smile.math.distance.SparseMinkowskiDistance Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2010-2020 Haifeng Li. All rights reserved.
*
* Smile is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
*
* Smile is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Smile. If not, see .
******************************************************************************/
package smile.math.distance;
import java.util.Arrays;
import java.util.Iterator;
import smile.util.SparseArray;
/**
* Minkowski distance of order p or Lp-norm, is a generalization of
* Euclidean distance that is actually L2-norm. You may also provide
* a specified weight vector.
*
* @author Haifeng Li
*/
public class SparseMinkowskiDistance implements Metric {
private static final long serialVersionUID = 1L;
/**
* The order of Minkowski distance.
*/
private int p;
/**
* The weights used in weighted distance.
*/
private double[] weight = null;
/**
* Constructor.
*/
public SparseMinkowskiDistance(int p) {
if (p <= 0) {
throw new IllegalArgumentException(String.format("The order p has to be larger than 0: p = d", p));
}
this.p = p;
}
/**
* Constructor.
*
* @param weight the weight vector.
*/
public SparseMinkowskiDistance(int p, double[] weight) {
if (p <= 0) {
throw new IllegalArgumentException(String.format("The order p has to be larger than 0: p = d", p));
}
for (int i = 0; i < weight.length; i++) {
if (weight[i] < 0) {
throw new IllegalArgumentException(String.format("Weight has to be nonnegative: %f", weight[i]));
}
}
this.p = p;
this.weight = weight;
}
@Override
public String toString() {
if (weight != null) {
return String.format("Weighted Minkowski Distance(p = %d, weight = %s)", p, Arrays.toString(weight));
} else {
return String.format("Minkowski Distance(p = %d)", p);
}
}
@Override
public double d(SparseArray x, SparseArray y) {
if (x.isEmpty()) {
throw new IllegalArgumentException("List x is empty.");
}
if (y.isEmpty()) {
throw new IllegalArgumentException("List y is empty.");
}
Iterator iterX = x.iterator();
Iterator iterY = y.iterator();
SparseArray.Entry a = iterX.hasNext() ? iterX.next() : null;
SparseArray.Entry b = iterY.hasNext() ? iterY.next() : null;
double dist = 0.0;
if (weight == null) {
while (a != null && b != null) {
if (a.i < b.i) {
double d = a.x;
dist += Math.pow(d, p);
a = iterX.hasNext() ? iterX.next() : null;
} else if (a.i > b.i) {
double d = b.x;
dist += Math.pow(d, p);
b = iterY.hasNext() ? iterY.next() : null;
} else {
double d = a.x - b.x;
dist += Math.pow(d, p);
a = iterX.hasNext() ? iterX.next() : null;
b = iterY.hasNext() ? iterY.next() : null;
}
}
while (a != null) {
double d = a.x;
dist += Math.pow(d, p);
a = iterX.hasNext() ? iterX.next() : null;
}
while (b != null) {
double d = b.x;
dist += Math.pow(d, p);
b = iterY.hasNext() ? iterY.next() : null;
}
} else {
while (a != null && b != null) {
if (a.i < b.i) {
double d = a.x;
dist += weight[a.i] * Math.pow(d, p);
a = iterX.hasNext() ? iterX.next() : null;
} else if (a.i > b.i) {
double d = b.x;
dist += weight[b.i] * Math.pow(d, p);
b = iterY.hasNext() ? iterY.next() : null;
} else {
double d = a.x - b.x;
dist += weight[a.i] * Math.pow(d, p);
a = iterX.hasNext() ? iterX.next() : null;
b = iterY.hasNext() ? iterY.next() : null;
}
}
while (a != null) {
double d = a.x;
dist += weight[a.i] * Math.pow(d, p);
a = iterX.hasNext() ? iterX.next() : null;
}
while (b != null) {
double d = b.x;
dist += weight[b.i] * Math.pow(d, p);
b = iterY.hasNext() ? iterY.next() : null;
}
}
return Math.pow(dist, 1.0/p);
}
}