
final double diff =
pair.getInput().getData(i) - this.network.getWeightsInputToInstar().get(i, winner);
distance += diff * diff;
}
distance = BoundMath.sqrt(distance);
if (distance > worstDistance) {
worstDistance = distance;
}
// train
for (int j = 0; j < this.network.getInputCount(); j++) {
final double delta =
this.learningRate
* (pair.getInput().getData(j)
- this.network.getWeightsInputToInstar().get(j, winner));
this.network.getWeightsInputToInstar().add(j, winner, delta);
}
}
setError(worstDistance);
}
