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

Introduction

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

Prototype

public abstract double getEntry(int index) throws OutOfRangeException;

Source Link

Document

Return the entry at the specified index.

Usage

From source file:org.grouplens.samantha.modeler.tree.RegressionTree.java

private int predictLeaf(LearningInstance instance) {
    int predNode = -1;
    if (variableSpace.getVectorVarSizeByName(treeName) > 0) {
        StandardLearningInstance ins = (StandardLearningInstance) instance;
        int node = 0;
        do {//  ww w.ja  v a 2 s . c o m
            predNode = node;
            RealVector nodeVec = variableSpace.getVectorVarByNameIndex(treeName, node);
            int splitIdx = (int) nodeVec.getEntry(0);
            if (splitIdx == -1) {
                return predNode;
            }
            double splitVal = nodeVec.getEntry(1);
            double feaVal = 0.0;
            if (ins.getFeatures().containsKey(splitIdx)) {
                feaVal = ins.getFeatures().get(splitIdx);
            }
            if (feaVal <= splitVal) {
                node = (int) nodeVec.getEntry(2);
            } else {
                node = (int) nodeVec.getEntry(3);
            }
            if (node == -1) {
                return predNode;
            }
        } while (node != -1);
    }
    return predNode;
}

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From source file:org.grouplens.samantha.server.retriever.FeatureSupportRetrieverConfig.java

public Retriever getRetriever(RequestContext requestContext) {
    List<EntityExpander> entityExpanders = ExpanderUtilities.getEntityExpanders(requestContext, expandersConfig,
            injector);//from   w w w  . j av a2 s .  co m
    FeatureSupportModelManager manager = new FeatureSupportModelManager(modelName, modelFile, injector);
    IndexedVectorModel model = (IndexedVectorModel) manager.manage(requestContext);
    List<ObjectNode> results = new ArrayList<>();
    int size = model.getIndexSize();
    for (int i = 0; i < size; i++) {
        ObjectNode one = Json.newObject();
        Map<String, String> keys = FeatureExtractorUtilities.decomposeKey(model.getKeyByIndex(i));
        RealVector vector = model.getIndexVector(i);
        one.put(supportAttr, vector.getEntry(0));
        for (String attr : itemAttrs) {
            one.put(attr, keys.get(attr));
        }
        results.add(one);
    }
    return new PrecomputedRetriever(results, entityExpanders, config);
}

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From source file:org.grouplens.samantha.server.space.RedisVariableSpace.java

public void setScalarVarByName(String name, RealVector vars) {
    for (int i = 0; i < vars.getDimension(); i++) {
        setScalarVarByNameIndex(name, i, vars.getEntry(i));
    }// w ww . ja  va2s.  co  m
}

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From source file:org.grouplens.samantha.server.space.RedisVariableSpace.java

public void setVectorVarByNameIndex(String name, int index, RealVector var) {
    String varIdxName = "IDX_V_" + name + "_" + Integer.valueOf(index).toString();
    ArrayNode values = Json.newArray();/*from  w ww.  j a  v a 2 s.  c  o m*/
    values.add(index);
    for (int i = 0; i < var.getDimension(); i++) {
        values.add(var.getEntry(i));
    }
    redisService.setValue(spaceIdentifier, varIdxName, values);
}

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From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealVector sqrt(final RealVector vector) {
    final RealVector result = vector.copy();
    for (int e = 0; e < result.getDimension(); e++) {
        result.setEntry(e, Math.sqrt(result.getEntry(e)));
    }/* ww  w .jav  a2  s  .  c o m*/
    return result;
}

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From source file:org.knime.knip.core.algorithm.convolvers.KernelTools.java

/**
 * Creates a numDims dimensions image where all dimensions d<numDims are of size 1 and the last dimensions contains
 * the vector./*from www .j  a v  a 2  s.com*/
 * 
 * @param <R>
 * @param ar
 * @param type
 * @param numDims number of dimensions
 * @return
 */
public static <R extends RealType<R>> Img<R> vectorToImage(final RealVector ar, final R type, final int numDims,
        final ImgFactory<R> fac) {
    long[] dims = new long[numDims];

    for (int i = 0; i < dims.length - 1; i++) {
        dims[i] = 1;
    }
    dims[dims.length - 1] = ar.getDimension();
    Img<R> res = fac.create(dims, type);
    Cursor<R> c = res.cursor();
    while (c.hasNext()) {
        c.fwd();
        c.get().setReal(ar.getEntry(c.getIntPosition(numDims - 1)));
    }

    return res;
}

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From source file:org.knime.knip.core.util.ApacheMathTools.java

/**
 * Creates a numDims dimensions image where all dimensions d<numDims are of size 1 and the last dimensions contains
 * the vector.//from w  ww .  j  a  va2 s  .co m
 * 
 * @param <R>
 * @param ar
 * @param type
 * @param numDims number of dimensions
 * @return
 */
public static <R extends RealType<R>> Img<R> vectorToImage(final RealVector ar, final R type, final int numDims,
        final ImgFactory<R> fac) {
    final long[] dims = new long[numDims];

    for (int i = 0; i < (dims.length - 1); i++) {
        dims[i] = 1;
    }
    dims[dims.length - 1] = ar.getDimension();
    final Img<R> res = fac.create(dims, type);
    final Cursor<R> c = res.cursor();
    while (c.hasNext()) {
        c.fwd();
        c.get().setReal(ar.getEntry(c.getIntPosition(numDims - 1)));
    }

    return res;
}

---

From source file:org.knowrob.vis.model.util.algorithm.ACCUM.java

/**
 * Calculates curvature for the vertices of a triangle. Based on
 * the trimesh2 (2.12) (Szymon Rusinkiewicz Princeton University)
 * curvature calculation algorithm.//from  w ww.j  a va 2 s.  c  o  m
 * 
 * @see <a href="https://github.com/fcole/qrtsc/tree/master/trimesh2">trimesh2</a>
 * 
 * @param curvatures
 *            vertex curvature mapping
 * @param tri
 *            triangle to calculate curvature for
 */
private static void calculateCurvatureForTriangle(HashMap<Vertex, Curvature> curvatures, Triangle tri) {
    // Edges
    Edge e[] = tri.getEdges();

