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

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

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Introduction

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

Prototype

void set(int index, double value);

Source Link

Document

Set the value at the given index

Usage

From source file:edu.snu.dolphin.bsp.examples.ml.data.ClusteringDataParser.java

License:Apache License

@Override
public void parse() {
    final List<Vector> points = new ArrayList<>();

    for (final Pair<LongWritable, Text> keyValue : dataSet) {
        final String text = keyValue.getSecond().toString().trim();
        if (text.startsWith("#") || text.length() == 0) {
            continue;
        }// w w w. j a v a 2  s .c om

        final String[] split = text.split("\\s+");
        final Vector point = new DenseVector(split.length);
        try {
            for (int i = 0; i < split.length; i++) {
                point.set(i, Double.valueOf(split[i]));
            }
            points.add(point);
        } catch (final NumberFormatException e) {
            parseException = new ParseException("Parse failed: each field should be a number");
            return;
        }
    }
    result = points;
}

From source file:edu.snu.dolphin.bsp.examples.ml.data.ClusterStats.java

License:Apache License

/**
 * Compute mean from the statistics./* w w w . j a  v a 2  s .  c  o  m*/
 * @return
 */
public Vector computeMean() {
    final Vector mean = new DenseVector(pointSum.size());
    for (int i = 0; i < mean.size(); i++) {
        mean.set(i, pointSum.get(i) / probSum);
    }
    return mean;
}

From source file:edu.snu.dolphin.bsp.examples.ml.data.RegressionDataParser.java

License:Apache License

@Override
public void parse() {
    LOG.log(Level.INFO, "Trying to parse!");
    result = new ArrayList<>();

    for (final Pair<LongWritable, Text> keyValue : dataSet) {

        final String text = keyValue.getSecond().toString().trim();
        if (text.startsWith("#") || text.length() == 0) {
            continue;
        }//from   ww w .  jav  a  2 s .c o m

        final String[] split = text.split("\\s+");
        if (split.length != dimension + 1) {
            parseException = new ParseException("Parse failed: the number of features is not " + dimension);
            return;
        }

        final double output;
        final Vector feature = new DenseVector(split.length);
        try {
            output = Double.valueOf(split[dimension]);
            for (int i = 0; i < dimension; i++) {
                feature.set(i, Double.valueOf(split[i]));
            }
            feature.set(dimension, 1);
        } catch (final NumberFormatException e) {
            parseException = new ParseException("Parse failed: numbers should be DOUBLE");
            return;
        }

        result.add(new Row(output, feature));
    }
}

From source file:edu.snu.dolphin.bsp.examples.ml.data.VectorSum.java

License:Apache License

public Vector computeVectorMean() {
    final Vector mean = new DenseVector(sum.size());
    for (int i = 0; i < mean.size(); i++) {
        mean.set(i, sum.get(i) / count);
    }/*from www  . ja  v a 2  s  . c o  m*/
    return mean;
}

From source file:edu.snu.dolphin.bsp.examples.ml.sub.CentroidListCodec.java

License:Apache License

@Override
public List<Vector> decode(final byte[] data) {
    final ByteArrayInputStream bais = new ByteArrayInputStream(data);
    final List<Vector> list = new ArrayList<>();
    int numClusters = 0;
    int dimension = 0;

    try (final DataInputStream dais = new DataInputStream(bais)) {
        numClusters = dais.readInt();/*  www .j a v  a  2 s. c o m*/
        dimension = dais.readInt();

        for (int clusterID = 0; clusterID < numClusters; clusterID++) {
            final Vector vector = new DenseVector(dimension);
            for (int i = 0; i < dimension; i++) {
                vector.set(i, dais.readDouble());
            }
            list.add(vector);
        }
    } catch (final IOException e) {
        throw new RuntimeException(e.getCause());
    }

    return list;
}

From source file:edu.snu.dolphin.bsp.examples.ml.sub.ClusterSummaryListCodec.java

License:Apache License

@Override
public List<ClusterSummary> decode(final byte[] data) {
    final ByteArrayInputStream bais = new ByteArrayInputStream(data);
    final List<ClusterSummary> resultList = new ArrayList<>();

    try (final DataInputStream dais = new DataInputStream(bais)) {
        final int numClusters = dais.readInt();
        final int dimension = dais.readInt();

