/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* MINND.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.mi;
import weka.classifiers.Classifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.AbstractClassifier;
import weka.core.DenseInstance;
/**
<!-- globalinfo-start -->
* Multiple-Instance Nearest Neighbour with Distribution learner.<br/>
* <br/>
* It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0. In order to avoid overfitting, it uses mean-square function (i.e. the Euclidean distance) to search for the weights.<br/>
* It then uses the weights to cleanse the training data. After that it searches for the weights again from the starting points of the weights searched before.<br/>
* Finally it uses the most updated weights to cleanse the test exemplar and then finds the nearest neighbour of the test exemplar using partly-weighted Kullback distance. But the variances in the Kullback distance are the ones before cleansing.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Xin Xu (2001). A nearest distribution approach to multiple-instance learning. Hamilton, NZ.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @misc{Xu2001,
* address = {Hamilton, NZ},
* author = {Xin Xu},
* note = {0657.591B},
* school = {University of Waikato},
* title = {A nearest distribution approach to multiple-instance learning},
* year = {2001}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -K <number of neighbours>
* Set number of nearest neighbour for prediction
* (default 1)</pre>
*
* <pre> -S <number of neighbours>
* Set number of nearest neighbour for cleansing the training data
* (default 1)</pre>
*
* <pre> -E <number of neighbours>
* Set number of nearest neighbour for cleansing the testing data
* (default 1)</pre>
*
<!-- options-end -->
*
* @author Xin Xu (xx5@cs.waikato.ac.nz)
* @version $Revision: 5527 $
*/
public class MINND
extends AbstractClassifier
implements OptionHandler, MultiInstanceCapabilitiesHandler,
TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -4512599203273864994L;
/** The number of nearest neighbour for prediction */
protected int m_Neighbour = 1;
/** The mean for each attribute of each exemplar */
protected double[][] m_Mean = null;
/** The variance for each attribute of each exemplar */
protected double[][] m_Variance = null;
/** The dimension of each exemplar, i.e. (numAttributes-2) */
protected int m_Dimension = 0;
/** header info of the data */
protected Instances m_Attributes;;
/** The class label of each exemplar */
protected double[] m_Class = null;
/** The number of class labels in the data */
protected int m_NumClasses = 0;
/** The weight of each exemplar */
protected double[] m_Weights = null;
/** The very small number representing zero */
static private double m_ZERO = 1.0e-45;
/** The learning rate in the gradient descent */
protected double m_Rate = -1;
/** The minimum values for numeric attributes. */
private double [] m_MinArray=null;
/** The maximum values for numeric attributes. */
private double [] m_MaxArray=null;
/** The stopping criteria of gradient descent*/
private double m_STOP = 1.0e-45;
/** The weights that alter the dimnesion of each exemplar */
private double[][] m_Change=null;
/** The noise data of each exemplar */
private double[][] m_NoiseM = null, m_NoiseV = null, m_ValidM = null,
m_ValidV = null;
/** The number of nearest neighbour instances in the selection of noises
in the training data*/
private int m_Select = 1;
/** The number of nearest neighbour exemplars in the selection of noises
in the test data */
private int m_Choose = 1;
/** The decay rate of learning rate */
private double m_Decay = 0.5;
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Multiple-Instance Nearest Neighbour with Distribution learner.\n\n"
+ "It uses gradient descent to find the weight for each dimension of "
+ "each exeamplar from the starting point of 1.0. In order to avoid "
+ "overfitting, it uses mean-square function (i.e. the Euclidean "
+ "distance) to search for the weights.\n "
+ "It then uses the weights to cleanse the training data. After that "
+ "it searches for the weights again from the starting points of the "
+ "weights searched before.\n "
+ "Finally it uses the most updated weights to cleanse the test exemplar "
+ "and then finds the nearest neighbour of the test exemplar using "
+ "partly-weighted Kullback distance. But the variances in the Kullback "
+ "distance are the ones before cleansing.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.MISC);
result.setValue(Field.AUTHOR, "Xin Xu");
result.setValue(Field.YEAR, "2001");
result.setValue(Field.TITLE, "A nearest distribution approach to multiple-instance learning");
result.setValue(Field.SCHOOL, "University of Waikato");
result.setValue(Field.ADDRESS, "Hamilton, NZ");
result.setValue(Field.NOTE, "0657.591B");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.RELATIONAL_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
// other
result.enable(Capability.ONLY_MULTIINSTANCE);
return result;
}
/**
* Returns the capabilities of this multi-instance classifier for the
* relational data.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getMultiInstanceCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.disableAllClasses();
result.enable(Capability.NO_CLASS);
return result;
}
/**
* As normal Nearest Neighbour algorithm does, it's lazy and simply
* records the exemplar information (i.e. mean and variance for each
* dimension of each exemplar and their classes) when building the model.
