smile.math.distance.EuclideanDistance Maven / Gradle / Ivy
/******************************************************************************
* Confidential Proprietary *
* (c) Copyright Haifeng Li 2011, All Rights Reserved *
******************************************************************************/
package smile.math.distance;
/**
* Euclidean distance. Use getInstance() to get the standard unweighted
* Euclidean distance. Or create an instance with a specified
* weight vector. For float or double arrays, missing values (i.e. NaN)
* are also handled. Also support sparse arrays of which zeros are excluded
* to save space.
*
* @author Haifeng Li
*/
public class EuclideanDistance implements Metric {
/**
* The weights used in weighted distance.
*/
private double[] weight = null;
/**
* Constructor. Standard (unweighted) Euclidean distance.
*/
public EuclideanDistance() {
}
/**
* Constructor with a given weight vector.
*
* @param weight the weight vector.
*/
public EuclideanDistance(double[] weight) {
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.weight = weight;
}
@Override
public String toString() {
if (weight != null)
return "weighted Euclidean distance";
else
return "Euclidean distance";
}
/**
* Euclidean distance between the two arrays of type integer. No missing
* value handling in this method.
*/
public double d(int[] x, int[] y) {
if (x.length != y.length)
throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length));
double dist = 0.0;
if (weight == null) {
for (int i = 0; i < x.length; i++) {
double d = x[i] - y[i];
dist += d * d;
}
} else {
if (x.length != weight.length)
throw new IllegalArgumentException(String.format("Input vectors and weight vector have different length: %d, %d", x.length, weight.length));
for (int i = 0; i < x.length; i++) {
double d = x[i] - y[i];
dist += weight[i] * d * d;
}
}
return Math.sqrt(dist);
}
/**
* Euclidean distance between the two arrays of type float.
* NaN will be treated as missing values and will be excluded from the
* calculation. Let m be the number nonmissing values, and n be the
* number of all values. The returned distance is sqrt(n * d / m),
* where d is the square of distance between nonmissing values.
*/
public double d(float[] x, float[] y) {
if (x.length != y.length)
throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length));
int n = x.length;
int m = 0;
double dist = 0.0;
if (weight == null) {
for (int i = 0; i < n; i++) {
if (!Float.isNaN(x[i]) && !Float.isNaN(y[i])) {
m++;
double d = x[i] - y[i];
dist += d * d;
}
}
} else {
if (x.length != weight.length)
throw new IllegalArgumentException(String.format("Input vectors and weight vector have different length: %d, %d", x.length, weight.length));
for (int i = 0; i < n; i++) {
if (!Float.isNaN(x[i]) && !Float.isNaN(y[i])) {
m++;
double d = x[i] - y[i];
dist += weight[i] * d * d;
}
}
}
if (m == 0)
dist = Double.NaN;
else
dist = n * dist / m;
return Math.sqrt(dist);
}
/**
* Euclidean distance between the two arrays of type double.
* NaN will be treated as missing values and will be excluded from the
* calculation. Let m be the number nonmissing values, and n be the
* number of all values. The returned distance is sqrt(n * d / m),
* where d is the square of distance between nonmissing values.
*/
@Override
public double d(double[] x, double[] y) {
if (x.length != y.length)
throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length));
int n = x.length;
int m = 0;
double dist = 0.0;
if (weight == null) {
for (int i = 0; i < n; i++) {
if (!Double.isNaN(x[i]) && !Double.isNaN(y[i])) {
m++;
double d = x[i] - y[i];
dist += d * d;
}
}
} else {
if (x.length != weight.length)
throw new IllegalArgumentException(String.format("Input vectors and weight vector have different length: %d, %d", x.length, weight.length));
for (int i = 0; i < n; i++) {
if (!Double.isNaN(x[i]) && !Double.isNaN(y[i])) {
m++;
double d = x[i] - y[i];
dist += weight[i] * d * d;
}
}
}
if (m == 0)
dist = Double.NaN;
else
dist = n * dist / m;
return Math.sqrt(dist);
}
}