classifier/ClassifierTS.java at master · TigranI/classifier · GitHub

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package classifier;

import java.util.ArrayList;

import java.util.HashSet;

import java.util.List;

import java.util.Map;

import java.util.Random;

import java.util.Set;

import classifier.PrimalSvmTree.TreeModel;

public class ClassifierTS {

int N;

int dim;

double[][] X; // training data

int[] Y; // +1, -1

double[][] Xt; // testing data

int[] Yt; // +1, -1

int Nplus;

int Nminus;

int majority;

int maxPoints;

int maxDims;

BinaryModel binaryModel = null;

public ClassifierTS(double[][] X, int[] Y, double[][] Xt, int[] Yt) {

this.X = X;

this.Y = Y;

this.Xt = Xt;

this.Yt = Yt;

N = X.length;

Nplus = 0;

Nminus = 0;

dim = X[0].length;

maxDims = 10;

maxPoints = 1000;

buildModel();

testModel();

}

public void buildModel() {

for(int j=0;j<N;j++) {

if(Y[j] == 1) {

Nplus++;

} else {

Nminus++;

}

if(Nplus >= Nminus) {

majority = 1;

} else {

majority = -1;

}

MdsData mdsData = null;

ScaleData scaleData = null;

if(dim > maxDims) {

Lmds lmds = new Lmds(X, maxDims);

X = lmds.getResultSpace();

mdsData = new MdsData(lmds.getLandmarks(), lmds.getMatrix(), lmds.getMeans());

}

dim = X[0].length;

scaleData = new ScaleData(scale());

TreeSvmModel treeSvmModel = decompose();

binaryModel = new BinaryModel(majority, mdsData, scaleData, treeSvmModel);

//binaryModel.print();

}

public void testModel() {

int Nt = Yt.length;

TreeSvmModel treeSvmModel = binaryModel.getTreeModel();

MdsData mdsData = binaryModel.getMdsData();

ScaleData scaleData = binaryModel.getScaleData();

if(mdsData != null) {

System.out.println("MDS transforming ...");

}

if(scaleData != null) {

System.out.println("Scale transforming ...");

}

int majority = binaryModel.getMajority();

int[] Ysvm = new int[Nt];

double testError = 0;

for(int i=0;i<Nt;i++) {

if(mdsData != null) {

Xt[i] = mdsData.applyToPoint(Xt[i]);

}

if(scaleData != null) {

Xt[i] = scaleData.applyToPoint(Xt[i]);

}

double value = treeSvmModel.applyToPoint(Xt[i]);

if(value != Double.NEGATIVE_INFINITY) {

Ysvm[i] = (int) value;

} else {

Ysvm[i] = majority;

}

if(Ysvm[i] * Yt[i] <= 0) {

testError += 1;

}

testError /= Nt;

System.out.println("Test error = " + testError);

}

public TreeSvmModel decompose() {

List<Integer> finalPoints = new ArrayList<Integer>();

Set<Integer> finalSet = new HashSet<Integer>();

List<Integer> plus = new ArrayList<Integer>();

List<Integer> minus = new ArrayList<Integer>();

for(int j=0;j<N;j++) {

if(Y[j] == 1) {

plus.add(j);

} else {

minus.add(j);

}

double plusRatio = Nplus / (double) N;

//double minusRatio = Nminus / (double) N;

int totalGroups = (int) Math.ceil(N / (double) maxPoints);

int groupSize = Math.min(N, maxPoints);

int plusSize = (int) (Math.round(plusRatio * groupSize));

int minusSize = groupSize - plusSize;

int numGroups = 0;

int iplus = 0;

int iminus = 0;

while(numGroups < totalGroups) {

List<Integer> subset = new ArrayList<Integer>();

int numplus = 0;

int numminus = 0;

while(numplus < plusSize && iplus < plus.size()) {

subset.add(plus.get(iplus));

iplus++;

numplus++;

}

while(numminus < minusSize && iminus < minus.size()) {

subset.add(minus.get(iminus));

iminus++;

numminus++;