* There is actually no need to store the exemplars themselves.
*
* @param exs the training exemplars
* @throws Exception if the model cannot be built properly
*/
public void buildClassifier(Instances exs)throws Exception{
// can classifier handle the data?
getCapabilities().testWithFail(exs);
// remove instances with missing class
Instances newData = new Instances(exs);
newData.deleteWithMissingClass();
int numegs = newData.numInstances();
m_Dimension = newData.attribute(1).relation().numAttributes();
m_Attributes = newData.stringFreeStructure();
m_Change = new double[numegs][m_Dimension];
m_NumClasses = exs.numClasses();
m_Mean = new double[numegs][m_Dimension];
m_Variance = new double[numegs][m_Dimension];
m_Class = new double[numegs];
m_Weights = new double[numegs];
m_NoiseM = new double[numegs][m_Dimension];
m_NoiseV = new double[numegs][m_Dimension];
m_ValidM = new double[numegs][m_Dimension];
m_ValidV = new double[numegs][m_Dimension];
m_MinArray = new double[m_Dimension];
m_MaxArray = new double[m_Dimension];
for(int v=0; v < m_Dimension; v++)
m_MinArray[v] = m_MaxArray[v] = Double.NaN;
for(int w=0; w < numegs; w++){
updateMinMax(newData.instance(w));
}
// Scale exemplars
Instances data = m_Attributes;
for(int x=0; x < numegs; x++){
Instance example = newData.instance(x);
example = scale(example);
for (int i=0; i<m_Dimension; i++) {
m_Mean[x][i] = example.relationalValue(1).meanOrMode(i);
m_Variance[x][i] = example.relationalValue(1).variance(i);
if(Utils.eq(m_Variance[x][i],0.0))
m_Variance[x][i] = m_ZERO;
m_Change[x][i] = 1.0;
}
/* for(int y=0; y < m_Variance[x].length; y++){
if(Utils.eq(m_Variance[x][y],0.0))
m_Variance[x][y] = m_ZERO;
m_Change[x][y] = 1.0;
} */
data.add(example);
m_Class[x] = example.classValue();
m_Weights[x] = example.weight();
}
for(int z=0; z < numegs; z++)
findWeights(z, m_Mean);
// Pre-process and record "true estimated" parameters for distributions
for(int x=0; x < numegs; x++){
Instance example = preprocess(data, x);
if (getDebug())
System.out.println("???Exemplar "+x+" has been pre-processed:"+
data.instance(x).relationalValue(1).sumOfWeights()+
"|"+example.relationalValue(1).sumOfWeights()+
"; class:"+m_Class[x]);
if(Utils.gr(example.relationalValue(1).sumOfWeights(), 0)){
for (int i=0; i<m_Dimension; i++) {
m_ValidM[x][i] = example.relationalValue(1).meanOrMode(i);
m_ValidV[x][i] = example.relationalValue(1).variance(i);
if(Utils.eq(m_ValidV[x][i],0.0))
m_ValidV[x][i] = m_ZERO;
}
/* for(int y=0; y < m_ValidV[x].length; y++){
if(Utils.eq(m_ValidV[x][y],0.0))
m_ValidV[x][y] = m_ZERO;
}*/
}
else{
m_ValidM[x] = null;
m_ValidV[x] = null;
}
}
for(int z=0; z < numegs; z++)
if(m_ValidM[z] != null)
findWeights(z, m_ValidM);
}
/**
* Pre-process the given exemplar according to the other exemplars
* in the given exemplars. It also updates noise data statistics.
