/**
* Determine the class using part of an array.
*
* @param pos The position to begin.
* @param data The array to check.
* @return The class item.
*/
public final ClassItem determineClass(final int pos, final double[] data) {
int resultIndex = 0;
final double[] d = new double[getColumnsNeeded()];
EngineArray.arrayCopy(data, pos, d, 0, d.length);
switch (this.action) {
case Equilateral:
resultIndex = this.eq.decode(d);
break;
case OneOf:
resultIndex = EngineArray.indexOfLargest(d);
break;
case SingleField:
resultIndex = (int) d[0];
break;
default:
throw new AnalystError("Invalid action: " + this.action);
}
if (resultIndex < 0) {
return null;
}
return this.classes.get(resultIndex);
}
/**
* Determine what class the specified data belongs to.
*
* @param data The data to analyze.
* @return The class the data belongs to.
*/
public final ClassItem determineClass(final double[] data) {
int resultIndex = 0;
switch (this.action) {
case Equilateral:
resultIndex = this.eq.decode(data);
break;
case OneOf:
resultIndex = EngineArray.indexOfLargest(data);
break;
case SingleField:
resultIndex = (int) data[0];
break;
default:
throw new AnalystError("Unknown action: " + this.action);
}
return this.classes.get(resultIndex);
}
/** {@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);
}