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Open Source Chemistry Library
/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
package smile.clustering.linkage;
/**
* Unweighted Pair Group Method using Centroids (also known as centroid linkage).
* The distance between two clusters is the Euclidean distance between their
* centroids, as calculated by arithmetic mean. Only valid for Euclidean
* distance based proximity matrix.
*
* @author Haifeng Li
*/
public class UPGMCLinkage extends Linkage {
/**
* The number of samples in each cluster.
*/
private int[] n;
/**
* Constructor.
* @param proximity the proximity matrix to store the distance measure of
* dissimilarity. To save space, we only need the lower half of matrix.
*/
public UPGMCLinkage(double[][] proximity) {
init(proximity);
n = new int[proximity.length];
for (int i = 0; i < n.length; i++) {
n[i] = 1;
}
for (int i = 0; i < this.proximity.length; i++) {
this.proximity[i] *= this.proximity[i];
}
}
@Override
public String toString() {
return "UPGMC linkage";
}
@Override
public void merge(int i, int j) {
float nij = n[i] + n[j];
for (int k = 0; k < i; k++) {
proximity[index(i, k)] = (d(i, k) * n[i] + d(j, k) * n[j] - d(j, i) * n[i] * n[j] / nij) / nij;
}
for (int k = i+1; k < j; k++) {
proximity[index(k, i)] = (d(k, i) * n[i] + d(j, k) * n[j] - d(j, i) * n[i] * n[j] / nij) / nij;
}
for (int k = j+1; k < size; k++) {
proximity[index(k, i)] = (d(k, i) * n[i] + d(k, j) * n[j] - d(j, i) * n[i] * n[j] / nij) / nij;
}
n[i] += n[j];
}
}