Example usage for org.apache.mahout.cf.taste.hadoop.item PrefAndSimilarityColumnWritable getPrefValue

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.hadoop.item PrefAndSimilarityColumnWritable getPrefValue.

Prototype

public float getPrefValue() 

Source Link

Usage

From source file:hadoop.api.AggregateAndRecommendReducer.java

License:Apache License

private void reduceNonBooleanData(VarLongWritable userID, Iterable<PrefAndSimilarityColumnWritable> values,
        Context context) throws IOException, InterruptedException {
    /* each entry here is the sum in the numerator of the prediction formula */
    Vector numerators = null;//from   w w  w.j  av  a  2s.com
    /* each entry here is the sum in the denominator of the prediction formula */
    Vector denominators = null;
    /* each entry here is the number of similar items used in the prediction formula */
    Vector numberOfSimilarItemsUsed = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);

    for (PrefAndSimilarityColumnWritable prefAndSimilarityColumn : values) {
        Vector simColumn = prefAndSimilarityColumn.getSimilarityColumn();
        float prefValue = prefAndSimilarityColumn.getPrefValue();
        /* count the number of items used for each prediction */
        for (Element e : simColumn.nonZeroes()) {
            int itemIDIndex = e.index();
            numberOfSimilarItemsUsed.setQuick(itemIDIndex, numberOfSimilarItemsUsed.getQuick(itemIDIndex) + 1);
        }

        if (denominators == null) {
            denominators = simColumn.clone();
        } else {
            denominators.assign(simColumn, Functions.PLUS_ABS);
        }

        if (numerators == null) {
            numerators = simColumn.clone();
            if (prefValue != BOOLEAN_PREF_VALUE) {
                numerators.assign(Functions.MULT, prefValue);
            }
        } else {
            if (prefValue != BOOLEAN_PREF_VALUE) {
                simColumn.assign(Functions.MULT, prefValue);
            }
            numerators.assign(simColumn, Functions.PLUS);
        }

    }

    if (numerators == null) {
        return;
    }

    Vector recommendationVector = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
    for (Element element : numerators.nonZeroes()) {
        int itemIDIndex = element.index();
        /* preference estimations must be based on at least 2 datapoints */
        if (numberOfSimilarItemsUsed.getQuick(itemIDIndex) > 1) {
            /* compute normalized prediction */
            double prediction = element.get() / denominators.getQuick(itemIDIndex);
            recommendationVector.setQuick(itemIDIndex, prediction);
        }
    }
    writeRecommendedItems(userID, recommendationVector, context);
}

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From source file:nl.gridline.zieook.inx.movielens.AggregateAndRecommendReducer.java

License:Apache License

private void reduceNonBooleanData(VarLongWritable userID, Iterable<PrefAndSimilarityColumnWritable> values,
        Context context) throws IOException, InterruptedException {
    /* each entry here is the sum in the numerator of the prediction formula */
    Vector numerators = null;/*ww w. j a  va 2s.  c o  m*/
    /* each entry here is the sum in the denominator of the prediction formula */
    Vector denominators = null;
    /* each entry here is the number of similar items used in the prediction formula */
    Vector numberOfSimilarItemsUsed = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);

    for (PrefAndSimilarityColumnWritable prefAndSimilarityColumn : values) {
        Vector simColumn = prefAndSimilarityColumn.getSimilarityColumn();
        float prefValue = prefAndSimilarityColumn.getPrefValue();
        /* count the number of items used for each prediction */
        Iterator<Vector.Element> usedItemsIterator = simColumn.iterateNonZero();
        while (usedItemsIterator.hasNext()) {
            int itemIDIndex = usedItemsIterator.next().index();
            numberOfSimilarItemsUsed.setQuick(itemIDIndex, numberOfSimilarItemsUsed.getQuick(itemIDIndex) + 1);
        }

        numerators = numerators == null
                ? prefValue == BOOLEAN_PREF_VALUE ? simColumn.clone() : simColumn.times(prefValue)
                : numerators.plus(prefValue == BOOLEAN_PREF_VALUE ? simColumn : simColumn.times(prefValue));

        simColumn.assign(ABSOLUTE_VALUES);
        denominators = denominators == null ? simColumn : denominators.plus(simColumn);
    }

    if (numerators == null) {
        return;
    }

    Vector recommendationVector = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
    Iterator<Vector.Element> iterator = numerators.iterateNonZero();
    while (iterator.hasNext()) {
        Vector.Element element = iterator.next();
        int itemIDIndex = element.index();
        /* preference estimations must be based on at least 2 datapoints */
        if (numberOfSimilarItemsUsed.getQuick(itemIDIndex) > 1) {
            /* compute normalized prediction */
            double prediction = element.get() / denominators.getQuick(itemIDIndex);
            recommendationVector.setQuick(itemIDIndex, prediction);
        }
    }
    writeRecommendedItems(userID, recommendationVector, context);
}

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