/** Computes the best split for a NUMERICAL attribute */
static Split numericalSplit(Data data, int attr) {
double[] values = data.values(attr).clone();
Arrays.sort(values);
double[] splitPoints = chooseNumericSplitPoints(values);
int numLabels = data.getDataset().nblabels();
int[][] counts = new int[splitPoints.length][numLabels];
int[] countAll = new int[numLabels];
int[] countLess = new int[numLabels];
computeFrequencies(data, attr, splitPoints, counts, countAll);
int size = data.size();
double hy = entropy(countAll, size);
double invDataSize = 1.0 / size;
int best = -1;
double bestIg = -1.0;
// try each possible split value
for (int index = 0; index < splitPoints.length; index++) {
double ig = hy;
DataUtils.add(countLess, counts[index]);
DataUtils.dec(countAll, counts[index]);
// instance with attribute value < values[index]
size = DataUtils.sum(countLess);
ig -= size * invDataSize * entropy(countLess, size);
// instance with attribute value >= values[index]
size = DataUtils.sum(countAll);
ig -= size * invDataSize * entropy(countAll, size);
if (ig > bestIg) {
bestIg = ig;
best = index;
}
}
if (best == -1) {
throw new IllegalStateException("no best split found !");
}
return new Split(attr, bestIg, splitPoints[best]);
}
/** Computes the split for a CATEGORICAL attribute */
private static Split categoricalSplit(Data data, int attr) {
double[] values = data.values(attr).clone();
double[] splitPoints = chooseCategoricalSplitPoints(values);
int numLabels = data.getDataset().nblabels();
int[][] counts = new int[splitPoints.length][numLabels];
int[] countAll = new int[numLabels];
computeFrequencies(data, attr, splitPoints, counts, countAll);
int size = data.size();
double hy = entropy(countAll, size); // H(Y)
double hyx = 0.0; // H(Y|X)
double invDataSize = 1.0 / size;
for (int index = 0; index < splitPoints.length; index++) {
size = DataUtils.sum(counts[index]);
hyx += size * invDataSize * entropy(counts[index], size);
}
double ig = hy - hyx;
return new Split(attr, ig);
}