*
* @param data the whole exemplars
* @param pos the position of given exemplar in data
* @return the processed exemplar
* @throws Exception if the returned exemplar is wrong
*/
public Instance preprocess(Instances data, int pos)
throws Exception{
Instance before = data.instance(pos);
if((int)before.classValue() == 0){
m_NoiseM[pos] = null;
m_NoiseV[pos] = null;
return before;
}
Instances after_relationInsts =before.attribute(1).relation().stringFreeStructure();
Instances noises_relationInsts =before.attribute(1).relation().stringFreeStructure();
Instances newData = m_Attributes;
Instance after = new DenseInstance(before.numAttributes());
Instance noises = new DenseInstance(before.numAttributes());
after.setDataset(newData);
noises.setDataset(newData);
for(int g=0; g < before.relationalValue(1).numInstances(); g++){
Instance datum = before.relationalValue(1).instance(g);
double[] dists = new double[data.numInstances()];
for(int i=0; i < data.numInstances(); i++){
if(i != pos)
dists[i] = distance(datum, m_Mean[i], m_Variance[i], i);
else
dists[i] = Double.POSITIVE_INFINITY;
}
int[] pred = new int[m_NumClasses];
for(int n=0; n < pred.length; n++)
pred[n] = 0;
for(int o=0; o<m_Select; o++){
int index = Utils.minIndex(dists);
pred[(int)m_Class[index]]++;
dists[index] = Double.POSITIVE_INFINITY;
}
int clas = Utils.maxIndex(pred);
if((int)before.classValue() != clas)
noises_relationInsts.add(datum);
else
after_relationInsts.add(datum);
}
int relationValue;
relationValue = noises.attribute(1).addRelation( noises_relationInsts);
noises.setValue(0,before.value(0));
noises.setValue(1, relationValue);
noises.setValue(2, before.classValue());
relationValue = after.attribute(1).addRelation( after_relationInsts);
after.setValue(0,before.value(0));
after.setValue(1, relationValue);
after.setValue(2, before.classValue());
if(Utils.gr(noises.relationalValue(1).sumOfWeights(), 0)){
for (int i=0; i<m_Dimension; i++) {
m_NoiseM[pos][i] = noises.relationalValue(1).meanOrMode(i);
m_NoiseV[pos][i] = noises.relationalValue(1).variance(i);
if(Utils.eq(m_NoiseV[pos][i],0.0))
m_NoiseV[pos][i] = m_ZERO;
}
/* for(int y=0; y < m_NoiseV[pos].length; y++){
if(Utils.eq(m_NoiseV[pos][y],0.0))
m_NoiseV[pos][y] = m_ZERO;
} */
}
else{
m_NoiseM[pos] = null;
m_NoiseV[pos] = null;
}
return after;
}
/**
* Calculates the distance between two instances
*
* @param first the first instance
* @param second the second instance
* @return the distance between the two given instances
*/
private double distance(Instance first, double[] mean, double[] var, int pos) {
double diff, distance = 0;
for(int i = 0; i < m_Dimension; i++) {
// If attribute is numeric
if(first.attribute(i).isNumeric()){
if (!first.isMissing(i)){
diff = first.value(i) - mean[i];
if(Utils.gr(var[i], m_ZERO))
distance += m_Change[pos][i] * var[i] * diff * diff;
else
distance += m_Change[pos][i] * diff * diff;
}
else{
if(Utils.gr(var[i], m_ZERO))
distance += m_Change[pos][i] * var[i];
else
distance += m_Change[pos][i] * 1.0;
}
}
}
return distance;
}
/**
* Updates the minimum and maximum values for all the attributes
* based on a new exemplar.
