com.expleague.ml.methods.multiclass.spoc.SPOCMethodProbsDecoder 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.ml.data.set.VecDataSet;
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.MulticlassCodingMatrixModelProbsDecoder;
/**
* User: qdeee
* Date: 23.05.14
*/
public class SPOCMethodProbsDecoder extends SPOCMethodClassic {
public static final double METRIC_STEP = 0.05;
public static final int METRIC_ITERS = 100;
public static final double METRIC_C = 0.5;
private final Mx S;
private final double metricStep;
private final double metricC;
public SPOCMethodProbsDecoder(final Mx codingMatrix, final Mx mxS, final VecOptimization weak) {
this(codingMatrix, mxS, weak, METRIC_STEP, METRIC_C);
}
public SPOCMethodProbsDecoder(final Mx codingMatrix, final Mx mxS, final VecOptimization weak, final double metricStep,
final double metricC) {
super(codingMatrix, weak);
this.S = mxS;
this.metricStep = metricStep;
this.metricC = metricC;
}
@Override
protected MulticlassCodingMatrixModelProbsDecoder createModel(final Func[] binClass, final VecDataSet learnDS, final BlockwiseMLLLogit llLogit) {
final CMLMetricOptimization metricOptimization = new CMLMetricOptimization(learnDS, llLogit, S, metricC, metricStep);
final Mx mu = metricOptimization.trainProbs(codeMatrix, binClass);
return new MulticlassCodingMatrixModelProbsDecoder(codeMatrix, binClass, MX_IGNORE_THRESHOLD, mu);
}
}
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