smile.mds.IsotonicMDS Maven / Gradle / Ivy
The newest version!
/*******************************************************************************
* 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.mds;
import smile.math.Math;
import smile.math.DifferentiableMultivariateFunction;
import smile.sort.QuickSort;
/**
* Kruskal's nonmetric MDS. In non-metric MDS, only the rank order of entries
* in the proximity matrix (not the actual dissimilarities) is assumed to
* contain the significant information. Hence, the distances of the final
* configuration should as far as possible be in the same rank order as the
* original data. Note that a perfect ordinal re-scaling of the data into
* distances is usually not possible. The relationship is typically found
* using isotonic regression.
*
* @author Haifeng Li
*/
public class IsotonicMDS {
/**
* The final stress achieved.
*/
private double stress;
/**
* Coordinate matrix.
*/
private double[][] coordinates;
/**
* Returns the final stress achieved.
*/
public double getStress() {
return stress;
}
/**
* Returns the coordinates of projected data.
*/
public double[][] getCoordinates() {
return coordinates;
}
/**
* Constructor. Learn a 2-dimensional Kruskal's non-metric MDS with default
* tolerance = 1E-4 and maxIter = 200.
* @param proximity the nonnegative proximity matrix of dissimilarities. The
* diagonal should be zero and all other elements should be positive and symmetric.
*/
public IsotonicMDS(double[][] proximity) {
this(proximity, 2);
}
/**
* Constructor. Learn Kruskal's non-metric MDS with default
* tolerance = 1E-4 and maxIter = 200.
* @param proximity the nonnegative proximity matrix of dissimilarities. The
* diagonal should be zero and all other elements should be positive and symmetric.
* @param k the dimension of the projection.
*/
public IsotonicMDS(double[][] proximity, int k) {
this(proximity, k, 1E-4, 200);
}
/**
* Constructor. Learn Kruskal's non-metric MDS with default
* tolerance = 1E-4 and maxIter = 100.
* @param proximity the nonnegative proximity matrix of dissimilarities. The
* diagonal should be zero and all other elements should be positive and symmetric.
* @param coordinates the initial projected coordinates, of which the column
* size is the projection dimension.
*/
public IsotonicMDS(double[][] proximity, double[][] coordinates) {
this(proximity, coordinates, 1E-4, 200);
}
/**
* Constructor. Learn Kruskal's non-metric MDS.
* @param proximity the nonnegative proximity matrix of dissimilarities. The
* diagonal should be zero and all other elements should be positive and symmetric.
* @param k the dimension of the projection.
* @param tol tolerance for stopping iterations.
* @param maxIter maximum number of iterations.
*/
public IsotonicMDS(double[][] proximity, int k, double tol, int maxIter) {
this(proximity, new MDS(proximity, k).getCoordinates(), tol, maxIter);
}
/**
* Constructor. Learn Kruskal's non-metric MDS.
* @param proximity the nonnegative proximity matrix of dissimilarities. The
* diagonal should be zero and all other elements should be positive and symmetric.
* @param init the initial projected coordinates, of which the column
* size is the projection dimension.
* @param tol tolerance for stopping iterations.
* @param maxIter maximum number of iterations.
*/
public IsotonicMDS(double[][] proximity, double[][] init, double tol, int maxIter) {
if (proximity.length != proximity[0].length) {
throw new IllegalArgumentException("The proximity matrix is not square.");
}
if (proximity.length != init.length) {
throw new IllegalArgumentException("The proximity matrix and the initial coordinates are of different size.");
}
coordinates = Math.clone(init);
int nr = proximity.length;
int nc = coordinates[0].length;
int n = nr * (nr - 1) / 2;
double[] d = new double[n];
for (int i = 0, l = 0; i < nr; i++) {
for (int j = i + 1; j < nr; j++, l++) {
d[l] = proximity[j][i];
}
}
double[] x = new double[nr * nc];
for (int i = 0, l = 0; i < nr; i++) {
for (int j = 0; j < nc; j++, l++) {
x[l] = coordinates[i][j];
}
}
int[] ord = QuickSort.sort(d);
int[] ord2 = QuickSort.sort(ord.clone());
ObjectiveFunction func = new ObjectiveFunction(nr, nc, d, ord, ord2);
stress = 0.0;
try {
stress = Math.min(func, 5, x, tol, maxIter);
} catch (Exception ex) {
// If L-BFGS doesn't work, let's try BFGS.
stress = Math.min(func, x, tol, maxIter);
}
if (stress == 0.0) {
System.out.format("Isotonic MDS: error = %.1f%%. The fit is perfect.\n", 100 * stress);
} else if (stress <= 0.025) {
System.out.format("Isotonic MDS: error = %.1f%%. The fit is excellent.\n", 100 * stress);
} else if (stress <= 0.05) {
System.out.format("Isotonic MDS: error = %.1f%%. The fit is good.\n", 100 * stress);
} else if (stress <= 0.10) {
System.out.format("Isotonic MDS: error = %.1f%%. The fit is fair.\n", 100 * stress);
} else {
System.out.format("Isotonic MDS: error = %.1f%%. The fit may be poor.\n", 100 * stress);
}
coordinates = new double[nr][nc];
for (int i = 0, l = 0; i < nr; i++) {
for (int j = 0; j < nc; j++, l++) {
coordinates[i][j] = x[l];
}
}
}
/**
* Isotonic regression.
