Example usage for org.apache.commons.math3.linear ArrayRealVector ArrayRealVector

Introduction

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

Prototype

public ArrayRealVector(ArrayRealVector v) throws NullArgumentException 

Source Link

Document

Construct a vector from another vector, using a deep copy.

Usage

From source file:com.github.thorbenlindhauer.test.util.LinearAlgebraUtil.java

public static RealVector asVector(double value) {
    return new ArrayRealVector(new double[] { value });
}

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From source file:EndmemberExtraction.java

public static ArrayList<Integer> ORASIS(double[][] data, int nData, int nDim, double threshold,
        int[] exemplarLabel) {
    ArrayRealVector vec;//from  ww w. ja va 2 s .c  o m
    ArrayList<ArrayRealVector> X = new ArrayList<>();
    ArrayList<ArrayRealVector> E = new ArrayList<>();
    ArrayList<Integer> exemplarIndex = new ArrayList<>();

    for (int i = 0; i < nData; i++) {
        vec = new ArrayRealVector(data[i]);
        vec.unitize();
        X.add(vec);
    }

    E.add(X.get(0));
    exemplarIndex.add(0);
    double t = Math.sqrt(2 * (1 - threshold));

    //Add first element of test spectra to set of exemplar spectra
    exemplarLabel[0] = 0;

    boolean flag;
    double maxCos, sigmaMin, sigmaMax, dotXR, dotER, cosTheta;

    double[] vecR = new double[nDim];
    for (int i = 0; i < nDim; i++) {
        vecR[i] = 1 / Math.sqrt(nDim);
    }
    ArrayRealVector R = new ArrayRealVector(vecR);

    ArrayRealVector exemplarSpec, testSpec;

    for (int i = 0; i < X.size(); i++) {
        if (i == 0 || exemplarLabel[i] == -1) {
            continue;
        }

        flag = false;
        maxCos = 0;
        testSpec = X.get(i);
        dotXR = testSpec.dotProduct(R);
        sigmaMin = dotXR - t;
        sigmaMax = dotXR + t;

        for (int j = 0; j < E.size(); j++) {
            exemplarSpec = E.get(j);
            dotER = exemplarSpec.dotProduct(R);

            if (dotER < sigmaMax && dotER > sigmaMin) {
                cosTheta = testSpec.dotProduct(exemplarSpec);

                if (cosTheta > threshold) {
                    //Test spectra is similar to one of the exemplar spectra
                    if (cosTheta > maxCos) {
                        maxCos = cosTheta;
                        exemplarLabel[i] = j;
                        //System.out.println("Count: "+i+"\texemplarLabel: "+exemplarLabel[i]);
                        flag = true;
                    }
                }
            }
        }

        if (!flag) {
            //Test spectra is unique, add it to set of exemplars
            E.add(testSpec);
            exemplarIndex.add(i);
            exemplarLabel[i] = E.size() - 1;
            //System.out.println("Count: "+i+"\texemplarLabel: "+exemplarLabel[i]);
        }

    }
    return exemplarIndex;
}

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From source file:com.cloudera.oryx.common.math.Vectors.java

/**
 * Constructs a dense {@link RealVector} of the given dimension.
 *
 * @param dimension The size of the dense vector to create
 *//*from   w w  w.  j  a v a2 s .  com*/
public static RealVector dense(int dimension) {
    return new ArrayRealVector(dimension);
}

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From source file:edu.oregonstate.eecs.mcplan.ml.WekaGlue.java

public static RealVector toRealVector(final Instance inst) {
    final RealVector v = new ArrayRealVector(inst.numAttributes());
    for (int i = 0; i < inst.numAttributes(); ++i) {
        v.setEntry(i, inst.value(i));//from  w  w  w .ja  v  a  2  s .  c o m
    }
    return v;
}

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From source file:com.cloudera.oryx.kmeans.computation.covariance.CovarianceDataBuilder.java

CovarianceDataBuilder(int n) {
    this.means = new ArrayRealVector(n);
    this.rawCoMoment = new Array2DRowRealMatrix(n, n);
}

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From source file:edu.byu.nlp.stats.VectorCategoricalDistributionTest.java

/**
 * Test method for {@link edu.byu.nlp.stats.VectorCategoricalDistribution#entropy()}.
 *///  w  ww.  java  2 s .c  o m
@Test
public void testEntropy() {
    double[] logProbs = new double[] { log(0.7), log(0.0), log(0.3) };
    VectorCategoricalDistribution dist = new VectorCategoricalDistribution(new ArrayRealVector(logProbs));
    assertThat(dist.entropy()).isEqualTo(-0.7 * log(0.7) - 0.3 * log(0.3), Delta.delta(1e-10));
}

