EndmemberExtraction.java Source code

Introduction

Here is the source code for EndmemberExtraction.java

Source

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import org.apache.commons.math3.linear.ArrayRealVector;

import java.io.File;
import java.io.FileNotFoundException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Scanner;

/**
 *
 * @author Jay
 */
public class EndmemberExtraction {

    public static ArrayList<Integer> ORASIS(double[][] data, int nData, int nDim, double threshold,
            int[] exemplarLabel) {
        ArrayRealVector vec;
        ArrayList<ArrayRealVector> X = new ArrayList<>();
        ArrayList<ArrayRealVector> E = new ArrayList<>();
        ArrayList<Integer> exemplarIndex = new ArrayList<>();

        for (int i = 0; i < nData; i++) {
            vec = new ArrayRealVector(data[i]);
            vec.unitize();
            X.add(vec);
        }

        E.add(X.get(0));
        exemplarIndex.add(0);
        double t = Math.sqrt(2 * (1 - threshold));

        //Add first element of test spectra to set of exemplar spectra
        exemplarLabel[0] = 0;

        boolean flag;
        double maxCos, sigmaMin, sigmaMax, dotXR, dotER, cosTheta;

        double[] vecR = new double[nDim];
        for (int i = 0; i < nDim; i++) {
            vecR[i] = 1 / Math.sqrt(nDim);
        }
        ArrayRealVector R = new ArrayRealVector(vecR);

        ArrayRealVector exemplarSpec, testSpec;

        for (int i = 0; i < X.size(); i++) {
            if (i == 0 || exemplarLabel[i] == -1) {
                continue;
            }

            flag = false;
            maxCos = 0;
            testSpec = X.get(i);
            dotXR = testSpec.dotProduct(R);
            sigmaMin = dotXR - t;
            sigmaMax = dotXR + t;

            for (int j = 0; j < E.size(); j++) {
                exemplarSpec = E.get(j);
                dotER = exemplarSpec.dotProduct(R);

                if (dotER < sigmaMax && dotER > sigmaMin) {
                    cosTheta = testSpec.dotProduct(exemplarSpec);

                    if (cosTheta > threshold) {
                        //Test spectra is similar to one of the exemplar spectra
                        if (cosTheta > maxCos) {
                            maxCos = cosTheta;
                            exemplarLabel[i] = j;
                            //System.out.println("Count: "+i+"\texemplarLabel: "+exemplarLabel[i]);
                            flag = true;
                        }
                    }
                }
            }

            if (!flag) {
                //Test spectra is unique, add it to set of exemplars
                E.add(testSpec);
                exemplarIndex.add(i);
                exemplarLabel[i] = E.size() - 1;
                //System.out.println("Count: "+i+"\texemplarLabel: "+exemplarLabel[i]);
            }

        }
        return exemplarIndex;
    }

    public static void exemplarFrequency(int[] exemplarLabel, double curThresholdAbundance) {
        int[] exemplarFreq = new int[100];
        HashSet<Integer> uniqueExemplars = new HashSet<>();
        int totalExemplars = 0;
        for (int i = 0; i < exemplarLabel.length; i++) {
            if (exemplarLabel[i] != -1) {
                exemplarFreq[exemplarLabel[i]]++;
                totalExemplars++;
                uniqueExemplars.add(exemplarLabel[i]);
            }
        }

        int nExemplar = uniqueExemplars.size();
        double[] fracAbundance = new double[nExemplar];
        boolean[] isRejected = new boolean[nExemplar];
        for (boolean i : isRejected) {
            i = false;
        }
        //System.out.println("nExemplar="+nExemplar);

        //System.out.println("Fractional abundance:");
        for (int i = 0; i < nExemplar; i++) {
            fracAbundance[i] = (double) exemplarFreq[i] / totalExemplars;

            if (fracAbundance[i] < curThresholdAbundance) {
                isRejected[i] = true;
            }
            //System.out.println(i+" : "+(fracAbundance[i]*100));
        }
        //System.out.println();
        int count = 0;
        for (int i = 0; i < exemplarLabel.length; i++) {
            if (exemplarLabel[i] != -1) {
                if (isRejected[exemplarLabel[i]]) {
                    exemplarLabel[i] = -1;
                }
            }
        }
    }

    public static void iterativeORASIS(double[][] data, int nData, int nDim, double minThresholdAngle,
            double maxThresholdAngle, double stepsize, double minThresholdAbundance, int[] exemplarLabel) {
        int itr = 1;
        double curThresholdAngle = minThresholdAngle, curThresholdAbundance = minThresholdAbundance;
        ArrayList<Integer> exemplarIndex;

        while (curThresholdAngle <= maxThresholdAngle) {
            exemplarIndex = ORASIS(data, nData, nDim, curThresholdAngle, exemplarLabel);

            //System.out.println("Itr="+itr);
            exemplarFrequency(exemplarLabel, curThresholdAbundance);

            curThresholdAngle += stepsize;
            //curThresholdAbundance+=.005;
            itr++;
        }
    }

    public static int[][] reshape(int[] index, int imgDim1, int imgDim2) {
        int count = 0;
        int[][] classificationMat = new int[imgDim1][imgDim2];
        for (int j = 0; j < imgDim2; j++)
            for (int i = 0; i < imgDim1; i++) {
                classificationMat[i][j] = index[count];
                count++;
            }
        return classificationMat;
    }

    public static int[] assignInitialLabels(double[][] data, int nData, int nDim, int imgDim1, int imgDim2,
            double minThresholdAngle, double maxThresholdAngle, double stepSize, double minThresholdAbundance,
            String filepath) {

        int[] exemplarLabel = new int[nData];
        int[][] exemplarMat;

        iterativeORASIS(data, nData, nDim, minThresholdAngle, maxThresholdAngle, stepSize, minThresholdAbundance,
                exemplarLabel);
        HashSet<Integer> uniqueExemplars = new HashSet<>();
        for (int i = 0; i < nData; i++) {
            uniqueExemplars.add(exemplarLabel[i]);
        }

        //System.out.println("Unique exemplars:");
        HashMap<Integer, Integer> exemplars = new HashMap<>();
        int count = 0;
        for (int i : uniqueExemplars) {
            if (i != -1) {

                exemplars.put(i, count);
                //System.out.print(i+"\t");
                count++;
            }
        }
        System.out.println("Exemplar Count:" + count);
        //System.out.println();

        for (int i = 0; i < nData; i++) {
            if (exemplarLabel[i] != -1) {
                exemplarLabel[i] = exemplars.get(exemplarLabel[i]);
            }
        }
        exemplarMat = reshape(exemplarLabel, imgDim1, imgDim2);
        IO.writeData(exemplarMat, imgDim1, imgDim2, filepath + "exemplarMat.txt");

        /*
        for(int i=0;i<imgDim1;i++){
        System.out.print(i+" : ");
        for(int j=0;j<imgDim2;j++){
            System.out.print(exemplarMat[i][j]+"\t");
        }
        System.out.println();
        }
        */

        //Display exemplar classification image
        File exemplarImg = new File(filepath + "ExemplarImg.png");
        ImageProc.imagesc(exemplarImg, exemplarMat, uniqueExemplars.size(), imgDim1, imgDim2);

        return exemplarLabel;

    }
}