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TLUtils.java
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/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/*
* TLUtils.java
* A Framework for Supervised Heterogeneous Transfer Learning
* using Dynamic Distribution Adaptation and Manifold Regularization
*
* @author Md Geaur Rahman and Md Zahidul Islam
* School of Computing, Mathematics & Engineering
* Charles Sturt University, Bathurst, NSW, Australia
*/
package transferlearning;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.StringTokenizer;
import weka.classifiers.Classifier;
import weka.classifiers.trees.RandomForest;
import weka.classifiers.trees.SysFor;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ArffSaver;
import weka.core.converters.ConverterUtils;
import Jama.Matrix;
import Jama.QRDecomposition;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.Remove;
/**
*
* @author Md Geaur Rahman <gea.bau.edu.bd>
*/
public class TLUtils {
public static int findMinLeafSize(int numRecords)
{
if(numRecords<=20)
return 2;
else if(numRecords<=100)
return (int)numRecords/10;
else if(numRecords<=10000)
return (int)Math.sqrt(numRecords);
else if(numRecords<=20000)
return 150;
else if(numRecords<=40000)
return 200;
else if(numRecords<=50000)
return 300;
else if(numRecords<=75000)
return 400;
else if(numRecords<=100000)
return 500;
else if(numRecords<=150000)
return 700;
else if(numRecords<=200000)
return 800;
else if(numRecords<=400000)
return 900;
else if(numRecords<=500000)
return 1000;
else
return 2000;
}
public static double calculateAccuracy(String modelFile,String testFile)
{
double acc=0;
try{
Classifier model = loadModel(modelFile);
if(model!=null)
acc=calculateAccuracy(model,testFile);
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return acc;
}
public static double calculateAccuracy(Classifier model, String testFile)
{
double acc=0;
try{
ConverterUtils.DataSource source=new ConverterUtils.DataSource(testFile);
Instances testdata=source.getDataSet();
acc=calculateAccuracy(model,testdata);
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return acc;
}
public static double calculateAccuracy(Classifier model,Instances testdata)
{
double acc=0;
try{
testdata.setClassIndex(testdata.numAttributes()-1);
int total=testdata.numInstances();
int correct=0;
for (int j=0;j<testdata.numInstances();j++){
double actualClass = testdata.instance(j).classValue();
String actual = testdata.classAttribute().value((int) actualClass);
Instance newInst = testdata.instance(j);
double preNB = model.classifyInstance(newInst);
String predString = testdata.classAttribute().value((int) preNB);
if(actual.equals(predString))correct++;
}
acc=100.0*correct/total;
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return acc;
}
public static Classifier loadModel(String modelFile)
{
Classifier model=null;
try{
ObjectInputStream ois = new ObjectInputStream(new FileInputStream(modelFile));
model = (Classifier) ois.readObject();
ois.close();
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return model;
}
public static void saveModel(Classifier model,String modelFile)
{
try{
ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(modelFile));
oos.writeObject(model);
oos.flush();
oos.close();
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
}
public static void writeARRF(Instances data, String file)
{
try{
ArffSaver saver = new ArffSaver();
saver.setInstances(data);
saver.setFile(new File(file));
saver.writeBatch();
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
}
public static Classifier buildSingleForest(String trainFile, int minRecords)
{
Classifier cls=null;
try{
ConverterUtils.DataSource source=new ConverterUtils.DataSource(trainFile);
Instances dataset=source.getDataSet();
cls=buildClassifier(dataset,1,minRecords,0);
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return cls;
}
public static Classifier buildSingleForest(String trainFile, int minRecords, int method)
{
Classifier cls=null;
try{
ConverterUtils.DataSource source=new ConverterUtils.DataSource(trainFile);
Instances dataset=source.getDataSet();
cls=buildClassifier(dataset,1,minRecords,method);
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return cls;
}
public static void buildSingleForest(String trainFile, int minRecords, String modelFile, int method)
{
try{
ConverterUtils.DataSource source=new ConverterUtils.DataSource(trainFile);
Instances dataset=source.getDataSet();