*
* @param ex the new exemplar
*/
private void updateMinMax(Instance ex) {
Instances insts = ex.relationalValue(1);
for (int j = 0;j < m_Dimension; j++) {
if (insts.attribute(j).isNumeric()){
for(int k=0; k < insts.numInstances(); k++){
Instance ins = insts.instance(k);
if(!ins.isMissing(j)){
if (Double.isNaN(m_MinArray[j])) {
m_MinArray[j] = ins.value(j);
m_MaxArray[j] = ins.value(j);
} else {
if (ins.value(j) < m_MinArray[j])
m_MinArray[j] = ins.value(j);
else if (ins.value(j) > m_MaxArray[j])
m_MaxArray[j] = ins.value(j);
}
}
}
}
}
}
/**
* Scale the given exemplar so that the returned exemplar
* has the value of 0 to 1 for each dimension
*
* @param before the given exemplar
* @return the resultant exemplar after scaling
* @throws Exception if given exampler cannot be scaled properly
*/
private Instance scale(Instance before) throws Exception{
Instances afterInsts = before.relationalValue(1).stringFreeStructure();
Instance after = new DenseInstance(before.numAttributes());
after.setDataset(m_Attributes);
for(int i=0; i < before.relationalValue(1).numInstances(); i++){
Instance datum = before.relationalValue(1).instance(i);
Instance inst = (Instance)datum.copy();
for(int j=0; j < m_Dimension; j++){
if(before.relationalValue(1).attribute(j).isNumeric())
inst.setValue(j, (datum.value(j) - m_MinArray[j])/(m_MaxArray[j] - m_MinArray[j]));
}
afterInsts.add(inst);
}
int attValue = after.attribute(1).addRelation(afterInsts);
after.setValue(0, before.value( 0));
after.setValue(1, attValue);
after.setValue(2, before.value( 2));
return after;
}
/**
* Use gradient descent to distort the MU parameter for
* the exemplar. The exemplar can be in the specified row in the
* given matrix, which has numExemplar rows and numDimension columns;
* or not in the matrix.
*
* @param row the given row index
* @param mean
*/
public void findWeights(int row, double[][] mean){
double[] neww = new double[m_Dimension];
double[] oldw = new double[m_Dimension];
System.arraycopy(m_Change[row], 0, neww, 0, m_Dimension);
//for(int z=0; z<m_Dimension; z++)
//System.out.println("mu("+row+"): "+origin[z]+" | "+newmu[z]);
double newresult = target(neww, mean, row, m_Class);
double result = Double.POSITIVE_INFINITY;
double rate= 0.05;
if(m_Rate != -1)
rate = m_Rate;
//System.out.println("???Start searching ...");
search:
while(Utils.gr((result-newresult), m_STOP)){ // Full step
oldw = neww;
neww= new double[m_Dimension];
double[] delta = delta(oldw, mean, row, m_Class);
for(int i=0; i < m_Dimension; i++)
if(Utils.gr(m_Variance[row][i], 0.0))
neww[i] = oldw[i] + rate * delta[i];
result = newresult;
newresult = target(neww, mean, row, m_Class);
//System.out.println("???old: "+result+"|new: "+newresult);
while(Utils.gr(newresult, result)){ // Search back
//System.out.println("search back");
if(m_Rate == -1){
rate *= m_Decay; // Decay
