/** Generate a random neighbor which differs in only one medoid with current clusters. */
private double getRandomNeighbor(T[] data, T[] medoids, int[] y, double[] d) {
int n = data.length;
int index = Math.randomInt(k);
T medoid = null;
boolean dup;
do {
dup = false;
medoid = data[Math.randomInt(n)];
for (int i = 0; i < k; i++) {
if (medoid == medoids[i]) {
dup = true;
break;
}
}
} while (dup);
medoids[index] = medoid;
for (int i = 0; i < n; i++) {
double dist = distance.d(data[i], medoid);
if (d[i] > dist) {
y[i] = index;
d[i] = dist;
} else if (y[i] == index) {
d[i] = dist;
y[i] = index;
for (int j = 0; j < k; j++) {
if (j != index) {
dist = distance.d(data[i], medoids[j]);
if (d[i] > dist) {
y[i] = j;
d[i] = dist;
}
}
}
}
}
return Math.sum(d);
}
/**
* Finds the best split cutoff for attribute j at the current node.
*
* @param n the number instances in this node.
* @param count the sample count in each class.
* @param falseCount an array to store sample count in each class for false child node.
* @param impurity the impurity of this node.
* @param j the attribute to split on.
*/
public Node findBestSplit(int n, int[] count, int[] falseCount, double impurity, int j) {
Node splitNode = new Node();
if (attributes[j].getType() == Attribute.Type.NOMINAL) {
int m = ((NominalAttribute) attributes[j]).size();
int[][] trueCount = new int[m][k];
for (int i = 0; i < x.length; i++) {
if (samples[i] > 0) {
trueCount[(int) x[i][j]][y[i]] += samples[i];
}
}
for (int l = 0; l < m; l++) {
int tc = Math.sum(trueCount[l]);
int fc = n - tc;
// If either side is empty, skip this feature.
if (tc < nodeSize || fc < nodeSize) {
continue;
}
for (int q = 0; q < k; q++) {
falseCount[q] = count[q] - trueCount[l][q];
}
int trueLabel = Math.whichMax(trueCount[l]);
int falseLabel = Math.whichMax(falseCount);
double gain =
impurity
- (double) tc / n * impurity(trueCount[l], tc)
- (double) fc / n * impurity(falseCount, fc);
if (gain > splitNode.splitScore) {
// new best split
splitNode.splitFeature = j;
splitNode.splitValue = l;
splitNode.splitScore = gain;
splitNode.trueChildOutput = trueLabel;
splitNode.falseChildOutput = falseLabel;
}
}
} else if (attributes[j].getType() == Attribute.Type.NUMERIC) {
int[] trueCount = new int[k];
double prevx = Double.NaN;
int prevy = -1;
for (int i : order[j]) {
if (samples[i] > 0) {
if (Double.isNaN(prevx) || x[i][j] == prevx || y[i] == prevy) {
prevx = x[i][j];
prevy = y[i];
trueCount[y[i]] += samples[i];
continue;
}
int tc = Math.sum(trueCount);
int fc = n - tc;
// If either side is empty, skip this feature.
if (tc < nodeSize || fc < nodeSize) {
prevx = x[i][j];
prevy = y[i];
trueCount[y[i]] += samples[i];
continue;
}
for (int l = 0; l < k; l++) {
falseCount[l] = count[l] - trueCount[l];
}
int trueLabel = Math.whichMax(trueCount);
int falseLabel = Math.whichMax(falseCount);
double gain =
impurity
- (double) tc / n * impurity(trueCount, tc)
- (double) fc / n * impurity(falseCount, fc);
if (gain > splitNode.splitScore) {
// new best split
splitNode.splitFeature = j;
splitNode.splitValue = (x[i][j] + prevx) / 2;
splitNode.splitScore = gain;
splitNode.trueChildOutput = trueLabel;
splitNode.falseChildOutput = falseLabel;
}
prevx = x[i][j];
prevy = y[i];
trueCount[y[i]] += samples[i];
}
}
} else {
throw new IllegalStateException("Unsupported attribute type: " + attributes[j].getType());
}
return splitNode;
}
