Example usage for org.apache.mahout.math QRDecomposition QRDecomposition

Introduction

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

Prototype

public QRDecomposition(Matrix a) 

Source Link

Document

Constructs and returns a new QR decomposition object; computed by Householder reflections; The decomposed matrices can be retrieved via instance methods of the returned decomposition object.

Usage

From source file:com.innometrics.integration.app.recommender.ml.als.AlternatingLeastSquaresSolver.java

License:Apache License

private static Vector solve(Matrix Ai, Matrix Vi) {
    return new QRDecomposition(Ai).solve(Vi).viewColumn(0);
}

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From source file:com.innometrics.integration.app.recommender.ml.als.ImplicitFeedbackAlternatingLeastSquaresSolver.java

License:Apache License

private static Vector solve(Matrix A, Matrix y) {
    return new QRDecomposition(A).solve(y).viewColumn(0);
}

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From source file:com.ydy.cf.solver.impl.AlternatingLeastSquaresSolver.java

License:Apache License

private Vector solve(Matrix Ai, Matrix Vi) {
    return new QRDecomposition(Ai).solve(Vi).viewColumn(0);
}

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

License:Apache License

/**
 * Compute the inverse of a given matrix.
 */// w ww.java2s .  co  m
private Matrix inverse(final Matrix matrix) {
    final int dimension = matrix.rowSize();
    final QRDecomposition qr = new QRDecomposition(matrix);
    return qr.solve(DiagonalMatrix.identity(dimension));
}

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From source file:org.qcri.pca.SPCADriver.java

private static Matrix inv(Matrix m) {
    // assume m is square
    QRDecomposition qr = new QRDecomposition(m);
    Matrix i = eye(m.numRows());// ww  w .j a  v a  2s.c  om
    Matrix res = qr.solve(i);
    Matrix densRes = toDenseMatrix(res); // to go around sparse matrix bug
    return densRes;
}

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From source file:org.qcri.sparkpca.PCAUtils.java

/**
 * Compute the inverse of a Matrix/*from  w  w w .j  av a2s.  co m*/
*/
public static Matrix inv(Matrix m) {
    // assume m is square
    QRDecomposition qr = new QRDecomposition(m);
    Matrix i = eye(m.numRows());
    Matrix res = qr.solve(i);
    Matrix densRes = toDenseMatrix(res); // to go around sparse matrix bug
    return densRes;
}

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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);//w  w  w  .ja  va2s  .c  o 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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