Example usage for org.apache.mahout.math.function Functions PLUS

List of usage examples for org.apache.mahout.math.function Functions PLUS

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Introduction

In this page you can find the example usage for org.apache.mahout.math.function Functions PLUS.

Prototype

DoubleDoubleFunction PLUS

To view the source code for org.apache.mahout.math.function Functions PLUS.

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Document

Function that returns a + b.

Usage

From source file:at.illecker.hama.rootbeer.examples.matrixmultiplication.compositeinput.cpu.MatrixMultiplicationBSPCpu.java

License:Apache License

@Override
public void cleanup(BSPPeer<IntWritable, TupleWritable, IntWritable, VectorWritable, MatrixRowMessage> peer)
        throws IOException {

    // MasterTask accumulates result
    if (peer.getPeerName().equals(masterTask)) {

        // SortedMap because the final matrix rows should be in order
        SortedMap<Integer, Vector> accumlatedRows = new TreeMap<Integer, Vector>();
        MatrixRowMessage currentMatrixRowMessage = null;

        // Collect messages
        while ((currentMatrixRowMessage = peer.getCurrentMessage()) != null) {
            int rowIndex = currentMatrixRowMessage.getRowIndex();
            Vector rowValues = currentMatrixRowMessage.getRowValues().get();

            if (isDebuggingEnabled) {
                logger.writeChars("bsp,gotMsg,key=" + rowIndex + ",value=" + rowValues.toString() + "\n");
            }//w  ww  .  j  a  va2  s .c o m

            if (accumlatedRows.containsKey(rowIndex)) {
                accumlatedRows.get(rowIndex).assign(rowValues, Functions.PLUS);
            } else {
                accumlatedRows.put(rowIndex, new RandomAccessSparseVector(rowValues));
            }
        }

        // Write accumulated results
        for (Map.Entry<Integer, Vector> row : accumlatedRows.entrySet()) {
            if (isDebuggingEnabled) {
                logger.writeChars(
                        "bsp,write,key=" + row.getKey() + ",value=" + row.getValue().toString() + "\n");
            }
            peer.write(new IntWritable(row.getKey()), new VectorWritable(row.getValue()));
        }

    }
}

From source file:com.elex.dmp.core.TopicModel.java

License:Apache License

public void updateTopic(int topic, Vector docTopicCounts) {
    topicTermCounts.viewRow(topic).assign(docTopicCounts, Functions.PLUS);
    topicSums.set(topic, topicSums.get(topic) + docTopicCounts.norm(1));
}

From source file:com.elex.dmp.core.TopicModel.java

License:Apache License

public void update(int termId, Vector topicCounts) {
    for (int x = 0; x < numTopics; x++) {
        Vector v = topicTermCounts.viewRow(x);
        v.set(termId, v.get(termId) + topicCounts.get(x));
    }/*from  ww w . ja v  a2s .  c  o  m*/
    topicSums.assign(topicCounts, Functions.PLUS);
}

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

License:Apache License

/** Y' (Cu - I) Y +  I */
private Matrix getYtransponseCuMinusIYPlusLambdaI(Vector userRatings) {
    Preconditions.checkArgument(userRatings.isSequentialAccess(), "need sequential access to ratings!");

    /* (Cu -I) Y */
    OpenIntObjectHashMap<Vector> CuMinusIY = new OpenIntObjectHashMap<Vector>(
            userRatings.getNumNondefaultElements());
    for (Element e : userRatings.nonZeroes()) {
        CuMinusIY.put(e.index(), Y.get(e.index()).times(confidence(e.get()) - 1));
    }/*from   ww  w .j a v  a2  s  . c  om*/

    Matrix YtransponseCuMinusIY = new DenseMatrix(numFeatures, numFeatures);

    /* Y' (Cu -I) Y by outer products */
    for (Element e : userRatings.nonZeroes()) {
        for (Vector.Element feature : Y.get(e.index()).all()) {
            Vector partial = CuMinusIY.get(e.index()).times(feature.get());
            YtransponseCuMinusIY.viewRow(feature.index()).assign(partial, Functions.PLUS);
        }
    }

    /* Y' (Cu - I) Y +  I  add lambda on the diagonal */
    for (int feature = 0; feature < numFeatures; feature++) {
        YtransponseCuMinusIY.setQuick(feature, feature,
                YtransponseCuMinusIY.getQuick(feature, feature) + lambda);
    }

    return YtransponseCuMinusIY;
}

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

License:Apache License

/** Y' Cu p(u) */
private Matrix getYtransponseCuPu(Vector userRatings) {
    Preconditions.checkArgument(userRatings.isSequentialAccess(), "need sequential access to ratings!");

