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

Introduction

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

Prototype

public double getNorm()

Source Link

Document

Returns the L₂ norm of the vector.

Usage

From source file:org.grouplens.lenskit.diffusion.Iterative.IterativeDiffusionItemScorer.java

/**
 * Score items by computing predicted ratings.
 *
 * @see ItemScoreAlgorithm#scoreItems(ItemItemModel, org.grouplens.lenskit.vectors.SparseVector, org.grouplens.lenskit.vectors.MutableSparseVector, NeighborhoodScorer)
 *//*from  w  w  w .j a v  a2  s .  c om*/
@Override
public void score(long user, @Nonnull MutableSparseVector scores) {
    UserHistory<? extends Event> history = dao.getEventsForUser(user, summarizer.eventTypeWanted());
    if (history == null) {
        history = History.forUser(user);
    }
    SparseVector summary = summarizer.summarize(history);
    VectorTransformation transform = normalizer.makeTransformation(user, summary);
    MutableSparseVector normed = summary.mutableCopy();
    transform.apply(normed);
    scores.clear();
    int numItems = 1682;
    //algorithm.scoreItems(model, normed, scores, scorer);
    int num_updates = 300;
    double update_rate = 1;
    double threshold = 0.01;
    RealVector z_out = diffusionMatrix.preMultiply(VectorUtils.toRealVector(numItems, normed));
    boolean updated = true;
    LongSortedSet known = normed.keySet();
    int count_iter = 0;
    for (int i = 0; i < num_updates && updated; i++) {
        updated = false;
        RealVector temp = diffusionMatrix.preMultiply(z_out);
        temp.mapMultiplyToSelf(z_out.getNorm() / temp.getNorm());
        RealVector temp_diff = z_out.add(temp.mapMultiplyToSelf(-1.0));
        for (int j = 0; j < numItems; j++) {
            if (!known.contains((long) (j + 1))) {
                //if the rating is not one of the known ones
                if (Math.abs(temp_diff.getEntry(j)) > threshold) {
                    // if difference is large enough, update
                    updated = true;
                    z_out.setEntry(j, (1.0 - update_rate) * z_out.getEntry(j) + update_rate * temp.getEntry(j));
                }
            }
        }
        count_iter++;
    }
    System.out.println(count_iter);
    LongSortedSet testDomain = scores.keyDomain();
    //fill up the score vector
    for (int i = 0; i < numItems; i++) {
        if (testDomain.contains((long) (i + 1))) {
            scores.set((long) (i + 1), z_out.getEntry(i));
        }
    }

    // untransform the scores
    transform.unapply(scores);
    System.out.println(scores);
}

From source file:org.grouplens.samantha.modeler.solver.L2Regularizer.java

public double getObjective(double l2coef, RealVector var) {
    double l2norm = var.getNorm();
    return l2coef * l2norm * l2norm;
}

From source file:org.grouplens.samantha.modeler.solver.L2Regularizer.java

public double getObjective(double l2coef, List<RealVector> vars) {
    double objVal = 0.0;
    for (RealVector realVector : vars) {
        double l2norm = realVector.getNorm();
        objVal += l2norm * l2norm;// www. jav a2 s.  c  o m
    }
    return objVal * l2coef;
}

From source file:org.lambda3.indra.composition.L2NormSumVectorComposer.java

@Override
public RealVector compose(List<RealVector> vectors) {
    RealVector sum = super.compose(vectors);
    if (vectors.size() > 1) {
        //norm is not calculated for single vectors.
        double norm = sum.getNorm();
        return sum.mapDivideToSelf(norm);
    }//from w w  w  . j a  va 2 s  . c  o  m

    return sum;
}

From source file:org.lenskit.mf.funksvd.FunkSVDModelBuilder.java

/**
 * Add a feature's summary to the feature info builder.
 *
 * @param ufv The user values.//from   w  w w.j a  va2  s .  c o  m
 * @param ifv The item values.
 * @param fib  The feature info builder.
 */
protected void summarizeFeature(RealVector ufv, RealVector ifv, FeatureInfo.Builder fib) {
    fib.setUserAverage(realVectorSum(ufv) / ufv.getDimension())
            .setItemAverage(realVectorSum(ifv) / ifv.getDimension())
            .setSingularValue(ufv.getNorm() * ifv.getNorm());
}

From source file:rcdemo.physics.FrictionForceModel.java

@Override
public RealVector getForce(RealVector x, RealVector v) {
    double rho = 1;
    double Cd = 1;
    double A = 1;
    double factor = 0.5 * rho * v.getNorm() * Cd * A;
    return v.mapMultiply(-factor);
}

From source file:trustframework.utils.Similarity.java

/**
 * Calculates cosine similarity between two frequencies
 * @param <T> Hash key class/*from   w  w w  .j  a  v a 2s  .  com*/
 * @param freq1 frequency 1
 * @param freq2 frequency 2
 * @return cosine similarity [0,1]
 */
public static <T> double cosineSimilarity(Map<T, Integer> freq1, Map<T, Integer> freq2) {
    Set<T> terms = new HashSet<>();
    terms.addAll(freq1.keySet());
    terms.addAll(freq2.keySet());
    RealVector v1 = buildRealVector(freq1, terms);
    RealVector v2 = buildRealVector(freq2, terms);
    return (v1.dotProduct(v2)) / (v1.getNorm() * v2.getNorm());
}