Classifier cls=buildClassifier(dataset,1,minRecords,method);
saveModel(cls,modelFile);
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
}
public static void buildSingleForest(String trainFile, int minRecords, String modelFile)
{
buildSingleForest(trainFile,minRecords,modelFile,0);
}
public static void buildClassifier(String trainFile, int numTrees, int minRecords, String modelFile)
{
buildClassifier(trainFile,numTrees,minRecords,modelFile,0);
}
public static void buildClassifier(String trainFile, int numTrees, int minRecords,
String modelFile, int method)
{
Classifier cls=null;
try{
ConverterUtils.DataSource source=new ConverterUtils.DataSource(trainFile);
Instances dataset=source.getDataSet();
cls=buildClassifier(dataset,numTrees,minRecords,method);
saveModel(cls,modelFile);
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
}
public static Classifier buildClassifier(String trainFile, int numTrees, int minRecords)
{
return buildClassifier(trainFile,numTrees,minRecords,0);
}
public static Classifier buildClassifier(String trainFile, int numTrees, int minRecords, int method)
{
Classifier cls=null;
try{
ConverterUtils.DataSource source=new ConverterUtils.DataSource(trainFile);
Instances dataset=source.getDataSet();
cls=buildClassifier(dataset,numTrees,minRecords,method);
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return cls;
}
public static Classifier buildClassifier(Instances dataset, int numTrees, int minRecords, int method)
{
Classifier cls=null;
try{
dataset.setClassIndex(dataset.numAttributes()-1);
if(method==1)//SysFor
{
SysFor sysFor=new SysFor();
sysFor.setMinRecLeaf(minRecords);
sysFor.setNumberTrees(numTrees);
sysFor.setGoodness(0.3f);
sysFor.setConfidence(0.25f);
sysFor.setSeparation(0.3f);
sysFor.buildClassifier(dataset);
// String tree=sysFor.toString();
cls=(Classifier) sysFor;
// System.out.println(tree);
}
else //RandomForest
{
String []options={"-P","100","-print","-I","1","-num-slots","1",
"-K","0","-M","2","-V","0.001","-S","1"};
options[4] = numTrees+"";
options[10] = minRecords+"";
RandomForest rf=new RandomForest();
rf.setOptions(options);
rf.buildClassifier(dataset);
cls=(Classifier) rf;
}
}
catch(Exception e)
{
System.out.println("Error: "+e);
}
return cls;
}
public static String majorityClassValue(Instances allData)
{
String []classNames = new String[allData.numClasses()];
for (int i = 0; i < allData.numClasses(); i++) {
classNames[i] = allData.classAttribute().value(i);
}
int[] classCounts=new int[allData.numClasses()];
for(Instance ins: allData)
classCounts[(int)(ins.classValue())]++;
int index=0;
for (int i = 1; i < allData.numClasses(); i++) {
if(classCounts[i]>classCounts[index]){
index=i;
}
}
return classNames[index];
}
/** Take a certain percentage of a set of instances.
* @param instances
* @param percentage
* @return a reduced set of instances according to the given percentage
*/
public static Instances trimInstances(Instances instances, double percentage) {
int numInstancesToKeep = (int) Math.ceil(instances.numInstances() * percentage);
return trimInstances(instances, numInstancesToKeep);
}
/** Take a certain number of a set of instances.
* @param instances
* @param numInstances the number of instances to keep
* @return a reduced set of instances according to the given number to keep
*/
public static Instances trimInstances(Instances instances, int numInstances) {
Instances trimmedInstances = new Instances(instances);
for (int i = trimmedInstances.numInstances() - 1; i >= numInstances; i--) {
trimmedInstances.delete(i);
}
return trimmedInstances;
}
/** Extract a particular subset of the instances.
* @param instances
* @param startIdx the start instance index
* @param numInstancesToRetrieve the number of instances to retrieve
* @return the specified subset of the instances.
*/
public static Instances subsetInstances(Instances instances, int startIdx, int numInstancesToRetrieve) {
double possibleNumInstancesToRetrieve = instances.numInstances()-startIdx;
if (numInstancesToRetrieve > possibleNumInstancesToRetrieve) {
throw new IllegalArgumentException("Cannot retrieve more than "+possibleNumInstancesToRetrieve+" instances.");
}
int endIdx = startIdx + numInstancesToRetrieve - 1;
// delete all instance indices outside of [startIdx, endIdx]
Instances subset = new Instances(instances);
for (int i=subset.numInstances()-1; i>= 0; i--) {
if (i < startIdx || i > endIdx)
subset.delete(i);
}
return subset;
}
/** Merge two instance sets.