    // N-T-B coordinate system per face
    Vector3f t = new Vector3f(e[0].getEdgeValue());
    t.normalize();
    Vector3f n = new Vector3f();
    n.cross(e[0].getEdgeValue(), e[1].getEdgeValue());
    //n.normalize();
    Vector3f b = new Vector3f();
    b.cross(n, t);
    b.normalize();

    // Estimate curvature based on variation of normals
    // along edges
    float m[] = { 0.0f, 0.0f, 0.0f };
    double w[][] = { { 0.0d, 0.0d, 0.0d }, { 0.0d, 0.0d, 0.0d }, { 0.0d, 0.0d, 0.0d } };
    for (int j = 0; j < 3; j++) {

        float u = e[j].getEdgeValue().dot(t);
        float v = e[j].getEdgeValue().dot(b);
        w[0][0] += u * u;
        w[0][1] += u * v;
        // w[1][1] += v*v + u*u;
        // w[1][2] += u*v;
        w[2][2] += v * v;
        Vector3f dn = new Vector3f(tri.getPosition()[(j + 2) % 3].getNormalVector());
        Vector3f dnn = new Vector3f(tri.getPosition()[(j + 1) % 3].getNormalVector());
        dn.sub(dnn);
        float dnu = dn.dot(t);
        float dnv = dn.dot(b);
        m[0] += dnu * u;
        m[1] += dnu * v + dnv * u;
        m[2] += dnv * v;
    }
    w[1][1] = w[0][0] + w[2][2];
    w[1][2] = w[0][1];

    RealMatrix coefficients = new Array2DRowRealMatrix(w, false);
    DecompositionSolver solver = new LUDecomposition(coefficients).getSolver();
    if (!solver.isNonSingular()) {
        return;
    }

    RealVector constants = new ArrayRealVector(new double[] { m[0], m[1], m[2] }, false);
    RealVector solution = solver.solve(constants);

    m[0] = (float) solution.getEntry(0);
    m[1] = (float) solution.getEntry(1);
    m[2] = (float) solution.getEntry(2);

    // flag for ill-solutioned system (NaN = no valid info on the curvatures => then consider 
    // the curvatures to be the ones of a simple plane)
    boolean okSolution = true;
    if (isNaN(m[0]) || isNaN(m[1]) || isNaN(m[2])) {
        okSolution = false;
    }

    // Push it back out to the vertices
    for (int j = 0; j < tri.getPosition().length; j++) {
        Vertex vj = tri.getPosition()[j];
        float c1, c12, c2;
        float[] ret;

        // guard against ill-conditioned systems (NaN curvatures are viewed as a simple planar
        // corresponding curves      
        if (okSolution) {
            ret = proj_curv(t, b, m[0], m[1], m[2], curvatures.get(vj).getPrincipleDirectionMax(),
                    curvatures.get(vj).getPrincipleDirectionMin());
            if (isNaN(ret[0]) || isNaN(ret[1]) || isNaN(ret[2])) {
                c1 = 0.0f;
                c12 = 0.0f;
                c2 = 0.0f;
            } else {
                c1 = ret[0];
                c12 = ret[1];
                c2 = ret[2];
            }
        } else {
            c1 = 0.0f;
            c12 = 0.0f;
            c2 = 0.0f;
        }

        Curvature c = curvatures.get(vj);

        double wt = 0.0d;
        if (vj.getPointarea() != 0.0f) {
            if (j == 0)
                wt = tri.getCornerarea().x / vj.getPointarea();
            else if (j == 1)
                wt = tri.getCornerarea().y / vj.getPointarea();
            else
                wt = tri.getCornerarea().z / vj.getPointarea();
        }

        synchronized (c) {
            c.setCurvatureMax((float) (c.getCurvatureMax() + wt * c1));
            c.setCurvatureMinMax((float) (c.getCurvatureMinMax() + wt * c12));
            c.setCurvatureMin((float) (c.getCurvatureMin() + wt * c2));
        }
    }
}

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From source file:org.lambda3.indra.core.function.AlphaSkewRelatednessFunction.java

@Override
public double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }//from  w w  w.  j  ava2 s . co m

    double alpha = 0.99;
    double divergence = 0.0;

    for (int i = 0; i < r1.getDimension(); ++i) {
        if (r1.getEntry(i) > 0.0 && r2.getEntry(i) > 0.0) {
            divergence += r1.getEntry(i)
                    * Math.log(r1.getEntry(i) / ((1 - alpha) * r1.getEntry(i) + alpha * r2.getEntry(i)));
        }
    }

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

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From source file:org.lambda3.indra.core.function.ChebyshevRelatednessFunction.java

@Override
public double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }//from  www.  jav  a 2s .  c  o m

    double max = 0;
    double tmp;

    for (int i = 0; i < r1.getDimension(); ++i) {
        tmp = Math.abs((r1.getEntry(i) - r2.getEntry(i)));
        max = (tmp > max ? tmp : max);
    }

    double result = 1 / (1 + (max == Double.NaN ? 0 : max));
    return Math.abs(result);
}

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