for(int i=0; i < m_Dimension; i++)
if(Utils.gr(m_Variance[row][i], 0.0))
neww[i] = oldw[i] + rate * delta[i];
newresult = target(neww, mean, row, m_Class);
}
else{
for(int i=0; i < m_Dimension; i++)
neww[i] = oldw[i];
break search;
}
}
}
//System.out.println("???Stop");
m_Change[row] = neww;
}
/**
* Delta of x in one step of gradient descent:
* delta(Wij) = 1/2 * sum[k=1..N, k!=i](sqrt(P)*(Yi-Yk)/D - 1) * (MUij -
* MUkj)^2 where D = sqrt(sum[j=1..P]Kkj(MUij - MUkj)^2)
* N is number of exemplars and P is number of dimensions
*
* @param x the weights of the exemplar in question
* @param rowpos row index of x in X
* @param Y the observed class label
* @return the delta for all dimensions
*/
private double[] delta(double[] x, double[][] X, int rowpos, double[] Y){
double y = Y[rowpos];
double[] delta=new double[m_Dimension];
for(int h=0; h < m_Dimension; h++)
delta[h] = 0.0;
for(int i=0; i < X.length; i++){
if((i != rowpos) && (X[i] != null)){
double var = (y==Y[i]) ? 0.0 : Math.sqrt((double)m_Dimension - 1);
double distance=0;
for(int j=0; j < m_Dimension; j++)
if(Utils.gr(m_Variance[rowpos][j], 0.0))
distance += x[j]*(X[rowpos][j]-X[i][j]) * (X[rowpos][j]-X[i][j]);
distance = Math.sqrt(distance);
if(distance != 0)
for(int k=0; k < m_Dimension; k++)
if(m_Variance[rowpos][k] > 0.0)
delta[k] += (var/distance - 1.0) * 0.5 *
(X[rowpos][k]-X[i][k]) *
(X[rowpos][k]-X[i][k]);
}
}
//System.out.println("???delta: "+delta);
return delta;
}
/**
* Compute the target function to minimize in gradient descent
* The formula is:<br/>
* 1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2 <p/>
* where p is the number of exemplars and Y is the class label.
* In the case of X=MU, f() is the Euclidean distance between two
* exemplars together with the related weights and var() is
* sqrt(numDimension)*(Y-Yi) where Y-Yi is either 0 (when Y==Yi)
* or 1 (Y!=Yi)
*
* @param x the weights of the exemplar in question
* @param rowpos row index of x in X
* @param Y the observed class label
* @return the result of the target function
*/
public double target(double[] x, double[][] X, int rowpos, double[] Y){
double y = Y[rowpos], result=0;
for(int i=0; i < X.length; i++){
if((i != rowpos) && (X[i] != null)){
double var = (y==Y[i]) ? 0.0 : Math.sqrt((double)m_Dimension - 1);
double f=0;
for(int j=0; j < m_Dimension; j++)
if(Utils.gr(m_Variance[rowpos][j], 0.0)){
f += x[j]*(X[rowpos][j]-X[i][j]) * (X[rowpos][j]-X[i][j]);
//System.out.println("i:"+i+" j: "+j+" row: "+rowpos);
}
f = Math.sqrt(f);
//System.out.println("???distance between "+rowpos+" and "+i+": "+f+"|y:"+y+" vs "+Y[i]);
if(Double.isInfinite(f))
System.exit(1);
result += 0.5 * (f - var) * (f - var);
}
}
//System.out.println("???target: "+result);
return result;
}
/**
* Use Kullback Leibler distance to find the nearest neighbours of
* the given exemplar.