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From source file:com.datumbox.opensource.examples.DPMMExample.java

/**
 * Demo of Dirichlet Process Mixture Model with Multinomial
 *//*  w w w.j  av a 2  s. co  m*/
public static void MDPMM() {
    System.out.println("Multinomial DPMM");

    //Data points to cluster
    List<Point> pointList = new ArrayList<>();
    //cluster 1
    pointList.add(new Point(0,
            new ArrayRealVector(new double[] { 10.0, 13.0, 5.0, 6.0, 5.0, 4.0, 0.0, 0.0, 0.0, 0.0 })));
    pointList.add(new Point(1,
            new ArrayRealVector(new double[] { 11.0, 11.0, 6.0, 7.0, 7.0, 3.0, 0.0, 0.0, 1.0, 0.0 })));
    pointList.add(new Point(2,
            new ArrayRealVector(new double[] { 12.0, 12.0, 10.0, 16.0, 4.0, 6.0, 0.0, 0.0, 0.0, 2.0 })));
    //cluster 2
    pointList.add(new Point(3,
            new ArrayRealVector(new double[] { 10.0, 13.0, 0.0, 0.0, 0.0, 0.0, 5.0, 6.0, 5.0, 4.0 })));
    pointList.add(new Point(4,
            new ArrayRealVector(new double[] { 11.0, 11.0, 0.0, 0.0, 1.0, 0.0, 6.0, 7.0, 7.0, 3.0 })));
    pointList.add(new Point(5,
            new ArrayRealVector(new double[] { 12.0, 12.0, 0.0, 0.0, 0.0, 2.0, 10.0, 16.0, 4.0, 6.0 })));
    //cluster 3
    pointList.add(new Point(6,
            new ArrayRealVector(new double[] { 10.0, 13.0, 5.0, 6.0, 5.0, 4.0, 5.0, 6.0, 5.0, 4.0 })));
    pointList.add(new Point(7,
            new ArrayRealVector(new double[] { 11.0, 11.0, 6.0, 7.0, 7.0, 3.0, 6.0, 7.0, 7.0, 3.0 })));
    pointList.add(new Point(8,
            new ArrayRealVector(new double[] { 12.0, 12.0, 10.0, 16.0, 4.0, 6.0, 10.0, 16.0, 4.0, 6.0 })));

    //Dirichlet Process parameter
    Integer dimensionality = 10;
    double alpha = 1.0;

    //Hyper parameters of Base Function
    double alphaWords = 1.0;

    //Create a DPMM object
    DPMM dpmm = new MultinomialDPMM(dimensionality, alpha, alphaWords);

    int maxIterations = 100;
    int performedIterations = dpmm.cluster(pointList, maxIterations);
    if (performedIterations < maxIterations) {
        System.out.println("Converged in " + String.valueOf(performedIterations));
    } else {
        System.out.println("Max iterations of " + String.valueOf(performedIterations)
                + " reached. Possibly did not converge.");
    }

    //get a list with the point ids and their assignments
    Map<Integer, Integer> zi = dpmm.getPointAssignments();
    System.out.println(zi.toString());

}

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From source file:game.plugins.valuetemplates.VectorTemplate.java

@Override
public RealVector loadValue(List<String> description) {
    RealVector ret = new ArrayRealVector(dimension);
    int i = 0;//ww w  . ja  v a2  s.  c  o m
    try {
        for (String s : description)
            ret.setEntry(i++, Double.parseDouble(s));
    } catch (Exception e) {
        System.out.println("oh");
    }
    return ret;
}

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From source file:game.plugins.weka.classifiers.WekaClassifier.java

@Override
public Data classify(Data input) {
    Data ret = new Data();

    for (Element e : input) {
        Instance i = new Instance(1.0, e.get(RealVector.class).toArray());
        i.setDataset(dataset);//from   w w w  .ja va  2 s .c o m
        try {
            ret.add(new Element(new ArrayRealVector(internal.distributionForInstance(i))));
        } catch (Exception ex) {
            ex.printStackTrace();
        }
    }

    return ret;
}

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From source file:gamlss.algorithm.GlimFitCG.java

public GlimFitCG(boolean copyOriginal, int size) {

    wls = new WLSMultipleLinearRegression(copyOriginal);

    adj = new ArrayRealVector(size);

}

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