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EndmemberExtraction.java
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EndmemberExtraction.java
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/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
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;
}
}