/*
* 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.
*/
/*
* TLD.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.mi;
import weka.classifiers.RandomizableClassifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Optimization;
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.Random;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Two-Level Distribution approach, changes the starting value of the searching algorithm, supplement the cut-off modification and check missing values.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Xin Xu (2003). Statistical learning in multiple instance problem. Hamilton, NZ.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @mastersthesis{Xu2003,
* address = {Hamilton, NZ},
* author = {Xin Xu},
* note = {0657.594},
* school = {University of Waikato},
* title = {Statistical learning in multiple instance problem},
* year = {2003}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -C
* Set whether or not use empirical
* log-odds cut-off instead of 0</pre>
*
* <pre> -R <numOfRuns>
* Set the number of multiple runs
* needed for searching the MLE.</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Xin Xu (xx5@cs.waikato.ac.nz)
* @version $Revision: 1.6 $
*/
public class TLD
extends RandomizableClassifier
implements OptionHandler, MultiInstanceCapabilitiesHandler,
TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 6657315525171152210L;
/** The mean for each attribute of each positive exemplar */
protected double[][] m_MeanP = null;
/** The variance for each attribute of each positive exemplar */
protected double[][] m_VarianceP = null;
/** The mean for each attribute of each negative exemplar */
protected double[][] m_MeanN = null;
/** The variance for each attribute of each negative exemplar */
protected double[][] m_VarianceN = null;
/** The effective sum of weights of each positive exemplar in each dimension*/
protected double[][] m_SumP = null;
/** The effective sum of weights of each negative exemplar in each dimension*/
protected double[][] m_SumN = null;
/** The parameters to be estimated for each positive exemplar*/
protected double[] m_ParamsP = null;
/** The parameters to be estimated for each negative exemplar*/
protected double[] m_ParamsN = null;
/** The dimension of each exemplar, i.e. (numAttributes-2) */
protected int m_Dimension = 0;
/** The class label of each exemplar */
protected double[] m_Class = null;
/** The number of class labels in the data */
protected int m_NumClasses = 2;
/** The very small number representing zero */
static public double ZERO = 1.0e-6;
/** The number of runs to perform */
protected int m_Run = 1;
protected double m_Cutoff;
protected boolean m_UseEmpiricalCutOff = false;
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Two-Level Distribution approach, changes the starting value of "
+ "the searching algorithm, supplement the cut-off modification and "
+ "check missing values.\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.MASTERSTHESIS);
result.setValue(Field.AUTHOR, "Xin Xu");
result.setValue(Field.YEAR, "2003");
result.setValue(Field.TITLE, "Statistical learning in multiple instance problem");
result.setValue(Field.SCHOOL, "University of Waikato");
result.setValue(Field.ADDRESS, "Hamilton, NZ");
result.setValue(Field.NOTE, "0657.594");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.RELATIONAL_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.BINARY_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();
// attributes
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.disableAllClasses();
result.enable(Capability.NO_CLASS);
return result;
}
/**
*
* @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
exs = new Instances(exs);
exs.deleteWithMissingClass();
int numegs = exs.numInstances();
m_Dimension = exs.attribute(1).relation(). numAttributes();
Instances pos = new Instances(exs, 0), neg = new Instances(exs, 0);
for(int u=0; u<numegs; u++){
Instance example = exs.instance(u);
if(example.classValue() == 1)
pos.add(example);
else
neg.add(example);
}
int pnum = pos.numInstances(), nnum = neg.numInstances();
m_MeanP = new double[pnum][m_Dimension];
m_VarianceP = new double[pnum][m_Dimension];
m_SumP = new double[pnum][m_Dimension];
m_MeanN = new double[nnum][m_Dimension];
m_VarianceN = new double[nnum][m_Dimension];
m_SumN = new double[nnum][m_Dimension];
m_ParamsP = new double[4*m_Dimension];
m_ParamsN = new double[4*m_Dimension];
// Estimation of the parameters: as the start value for search
double[] pSumVal=new double[m_Dimension], // for m
nSumVal=new double[m_Dimension];
double[] maxVarsP=new double[m_Dimension], // for a
maxVarsN=new double[m_Dimension];
// Mean of sample variances: for b, b=a/E(\sigma^2)+2
double[] varMeanP = new double[m_Dimension],
varMeanN = new double[m_Dimension];
// Variances of sample means: for w, w=E[var(\mu)]/E[\sigma^2]
double[] meanVarP = new double[m_Dimension],
meanVarN = new double[m_Dimension];
// number of exemplars without all values missing
double[] numExsP = new double[m_Dimension],
numExsN = new double[m_Dimension];
// Extract metadata fro both positive and negative bags
for(int v=0; v < pnum; v++){
/*Exemplar px = pos.exemplar(v);
m_MeanP[v] = px.meanOrMode();
m_VarianceP[v] = px.variance();
Instances pxi = px.getInstances();
*/
Instances pxi = pos.instance(v).relationalValue(1);
for (int k=0; k<pxi.numAttributes(); k++) {
m_MeanP[v][k] = pxi.meanOrMode(k);
m_VarianceP[v][k] = pxi.variance(k);
}
for (int w=0,t=0; w < m_Dimension; w++,t++){
//if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
if(!Double.isNaN(m_MeanP[v][w])){
for(int u=0;u<pxi.numInstances();u++){
Instance ins = pxi.instance(u);
if(!ins.isMissing(t))
m_SumP[v][w] += ins.weight();
}
numExsP[w]++;
pSumVal[w] += m_MeanP[v][w];
meanVarP[w] += m_MeanP[v][w]*m_MeanP[v][w];
if(maxVarsP[w] < m_VarianceP[v][w])
maxVarsP[w] = m_VarianceP[v][w];
varMeanP[w] += m_VarianceP[v][w];
m_VarianceP[v][w] *= (m_SumP[v][w]-1.0);
if(m_VarianceP[v][w] < 0.0)
m_VarianceP[v][w] = 0.0;
}
}
}
for(int v=0; v < nnum; v++){
/*Exemplar nx = neg.exemplar(v);
m_MeanN[v] = nx.meanOrMode();
m_VarianceN[v] = nx.variance();
Instances nxi = nx.getInstances();
*/
Instances nxi = neg.instance(v).relationalValue(1);
for (int k=0; k<nxi.numAttributes(); k++) {
m_MeanN[v][k] = nxi.meanOrMode(k);
m_VarianceN[v][k] = nxi.variance(k);
}
for (int w=0,t=0; w < m_Dimension; w++,t++){
//if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
if(!Double.isNaN(m_MeanN[v][w])){
for(int u=0;u<nxi.numInstances();u++)
if(!nxi.instance(u).isMissing(t))
m_SumN[v][w] += nxi.instance(u).weight();
numExsN[w]++;
nSumVal[w] += m_MeanN[v][w];
meanVarN[w] += m_MeanN[v][w]*m_MeanN[v][w];
if(maxVarsN[w] < m_VarianceN[v][w])
maxVarsN[w] = m_VarianceN[v][w];
varMeanN[w] += m_VarianceN[v][w];
m_VarianceN[v][w] *= (m_SumN[v][w]-1.0);
if(m_VarianceN[v][w] < 0.0)
m_VarianceN[v][w] = 0.0;
}
}
}
for(int w=0; w<m_Dimension; w++){
pSumVal[w] /= numExsP[w];
nSumVal[w] /= numExsN[w];
if(numExsP[w]>1)
meanVarP[w] = meanVarP[w]/(numExsP[w]-1.0)
- pSumVal[w]*numExsP[w]/(numExsP[w]-1.0);
if(numExsN[w]>1)
meanVarN[w] = meanVarN[w]/(numExsN[w]-1.0)
- nSumVal[w]*numExsN[w]/(numExsN[w]-1.0);
varMeanP[w] /= numExsP[w];
varMeanN[w] /= numExsN[w];
}
//Bounds and parameter values for each run
double[][] bounds = new double[2][4];
double[] pThisParam = new double[4],
nThisParam = new double[4];
// Initial values for parameters
double a, b, w, m;
// Optimize for one dimension
for (int x=0; x < m_Dimension; x++){
if (getDebug())
System.err.println("\n\n!!!!!!!!!!!!!!!!!!!!!!???Dimension #"+x);
// Positive examplars: first run
a = (maxVarsP[x]>ZERO) ? maxVarsP[x]:1.0;
if (varMeanP[x]<=ZERO) varMeanP[x] = ZERO; // modified by LinDong (09/2005)
b = a/varMeanP[x]+2.0; // a/(b-2) = E(\sigma^2)
w = meanVarP[x]/varMeanP[x]; // E[var(\mu)] = w*E[\sigma^2]
if(w<=ZERO) w=1.0;
m = pSumVal[x];
pThisParam[0] = a; // a
pThisParam[1] = b; // b
pThisParam[2] = w; // w
pThisParam[3] = m; // m
// Negative examplars: first run
a = (maxVarsN[x]>ZERO) ? maxVarsN[x]:1.0;
if (varMeanN[x]<=ZERO) varMeanN[x] = ZERO; // modified by LinDong (09/2005)
b = a/varMeanN[x]+2.0; // a/(b-2) = E(\sigma^2)
w = meanVarN[x]/varMeanN[x]; // E[var(\mu)] = w*E[\sigma^2]
if(w<=ZERO) w=1.0;
m = nSumVal[x];
nThisParam[0] = a; // a
nThisParam[1] = b; // b
nThisParam[2] = w; // w
nThisParam[3] = m; // m
// Bound constraints
bounds[0][0] = ZERO; // a > 0
bounds[0][1] = 2.0+ZERO; // b > 2
bounds[0][2] = ZERO; // w > 0
bounds[0][3] = Double.NaN;
for(int t=0; t<4; t++){
bounds[1][t] = Double.NaN;
m_ParamsP[4*x+t] = pThisParam[t];
m_ParamsN[4*x+t] = nThisParam[t];
}
double pminVal=Double.MAX_VALUE, nminVal=Double.MAX_VALUE;
Random whichEx = new Random(m_Seed);
TLD_Optm pOp=null, nOp=null;
boolean isRunValid = true;
double[] sumP=new double[pnum], meanP=new double[pnum],
varP=new double[pnum];
double[] sumN=new double[nnum], meanN=new double[nnum],
varN=new double[nnum];
// One dimension
for(int p=0; p<pnum; p++){
sumP[p] = m_SumP[p][x];
meanP[p] = m_MeanP[p][x];
varP[p] = m_VarianceP[p][x];
}
for(int q=0; q<nnum; q++){
sumN[q] = m_SumN[q][x];
meanN[q] = m_MeanN[q][x];
varN[q] = m_VarianceN[q][x];
}
for(int y=0; y<m_Run;){
if (getDebug())
System.err.println("\n\n!!!!!!!!!!!!!!!!!!!!!!???Run #"+y);
double thisMin;
if (getDebug())
System.err.println("\nPositive exemplars");
pOp = new TLD_Optm();
pOp.setNum(sumP);
pOp.setSSquare(varP);
pOp.setXBar(meanP);
pThisParam = pOp.findArgmin(pThisParam, bounds);
while(pThisParam==null){
pThisParam = pOp.getVarbValues();
if (getDebug())
System.err.println("!!! 200 iterations finished, not enough!");
pThisParam = pOp.findArgmin(pThisParam, bounds);
}
thisMin = pOp.getMinFunction();
if(!Double.isNaN(thisMin) && (thisMin<pminVal)){
pminVal = thisMin;
for(int z=0; z<4; z++)
m_ParamsP[4*x+z] = pThisParam[z];
}
if(Double.isNaN(thisMin)){
pThisParam = new double[4];
isRunValid =false;
}
if (getDebug())
System.err.println("\nNegative exemplars");
nOp = new TLD_Optm();
nOp.setNum(sumN);
nOp.setSSquare(varN);
nOp.setXBar(meanN);
nThisParam = nOp.findArgmin(nThisParam, bounds);
while(nThisParam==null){
nThisParam = nOp.getVarbValues();
if (getDebug())
System.err.println("!!! 200 iterations finished, not enough!");
nThisParam = nOp.findArgmin(nThisParam, bounds);
}
thisMin = nOp.getMinFunction();
if(!Double.isNaN(thisMin) && (thisMin<nminVal)){
nminVal = thisMin;
for(int z=0; z<4; z++)
m_ParamsN[4*x+z] = nThisParam[z];
}
if(Double.isNaN(thisMin)){
nThisParam = new double[4];
isRunValid =false;
}
if(!isRunValid){ y--; isRunValid=true; }
if(++y<m_Run){
// Change the initial parameters and restart
int pone = whichEx.nextInt(pnum), // Randomly pick one pos. exmpl.
none = whichEx.nextInt(nnum);
// Positive exemplars: next run
while((m_SumP[pone][x]<=1.0)||Double.isNaN(m_MeanP[pone][x]))
pone = whichEx.nextInt(pnum);
a = m_VarianceP[pone][x]/(m_SumP[pone][x]-1.0);
if(a<=ZERO) a=m_ParamsN[4*x]; // Change to negative params
m = m_MeanP[pone][x];
double sq = (m-m_ParamsP[4*x+3])*(m-m_ParamsP[4*x+3]);
b = a*m_ParamsP[4*x+2]/sq+2.0; // b=a/Var+2, assuming Var=Sq/w'
if((b<=ZERO) || Double.isNaN(b) || Double.isInfinite(b))
b=m_ParamsN[4*x+1];
w = sq*(m_ParamsP[4*x+1]-2.0)/m_ParamsP[4*x];//w=Sq/Var, assuming Var=a'/(b'-2)
if((w<=ZERO) || Double.isNaN(w) || Double.isInfinite(w))
w=m_ParamsN[4*x+2];
pThisParam[0] = a; // a
pThisParam[1] = b; // b
pThisParam[2] = w; // w
pThisParam[3] = m; // m
// Negative exemplars: next run
while((m_SumN[none][x]<=1.0)||Double.isNaN(m_MeanN[none][x]))
none = whichEx.nextInt(nnum);
a = m_VarianceN[none][x]/(m_SumN[none][x]-1.0);
if(a<=ZERO) a=m_ParamsP[4*x];
m = m_MeanN[none][x];
sq = (m-m_ParamsN[4*x+3])*(m-m_ParamsN[4*x+3]);
b = a*m_ParamsN[4*x+2]/sq+2.0; // b=a/Var+2, assuming Var=Sq/w'
if((b<=ZERO) || Double.isNaN(b) || Double.isInfinite(b))
b=m_ParamsP[4*x+1];
w = sq*(m_ParamsN[4*x+1]-2.0)/m_ParamsN[4*x];//w=Sq/Var, assuming Var=a'/(b'-2)
if((w<=ZERO) || Double.isNaN(w) || Double.isInfinite(w))
w=m_ParamsP[4*x+2];
nThisParam[0] = a; // a
nThisParam[1] = b; // b
nThisParam[2] = w; // w
nThisParam[3] = m; // m
}
}
}
for (int x=0, y=0; x<m_Dimension; x++, y++){
//if((x==exs.classIndex()) || (x==exs.idIndex()))
//y++;
a=m_ParamsP[4*x]; b=m_ParamsP[4*x+1];
w=m_ParamsP[4*x+2]; m=m_ParamsP[4*x+3];
if (getDebug())
System.err.println("\n\n???Positive: ( "+exs.attribute(1).relation().attribute(y)+
"): a="+a+", b="+b+", w="+w+", m="+m);
a=m_ParamsN[4*x]; b=m_ParamsN[4*x+1];
w=m_ParamsN[4*x+2]; m=m_ParamsN[4*x+3];
if (getDebug())
System.err.println("???Negative: ("+exs.attribute(1).relation().attribute(y)+
"): a="+a+", b="+b+", w="+w+", m="+m);
}
if(m_UseEmpiricalCutOff){
// Find the empirical cut-off
double[] pLogOdds=new double[pnum], nLogOdds=new double[nnum];
for(int p=0; p<pnum; p++)
pLogOdds[p] =
likelihoodRatio(m_SumP[p], m_MeanP[p], m_VarianceP[p]);
for(int q=0; q<nnum; q++)
nLogOdds[q] =
likelihoodRatio(m_SumN[q], m_MeanN[q], m_VarianceN[q]);
// Update m_Cutoff
findCutOff(pLogOdds, nLogOdds);
}
else
m_Cutoff = -Math.log((double)pnum/(double)nnum);
if (getDebug())
System.err.println("???Cut-off="+m_Cutoff);
}
/**
*
* @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{
//Exemplar ex = new Exemplar(e);
Instances exi = ex.relationalValue(1);
double[] n = new double[m_Dimension];
double [] xBar = new double[m_Dimension];
double [] sSq = new double[m_Dimension];
for (int i=0; i<exi.numAttributes() ; i++){
xBar[i] = exi.meanOrMode(i);
sSq[i] = exi.variance(i);
}
for (int w=0, t=0; w < m_Dimension; w++, t++){
