/**
* Query a regression algorithm using equilateral encoding.
*
* @param alg The algorithm being used.
* @param theTrainingData The training data.
* @param items The category items classified.
* @param high The high value.
* @param low The low value.
*/
public static void queryEquilateral(
final RegressionAlgorithm alg,
final List<BasicData> theTrainingData,
final Map<String, Integer> items,
final double high,
final double low) {
// first, we need to invert the items. Right now it maps from category to index. We need index
// to category.
final Map<Integer, String> invMap = new HashMap<>();
for (final Map.Entry<String, Integer> entry : items.entrySet()) {
invMap.put(entry.getValue(), entry.getKey());
}
// now we can query
final Equilateral eq = new Equilateral(items.size(), high, low);
for (final BasicData data : theTrainingData) {
final double[] output = alg.computeRegression(data.getInput());
final int idealIndex = eq.decode(data.getIdeal());
final int actualIndex = eq.decode(output);
System.out.println(
Arrays.toString(data.getInput())
+ " -> "
+ invMap.get(actualIndex)
+ ", Ideal: "
+ invMap.get(idealIndex));
}
}
/**
* Query a regression algorithm and see how close it matches the training data.
*
* @param alg The algorithm to evaluate.
* @param theTrainingData The training data.
*/
public static void query(final RegressionAlgorithm alg, final List<BasicData> theTrainingData) {
for (final BasicData data : theTrainingData) {
final double[] output = alg.computeRegression(data.getInput());
System.out.println(
Arrays.toString(data.getInput())
+ " -> "
+ Arrays.toString(output)
+ ", Ideal: "
+ Arrays.toString(data.getIdeal()));
}
}