annotate src/samer/mds/CovarianceTask.java @ 8:5e3cbbf173aa tip

Reorganise some more
author samer
date Fri, 05 Apr 2019 22:41:58 +0100
parents bf79fb79ee13
children
rev   line source
samer@0 1 package samer.mds;
samer@0 2
samer@0 3 // import samer.core.*;
samer@0 4 import samer.maths.*;
samer@0 5 import samer.tools.*;
samer@0 6
samer@0 7 /**
samer@0 8 Transfer covariance matrix to MDS distances
samer@0 9 Assumes that elements are not normalised to unit variance
samer@0 10 */
samer@0 11 public class CovarianceTask extends AnonymousTask
samer@0 12 {
samer@0 13 int N;
samer@0 14 double [] d; // linear array of distances
samer@0 15 double [][] _C; // matrix of covariances
samer@0 16 double [] var; // array of variances
samer@0 17
samer@0 18 /** link each object to all the others using distances in matrix, returns a task
samer@0 19 that can be used to refresh distances from original matrix */
samer@0 20 public CovarianceTask(MDSBase mds, Matrix C) {
samer@0 21 N = C.getRowDimension();
samer@0 22
samer@0 23 d=new double[N*(N - 1)/2];
samer@0 24 var=new double[N];
samer@0 25 _C=C.getArray();
samer@0 26
samer@0 27 mds.clearLinks(d);
samer@0 28 for (int k=0, i=0; i<N; i++)
samer@0 29 for (int j=0; j<i; j++) mds.setLink(k++,i,j);
samer@0 30
samer@0 31 run();
samer@0 32 }
samer@0 33
samer@0 34 public Vec getDistances() { return new Vec.ForArray(d); }
samer@0 35 public void run() {
samer@0 36 for (int i=0; i<N; i++) var[i]=_C[i][i];
samer@0 37 for (int k=0, i=0; i<N; i++) {
samer@0 38 double [] Ci=_C[i];
samer@0 39 double vari=var[i];
samer@0 40 for (int j=0; j<i; j++)
samer@0 41 d[k++]=Math.sqrt(0.5*Math.log(vari*var[j]/(Ci[j]*Ci[j])));
samer@0 42 }
samer@0 43 }
samer@0 44 }