    Vector YtransponseCuPu = new DenseVector(numFeatures);

    for (Element e : userRatings.nonZeroes()) {
        YtransponseCuPu.assign(Y.get(e.index()).times(confidence(e.get())), Functions.PLUS);
    }/*from w  w w .  j a va 2s. com*/

    return columnVectorAsMatrix(YtransponseCuPu);
}

From source file:com.memonews.mahout.sentiment.SGDHelper.java

License:Apache License

static void analyzeState(final SGDInfo info, final int leakType, final int k,
        final State<AdaptiveLogisticRegression.Wrapper, CrossFoldLearner> best) throws IOException {
    final int bump = info.getBumps()[(int) Math.floor(info.getStep()) % info.getBumps().length];
    final int scale = (int) Math.pow(10, Math.floor(info.getStep() / info.getBumps().length));
    double maxBeta;
    double nonZeros;
    double positive;
    double norm;//w  w w  .  ja  v  a2 s.  co m

    double lambda = 0;
    double mu = 0;

    if (best != null) {
        final CrossFoldLearner state = best.getPayload().getLearner();
        info.setAverageCorrect(state.percentCorrect());
        info.setAverageLL(state.logLikelihood());

        final OnlineLogisticRegression model = state.getModels().get(0);
        // finish off pending regularization
        model.close();

        final Matrix beta = model.getBeta();
        maxBeta = beta.aggregate(Functions.MAX, Functions.ABS);
        nonZeros = beta.aggregate(Functions.PLUS, new DoubleFunction() {
            @Override
            public double apply(final double v) {
                return Math.abs(v) > 1.0e-6 ? 1 : 0;
            }
        });
        positive = beta.aggregate(Functions.PLUS, new DoubleFunction() {
            @Override
            public double apply(final double v) {
                return v > 0 ? 1 : 0;
            }
        });
        norm = beta.aggregate(Functions.PLUS, Functions.ABS);

        lambda = best.getMappedParams()[0];
        mu = best.getMappedParams()[1];
    } else {
        maxBeta = 0;
        nonZeros = 0;
        positive = 0;
        norm = 0;
    }
    if (k % (bump * scale) == 0) {
        if (best != null) {
            ModelSerializer.writeBinary("/tmp/news-group-" + k + ".model",
                    best.getPayload().getLearner().getModels().get(0));
        }

        info.setStep(info.getStep() + 0.25);
        System.out.printf("%.2f\t%.2f\t%.2f\t%.2f\t%.8g\t%.8g\t", maxBeta, nonZeros, positive, norm, lambda,
                mu);
        System.out.printf("%d\t%.3f\t%.2f\t%s\n", k, info.getAverageLL(), info.getAverageCorrect() * 100,
                LEAK_LABELS[leakType % 3]);
    }
}

From source file:com.netease.news.classifier.naivebayes.WeightsMapper.java

License:Apache License

@Override
protected void map(IntWritable index, VectorWritable value, Context ctx)
        throws IOException, InterruptedException {
    Vector instance = value.get();
    if (weightsPerFeature == null) {
        weightsPerFeature = new RandomAccessSparseVector(instance.size(), instance.getNumNondefaultElements());
    }//from w w  w .j av a  2 s .c  om

    int label = index.get();
    weightsPerFeature.assign(instance, Functions.PLUS);
    weightsPerLabel.set(label, weightsPerLabel.get(label) + instance.zSum());
}

From source file:com.skp.experiment.math.als.hadoop.DistributedImplicitFeedbackAlternatingLeastSquaresSolver.java

License:Apache License

public Vector solve(Vector userRatings) throws IOException {
    Preconditions.checkArgument(userRatings.isSequentialAccess(), "need sequential access to ratings!");
    //Matrix sparseY = getSparseMatrix(userRatings);
    getSparseMatrix(userRatings);/*from   w w w  . j av a  2s. co m*/
    /* Y' (Cu - I) Y +  I */
    /* Y' Cu p(u) */
    Vector YtransponseCuPu = new DenseVector(numFeatures);
    /* (Cu -I) Y */
    OpenIntObjectHashMap<Vector> CuMinusIY = new OpenIntObjectHashMap<Vector>();

    Iterator<Vector.Element> ratings = userRatings.iterateNonZero();
    while (ratings.hasNext()) {
        Vector.Element e = ratings.next();
        CuMinusIY.put(e.index(), sparseY.get(e.index()).times(confidence(e.get()) - 1));
        /* Y' Cu p(u) */
        YtransponseCuPu.assign(sparseY.get(e.index()).times(confidence(e.get())), Functions.PLUS);
    }