* It also uses K-Nearest Neighbour algorithm to classify the
* test exemplar
*
* @param ex the given test exemplar
* @return the classification
* @throws Exception if the exemplar could not be classified
* successfully
*/
public double classifyInstance(Instance ex)throws Exception{
ex = scale(ex);
double[] var = new double [m_Dimension];
for (int i=0; i<m_Dimension; i++)
var[i]= ex.relationalValue(1).variance(i);
// The Kullback distance to all exemplars
double[] kullback = new double[m_Class.length];
// The first K nearest neighbours' predictions */
double[] predict = new double[m_NumClasses];
for(int h=0; h < predict.length; h++)
predict[h] = 0;
ex = cleanse(ex);
if(ex.relationalValue(1).numInstances() == 0){
if (getDebug())
System.out.println("???Whole exemplar falls into ambiguous area!");
return 1.0; // Bias towards positive class
}
double[] mean = new double[m_Dimension];
for (int i=0; i<m_Dimension; i++)
mean [i]=ex.relationalValue(1).meanOrMode(i);
// Avoid zero sigma
for(int h=0; h < var.length; h++){
if(Utils.eq(var[h],0.0))
var[h] = m_ZERO;
}
for(int i=0; i < m_Class.length; i++){
if(m_ValidM[i] != null)
kullback[i] = kullback(mean, m_ValidM[i], var, m_Variance[i], i);
else
kullback[i] = Double.POSITIVE_INFINITY;
}
for(int j=0; j < m_Neighbour; j++){
int pos = Utils.minIndex(kullback);
predict[(int)m_Class[pos]] += m_Weights[pos];
kullback[pos] = Double.POSITIVE_INFINITY;
}
if (getDebug())
System.out.println("???There are still some unambiguous instances in this exemplar! Predicted as: "+Utils.maxIndex(predict));
return (double)Utils.maxIndex(predict);
}
/**
* Cleanse the given exemplar according to the valid and noise data
* statistics
*
* @param before the given exemplar
* @return the processed exemplar
* @throws Exception if the returned exemplar is wrong
*/
public Instance cleanse(Instance before) throws Exception{
Instances insts = before.relationalValue(1).stringFreeStructure();
Instance after = new DenseInstance (before.numAttributes());
after.setDataset(m_Attributes);
for(int g=0; g < before.relationalValue(1).numInstances(); g++){
Instance datum = before.relationalValue(1).instance(g);
double[] minNoiDists = new double[m_Choose];
double[] minValDists = new double[m_Choose];
int noiseCount = 0, validCount = 0;
double[] nDist = new double[m_Mean.length];
double[] vDist = new double[m_Mean.length];
for(int h=0; h < m_Mean.length; h++){
if(m_ValidM[h] == null)
vDist[h] = Double.POSITIVE_INFINITY;
else
vDist[h] = distance(datum, m_ValidM[h], m_ValidV[h], h);
if(m_NoiseM[h] == null)
nDist[h] = Double.POSITIVE_INFINITY;
else
nDist[h] = distance(datum, m_NoiseM[h], m_NoiseV[h], h);
}
for(int k=0; k < m_Choose; k++){
int pos = Utils.minIndex(vDist);
minValDists[k] = vDist[pos];
vDist[pos] = Double.POSITIVE_INFINITY;
pos = Utils.minIndex(nDist);
minNoiDists[k] = nDist[pos];
nDist[pos] = Double.POSITIVE_INFINITY;
}
int x = 0,y = 0;
while((x+y) < m_Choose){
if(minValDists[x] <= minNoiDists[y]){
validCount++;
x++;
}
else{
noiseCount++;
y++;
}
}
if(x >= y)
insts.add (datum);
}
after.setValue(0, before.value( 0));
after.setValue(1, after.attribute(1).addRelation(insts));
after.setValue(2, before.value( 2));
return after;
}
/**
* This function calculates the Kullback Leibler distance between
* two normal distributions. This distance is always positive.
* Kullback Leibler distance = integral{f(X)ln(f(X)/g(X))}
* Note that X is a vector. Since we assume dimensions are independent
* f(X)(g(X) the same) is actually the product of normal density
* functions of each dimensions. Also note that it should be log2
* instead of (ln) in the formula, but we use (ln) simply for computational
* convenience.
