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

List of usage examples for org.apache.commons.math3.linear RealVector getDimension

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Introduction

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

Prototype

public abstract int getDimension();

Source Link

Document

Returns the size of the vector.

Usage

From source file:org.lambda3.indra.core.impl.DiceClient.java

@Override
protected double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }/*from   ww  w.ja va2s  . co  m*/

    double min = 0.0;
    double sum = 0.0;

    for (int i = 0; i < r1.getDimension(); ++i) {
        if (r1.getEntry(i) > r2.getEntry(i)) {
            min += r2.getEntry(i);
        } else {
            min += r1.getEntry(i);
        }
        sum += r1.getEntry(i) + r2.getEntry(i);
    }

    if (sum == 0) {
        return 0;
    }

    double result = 2 * min / sum;
    return Math.abs(result);
}

From source file:org.lambda3.indra.core.impl.Jaccard2Client.java

@Override
protected double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }/*from ww  w.j  a v  a2s . com*/

    double min = 0.0;
    double max = 0.0;

    for (int i = 0; i < r1.getDimension(); ++i) {
        if (r1.getEntry(i) > r2.getEntry(i)) {
            min += r2.getEntry(i);
            max += r1.getEntry(i);
        } else {
            min += r1.getEntry(i);
            max += r2.getEntry(i);
        }
    }

    if (max == 0) {
        return 0;
    }

    return Math.abs(min / max);
}

From source file:org.lambda3.indra.core.impl.JensenShannonClient.java

@Override
protected double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }/*from   www.  j  ava2s  .  c om*/

    double divergence = 0.0;
    double avr = 0.0;

    for (int i = 0; i < r1.getDimension(); ++i) {
        avr = (r1.getEntry(i) + r2.getEntry(i)) / 2;

        if (r1.getEntry(i) > 0.0 && avr > 0.0) {
            divergence += r1.getEntry(i) * Math.log(r1.getEntry(i) / avr);
        }
    }
    for (int i = 0; i < r2.getDimension(); ++i) {
        avr = (r1.getEntry(i) + r2.getEntry(i)) / 2;

        if (r2.getEntry(i) > 0.0 && avr > 0.0) {
            divergence += r1.getEntry(i) * Math.log(r2.getEntry(i) / avr);
        }
    }

    double result = 1 - (divergence / (2 * Math.sqrt(2 * Math.log(2))));
    return Math.abs(result);
}

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

@Override
protected double computeScore(double bias, @Nonnull RealVector user, @Nonnull RealVector item) {
    if (domain == null) {
        return super.computeScore(bias, user, item);
    } else {/*  w w  w  . j a va  2  s.c om*/
        double result = bias;
        int n = user.getDimension();
        for (int i = 0; i < n; i++) {
            result = domain.clampValue(result + user.getEntry(i) * item.getEntry(i));
        }
        return result;
    }
}

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 ww. j a v  a 2s . com*/
 * @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:org.lenskit.mf.svd.DomainClampingKernel.java

@Override
public double apply(double bias, @Nonnull RealVector user, @Nonnull RealVector item) {
    final int n = user.getDimension();
    Preconditions.checkArgument(item.getDimension() == n, "vectors have different lengths");

    double result = bias;
    for (int i = 0; i < n; i++) {
        result = domain.clampValue(result + user.getEntry(i) * item.getEntry(i));
    }//from   ww w .  j a  v a 2  s .  c o m
    return result;
}

From source file:org.lenskit.pf.HPFModelProvider.java

public void logNormalize(RealVector phi) {
    final int size = phi.getDimension();
    if (size == 1) {
        phi.setEntry(0, 1.0);//from   ww  w.ja v a  2 s . com
    }

    if (size > 1) {
        double logsum = phi.getEntry(0);
        for (int i = 1; i < size; i++) {
            double phiK = phi.getEntry(i);
            if (phiK < logsum) {
                logsum = logsum + Math.log(1 + Math.exp(phiK - logsum));
            } else {
                logsum = phiK + Math.log(1 + Math.exp(logsum - phiK));
            }
        }

        for (int k = 0; k < size; k++) {
            double phiK = phi.getEntry(k);
            double normalized = Math.exp(phiK - logsum);
            phi.setEntry(k, normalized);
        }
    }

}

From source file:org.lenskit.pf.PMFModel.java

private static void logNormalize(RealVector phi) {
    final int size = phi.getDimension();
    if (size == 1) {
        phi.setEntry(0, 1.0);/*from   w ww . j a v  a2 s  . co  m*/
    }

    if (size > 1) {
        double logsum = phi.getEntry(0);
        for (int k = 1; k < size; k++) {
            double phiK = phi.getEntry(k);
            if (phiK < logsum) {
                logsum = logsum + Math.log(1 + Math.exp(phiK - logsum));
            } else {
                logsum = phiK + Math.log(1 + Math.exp(logsum - phiK));
            }
        }

        for (int k = 0; k < size; k++) {
            double phiK = phi.getEntry(k);
            double normalized = Math.exp(phiK - logsum);
            phi.setEntry(k, normalized);
        }
    }

}

From source file:org.lenskit.predict.ordrec.OrdRecRatingPredictor.java

/**
 * The train function of OrdRec. Get all parameters after learning process.
 *//*from   w  ww. ja  va  2  s .c o  m*/
@SuppressWarnings("ConstantConditions")
private void trainModel(OrdRecModel model, SparseVector ratings, MutableSparseVector scores) {
    RealVector beta = model.getBeta();
    RealVector deltaBeta = new ArrayRealVector(beta.getDimension());
    double dt1;
    // n is the number of iteration;
    for (int j = 0; j < iterationCount; j++) {
        for (VectorEntry rating : ratings) {
            long iid = rating.getKey();
            double score = scores.get(iid);
            int r = quantizer.index(rating.getValue());

            double probEqualR = model.getProbEQ(score, r);
            double probLessR = model.getProbLE(score, r);
            double probLessR_1 = model.getProbLE(score, r - 1);

            double t1 = model.getT1();
            dt1 = learningRate / probEqualR * (probLessR * (1 - probLessR) * dBeta(r, 0, t1)
                    - probLessR_1 * (1 - probLessR_1) * dBeta(r - 1, 0, t1) - regTerm * t1);

            double dbetaK;
            for (int k = 0; k < beta.getDimension(); k++) {
                dbetaK = learningRate / probEqualR
                        * (probLessR * (1 - probLessR) * dBeta(r, k + 1, beta.getEntry(k))
                                - probLessR_1 * (1 - probLessR_1) * dBeta(r - 1, k + 1, beta.getEntry(k))
                                - regTerm * beta.getEntry(k));
                deltaBeta.setEntry(k, dbetaK);
            }
            model.update(dt1, deltaBeta);
        }
    }
}

From source file:org.lenskit.transform.quantize.PreferenceDomainQuantizerTest.java

@Test
public void testMakeValues() {
    RealVector vals = PreferenceDomainQuantizer.makeValues(domain);
    RealVector evals = new ArrayRealVector(new double[] { 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 });
    assertThat(vals.getDimension(), equalTo(evals.getDimension()));
    for (int i = 0; i < vals.getDimension(); i++) {
        assertThat("element " + i, vals.getEntry(i), closeTo(evals.getEntry(i), 1.0e-6));
    }/*from  w  ww  .jav a 2  s . c  o m*/
}