*/
static class ObjectiveFunction implements DifferentiableMultivariateFunction {
int[] ord; /* ranks of dissimilarities */
int[] ord2; /* inverse ordering (which one is rank i?) */
int n; /* number of dissimilarities */
int nr; /* number of data points */
int nc; /* # cols of fitted configuration */
int dimx; /* Size of configuration array */
double[] d; /* dissimilarities */
double[] y; /* fitted distances (in rank of d order) */
double[] yc; /* cumulative fitted distances (in rank of d order) */
double[] yf; /* isotonic regression fitted values (ditto) */
ObjectiveFunction(int nr, int nc, double[] d, int[] ord, int[] ord2) {
this.d = d;
this.ord = ord;
this.ord2 = ord2;
this.nr = nr;
this.nc = nc;
this.n = d.length;
this.y = new double[n];
this.yf = new double[n];
this.yc = new double[n + 1];
}
void dist(double[] x) {
int index = 0;
for (int i = 0; i < nr; i++) {
for (int j = i + 1; j < nr; j++) {
double tmp = 0.0;
for (int c = 0; c < nc; c++) {
tmp += Math.sqr(x[i * nc + c] - x[j * nc + c]);
}
d[index++] = Math.sqrt(tmp);
}
}
for (index = 0; index < n; index++) {
y[index] = d[ord[index]];
}
}
@Override
public double f(double[] x) {
dist(x);
yc[0] = 0.0;
double tmp = 0.0;
for (int i = 0; i < n; i++) {
tmp += y[i];
yc[i + 1] = tmp;
}
int ip = 0;
int known = 0;
do {
double slope = 1.0e+200;
for (int i = known + 1; i <= n; i++) {
tmp = (yc[i] - yc[known]) / (i - known);
if (tmp < slope) {
slope = tmp;
ip = i;
}
}
for (int i = known; i < ip; i++) {
yf[i] = (yc[ip] - yc[known]) / (ip - known);
}
} while ((known = ip) < n);
double sstar = 0.0;
double tstar = 0.0;
for (int i = 0; i < n; i++) {
tmp = y[i] - yf[i];
sstar += tmp * tmp;
tstar += y[i] * y[i];
}
double ssq = Math.sqrt(sstar / tstar);
return ssq;
}
@Override
public double f(double[] x, double[] g) {
dist(x);
yc[0] = 0.0;
double tmp = 0.0;
for (int i = 0; i < n; i++) {
tmp += y[i];
yc[i + 1] = tmp;
}
int ip = 0;
int known = 0;
do {
double slope = 1.0e+200;
for (int i = known + 1; i <= n; i++) {
tmp = (yc[i] - yc[known]) / (i - known);
if (tmp < slope) {
slope = tmp;
ip = i;
}
}
for (int i = known; i < ip; i++) {
yf[i] = (yc[ip] - yc[known]) / (ip - known);
}
} while ((known = ip) < n);
double sstar = 0.0;
double tstar = 0.0;
for (int i = 0; i < n; i++) {
tmp = y[i] - yf[i];
sstar += tmp * tmp;
tstar += y[i] * y[i];
}
double ssq = Math.sqrt(sstar / tstar);
int k = 0;
for (int u = 0; u < nr; u++) {
for (int i = 0; i < nc; i++) {
tmp = 0.0;
for (int s = 0; s < nr; s++) {
if (s == u) {
continue;
}
if (s > u) {
k = nr * u - u * (u + 1) / 2 + s - u;
} else if (s < u) {
k = nr * s - s * (s + 1) / 2 + u - s;
}
k = ord2[k - 1];
if (k >= n) {
continue;
}
double tmp1 = (x[u * nc + i] - x[s * nc + i]);
double sgn = (tmp1 >= 0) ? 1 : -1;
tmp1 = Math.abs(tmp1) / y[k];
tmp += ((y[k] - yf[k]) / sstar - y[k] / tstar) * sgn * tmp1;
}
g[u * nc + i] = tmp * ssq;
}
}
return ssq;
}
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy