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package phrase;
import java.io.PrintStream;
import java.util.Arrays;
import optimization.gradientBasedMethods.ProjectedGradientDescent;
import optimization.gradientBasedMethods.ProjectedObjective;
import optimization.gradientBasedMethods.stats.OptimizerStats;
import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;
import optimization.linesearch.InterpolationPickFirstStep;
import optimization.linesearch.LineSearchMethod;
import optimization.linesearch.WolfRuleLineSearch;
import optimization.projections.SimplexProjection;
import optimization.stopCriteria.CompositeStopingCriteria;
import optimization.stopCriteria.ProjectedGradientL2Norm;
import optimization.stopCriteria.StopingCriteria;
import optimization.stopCriteria.ValueDifference;
import optimization.util.MathUtils;
public class PhraseObjective extends ProjectedObjective{
private static final double GRAD_DIFF = 0.002;
public static double INIT_STEP_SIZE=1;
public static double VAL_DIFF=0.001;
private double c1=0.0001;
private double c2=0.9;
private PhraseCluster c;
/**@brief
* for debugging purposes
*/
public static PrintStream ps;
/**@brief current phrase being optimzed*/
public int phrase;
/**@brief un-regularized posterior
* unnormalized
* p[edge][tag]
* P(tag|edge) \propto P(tag|phrase)P(context|tag)
*/
private double[][]p;
/**@brief regularized posterior
* q[edge][tag] propto p[edge][tag]*exp(-lambda)
*/
private double q[][];
private int data[][];
/**@brief log likelihood of the associated phrase
*
*/
private double loglikelihood;
private SimplexProjection projection;
double[] newPoint ;
private int n_param;
/**@brief likelihood under p
*
*/
private double llh;
public PhraseObjective(PhraseCluster cluster, int phraseIdx){
phrase=phraseIdx;
c=cluster;
data=c.c.data[phrase];
n_param=data.length*c.K;
parameters=new double [n_param];
newPoint = new double[n_param];
gradient = new double[n_param];
initP();
projection=new SimplexProjection(c.scale);
q=new double [data.length][c.K];
setParameters(parameters);
}
private void initP(){
int countIdx=data[0].length-1;
p=new double[data.length][];
for(int edge=0;edge<data.length;edge++){
p[edge]=c.posterior(phrase,data[edge]);
}
for(int edge=0;edge<data.length;edge++){
llh+=Math.log
(data[edge][countIdx]*arr.F.l1norm(p[edge]));
arr.F.l1normalize(p[edge]);
}
}
@Override
public void setParameters(double[] params) {
super.setParameters(params);
updateFunction();
}
private void updateFunction(){
updateCalls++;
loglikelihood=0;
int countIdx=data[0].length-1;
for(int tag=0;tag<c.K;tag++){
for(int edge=0;edge<data.length;edge++){
q[edge][tag]=p[edge][tag]*
Math.exp(-parameters[tag*data.length+edge]/data[edge][countIdx]);
}
}
for(int edge=0;edge<data.length;edge++){
loglikelihood+=data[edge][countIdx] * Math.log(arr.F.l1norm(q[edge]));
arr.F.l1normalize(q[edge]);
}
for(int tag=0;tag<c.K;tag++){
for(int edge=0;edge<data.length;edge++){
gradient[tag*data.length+edge]=-q[edge][tag];
}
}
}
@Override
// TODO Auto-generated method stub
public double[] projectPoint(double[] point) {
double toProject[]=new double[data.length];
for(int tag=0;tag<c.K;tag++){
for(int edge=0;edge<data.length;edge++){
toProject[edge]=point[tag*data.length+edge];
}
projection.project(toProject);
for(int edge=0;edge<data.length;edge++){
newPoint[tag*data.length+edge]=toProject[edge];
}
}
return newPoint;
}
@Override
public double[] getGradient() {
// TODO Auto-generated method stub
gradientCalls++;
return gradient;
}
@Override
public double getValue() {
// TODO Auto-generated method stub
functionCalls++;
return loglikelihood;
}
@Override
public String toString() {
// TODO Auto-generated method stub
return "";
}
public double [][]posterior(){
return q;
}
public void optimizeWithProjectedGradientDescent(){
LineSearchMethod ls =
new ArmijoLineSearchMinimizationAlongProjectionArc
(new InterpolationPickFirstStep(INIT_STEP_SIZE));
//LineSearchMethod ls = new WolfRuleLineSearch(
// (new InterpolationPickFirstStep(INIT_STEP_SIZE)), c1, c2);
OptimizerStats stats = new OptimizerStats();
ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);
StopingCriteria stopValue = new ValueDifference(VAL_DIFF);
CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
compositeStop.add(stopGrad);
compositeStop.add(stopValue);
optimizer.setMaxIterations(100);
updateFunction();
boolean succed = optimizer.optimize(this,stats,compositeStop);
// System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));
if(succed){
System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
}else{
System.out.println("Failed to optimize");
}
// ps.println(Arrays.toString(parameters));
// for(int edge=0;edge<data.length;edge++){
// ps.println(Arrays.toString(q[edge]));
// }
}
/**
* L - KL(q||p) -
* scale * \sum_{tag,phrase} max_i P(tag|i th occurrence of phrase)
* @return
*/
public double primal()
{
double l=llh;
// ps.print("Phrase "+phrase+": "+l);
double kl=-loglikelihood
+MathUtils.dotProduct(parameters, gradient);
// ps.print(", "+kl);
l=l-kl;
double sum=0;
for(int tag=0;tag<c.K;tag++){
double max=0;
for(int edge=0;edge<data.length;edge++){
if(q[edge][tag]>max){
max=q[edge][tag];
}
}
sum+=max;
}
// ps.println(", "+sum);
l=l-c.scale*sum;
return l;
}
}
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