Example usage for org.apache.commons.math3.linear RealVector getLInfNorm

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealVector getLInfNorm.

Prototype

public double getLInfNorm() 

Source Link

Document

Returns the L_∞ norm of the vector.

Usage

From source file:com.opengamma.strata.math.impl.matrix.CommonsMatrixAlgebra.java

@Override
public double getNormInfinity(Matrix m) {
    ArgChecker.notNull(m, "m");
    if (m instanceof DoubleArray) {
        RealVector temp = CommonsMathWrapper.wrap((DoubleArray) m);
        return temp.getLInfNorm();
    } else if (m instanceof DoubleMatrix) {
        RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix) m);
        //REVIEW Commons getNorm() is wrong - it returns the column norm
        // TODO find if commons implements this anywhere, so we are not doing it
        // by hand
        double max = 0.0;
        for (int row = temp.getRowDimension() - 1; row >= 0; row--) {
            max = Math.max(max, temp.getRowVector(row).getL1Norm());
        }//  w  w w .  j a v  a2s. c  om
        return max;
    }
    throw new IllegalArgumentException("Can only find normInfinity of DoubleMatrix; have " + m.getClass());
}

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From source file:org.apache.predictionio.examples.java.recommendations.tutorial4.FeatureBasedAlgorithm.java

public FeatureBasedModel train(PreparedData data) {
    Map<Integer, RealVector> userFeatures = new HashMap<Integer, RealVector>();
    Map<Integer, Integer> userActions = new HashMap<Integer, Integer>();

    for (Integer uid : data.userInfo.keySet()) {
        userFeatures.put(uid, new ArrayRealVector(data.featureCount));
        userActions.put(uid, 0);/*from www. j  a va2 s.  c  om*/
    }

    for (TrainingData.Rating rating : data.ratings) {
        final int uid = rating.uid;
        final int iid = rating.iid;
        final double rate = rating.rating;

        // Skip features outside the range.
        if (!(params.min <= rate && rate <= params.max))
            continue;

        final double actualRate = (rate - params.drift) * params.scale;
        final RealVector userFeature = userFeatures.get(uid);
        final RealVector itemFeature = data.itemFeatures.get(iid);
        userFeature.combineToSelf(1, actualRate, itemFeature);

        userActions.put(uid, userActions.get(uid) + 1);
    }

    // Normalize userFeatures by l-inf-norm
    for (Integer uid : userFeatures.keySet()) {
        final RealVector feature = userFeatures.get(uid);
        feature.mapDivideToSelf(feature.getLInfNorm());
    }

    // Normalize itemFeatures by weight
    Map<Integer, RealVector> itemFeatures = new HashMap<Integer, RealVector>();
    for (Integer iid : data.itemFeatures.keySet()) {
        final RealVector feature = data.itemFeatures.get(iid);
        final RealVector normalizedFeature = feature.mapDivide(feature.getL1Norm());
        itemFeatures.put(iid, normalizedFeature);
    }

    return new FeatureBasedModel(userFeatures, userActions, itemFeatures);
}

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