//if((t==m_ClassIndex) || (t==m_IdIndex))
//t++;
for(int u=0;u<exi.numInstances();u++)
if(!exi.instance(u).isMissing(t))
n[w] += exi.instance(u).weight();
sSq[w] = sSq[w]*(n[w]-1.0);
if(sSq[w] <= 0.0)
sSq[w] = 0.0;
}
double logOdds = likelihoodRatio(n, xBar, sSq);
return (logOdds > m_Cutoff) ? 1 : 0 ;
}
private double likelihoodRatio(double[] n, double[] xBar, double[] sSq){
double LLP = 0.0, LLN = 0.0;
for (int x=0; x<m_Dimension; x++){
if(Double.isNaN(xBar[x])) continue; // All missing values
int halfN = ((int)n[x])/2;
//Log-likelihood for positive
double a=m_ParamsP[4*x], b=m_ParamsP[4*x+1],
w=m_ParamsP[4*x+2], m=m_ParamsP[4*x+3];
LLP += 0.5*b*Math.log(a) + 0.5*(b+n[x]-1.0)*Math.log(1.0+n[x]*w)
- 0.5*(b+n[x])*Math.log((1.0+n[x]*w)*(a+sSq[x])+
n[x]*(xBar[x]-m)*(xBar[x]-m))
- 0.5*n[x]*Math.log(Math.PI);
for(int y=1; y<=halfN; y++)
LLP += Math.log(b/2.0+n[x]/2.0-(double)y);
if(n[x]/2.0 > halfN) // n is odd
LLP += TLD_Optm.diffLnGamma(b/2.0);
//Log-likelihood for negative
a=m_ParamsN[4*x];
b=m_ParamsN[4*x+1];
w=m_ParamsN[4*x+2];
m=m_ParamsN[4*x+3];
LLN += 0.5*b*Math.log(a) + 0.5*(b+n[x]-1.0)*Math.log(1.0+n[x]*w)
- 0.5*(b+n[x])*Math.log((1.0+n[x]*w)*(a+sSq[x])+
n[x]*(xBar[x]-m)*(xBar[x]-m))
- 0.5*n[x]*Math.log(Math.PI);
for(int y=1; y<=halfN; y++)
LLN += Math.log(b/2.0+n[x]/2.0-(double)y);
if(n[x]/2.0 > halfN) // n is odd
LLN += TLD_Optm.diffLnGamma(b/2.0);
}
return LLP - LLN;
}
private void findCutOff(double[] pos, double[] neg){
int[] pOrder = Utils.sort(pos),
nOrder = Utils.sort(neg);
/*
System.err.println("\n\n???Positive: ");
for(int t=0; t<pOrder.length; t++)
System.err.print(t+":"+Utils.doubleToString(pos[pOrder[t]],0,2)+" ");
System.err.println("\n\n???Negative: ");
for(int t=0; t<nOrder.length; t++)
System.err.print(t+":"+Utils.doubleToString(neg[nOrder[t]],0,2)+" ");
*/
int pNum = pos.length, nNum = neg.length, count, p=0, n=0;
double fstAccu=0.0, sndAccu=(double)pNum, split;
double maxAccu = 0, minDistTo0 = Double.MAX_VALUE;
// Skip continuous negatives
for(;(n<nNum)&&(pos[pOrder[0]]>=neg[nOrder[n]]); n++, fstAccu++);
if(n>=nNum){ // totally seperate
m_Cutoff = (neg[nOrder[nNum-1]]+pos[pOrder[0]])/2.0;
//m_Cutoff = neg[nOrder[nNum-1]];
return;
}
count=n;
while((p<pNum)&&(n<nNum)){
// Compare the next in the two lists
if(pos[pOrder[p]]>=neg[nOrder[n]]){ // Neg has less log-odds
fstAccu += 1.0;
split=neg[nOrder[n]];
n++;
}
else{
sndAccu -= 1.0;
split=pos[pOrder[p]];
p++;
}
count++;
if((fstAccu+sndAccu > maxAccu)
|| ((fstAccu+sndAccu == maxAccu) && (Math.abs(split)<minDistTo0))){
maxAccu = fstAccu+sndAccu;
m_Cutoff = split;
minDistTo0 = Math.abs(split);
}
}
}
/**
* 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 whether or not use empirical\n"
+ "\tlog-odds cut-off instead of 0",
"C", 0, "-C"));
result.addElement(new Option(
"\tSet the number of multiple runs \n"
+ "\tneeded for searching the MLE.",