        for (int i = 0; i < numClusters; i++) {
            final double prior = dais.readDouble();
            final Vector vector = new DenseVector(dimension);
            for (int j = 0; j < dimension; j++) {
                vector.set(j, dais.readDouble());
            }/*w  w  w  .  j a v a 2 s . c om*/
            Matrix matrix = null;
            if (isDiagonalCovariance) {
                matrix = new SparseMatrix(dimension, dimension);
                for (int j = 0; j < dimension; j++) {
                    matrix.set(j, j, dais.readDouble());
                }
            } else {
                matrix = new DenseMatrix(dimension, dimension);
                for (int j = 0; j < dimension; j++) {
                    for (int k = 0; k < dimension; k++) {
                        matrix.set(j, k, dais.readDouble());
                    }
                }
            }
            resultList.add(new ClusterSummary(prior, vector, matrix));
        }
    } catch (final IOException e) {
        throw new RuntimeException(e.getCause());
    }

    return resultList;
}

From source file:edu.snu.dolphin.bsp.examples.ml.sub.LinearModelCodec.java

License:Apache License

@Override
public LinearModel decode(final byte[] data) {
    final ByteArrayInputStream bais = new ByteArrayInputStream(data);
    final LinearModel model;
    try (final DataInputStream dais = new DataInputStream(bais)) {
        final int vecSize = dais.readInt();
        final Vector v = new DenseVector(vecSize);
        for (int i = 0; i < vecSize; i++) {
            v.set(i, dais.readDouble());
        }/*from  w w w  .j ava 2 s  .com*/
        model = new LinearModel(v);
    } catch (final IOException e) {
        throw new RuntimeException(e.getCause());
    }
    return model;
}

From source file:edu.snu.dolphin.bsp.examples.ml.sub.LinearRegSummaryCodec.java

License:Apache License

@Override
public LinearRegSummary decode(final byte[] data) {
    final ByteArrayInputStream bais = new ByteArrayInputStream(data);
    final LinearModel model;
    final int count;
    final double loss;

    try (final DataInputStream dais = new DataInputStream(bais)) {
        count = dais.readInt();//from  www  . ja  va2  s.  c  o m
        loss = dais.readDouble();
        final int vecSize = dais.readInt();
        final Vector v = new DenseVector(vecSize);
        for (int i = 0; i < vecSize; i++) {
            v.set(i, dais.readDouble());
        }
        model = new LinearModel(v);
    } catch (final IOException e) {
        throw new RuntimeException(e.getCause());
    }
    return new LinearRegSummary(model, count, loss);
}

From source file:edu.snu.dolphin.bsp.examples.ml.sub.LogisticRegSummaryCodec.java

License:Apache License

@Override
public LogisticRegSummary decode(final byte[] data) {
    final ByteArrayInputStream bais = new ByteArrayInputStream(data);
    final LinearModel model;
    final int count;
    final int posNum;
    final int negNum;

    try (final DataInputStream dais = new DataInputStream(bais)) {
        count = dais.readInt();/*from w w  w  .ja  va 2 s .com*/
        posNum = dais.readInt();
        negNum = dais.readInt();
        final int vecSize = dais.readInt();
        final Vector v = new DenseVector(vecSize);
        for (int i = 0; i < vecSize; i++) {
            v.set(i, dais.readDouble());
        }
        model = new LinearModel(v);

    } catch (final IOException e) {
        throw new RuntimeException(e.getCause());
    }

    return new LogisticRegSummary(model, count, posNum, negNum);
}

From source file:edu.snu.dolphin.bsp.examples.ml.sub.MapOfIntClusterStatsCodec.java

License:Apache License

@Override
public Map<Integer, ClusterStats> decode(final byte[] data) {
    final ByteArrayInputStream bais = new ByteArrayInputStream(data);
    final Map<Integer, ClusterStats> resultMap = new HashMap<>();

    try (final DataInputStream dais = new DataInputStream(bais)) {
        final int mapSize = dais.readInt();
        final int dimension = dais.readInt();
        for (int i = 0; i < mapSize; i++) {
            final int id = dais.readInt();
            final double probSum = dais.readDouble();
            final Vector pointSum = new DenseVector(dimension);
            for (int j = 0; j < dimension; j++) {
                pointSum.set(j, dais.readDouble());
            }/*from   ww w  . j a va  2  s .  com*/
            Matrix outProdSum = null;
            if (diagonalCovariance) {
                outProdSum = new SparseMatrix(dimension, dimension);
                for (int j = 0; j < dimension; j++) {
                    outProdSum.set(j, j, dais.readDouble());
                }
            } else {
                outProdSum = new DenseMatrix(dimension, dimension);
                for (int j = 0; j < dimension; j++) {
                    for (int k = 0; k < dimension; k++) {
                        outProdSum.set(j, k, dais.readDouble());
                    }
                }
            }
            resultMap.put(id, new ClusterStats(outProdSum, pointSum, probSum));
        }
    } catch (final IOException e) {
        throw new RuntimeException(e.getCause());
    }

    return resultMap;
}