Example usage for org.apache.commons.math.linear RealVector mapDivide

Introduction

In this page you can find the example usage for org.apache.commons.math.linear RealVector mapDivide.

Prototype

RealVector mapDivide(double d);

Source Link

Document

Divide each entry.

Usage

From source file:io.seldon.mf.RecentMfRecommender.java

public double[] createAvgProductVector(List<Long> recentitemInteractions, Map<Long, float[]> productFeatures) {
    int numLatentFactors = productFeatures.values().iterator().next().length;
    double[] userFeatures = new double[numLatentFactors];
    for (Long item : recentitemInteractions) {
        float[] productFactors = productFeatures.get(item);
        if (productFactors != null) {
            for (int feature = 0; feature < numLatentFactors; feature++) {
                userFeatures[feature] += productFactors[feature];
            }//from  w  w w . jav  a  2  s .c  o  m
        }
    }
    RealVector userFeaturesAsVector = new ArrayRealVector(userFeatures);
    RealVector normalised = userFeaturesAsVector.mapDivide(userFeaturesAsVector.getL1Norm());

    return normalised.getData();
}

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From source file:org.plista.kornakapi.core.recommender.FoldingFactorization.java

public double[] foldInAnonymousUser(long[] itemIDs) throws NoSuchItemException {

    double[] userFeatures = new double[factorization.numFeatures()];
    for (long itemID : itemIDs) {
        try {/*from   ww  w.j  a  v  a2  s .c o  m*/
            int itemIndex = factorization.itemIndex(itemID);
            for (int feature = 0; feature < factorization.numFeatures(); feature++) {
                userFeatures[feature] += factorization.allItemFeatures()[itemIndex][feature];
            }
        } catch (NoSuchItemException e) {
            if (log.isDebugEnabled()) {
                log.debug("Item unknown: {}", itemID);
                if (itemIDs.length == 1) {
                    throw new NoSuchItemException("At least one item must be known");
                }
            }
        }
    }
    RealVector userFeaturesAsVector = new ArrayRealVector(userFeatures);
    RealVector normalised = userFeaturesAsVector.mapDivide(userFeaturesAsVector.getL1Norm());

    return normalised.getData();
}

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