* @param instances1
* @param instances2
* @return the merged instance sets
*/
public static Instances mergeInstances(Instances instances1, Instances instances2) {
if(instances1 == null)
return instances2;
if(instances2 == null)
return instances1;
if (!instances1.checkInstance(instances2.firstInstance()))
throw new IllegalArgumentException("The instance sets are incompatible.");
Instances mergedInstances = new Instances(instances1);
Instances tempInstances = new Instances(instances2);
for (int i = 0; i < tempInstances.numInstances(); i++) {
mergedInstances.add(tempInstances.instance(i));
}
return mergedInstances;
}
/** check domain values of projected data and test data.
* @param instances1
* @param instances2
* @return the compitable test data
*/
public static Instances checkProjectedAndTestData(Instances proj, Instances test) {
if(proj == null)
return test;
String relation=test.relationName();
int numAttr=test.numAttributes();
ArrayList<Attribute> testAtts= new ArrayList<Attribute>();
ArrayList<String> []testVals=new ArrayList[numAttr];
for(int i=0;i<numAttr;i++)
{
if(test.attribute(i).isNominal())
{
testVals[i] = new ArrayList<String>();
int numv=test.attribute(i).numValues();
for(int k=0;k<numv;k++)
testVals[i].add(test.attribute(i).value(k));
numv=proj.attribute(i).numValues();
for(int k=0;k<numv;k++)
{
String dv=proj.attribute(i).value(k);
if(!testVals[i].contains(dv))
testVals[i].add(dv);
}
testAtts.add(new Attribute(test.attribute(i).name(),testVals[i]));
}
else{
testAtts.add(new Attribute(test.attribute(i).name()));
}
}
int t=test.numInstances();
double [][]alldata=new double[t][numAttr];
//src data
for(int i=0;i<t;i++)
{
Instance inst=test.get(i);
for(int j=0;j<numAttr;j++)
{
if(test.attribute(j).isNominal())
{
double v=inst.value(j);
String tv=test.attribute(j).value((int)v);
alldata[i][j]=testVals[j].indexOf(tv);
}
else
{
alldata[i][j]=inst.value(j);
}
}
}
Instances checkTestInstances = new Instances(relation, testAtts, 0);
for(int i=0;i<t;i++)
{
checkTestInstances.add(new DenseInstance(1.0, alldata[i]));
}
checkTestInstances.setClassIndex(test.classIndex());
return checkTestInstances;
}
/** Merge two instance sets of two similar (not same) domains,
* but they have the same attributes.
* @param src
* @param tgt
* @return the merged instance sets
*/
public static Instances mergeInstancesTL(Instances src, Instances tgt) {
if(src == null)
return tgt;
if(tgt == null)
return src;
String relation=src.relationName();
int numAttr=src.numAttributes();
ArrayList<Attribute> testAtts= new ArrayList<Attribute>();
ArrayList<String> []testVals=new ArrayList[numAttr];
for(int i=0;i<numAttr;i++)
{
if(src.attribute(i).isNominal())
{
testVals[i] = new ArrayList<String>();
int numv=src.attribute(i).numValues();
for(int k=0;k<numv;k++)
testVals[i].add(src.attribute(i).value(k));
numv=tgt.attribute(i).numValues();
for(int k=0;k<numv;k++)
{
String dv=tgt.attribute(i).value(k);
if(!testVals[i].contains(dv))
testVals[i].add(dv);
}
testAtts.add(new Attribute(src.attribute(i).name(),testVals[i]));
}
else{
testAtts.add(new Attribute(src.attribute(i).name()));
}
}
int s=src.numInstances();
int t=tgt.numInstances();
int total=s+t;
double [][]alldata=new double[total][numAttr];
//src data
for(int i=0;i<s;i++)
{
Instance inst=src.get(i);
for(int j=0;j<numAttr;j++)
{
if(src.attribute(j).isNominal())
{
double v=inst.value(j);
String tv=src.attribute(j).value((int)v);
alldata[i][j]=testVals[j].indexOf(tv);
}
else
{
alldata[i][j]=inst.value(j);
}
}
}
//merging data
for(int i=0,r=s;i<t && r<total;i++,r++)
{
Instance inst=tgt.get(i);
for(int j=0;j<numAttr;j++)
{
if(src.attribute(j).isNominal())
{
double v=inst.value(j);
String tv=tgt.attribute(j).value((int)v);
alldata[r][j]=testVals[j].indexOf(tv);
}
else
{
alldata[r][j]=inst.value(j);
}
}
}
Instances mergedInstances = new Instances(relation, testAtts, 0);
for(int i=0;i<total;i++)
{
mergedInstances.add(new DenseInstance(1.0, alldata[i]));
}
return mergedInstances;
}
/** change the class values with cv.
