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

Introduction

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

Prototype

Matrix cross(Vector other);

Source Link

Document

Return the cross product of the receiver and the other vector

Usage

From source file:edu.snu.dolphin.bsp.examples.ml.algorithms.clustering.em.EMMainCmpTask.java

License:Apache License

@Override
public void run(final int iteration) {
    clusterToStats = new HashMap<>();
    final int numClusters = clusterSummaries.size();

    // Compute the partial statistics of each cluster
    for (final Vector vector : points) {
        final int dimension = vector.size();
        Matrix outProd = null;/*from  w w  w.  j av a2  s.  com*/

        if (isCovarianceDiagonal) {
            outProd = new SparseMatrix(dimension, dimension);
            for (int j = 0; j < dimension; j++) {
                outProd.set(j, j, vector.get(j) * vector.get(j));
            }
        } else {
            outProd = vector.cross(vector);
        }

        double denominator = 0;
        final double[] numerators = new double[numClusters];
        for (int i = 0; i < numClusters; i++) {
            final ClusterSummary clusterSummary = clusterSummaries.get(i);
            final Vector centroid = clusterSummary.getCentroid();
            final Matrix covariance = clusterSummary.getCovariance();
            final Double prior = clusterSummary.getPrior();

            final Vector differ = vector.minus(centroid);
            numerators[i] = prior / Math.sqrt(covariance.determinant())
                    * Math.exp(differ.dot(inverse(covariance).times(differ)) / (-2));
            denominator += numerators[i];
        }

        for (int i = 0; i < numClusters; i++) {
            final double posterior = denominator == 0 ? 1.0 / numerators.length : numerators[i] / denominator;
            if (!clusterToStats.containsKey(i)) {
                clusterToStats.put(i,
                        new ClusterStats(times(outProd, posterior), vector.times(posterior), posterior, false));
            } else {
                clusterToStats.get(i).add(
                        new ClusterStats(times(outProd, posterior), vector.times(posterior), posterior, false));
            }
        }
    }
}

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From source file:org.trustedanalytics.atk.giraph.algorithms.als.AlternatingLeastSquaresComputation.java

License:Apache License

@Override
public void compute(Vertex<CFVertexId, VertexData4CFWritable, EdgeData4CFWritable> vertex,
        Iterable<MessageData4CFWritable> messages) throws IOException {
    long step = getSuperstep();
    if (step == 0) {
        initialize(vertex);/*  ww w  .j av  a 2  s .co  m*/
        vertex.voteToHalt();
        return;
    }

    Vector currentValue = vertex.getValue().getVector();
    double currentBias = vertex.getValue().getBias();
    // update aggregators every (2 * interval) super steps
    if ((step % (2 * learningCurveOutputInterval)) == 0) {
        double errorOnTrain = 0d;
        double errorOnValidate = 0d;
        double errorOnTest = 0d;
        int numTrain = 0;
        for (MessageData4CFWritable message : messages) {
            EdgeType et = message.getType();
            double weight = message.getWeight();
            Vector vector = message.getVector();
            double otherBias = message.getBias();
            double predict = currentBias + otherBias + currentValue.dot(vector);
            double e = weight - predict;
            switch (et) {
            case TRAIN:
                errorOnTrain += e * e;
                numTrain++;
                break;
            case VALIDATE:
                errorOnValidate += e * e;
                break;
            case TEST:
                errorOnTest += e * e;
                break;
            default:
                throw new IllegalArgumentException("Unknown recognized edge type: " + et.toString());
            }
        }
        double costOnTrain = 0d;
        if (numTrain > 0) {
            costOnTrain = errorOnTrain / numTrain
                    + lambda * (currentBias * currentBias + currentValue.dot(currentValue));
        }
        aggregate(SUM_TRAIN_COST, new DoubleWritable(costOnTrain));
        aggregate(SUM_VALIDATE_ERROR, new DoubleWritable(errorOnValidate));
        aggregate(SUM_TEST_ERROR, new DoubleWritable(errorOnTest));
    }

    // update vertex value
    if (step < maxSupersteps) {
        // xxt records the result of x times x transpose
        Matrix xxt = new DenseMatrix(featureDimension, featureDimension);
        xxt = xxt.assign(0d);
        // xr records the result of x times rating
        Vector xr = currentValue.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();
                xxt = xxt.plus(vector.cross(vector));
                xr = xr.plus(vector.times(weight - currentBias - otherBias));
                numTrain++;
            }
        }
        xxt = xxt.plus(new DiagonalMatrix(lambda * numTrain, featureDimension));
        Matrix bMatrix = new DenseMatrix(featureDimension, 1).assignColumn(0, xr);
        Vector value = new QRDecomposition(xxt).solve(bMatrix).viewColumn(0);
        vertex.getValue().setVector(value);

        // update vertex bias
        if (biasOn) {
            double bias = computeBias(value, messages);
            vertex.getValue().setBias(bias);
        }

        // send out messages
        for (Edge<CFVertexId, EdgeData4CFWritable> edge : vertex.getEdges()) {
            MessageData4CFWritable newMessage = new MessageData4CFWritable(vertex.getValue(), edge.getValue());
            sendMessage(edge.getTargetVertexId(), newMessage);
        }
    }

    vertex.voteToHalt();
}

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