}

if(subset.size() < groupSize && numGroups == totalGroups - 1 && totalGroups > 1) {

iplus = 0;

iminus = 0;

while(numplus < plusSize && iplus < plus.size()) {

subset.add(plus.get(iplus));

iplus++;

numplus++;

}

while(numminus < minusSize && iminus < minus.size()) {

subset.add(minus.get(iminus));

iminus++;

numminus++;

}

int M = subset.size();

double[][] subsetX = new double[M][dim+1];

int[] subsetY = new int[M];

int numSV = Math.min(N, (int) (maxPoints / (double) totalGroups));

for(int i=0;i<M;i++) {

for(int j=0;j<dim;j++) {

subsetX[i][j] = X[subset.get(i)][j];

subsetY[i] = Y[subset.get(i)];

}

subsetX[i][dim] = 1.0; // b for svm

}

System.out.println("computing model for group " + numGroups + " out of " + totalGroups + " : " + M + " " + numSV + " " + plusRatio + " " +

subsetX.length + " " + subsetY.length);

PrimalSvmTree svmtree = new PrimalSvmTree(subsetX, subsetY, numSV);

TreeModel treemodel = svmtree.getModel();

for(int index : treemodel.getSVs()) {

finalSet.add(subset.get(index));

}

numGroups++;

}

// compute final model

int T = Math.min(N, maxPoints);

int index = 0;

while(finalSet.size() < T) {

finalSet.add(index);

index++;

}

for(int pt : finalSet) {

finalPoints.add(pt);

}

int M = finalPoints.size();

double[][] subsetX = new double[M][dim+1];

int[] subsetY = new int[M];

int numSV = 0;

for(int i=0;i<M;i++) {

for(int j=0;j<dim;j++) {

subsetX[i][j] = X[finalPoints.get(i)][j];

subsetY[i] = Y[finalPoints.get(i)];

}

subsetX[i][dim] = 1.0; // b for svm

}

System.out.println("computing final model : " + M);

PrimalSvmTree svmtree = new PrimalSvmTree(subsetX, subsetY, numSV);

TreeModel finalTreeModel = svmtree.getModel(); // full model

TreeSvmModel treeSvmModel = new TreeSvmModel(finalTreeModel); // only takes ws

return treeSvmModel;

}

// scales X, returns scaling coefficients for the classification model

public double[][] scale() {

double[][] range = new double[dim][2];

for(int i=0;i<dim;i++) {

range[i][0] = Double.POSITIVE_INFINITY;

range[i][1] = Double.NEGATIVE_INFINITY;

}

for(int i=0;i<dim;i++) {

for(int j=0;j<N;j++) {

range[i][0] = (range[i][0] < X[j][i]) ? range[i][0] : X[j][i];

range[i][1] = (range[i][1] > X[j][i]) ? range[i][1] : X[j][i];

}

double[][] coefs = new double[dim][2];

for(int i=0;i<dim;i++) {

double a = range[i][0];

double b = range[i][1];

if(b > a) {

coefs[i][0] = 2.0 / (b - a);

coefs[i][1] = (a + b)/(a - b);

} else {

coefs[i][0] = 0.0;

coefs[i][1] = 0.0;

}

for(int j=0;j<N;j++) {

for(int i=0;i<dim;i++) {

X[j][i] = coefs[i][0] * X[j][i] + coefs[i][1];

}

return coefs;

}

class BinaryModel {

int majority;

MdsData mdsData;

ScaleData scaleData;

TreeSvmModel treeSvmModel;

public BinaryModel(int majority, MdsData mdsData, ScaleData scaleData, TreeSvmModel treeSvmModel) {

this.majority = majority;

this.mdsData = mdsData;

this.scaleData = scaleData;

this.treeSvmModel = treeSvmModel;

}

public int getMajority() {

return majority;

}

public MdsData getMdsData() {

return mdsData;

}

public ScaleData getScaleData() {

return scaleData;

}

public TreeSvmModel getTreeModel() {

return treeSvmModel;