    Matrix YtransponseCuMinusIY = new DenseMatrix(numFeatures, numFeatures);

    /* Y' (Cu -I) Y by outer products */
    ratings = userRatings.iterateNonZero();
    while (ratings.hasNext()) {
        Vector.Element e = ratings.next();
        for (Vector.Element feature : sparseY.get(e.index())) {
            Vector partial = CuMinusIY.get(e.index()).times(feature.get());
            YtransponseCuMinusIY.viewRow(feature.index()).assign(partial, Functions.PLUS);
        }
    }

    /* Y' (Cu - I) Y +  I  add lambda on the diagonal */
    for (int feature = 0; feature < numFeatures; feature++) {
        YtransponseCuMinusIY.setQuick(feature, feature,
                YtransponseCuMinusIY.getQuick(feature, feature) + lambda);
    }

    Matrix YtransposeCuPu = columnVectorAsMatrix(YtransponseCuPu);
    return solve(YtransposeY.plus(YtransponseCuMinusIY), YtransposeCuPu);
    //return YtransponseCuMinusIY;
}

From source file:com.skp.experiment.math.als.hadoop.ImplicitFeedbackAlternatingLeastSquaresReasonSolver.java

License:Apache License

/** Y' (Cu - I) Y +  I */
private Matrix YtransponseCuMinusIYPlusLambdaI(Vector userRatings) {
    Preconditions.checkArgument(userRatings.isSequentialAccess(), "need sequential access to ratings!");

    /* (Cu -I) Y */
    OpenIntObjectHashMap<Vector> CuMinusIY = new OpenIntObjectHashMap<Vector>();
    Iterator<Vector.Element> ratings = userRatings.iterateNonZero();
    while (ratings.hasNext()) {
        Vector.Element e = ratings.next();
        CuMinusIY.put(e.index(), Y.get(e.index()).times(confidence(e.get()) - 1));
    }//from   w ww  .  j a va  2s . co m

    Matrix YtransponseCuMinusIY = new DenseMatrix(numFeatures, numFeatures);

    /* Y' (Cu -I) Y by outer products */
    ratings = userRatings.iterateNonZero();
    while (ratings.hasNext()) {
        Vector.Element e = ratings.next();
        for (Vector.Element feature : Y.get(e.index())) {
            Vector partial = CuMinusIY.get(e.index()).times(feature.get());
            YtransponseCuMinusIY.viewRow(feature.index()).assign(partial, Functions.PLUS);
        }
    }

    /* Y' (Cu - I) Y +  I  add lambda on the diagonal */
    for (int feature = 0; feature < numFeatures; feature++) {
        YtransponseCuMinusIY.setQuick(feature, feature,
                YtransponseCuMinusIY.getQuick(feature, feature) + lambda);
    }

    return YtransponseCuMinusIY;
}

From source file:com.skp.experiment.math.als.hadoop.ImplicitFeedbackAlternatingLeastSquaresSolver.java

License:Apache License

/** Y' (Cu - I) Y +  I */
private Matrix YtransponseCuMinusIYPlusLambdaI(Vector userRatings) {
    Preconditions.checkArgument(userRatings.isSequentialAccess(), "need sequential access to ratings!");

    /* (Cu -I) Y */
    OpenIntObjectHashMap<Vector> CuMinusIY = new OpenIntObjectHashMap<Vector>();
    Iterator<Vector.Element> ratings = userRatings.iterateNonZero();
    while (ratings.hasNext()) {
        Vector.Element e = ratings.next();
        CuMinusIY.put(e.index(), Y.viewRow(e.index()).times(confidence(e.get()) - 1));
        //CuMinusIY.put(e.index(), Y.get(e.index()).times(confidence(e.get()) - 1));
    }/*from   ww w. j a  va2 s .  com*/

    Matrix YtransponseCuMinusIY = new DenseMatrix(numFeatures, numFeatures);

    /* Y' (Cu -I) Y by outer products */
    ratings = userRatings.iterateNonZero();
    while (ratings.hasNext()) {
        Vector.Element e = ratings.next();
        //for (Vector.Element feature : Y.get(e.index())) {
        for (Vector.Element feature : Y.viewRow(e.index())) {
            Vector partial = CuMinusIY.get(e.index()).times(feature.get());
            YtransponseCuMinusIY.viewRow(feature.index()).assign(partial, Functions.PLUS);
        }
    }

    /* Y' (Cu - I) Y +  I  add lambda on the diagonal */
    for (int feature = 0; feature < numFeatures; feature++) {
        YtransponseCuMinusIY.setQuick(feature, feature,
                YtransponseCuMinusIY.getQuick(feature, feature) + lambda);
    }

    return YtransponseCuMinusIY;
}