* @param instances
* @param cv
* @return the changed instance sets
*/
public static Instances changeClassValue(Instances instances, String cv) {
String relation=instances.relationName();
int numAttr=instances.numAttributes();
ArrayList<Attribute> testAtts= new ArrayList<Attribute>();
ArrayList<String> []testVals=new ArrayList[numAttr];
for(int i=0;i<numAttr;i++)
{
if(instances.attribute(i).isNominal())
{
testVals[i] = new ArrayList<String>();
if(i==instances.classIndex())
{
testVals[i].add(cv);
}
else{
int numv=instances.attribute(i).numValues();
for(int k=0;k<numv;k++)
testVals[i].add(instances.attribute(i).value(k));
}
testAtts.add(new Attribute(instances.attribute(i).name(),testVals[i]));
}
else{
testAtts.add(new Attribute(instances.attribute(i).name()));
}
}
int s=instances.numInstances();
double [][]alldata=new double[s][numAttr];
//src data
for(int i=0;i<s;i++)
{
Instance inst=instances.get(i);
for(int j=0;j<numAttr;j++)
{
if(instances.attribute(j).isNominal())
{
if(j==instances.classIndex())
{
alldata[i][j]=testVals[j].indexOf(cv);
}
else{
double v=inst.value(j);
String tv=instances.attribute(j).value((int)v);
alldata[i][j]=testVals[j].indexOf(tv);
}
}
else
{
alldata[i][j]=inst.value(j);
}
}
}
Instances newInstances = new Instances(relation, testAtts, 0);
for(int i=0;i<s;i++)
{
newInstances.add(new DenseInstance(1.0, alldata[i]));
}
return newInstances;
}
/** remove attributes.
* @param instances
* @param indices
* @return the final instance sets
*/
public static Instances removeAttributes(Instances instances, String indices) throws Exception{
Remove remove = new Remove();
remove.setAttributeIndices(indices);
remove.setInputFormat(instances);
Instances instNew = Filter.useFilter(instances, remove);
return instNew;
}
/** remove an attribute.
* @param instances
* @param comAttr
* @param index
* @return the final instance sets
*/
public static Instances removeAttributes(Instances instances, int comAttr[][],int index){
String indices="";
boolean first=true;
for(int k=0;k<instances.numAttributes();k++)
{
boolean found=false;
for(int i=0;i<comAttr.length;i++)
{
if(k==comAttr[i][index])
{
found=true;break;
}
}
if(!found)
{
if(first)
{
indices=(k+1)+"";
first=false;
}
else{
indices=","+(k+1);
}
}
}
Instances instNew=null;
try{
instNew =removeAttributes(instances, indices);
}
catch(Exception e)
{
}
return instNew;
}
/**
* Converts an instance to a feature vector excluding the class attribute.
* @param instance The instance.
* @return A vector representation of the instance excluding the class attribute
*/
public static double[] instanceToDoubleArray(Instance instance) {
double[] vector = new double[(instance.classIndex() != -1) ? instance.numAttributes() - 1 : instance.numAttributes()];
double[] instanceDoubleArray = instance.toDoubleArray();
int attIdx = 0;
for (int i = 0; i < vector.length; i++) {
if (i == instance.classIndex()) {
attIdx++;
}
vector[i] = instanceDoubleArray[attIdx++];
}
return vector;
}
/**
* Converts a set of instances to an array of vectors
* @param instances The set of instances.