"R", 1, "-R <numOfRuns>"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
result.addElement(enu.nextElement());
}
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -C
* Set whether or not use empirical
* log-odds cut-off instead of 0</pre>
*
* <pre> -R <numOfRuns>
* Set the number of multiple runs
* needed for searching the MLE.</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</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));
setUsingCutOff(Utils.getFlag('C', options));
String runString = Utils.getOption('R', options);
if (runString.length() != 0)
setNumRuns(Integer.parseInt(runString));
else
setNumRuns(1);
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector result;
String[] options;
int i;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
if (getDebug())
result.add("-D");
if (getUsingCutOff())
result.add("-C");
result.add("-R");
result.add("" + getNumRuns());
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 numRunsTipText() {
return "The number of runs to perform.";
}
/**
* Sets the number of runs to perform.
*
* @param numRuns the number of runs to perform
*/
public void setNumRuns(int numRuns) {
m_Run = numRuns;
}
/**
* Returns the number of runs to perform.
*
* @return the number of runs to perform
*/
public int getNumRuns() {
return m_Run;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String usingCutOffTipText() {
return "Whether to use an empirical cutoff.";
}
/**
* Sets whether to use an empirical cutoff.
*
* @param cutOff whether to use an empirical cutoff
*/
public void setUsingCutOff (boolean cutOff) {
m_UseEmpiricalCutOff = cutOff;
}
/**
* Returns whether an empirical cutoff is used
*
* @return true if an empirical cutoff is used
*/
public boolean getUsingCutOff() {
return m_UseEmpiricalCutOff;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.6 $");
}
/**
* Main method for testing.
*
* @param args the options for the classifier
*/
public static void main(String[] args) {
runClassifier(new TLD(), args);
}
}
class TLD_Optm extends Optimization {
private double[] num;
private double[] sSq;
private double[] xBar;
public void setNum(double[] n) {num = n;}
public void setSSquare(double[] s){sSq = s;}
public void setXBar(double[] x){xBar = x;}
/**
* Compute Ln[Gamma(b+0.5)] - Ln[Gamma(b)]
*
* @param b the value in the above formula
* @return the result
*/
public static double diffLnGamma(double b){
double[] coef= {76.18009172947146, -86.50532032941677,
24.01409824083091, -1.231739572450155,
0.1208650973866179e-2, -0.5395239384953e-5};
double rt = -0.5;
rt += (b+1.0)*Math.log(b+6.0) - (b+0.5)*Math.log(b+5.5);
double series1=1.000000000190015, series2=1.000000000190015;
for(int i=0; i<6; i++){
series1 += coef[i]/(b+1.5+(double)i);
series2 += coef[i]/(b+1.0+(double)i);
}
rt += Math.log(series1*b)-Math.log(series2*(b+0.5));
return rt;
