Example usage for org.apache.mahout.math.function Functions PLUS_ABS

Introduction

In this page you can find the example usage for org.apache.mahout.math.function Functions PLUS_ABS.

Prototype

DoubleDoubleFunction PLUS_ABS

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Document

Function that returns Math.abs(a) + Math.abs(b).

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;/*  www  . j av a 2  s  . c om*/
    /* 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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