* @return The array of feature vectors.
*/
public static double[][] instancesToDoubleArrays(Instances instances) {
double[][] vectors = new double[instances.numInstances()][];
for (int instIdx = 0; instIdx < instances.numInstances(); instIdx++) {
vectors[instIdx] = instanceToDoubleArray(instances.instance(instIdx));
}
return vectors;
}
public static Instances doubleArraysToInstances(Instances instances, double [][]values)
{
Instances data=new Instances(instances,0);
for(int i=0;i<values.length;i++)
{
data.add(new DenseInstance(1.0, values[i]));
}
return data;
}
public static String preprocessTree(String treeStr)
{
String []rules=treeStr.split("\n");
String rec="";int t=0;
for(int i=0;i<rules.length;i++)
{
if(rules[i].equals("RandomTree"))
{
t++;
rec=rec+"Tree "+t+":\n";i++;
while(i<rules.length)
{
if(rules[i].equals("RandomTree"))
{
i--;break;
}
else
{
int f=0;
if(rules[i].length()>16)
{
if(rules[i].substring(0, 16).equals("Size of the tree"))f=1;
}
if(f==0&&!rules[i].equals("") && !rules[i].equals("=========="))
{
rec=rec+rules[i]+"\n";
}
i++;
}
}
}
}
return rec;
}
public static String processConditionPart(String con)
{
String var=con.substring(con.indexOf(":")+1, con.indexOf("]"));
int l=con.indexOf("]")+1;
String op=con.substring(l, l+3).trim();
String val=con.substring(con.indexOf(op)+op.length(), con.length()-1).trim();
if(val.contains(":")){
val=val.substring(val.indexOf(":")+1, val.indexOf("}"));
}
return var+" "+op+" "+val;
}
public static String processLeafPart(String con)
{
String rec=con.substring(con.indexOf("<")+1, con.indexOf(">"));
return rec.substring(rec.indexOf(":")+1);
}
//Find the number of conditions of each trees
public static String[] processTrees(String []trees, int []Loc)
{
int numTree=Loc.length;
for(int i=0;i<numTree;i++)
{
Loc[i]=0;
}
int count=0;
int pos=0;
int tlines=trees.length;
ArrayList<String> Conditions=new ArrayList<String>();
for(int i=0;i<tlines;i++)
{
if(!trees[i].equals("")){
if(trees[i].startsWith("Tree"))
{
Loc[count]=pos;
count++;
}
else
{
String []cac=separateConditionClass(trees[i]);
if(!cac[1].isEmpty())
{
StringTokenizer tokenizer= new StringTokenizer(cac[1], " {}():\t\n\r\f");
if(tokenizer.hasMoreTokens()) cac[1]=tokenizer.nextToken();
cac[0]=cac[0].trim()+": "+cac[1];
}
Conditions.add(cac[0]);
pos++;
}
}
}
return Conditions.toArray(new String[0]);
}
//count the number of trees
public static int countTrees(String []trees)
{
int count=0;
int tlines=trees.length;
for(int i=0;i<tlines;i++)
{
if(!trees[i].equals("") && trees[i].length()>4)
{
if(trees[i].substring(0, 4).equals("Tree"))
{
count++;
}
}
}
return count;
}
public static boolean isConditionALeaf(String condition)
{
return condition.contains(":");
}
public static String [] separateConditionClass(String condition)
{
String []cac=new String[2];
cac[0]=condition;
cac[1]="";
if(isConditionALeaf(condition))
{
cac[1]=condition.substring(condition.indexOf(":")+1, condition.length());
cac[1]=cac[1].trim();
cac[0]=cac[0].substring(0, cac[0].indexOf(":"));
cac[0]=cac[0].trim();
}
return cac;
}
public static String[]convertStringToArray(String strVal)
{
ArrayList<String> arrVal=new ArrayList<String>();
StringTokenizer tokenizer= new StringTokenizer(strVal, " :\t\n\r\f");
int n=tokenizer.countTokens();
for(int i=0;i<n;i++)
{
arrVal.add(tokenizer.nextToken());
}
return arrVal.toArray(new String[0]);
}
