Example usage for org.apache.mahout.math Vector divide

Introduction

In this page you can find the example usage for org.apache.mahout.math Vector divide.

Prototype

Vector divide(double x);

Source Link

Document

Return a new vector containing the values of the recipient divided by the argument

Usage

From source file:DisplayClustering.java

License:Apache License

/**
 * Identical to plotRectangle(), but with the option of setting the color of
 * the rectangle's stroke./*  ww  w . ja v  a2 s .  com*/
 *
 * NOTE: This should probably be refactored with plotRectangle() since most of
 * the code here is direct copy/paste from that method.
 *
 * @param g2
 *          A Graphics2D context.
 * @param v
 *          A vector for the rectangle's center.
 * @param dv
 *          A vector for the rectangle's dimensions.
 * @param color
 *          The color of the rectangle's stroke.
 */
protected static void plotClusteredRectangle(Graphics2D g2, Vector v, Vector dv, Color color) {
    double[] flip = { 1, -1 };
    Vector v2 = v.times(new DenseVector(flip));
    v2 = v2.minus(dv.divide(2));
    int h = SIZE / 2;
    double x = v2.get(0) + h;
    double y = v2.get(1) + h;

    g2.setStroke(new BasicStroke(1));
    g2.setColor(color);
    g2.draw(new Rectangle2D.Double(x * DS, y * DS, dv.get(0) * DS, dv.get(1) * DS));
}

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From source file:DisplayClustering.java

License:Apache License

/**
 * Draw a rectangle on the graphics context
 *
 * @param g2//from  w ww.j a  v a 2  s .c o m
 *          a Graphics2D context
 * @param v
 *          a Vector of rectangle center
 * @param dv
 *          a Vector of rectangle dimensions
 */
protected static void plotRectangle(Graphics2D g2, Vector v, Vector dv) {
    double[] flip = { 1, -1 };
    Vector v2 = v.times(new DenseVector(flip));
    v2 = v2.minus(dv.divide(2));
    int h = SIZE / 2;
    double x = v2.get(0) + h;
    double y = v2.get(1) + h;
    g2.draw(new Rectangle2D.Double(x * DS, y * DS, dv.get(0) * DS, dv.get(1) * DS));
}

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From source file:DisplayClustering.java

License:Apache License

/**
 * Draw an ellipse on the graphics context
 *
 * @param g2/*from w w w. j  av  a2 s.com*/
 *          a Graphics2D context
 * @param v
 *          a Vector of ellipse center
 * @param dv
 *          a Vector of ellipse dimensions
 */
protected static void plotEllipse(Graphics2D g2, Vector v, Vector dv) {
    double[] flip = { 1, -1 };
    Vector v2 = v.times(new DenseVector(flip));
    v2 = v2.minus(dv.divide(2));
    int h = SIZE / 2;
    double x = v2.get(0) + h;
    double y = v2.get(1) + h;
    g2.draw(new Ellipse2D.Double(x * DS, y * DS, dv.get(0) * DS, dv.get(1) * DS));
}

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From source file:com.cloudera.science.ml.kmeans.core.KMeans.java

License:Open Source License

/**
 * Compute the {@code Vector} that is the centroid of the given weighted points.
 * //w  ww .  jav a  2 s.c  om
 * @param points The weighted points
 * @return The centroid of the weighted points
 */
public <V extends Vector> Vector centroid(Collection<Weighted<V>> points) {
    Vector center = null;
    long sz = 0;
    for (Weighted<V> v : points) {
        Vector weighted = v.thing().times(v.weight());
        if (center == null) {
            center = weighted;
        } else {
            center = center.plus(weighted);
        }
        sz += v.weight();
    }
    return center.divide(sz);
}

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From source file:com.cloudera.science.ml.kmeans.core.LloydsUpdateStrategy.java

License:Open Source License

/**
 * Compute the {@code Vector} that is the centroid of the given weighted points.
 * /*from   www  .  ja va 2 s  .  co m*/
 * @param points The weighted points
 * @return The centroid of the weighted points
 */
public <V extends Vector> Vector centroid(Collection<Weighted<V>> points) {
    Vector center = null;
    double sz = 0.0;
    for (Weighted<V> v : points) {
        Vector weighted = v.thing().times(v.weight());
        if (center == null) {
            center = weighted;
        } else {
            center = center.plus(weighted);
        }
        sz += v.weight();
    }
    return center.divide(sz);
}

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From source file:org.trustedanalytics.atk.giraph.algorithms.cgd.ConjugateGradientDescentComputation.java

License:Apache License

/**
 * Compute gradient//from  w  w  w.  j a v  a 2s .c om
 *
 * @param bias of type double
 * @param value of type Vector
 * @param messages of type Iterable
 * @return gradient of type Vector
 */
private Vector computeGradient(double bias, Vector value, Iterable<MessageData4CFWritable> messages) {
    Vector xr = value.clone().assign(0d);
    int numTrain = 0;
    for (MessageData4CFWritable message : messages) {
        EdgeType et = message.getType();
        if (et == EdgeType.TRAIN) {
            double weight = message.getWeight();
            Vector vector = message.getVector();
            double otherBias = message.getBias();
            double predict = bias + otherBias + value.dot(vector);
            double e = predict - weight;
            xr = xr.plus(vector.times(e));
            numTrain++;
        }
    }
    Vector gradient = value.clone().assign(0d);
    if (numTrain > 0) {
        gradient = xr.divide(numTrain).plus(value.times(lambda));
    }
    return gradient;
}

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From source file:org.trustedanalytics.atk.giraph.algorithms.lda.GiraphLdaComputation.java

License:Apache License

/**
 * Compute the conditional probability of topics given word
 *
 * Each element of the vector contains the conditional probability of topic k given word
 * @param vertex of the graph/*ww  w. ja  va  2s.c  o m*/
 */
private void setTopicGivenWord(Vertex<LdaVertexId, LdaVertexData, LdaEdgeData> vertex) {
    if (vertex.getId().isDocument()) {
        return;
    }

    double wordCount = 0d;

    // LDA result before normalization contains word_count * probability of topic given word and document
    Vector weightedGamma = vertex.getValue().getLdaResult();

    for (Edge<LdaVertexId, LdaEdgeData> edge : vertex.getMutableEdges()) {
        wordCount += edge.getValue().getWordCount();
    }

    Vector topicsGivenWord = new DenseVector(new double[config.numTopics()]);
    if (wordCount > 0d) {
        topicsGivenWord = weightedGamma.divide(wordCount);
    }
    vertex.getValue().setTopicGivenWord(topicsGivenWord);
}

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