edu.umn.msi.tropix.proteomics.itraqquantitation.impl.TrainingWeightFunctionImpl.java Source code

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Here is the source code for edu.umn.msi.tropix.proteomics.itraqquantitation.impl.TrainingWeightFunctionImpl.java

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/********************************************************************************
 * Copyright (c) 2009 Regents of the University of Minnesota
 *
 * This Software was written at the Minnesota Supercomputing Institute
 * http://msi.umn.edu
 *
 * All rights reserved. The following statement of license applies
 * only to this file, and and not to the other files distributed with it
 * or derived therefrom.  This file is made available under the terms of
 * the Eclipse Public License v1.0 which accompanies this distribution,
 * and is available at http://www.eclipse.org/legal/epl-v10.html
 *
 * Contributors:
 * Minnesota Supercomputing Institute - initial API and implementation
 *******************************************************************************/

package edu.umn.msi.tropix.proteomics.itraqquantitation.impl;

import java.util.Collections;
import java.util.List;
import java.util.Map;

import com.google.common.base.Function;
import com.google.common.collect.Lists;
import com.google.common.collect.Maps;

import edu.umn.msi.tropix.proteomics.itraqquantitation.weight.QuantificationWeight;
import edu.umn.msi.tropix.proteomics.itraqquantitation.weight.QuantificationWeights;

class TrainingWeightFunctionImpl implements Function<Double, Double> {
    private final double[][] trainingMatrix;

    /*
    TrainingWeightFunctionImpl(final List<ITraqMatch> trainingMatches, final List<ITraqLabel> labels, QuantiationTrainingParameters trainingParameters) {
      final Map<ITraqLabel, double[]> intensities = Maps.newHashMap();
      final int numMatches = trainingMatches.size();
      for(ITraqLabel label : labels) {
        intensities.put(label, new double[numMatches]);
      }
      int i = 0;
      for(ITraqMatch match : trainingMatches) {
        for(ITraqLabel label : labels) {
    intensities.get(label)[i] = match.getScan().getIntensity(label);
        }
        i++;
      }
      this.trainingMatrix = Variance.createVarianceMatrix(labels, intensities, trainingParameters.getExpectedRatios(), trainingParameters.getNumBins());  
    }
    */

    TrainingWeightFunctionImpl(final QuantificationWeights weights) {
        final Map<Double, Double> weightMap = Maps.newHashMapWithExpectedSize(weights.getWeight().length);
        for (QuantificationWeight weight : weights.getWeight()) {
            weightMap.put(weight.getIntensity(), weight.getWeight());
        }
        final List<Double> sortedIntensities = Lists.newArrayList(weightMap.keySet());
        Collections.sort(sortedIntensities);
        this.trainingMatrix = new double[sortedIntensities.size()][2];
        int i = 0;
        for (final Double intensity : sortedIntensities) {
            trainingMatrix[i][0] = intensity;
            trainingMatrix[i][1] = weightMap.get(intensity);
            i++;
        }
    }

    public Double apply(final Double weightIndex) {
        final int numBins = trainingMatrix.length;
        double weight = trainingMatrix[numBins - 1][1];
        for (int i = numBins - 1; i >= 0; i--) {
            if (weightIndex <= trainingMatrix[i][0]) {
                weight = trainingMatrix[i][1];
            } else {
                break;
            }
        }
        return weight;
    }

}