com.expleague.ml.methods.multiclass.spoc.SPOCMethodClassic Maven / Gradle / Ivy
package com.expleague.ml.methods.multiclass.spoc;
import com.expleague.commons.math.Func;
import com.expleague.commons.math.vectors.Mx;
import com.expleague.commons.math.vectors.VecTools;
import com.expleague.commons.math.vectors.impl.idxtrans.RowsPermutation;
import com.expleague.commons.math.vectors.impl.mx.VecBasedMx;
import com.expleague.commons.math.vectors.impl.vectors.ArrayVec;
import com.expleague.commons.math.vectors.impl.vectors.IndexTransVec;
import com.expleague.ml.data.set.VecDataSet;
import com.expleague.ml.data.set.impl.VecDataSetImpl;
import com.expleague.ml.data.tools.MCTools;
import com.expleague.ml.loss.LLLogit;
import com.expleague.ml.loss.blockwise.BlockwiseMLLLogit;
import com.expleague.ml.methods.VecOptimization;
import com.expleague.ml.models.multiclass.MulticlassCodingMatrixModel;
import gnu.trove.list.TDoubleList;
import gnu.trove.list.TIntList;
import gnu.trove.list.linked.TDoubleLinkedList;
import gnu.trove.list.linked.TIntLinkedList;
import gnu.trove.map.TIntObjectMap;
/**
* User: qdeee
* Date: 07.05.14
*/
public class SPOCMethodClassic extends VecOptimization.Stub {
protected static final double MX_IGNORE_THRESHOLD = 0.1;
protected final VecOptimization weak;
protected final Mx codeMatrix;
public SPOCMethodClassic(final Mx codeMatrix, final VecOptimization weak) {
this.weak = weak;
this.codeMatrix = VecTools.copy(codeMatrix);
CMLHelper.normalizeMx(this.codeMatrix, MX_IGNORE_THRESHOLD);
}
@Override
public MulticlassCodingMatrixModel fit(final VecDataSet learn, final BlockwiseMLLLogit llLogit) {
// System.out.println("coding matrix: \n" + codeMatrix.toString());
final TIntObjectMap indexes = MCTools.splitClassesIdxs(llLogit.labels());
final int k = codeMatrix.rows();
final int l = codeMatrix.columns();
final Func[] binClassifiers = new Func[l];
for (int j = 0; j < l; j++) {
final TIntList learnIdxs = new TIntLinkedList();
final TDoubleList target = new TDoubleLinkedList();
for (int i = 0; i < k; i++) {
final double code = codeMatrix.get(i, j);
if (Math.abs(code) > MX_IGNORE_THRESHOLD) {
final TIntList classIdxs = indexes.get(i);
target.fill(target.size(), target.size() + classIdxs.size(), Math.signum(code));
learnIdxs.addAll(classIdxs);
}
}
final VecDataSet dataSet = new VecDataSetImpl(
new VecBasedMx(
learn.xdim(),
new IndexTransVec(
learn.data(),
new RowsPermutation(learnIdxs.toArray(), learn.xdim())
)
), learn);
final LLLogit loss = new LLLogit(new ArrayVec(target.toArray()), learn);
binClassifiers[j] = (Func) weak.fit(dataSet, loss);
}
return createModel(binClassifiers, learn, llLogit);
}
protected MulticlassCodingMatrixModel createModel(final Func[] binClass, final VecDataSet learnDS, final BlockwiseMLLLogit llLogit) {
return new MulticlassCodingMatrixModel(codeMatrix, binClass, MX_IGNORE_THRESHOLD);
}
}
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