package classification;
/***
* This class implements one best structured Mira
* @author Kuzman Ganchev and Georgi Georgiev
* <A HREF="mailto:georgiev@ontotext.com>georgi.georgiev@ontotext.com</A>
* <A HREF="mailto:ganchev@ontotext.com>kuzman.ganchev@ontotext.com</A>
* Date: Thu Feb 26 12:27:56 EET 2009
*/
import gnu.trove.TIntDoubleHashMap;
import gnu.trove.TIntDoubleIterator;
import java.util.ArrayList;
import types.Alphabet;
import types.ClassificationInstance;
import types.LinearClassifier;
import types.SparseVector;
import types.StaticUtils;
public class Mira {
boolean performAveraging;
int numIterations;
Alphabet xAlphabet;
Alphabet yAlphabet;
CompleteFeatureFunction fxy;
Loss loss;
public Mira(boolean performAveraging, int numIterations,
Alphabet xAlphabet, Alphabet yAlphabet,
CompleteFeatureFunction fxy, Loss loss) {
this.performAveraging = performAveraging;
this.numIterations = numIterations;
this.xAlphabet = xAlphabet;
this.yAlphabet = yAlphabet;
this.fxy = fxy;
this.loss = loss;
}
private double calculateDenom(SparseVector a, SparseVector b) {
double result = 0;
TIntDoubleHashMap diff = new TIntDoubleHashMap();
for (int i = 0; i < a.numEntries(); i++) {
int ind = a.getIndexAt(i);
double val = a.getValueAt(i);
if (!diff.containsKey(ind)) {
diff.put(ind, 0);
}
diff.put(ind, diff.get(ind) + val);
}
for (int i = 0; i < b.numEntries(); i++) {
int ind = b.getIndexAt(i);
double val = b.getValueAt(i);
if (!diff.containsKey(ind)) {
diff.put(ind, 0);
}
diff.put(ind, diff.get(ind) - val);
}
for (TIntDoubleIterator iterator = diff.iterator(); iterator.hasNext();) {
iterator.advance();
result += Math.pow(iterator.value(), 2);
}
return result;
}
public LinearClassifier batchTrain(
ArrayList<ClassificationInstance> trainingData) {
LinearClassifier w = new LinearClassifier(this.xAlphabet,
this.yAlphabet, this.fxy);
LinearClassifier theta = null;
if (this.performAveraging) {
theta = new LinearClassifier(this.xAlphabet, this.yAlphabet,
this.fxy);
}
for (int iter = 0; iter < this.numIterations; iter++) {
for (ClassificationInstance inst : trainingData) {
int yhat = w.label(inst.x);
// calculate loss
double lloss = this.loss.calculate(inst.y, yhat);
// calculate alpha
double alpha = lloss
+ StaticUtils.dotProduct(this.fxy.apply(inst.x, yhat),
w.w)
- StaticUtils.dotProduct(
this.fxy.apply(inst.x, inst.y), w.w);
if (alpha <= 0) {
continue;
}
alpha = alpha
/ this.calculateDenom(this.fxy.apply(inst.x, inst.y),
this.fxy.apply(inst.x, yhat));
// if y = yhat then this update will not change w.
StaticUtils.plusEquals(w.w, this.fxy.apply(inst.x, inst.y),
alpha);
StaticUtils.plusEquals(w.w, this.fxy.apply(inst.x, yhat),
-alpha);
if (this.performAveraging) {
StaticUtils.plusEquals(theta.w, w.w, 1);
}
}
}
if (this.performAveraging) {
return theta;
}
return w;
}
}