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

List of usage examples for org.apache.mahout.math Vector dot

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Introduction

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

Prototype

double dot(Vector x);

Source Link

Document

Return the dot product of the recipient and the argument

Usage

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

License:Apache License

/**
 * Compute bias//from w  w w  .  j a  v a  2 s.  c  om
 *
 * @param value of type Vector
 * @param messages of type Iterable
 * @return bias of type double
 */
private double computeBias(Vector value, Iterable<MessageData4CFWritable> messages) {
    double errorOnTrain = 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 = otherBias + value.dot(vector);
            double e = weight - predict;
            errorOnTrain += e;
            numTrain++;
        }
    }
    double bias = 0d;
    if (numTrain > 0) {
        bias = errorOnTrain / ((1 + lambda) * numTrain);
    }
    return bias;
}

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 v a 2 s  . com
        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();
}

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

License:Apache License

/**
 * Compute gradient//from w  w w . java2  s  .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;
}

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

License:Apache License

/**
 * Compute alpha//from w w w . j a va  2s.co m
 *
 * @param gradient of type Vector
 * @param conjugate of type Vector
 * @param messages of type Iterable
 * @return alpha of type double
 */
private double computeAlpha(Vector gradient, Vector conjugate, Iterable<MessageData4CFWritable> messages) {
    double alpha = 0d;
    if (conjugate.norm(1d) == 0d) {
        return alpha;
    }
    double predictSquared = 0d;
    int numTrain = 0;
    for (MessageData4CFWritable message : messages) {
        EdgeType et = message.getType();
        if (et == EdgeType.TRAIN) {
            Vector vector = message.getVector();
            double predict = conjugate.dot(vector);
            predictSquared += predict * predict;
            numTrain++;
        }
    }
    if (numTrain > 0) {
        alpha = -gradient.dot(conjugate) / (predictSquared / numTrain + lambda * conjugate.dot(conjugate));
    }
    return alpha;
}

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

License:Apache License

/**
 * Compute beta according to Hestenes-Stiefel formula
 *
 * @param gradient of type Vector//from w ww  .  j a v a  2 s .  com
 * @param conjugate of type Vector
 * @param gradientNext of type Vector
 * @return beta of type double
 */
private double computeBeta(Vector gradient, Vector conjugate, Vector gradientNext) {
    double beta = 0d;
    if (conjugate.norm(1d) == 0d) {
        return beta;
    }
    Vector deltaVector = gradientNext.minus(gradient);
    beta = -gradientNext.dot(deltaVector) / conjugate.dot(deltaVector);
    return beta;
}

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

License:Apache License

@Override
public void compute(Vertex<CFVertexId, VertexData4CGDWritable, EdgeData4CFWritable> vertex,
        Iterable<MessageData4CFWritable> messages) throws IOException {
    long step = getSuperstep();
    if (step == 0) {
        initialize(vertex);/*from  w  ww.  ja  v a2s.c  om*/
        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));
    }

    if (step < maxSupersteps) {
        // implement CGD iterations
        Vector value0 = vertex.getValue().getVector();
        Vector gradient0 = vertex.getValue().getGradient();
        Vector conjugate0 = vertex.getValue().getConjugate();
        double bias0 = vertex.getValue().getBias();
        for (int i = 0; i < numCGDIters; i++) {
            double alpha = computeAlpha(gradient0, conjugate0, messages);
            Vector value = value0.plus(conjugate0.times(alpha));
            Vector gradient = computeGradient(bias0, value, messages);
            double beta = computeBeta(gradient0, conjugate0, gradient);
            Vector conjugate = conjugate0.times(beta).minus(gradient);
            value0 = value;
            gradient0 = gradient;
            conjugate0 = conjugate;
        }
        // update vertex values
        vertex.getValue().setVector(value0);
        vertex.getValue().setConjugate(conjugate0);
        vertex.getValue().setGradient(gradient0);

        // update vertex bias
        if (biasOn) {
            double bias = computeBias(value0, 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();
}

From source file:org.trustedanalytics.atk.giraph.algorithms.lda.CVB0LDAComputation.java

License:Apache License

/**
 * Evaluate cost according to vertex and messages
 *
 * @param vertex of the graph/*from ww w. jav a  2 s  .c o  m*/
 * @param messages of type iterable
 * @param map of type HashMap
 */
private void evaluateCost(Vertex<LdaVertexId, LdaVertexData, LdaEdgeData> vertex, Iterable<LdaMessage> messages,
        HashMap<LdaVertexId, Vector> map) {

    if (vertex.getId().isDocument()) {
        return;
    }
    Vector vector = vertex.getValue().getLdaResult();
    vector = vector.plus(config.beta()).times(nk.plus(numWords * config.beta()).assign(Functions.INV));

    double cost = 0d;
    for (Edge<LdaVertexId, LdaEdgeData> edge : vertex.getEdges()) {
        double weight = edge.getValue().getWordCount();
        LdaVertexId id = edge.getTargetVertexId();
        if (map.containsKey(id)) {
            Vector otherVector = map.get(id);
            otherVector = otherVector.plus(config.alpha()).normalize(1d);
            cost -= weight * Math.log(vector.dot(otherVector));
        } else {
            throw new IllegalArgumentException(
                    String.format("Vertex ID %s: A message is mis-matched", vertex.getId().getValue()));
        }
    }
    aggregate(SUM_COST, new DoubleWritable(cost));
}