diff options
Diffstat (limited to 'gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java')
| -rw-r--r-- | gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java | 224 |
1 files changed, 0 insertions, 224 deletions
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java deleted file mode 100644 index ac73a075..00000000 --- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java +++ /dev/null @@ -1,224 +0,0 @@ -package phrase;
-
-import java.util.Arrays;
-import java.util.List;
-
-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
-{
- static final double GRAD_DIFF = 0.00002;
- static double INIT_STEP_SIZE = 300;
- static double VAL_DIFF = 1e-8; // tuned to BTEC subsample
- static int ITERATIONS = 100;
- 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 List<Corpus.Edge> data;
-
- /**@brief log likelihood of the associated phrase
- *
- */
- private double loglikelihood;
- private SimplexProjection projection;
-
- double[] newPoint ;
-
- private int n_param;
-
- /**@brief likelihood under p
- *
- */
- public double llh;
-
- public PhraseObjective(PhraseCluster cluster, int phraseIdx, double scale, double[] lambda){
- phrase=phraseIdx;
- c=cluster;
- data=c.c.getEdgesForPhrase(phrase);
- n_param=data.size()*c.K;
- //System.out.println("Num parameters " + n_param + " for phrase #" + phraseIdx);
-
- if (lambda==null)
- lambda=new double[n_param];
-
- parameters = lambda;
- newPoint = new double[n_param];
- gradient = new double[n_param];
- initP();
- projection=new SimplexProjection(scale);
- q=new double [data.size()][c.K];
-
- setParameters(parameters);
- }
-
- private void initP(){
- p=new double[data.size()][];
- for(int edge=0;edge<data.size();edge++){
- p[edge]=c.posterior(data.get(edge));
- llh += data.get(edge).getCount() * Math.log(arr.F.l1norm(p[edge])); // Was bug here - count inside log!
- arr.F.l1normalize(p[edge]);
- }
- }
-
- @Override
- public void setParameters(double[] params) {
- super.setParameters(params);
- updateFunction();
- }
-
- private void updateFunction(){
- updateCalls++;
- loglikelihood=0;
-
- for(int tag=0;tag<c.K;tag++){
- for(int edge=0;edge<data.size();edge++){
- q[edge][tag]=p[edge][tag]*
- Math.exp(-parameters[tag*data.size()+edge]/data.get(edge).getCount());
- }
- }
-
- for(int edge=0;edge<data.size();edge++){
- loglikelihood+=data.get(edge).getCount() * 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.size();edge++){
- gradient[tag*data.size()+edge]=-q[edge][tag];
- }
- }
- }
-
- @Override
- public double[] projectPoint(double[] point)
- {
- double toProject[]=new double[data.size()];
- for(int tag=0;tag<c.K;tag++){
- for(int edge=0;edge<data.size();edge++){
- toProject[edge]=point[tag*data.size()+edge];
- }
- projection.project(toProject);
- for(int edge=0;edge<data.size();edge++){
- newPoint[tag*data.size()+edge]=toProject[edge];
- }
- }
- return newPoint;
- }
-
- @Override
- public double[] getGradient() {
- gradientCalls++;
- return gradient;
- }
-
- @Override
- public double getValue() {
- functionCalls++;
- return loglikelihood;
- }
-
- @Override
- public String toString() {
- return Arrays.toString(parameters);
- }
-
- public double [][]posterior(){
- return q;
- }
-
- long optimizationTime;
-
- public boolean optimizeWithProjectedGradientDescent(){
- long start = System.currentTimeMillis();
-
- 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*(-llh));
- CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
- compositeStop.add(stopGrad);
- compositeStop.add(stopValue);
- optimizer.setMaxIterations(ITERATIONS);
- updateFunction();
- boolean success = 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");
- //}
- //System.out.println(Arrays.toString(parameters));
-
- // for(int edge=0;edge<data.getSize();edge++){
- // ps.println(Arrays.toString(q[edge]));
- // }
-
- return success;
- }
-
- public double KL_divergence()
- {
- return -loglikelihood + MathUtils.dotProduct(parameters, gradient);
- }
-
- public double loglikelihood()
- {
- return llh;
- }
-
- public double l1lmax()
- {
- double sum=0;
- for(int tag=0;tag<c.K;tag++){
- double max=0;
- for(int edge=0;edge<data.size();edge++){
- if(q[edge][tag]>max)
- max=q[edge][tag];
- }
- sum+=max;
- }
- return sum;
- }
-
- public double primal(double scale)
- {
- return loglikelihood() - KL_divergence() - scale * l1lmax();
- }
-}
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