}
/**
* Compute dLn[Gamma(x+0.5)]/dx - dLn[Gamma(x)]/dx
*
* @param x the value in the above formula
* @return the result
*/
protected double diffFstDervLnGamma(double x){
double rt=0, series=1.0;// Just make it >0
for(int i=0;series>=m_Zero*1e-3;i++){
series = 0.5/((x+(double)i)*(x+(double)i+0.5));
rt += series;
}
return rt;
}
/**
* Compute {Ln[Gamma(x+0.5)]}'' - {Ln[Gamma(x)]}''
*
* @param x the value in the above formula
* @return the result
*/
protected double diffSndDervLnGamma(double x){
double rt=0, series=1.0;// Just make it >0
for(int i=0;series>=m_Zero*1e-3;i++){
series = (x+(double)i+0.25)/
((x+(double)i)*(x+(double)i)*(x+(double)i+0.5)*(x+(double)i+0.5));
rt -= series;
}
return rt;
}
/**
* Implement this procedure to evaluate objective
* function to be minimized
*/
protected double objectiveFunction(double[] x){
int numExs = num.length;
double NLL = 0; // Negative Log-Likelihood
double a=x[0], b=x[1], w=x[2], m=x[3];
for(int j=0; j < numExs; j++){
if(Double.isNaN(xBar[j])) continue; // All missing values
NLL += 0.5*(b+num[j])*
Math.log((1.0+num[j]*w)*(a+sSq[j]) +
num[j]*(xBar[j]-m)*(xBar[j]-m));
if(Double.isNaN(NLL) && m_Debug){
System.err.println("???????????1: "+a+" "+b+" "+w+" "+m
+"|x-: "+xBar[j] +
"|n: "+num[j] + "|S^2: "+sSq[j]);
System.exit(1);
}
// Doesn't affect optimization
//NLL += 0.5*num[j]*Math.log(Math.PI);
NLL -= 0.5*(b+num[j]-1.0)*Math.log(1.0+num[j]*w);
if(Double.isNaN(NLL) && m_Debug){
System.err.println("???????????2: "+a+" "+b+" "+w+" "+m
+"|x-: "+xBar[j] +
"|n: "+num[j] + "|S^2: "+sSq[j]);
System.exit(1);
}
int halfNum = ((int)num[j])/2;
for(int z=1; z<=halfNum; z++)
NLL -= Math.log(0.5*b+0.5*num[j]-(double)z);
if(0.5*num[j] > halfNum) // num[j] is odd
NLL -= diffLnGamma(0.5*b);
if(Double.isNaN(NLL) && m_Debug){
System.err.println("???????????3: "+a+" "+b+" "+w+" "+m
+"|x-: "+xBar[j] +
"|n: "+num[j] + "|S^2: "+sSq[j]);
System.exit(1);
}
NLL -= 0.5*Math.log(a)*b;
if(Double.isNaN(NLL) && m_Debug){
System.err.println("???????????4:"+a+" "+b+" "+w+" "+m);
System.exit(1);
}
}
if(m_Debug)
System.err.println("?????????????5: "+NLL);
if(Double.isNaN(NLL))
System.exit(1);
return NLL;
}
/**
* Subclass should implement this procedure to evaluate gradient
* of the objective function
*/
protected double[] evaluateGradient(double[] x){
double[] g = new double[x.length];
int numExs = num.length;
double a=x[0],b=x[1],w=x[2],m=x[3];
double da=0.0, db=0.0, dw=0.0, dm=0.0;
for(int j=0; j < numExs; j++){
if(Double.isNaN(xBar[j])) continue; // All missing values
double denorm = (1.0+num[j]*w)*(a+sSq[j]) +
num[j]*(xBar[j]-m)*(xBar[j]-m);
da += 0.5*(b+num[j])*(1.0+num[j]*w)/denorm-0.5*b/a;
db += 0.5*Math.log(denorm)
- 0.5*Math.log(1.0+num[j]*w)
- 0.5*Math.log(a);
int halfNum = ((int)num[j])/2;
for(int z=1; z<=halfNum; z++)
db -= 1.0/(b+num[j]-2.0*(double)z);