*
* The result is as follows, suppose there are P dimensions, and f(X)
* is the first distribution and g(X) is the second:
* Kullback = sum[1..P](ln(SIGMA2/SIGMA1)) +
* sum[1..P](SIGMA1^2 / (2*(SIGMA2^2))) +
* sum[1..P]((MU1-MU2)^2 / (2*(SIGMA2^2))) -
* P/2
*
* @param mu1 mu of the first normal distribution
* @param mu2 mu of the second normal distribution
* @param var1 variance(SIGMA^2) of the first normal distribution
* @param var2 variance(SIGMA^2) of the second normal distribution
* @return the Kullback distance of two distributions
*/
public double kullback(double[] mu1, double[] mu2,
double[] var1, double[] var2, int pos){
int p = mu1.length;
double result = 0;
for(int y=0; y < p; y++){
if((Utils.gr(var1[y], 0)) && (Utils.gr(var2[y], 0))){
result +=
((Math.log(Math.sqrt(var2[y]/var1[y]))) +
(var1[y] / (2.0*var2[y])) +
(m_Change[pos][y] * (mu1[y]-mu2[y])*(mu1[y]-mu2[y]) / (2.0*var2[y])) -
0.5);
}
}
return result;
}
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Vector result = new Vector();
result.addElement(new Option(
"\tSet number of nearest neighbour for prediction\n"
+ "\t(default 1)",
"K", 1, "-K <number of neighbours>"));
result.addElement(new Option(
"\tSet number of nearest neighbour for cleansing the training data\n"
+ "\t(default 1)",
"S", 1, "-S <number of neighbours>"));
result.addElement(new Option(
"\tSet number of nearest neighbour for cleansing the testing data\n"
+ "\t(default 1)",
"E", 1, "-E <number of neighbours>"));
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -K <number of neighbours>
* Set number of nearest neighbour for prediction
* (default 1)</pre>
*
* <pre> -S <number of neighbours>
* Set number of nearest neighbour for cleansing the training data
* (default 1)</pre>
*
* <pre> -E <number of neighbours>
* Set number of nearest neighbour for cleansing the testing data
* (default 1)</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception{
setDebug(Utils.getFlag('D', options));
String numNeighbourString = Utils.getOption('K', options);
if (numNeighbourString.length() != 0)
setNumNeighbours(Integer.parseInt(numNeighbourString));
else
setNumNeighbours(1);
numNeighbourString = Utils.getOption('S', options);
if (numNeighbourString.length() != 0)
setNumTrainingNoises(Integer.parseInt(numNeighbourString));
else
setNumTrainingNoises(1);
numNeighbourString = Utils.getOption('E', options);
if (numNeighbourString.length() != 0)
setNumTestingNoises(Integer.parseInt(numNeighbourString));
else
setNumTestingNoises(1);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector result;
result = new Vector();
if (getDebug())
result.add("-D");
result.add("-K");
result.add("" + getNumNeighbours());
result.add("-S");
result.add("" + getNumTrainingNoises());
result.add("-E");
result.add("" + getNumTestingNoises());
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numNeighboursTipText() {
return "The number of nearest neighbours to the estimate the class prediction of test bags.";
}
/**
* Sets the number of nearest neighbours to estimate
* the class prediction of tests bags
* @param numNeighbour the number of citers
*/
public void setNumNeighbours(int numNeighbour){
m_Neighbour = numNeighbour;
}
/**
* Returns the number of nearest neighbours to estimate
* the class prediction of tests bags
* @return the number of neighbours
*/
public int getNumNeighbours(){
return m_Neighbour;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numTrainingNoisesTipText() {
return "The number of nearest neighbour instances in the selection of noises in the training data.";
}
/**
* Sets the number of nearest neighbour instances in the
* selection of noises in the training data
*
* @param numTraining the number of noises in training data
*/
public void setNumTrainingNoises (int numTraining){
m_Select = numTraining;
}
/**
* Returns the number of nearest neighbour instances in the
* selection of noises in the training data
*
* @return the number of noises in training data
*/
public int getNumTrainingNoises(){
return m_Select;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numTestingNoisesTipText() {
return "The number of nearest neighbour instances in the selection of noises in the test data.";
}
/**
* Returns The number of nearest neighbour instances in the
* selection of noises in the test data
* @return the number of noises in test data
*/
public int getNumTestingNoises(){
return m_Choose;
}
/**
* Sets The number of nearest neighbour exemplars in the
* selection of noises in the test data
* @param numTesting the number of noises in test data
*/
public void setNumTestingNoises (int numTesting){
m_Choose = numTesting;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5527 $");
}
/**
* Main method for testing.
*
* @param args the options for the classifier
*/
public static void main(String[] args) {
runClassifier(new MINND(), args);
}
}