public static int findAttrType(String currentAttr, Instances dataset)
{
if(dataset.attribute(currentAttr).isNumeric())
return 1;
else
return 0;
}
//find shared contribution
public static int[][]findPivots(double [][]JSD, double similarityThreshold)
{
ArrayList<Integer> sourcePivot=new ArrayList<Integer>();
ArrayList<Integer> targetPivot=new ArrayList<Integer>();
for(int i=0;i<JSD.length;i++)
{
int p=-1;
for(int j=0;j<JSD[i].length;j++)
{
if(JSD[i][j]>=similarityThreshold)
{
if(p==-1)
p=j;
else if(JSD[i][j]>JSD[i][p])
p=j;
}
}
if(p>=0){
if(!sourcePivot.contains(i)&& !targetPivot.contains(p)){
sourcePivot.add(i);
targetPivot.add(p);
}
}
}
int np=sourcePivot.size();
int [][]pivots=new int[np][2];
for(int i=0;i<np;i++)
{
pivots[i][0]=sourcePivot.get(i);
pivots[i][1]=targetPivot.get(i);
}
return pivots.clone();
}
//find pivot attribute contribution
public static double [][]findAttributeContribution(double [][]contribution,int [][]pivots, int domain)
{
int np= pivots.length;
int noa=contribution[0].length;
double [][]st=new double[np][noa];
for(int i=0;i<np;i++)
{
int r=pivots[i][domain];
for(int c=0;c<noa;c++)
{
st[i][c]=contribution[r][c];
}
}
return st.clone();
}
//find pivot attribute contribution
public static String [][]findAttributeContribution(String [][]contribution,int [][]pivots, int domain)
{
int np= pivots.length;
int noa=contribution[0].length-1;
String [][]st=new String[np][noa];
for(int i=0;i<np;i++)
{
int r=pivots[i][domain];
for(int c=0;c<noa;c++)
{
st[i][c]=contribution[r][c];
}
}
return st.clone();
}
public static void displayProjection(double[][]projection)
{
System.out.println("\nProjection:");
for (int j = 0; j < projection.length; j++) {
for(int t=0; t<projection[j].length;t++)
{
if(t==projection[j].length-1)
System.out.println(projection[j][t]);
else
System.out.print(projection[j][t]+", ");
}
}
}
public static void display(int[][]projection, String msg)
{
System.out.println("\n"+msg+":");
for (int j = 0; j < projection.length; j++) {
for(int t=0; t<projection[j].length;t++)
{
if(t==projection[j].length-1)
System.out.println(projection[j][t]);
else
System.out.print(projection[j][t]+", ");
}
}
}
public static void display(String[][]projection, String msg)
{
System.out.println("\n"+msg+":");
for (int j = 0; j < projection.length; j++) {
for(int t=0; t<projection[j].length;t++)
{
if(t==projection[j].length-1)
System.out.println(projection[j][t]);
else
System.out.print(projection[j][t]+", ");
}
}
}
public static void display(double[][]projection, String msg)
{
System.out.println("\n"+msg+":");
for (int j = 0; j < projection.length; j++) {
for(int t=0; t<projection[j].length;t++)
{
if(t==projection[j].length-1)
System.out.println(projection[j][t]);
else
System.out.print(projection[j][t]+", ");
}
}
}
public static void display(double[][]centroids, int k)
{
System.out.println("\nCentrods:");
for (int j = 0; j < k; j++) {
double []x=centroids[j];
for(int t=0; t<x.length;t++)
{
if(t==x.length-1)
System.out.println(x[t]);
else
System.out.print(x[t]+", ");
}
}
}
public static double[][] normalize(double[][] arr)
{
int row=arr.length;
int col=arr[0].length;
for (int j = 0; j < col; j++) {
double min = Double.MAX_VALUE;
double max = Double.MIN_VALUE;
for (int i = 0; i < row; i++) {
max = Math.max(arr[i][j],max);
min = Math.min(arr[i][j],min);
}
for (int i = 0; i < row; i++) {
arr[i][j] = (arr[i][j] - min)/(max-min);
}
}