}

public void print() {

Map<String, double[]> ws = treeSvmModel.getWs();

for(String address : ws.keySet()) {

System.out.println(address + " : " + ws.get(address)[0]);

}

class MdsData {

double[][] landmarks;

double[][] L;

double[] lmeans;

public MdsData(double[][] landmarks, double[][] L, double[] lmeans) {

this.landmarks = landmarks;

this.L = L;

this.lmeans = lmeans;

}

public double[][] getLandmarks() {

return landmarks;

}

public double[][] getL() {

return L;

}

public double[] getLmeans() {

return lmeans;

}

public double[] applyToPoint(double[] x) {

int k = landmarks.length;

double[] z = new double[k];

for(int j=0;j<k;j++) {

z[j] = lmeans[j] - sqL2_dist(x, landmarks[j]);

}

double[] w = mult(mult(z, L), 0.5);

return w;

}

double sqL2_dist(double[] x, double[] y) {

double dist = 0;

for(int i=0;i<x.length;i++)

dist += (x[i] - y[i]) * (x[i] - y[i]);

return dist;

// return Math.sqrt(dist);

}

// v * A

public double[] mult(double[] v, double[][] A) {

int N = A[0].length;

int K = v.length;

double[] w = new double[N];

for(int i=0;i<N;i++) {

for(int j=0;j<K;j++) {

w[i] += A[j][i] * v[j];

}

return w;

}

// v * A

public double[] mult(double[] v, double a) {

int K = v.length;

double[] w = new double[K];

for(int j=0;j<K;j++) {

w[j] = a * v[j];

}

return w;

}

class ScaleData {

double[][] coefs; // coefficients of linear scaling to [-1, 1] in each dimension

public ScaleData(double[][] coefs) {

this.coefs = coefs;

}

public double[][] getScalingCoefs() {

return coefs;

}

public double[] applyToPoint(double[] x) {

double[] sx = new double[x.length];

for(int i=0;i<x.length;i++) {

sx[i] = coefs[i][0] * x[i] + coefs[i][1];

}

return sx;

}

class TreeSvmModel {

Map<String, double[]> ws;

public TreeSvmModel(TreeModel treemodel) {

ws = treemodel.getAllW();

}

public Map<String, double[]> getWs() {

return ws;

}

public double applyToPoint(double[] x) {

double value = Double.NEGATIVE_INFINITY;

String address = "r";

boolean flag = true;

while(flag) {

if(ws.containsKey(address)) {

double[] w = ws.get(address);

value = Math.signum(f(w, x));

if(value >= 0) {

address += "+";

} else {

address += "-";

}

else {

flag = false;

}

return value;

}

public double f(double[] w, double[] x) {

double value = dot(w, x);

return value;

}

public double dot(double[] v, double[] w) {

double value = 0;

int len = Math.min(v.length, w.length);

for(int i=0;i<len;i++) {

value += v[i] * w[i];

}

if(v.length > len) {

value += v[len];

}

if(w.length > len) {

value += w[len];

}

return value;

}

public static void main(String[] args) {

int N = 800;

int dim = 2;

double[][] X = new double[N][dim];

int[] Y = new int[N];

double[][] Xt = new double[N][dim];

int[] Yt = new int[N];

double a = Math.sqrt(Math.PI / 2.0);

Random rand = new Random(0);

for(int i=0;i<N;i++) {

for(int j=0;j<dim;j++) {

X[i][j] = 2 * a * rand.nextDouble() - a;

}

if(X[i][0] * X[i][0] + X[i][1] * X[i][1] < 1) {

Y[i] = -1;

}

else {

Y[i] = 1;

}

for(int i=0;i<N;i++) {

for(int j=0;j<dim;j++) {

Xt[i][j] = 2 * a * rand.nextDouble() - a;

}

if(Xt[i][0] * Xt[i][0] + Xt[i][1] * Xt[i][1] < 1) {

Yt[i] = -1;

}

else

Yt[i] = 1;

}

ClassifierTS cts = new ClassifierTS(X, Y, Xt, Yt);

}

classifier/ClassifierTS.java at master · TigranI/classifier · GitHub

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