if(num[j]/2.0 > halfNum) // num[j] is odd
db -= 0.5*diffFstDervLnGamma(0.5*b);
dw += 0.5*(b+num[j])*(a+sSq[j])*num[j]/denorm -
0.5*(b+num[j]-1.0)*num[j]/(1.0+num[j]*w);
dm += num[j]*(b+num[j])*(m-xBar[j])/denorm;
}
g[0] = da;
g[1] = db;
g[2] = dw;
g[3] = dm;
return g;
}
/**
* Subclass should implement this procedure to evaluate second-order
* gradient of the objective function
*/
protected double[] evaluateHessian(double[] x, int index){
double[] h = new double[x.length];
// # of exemplars, # of dimensions
// which dimension and which variable for 'index'
int numExs = num.length;
double a,b,w,m;
// Take the 2nd-order derivative
switch(index){
case 0: // a
a=x[0];b=x[1];w=x[2];m=x[3];
for(int j=0; j < numExs; j++){
if(Double.isNaN(xBar[j])) continue; //All missing values
double denorm = (1.0+num[j]*w)*(a+sSq[j]) +
num[j]*(xBar[j]-m)*(xBar[j]-m);
h[0] += 0.5*b/(a*a)
- 0.5*(b+num[j])*(1.0+num[j]*w)*(1.0+num[j]*w)
/(denorm*denorm);
h[1] += 0.5*(1.0+num[j]*w)/denorm - 0.5/a;
h[2] += 0.5*num[j]*num[j]*(b+num[j])*
(xBar[j]-m)*(xBar[j]-m)/(denorm*denorm);
h[3] -= num[j]*(b+num[j])*(m-xBar[j])
*(1.0+num[j]*w)/(denorm*denorm);
}
break;
case 1: // b
a=x[0];b=x[1];w=x[2];m=x[3];
for(int j=0; j < numExs; j++){
if(Double.isNaN(xBar[j])) continue; //All missing values
double denorm = (1.0+num[j]*w)*(a+sSq[j]) +
num[j]*(xBar[j]-m)*(xBar[j]-m);
h[0] += 0.5*(1.0+num[j]*w)/denorm - 0.5/a;
int halfNum = ((int)num[j])/2;
for(int z=1; z<=halfNum; z++)
h[1] +=
1.0/((b+num[j]-2.0*(double)z)*(b+num[j]-2.0*(double)z));
if(num[j]/2.0 > halfNum) // num[j] is odd
h[1] -= 0.25*diffSndDervLnGamma(0.5*b);
h[2] += 0.5*(a+sSq[j])*num[j]/denorm -
0.5*num[j]/(1.0+num[j]*w);
h[3] += num[j]*(m-xBar[j])/denorm;
}
break;
case 2: // w
a=x[0];b=x[1];w=x[2];m=x[3];
for(int j=0; j < numExs; j++){
if(Double.isNaN(xBar[j])) continue; //All missing values
double denorm = (1.0+num[j]*w)*(a+sSq[j]) +
num[j]*(xBar[j]-m)*(xBar[j]-m);
h[0] += 0.5*num[j]*num[j]*(b+num[j])*
(xBar[j]-m)*(xBar[j]-m)/(denorm*denorm);
h[1] += 0.5*(a+sSq[j])*num[j]/denorm -
0.5*num[j]/(1.0+num[j]*w);
h[2] += 0.5*(b+num[j]-1.0)*num[j]*num[j]/
((1.0+num[j]*w)*(1.0+num[j]*w)) -
0.5*(b+num[j])*(a+sSq[j])*(a+sSq[j])*
num[j]*num[j]/(denorm*denorm);
h[3] -= num[j]*num[j]*(b+num[j])*
(m-xBar[j])*(a+sSq[j])/(denorm*denorm);
}
break;
case 3: // m
a=x[0];b=x[1];w=x[2];m=x[3];
for(int j=0; j < numExs; j++){
if(Double.isNaN(xBar[j])) continue; //All missing values
double denorm = (1.0+num[j]*w)*(a+sSq[j]) +
num[j]*(xBar[j]-m)*(xBar[j]-m);
h[0] -= num[j]*(b+num[j])*(m-xBar[j])
*(1.0+num[j]*w)/(denorm*denorm);
h[1] += num[j]*(m-xBar[j])/denorm;
h[2] -= num[j]*num[j]*(b+num[j])*
(m-xBar[j])*(a+sSq[j])/(denorm*denorm);
h[3] += num[j]*(b+num[j])*
((1.0+num[j]*w)*(a+sSq[j])-
num[j]*(m-xBar[j])*(m-xBar[j]))
/(denorm*denorm);
}
}
return h;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.6 $");
}
}