Examples of org.apache.mahout.math.neighborhood.BruteSearch.search()

Package org.apache.mahout.math.neighborhood

Class org.apache.mahout.math.neighborhood.BruteSearch

Examples of org.apache.mahout.math.neighborhood.BruteSearch.search()

org.apache.mahout.math.neighborhood.BruteSearch.search()
Scans the list of reference vectors one at a time for @limit neighbors of the query vector. The weights of the WeightedVectors are not taken into account. @param query The query vector. @param limit The number of results to returned; must be at least 1. @return A list of the closest @limit neighbors for the given query.

    int numCols = columnCentroids.size();
    Matrix confusionMatrix = new DenseMatrix(numRows, numCols);


    for (Vector vector : datapoints) {
      WeightedThing<Vector> closestRowCentroid = rowSearcher.search(vector, 1).get(0);
      WeightedThing<Vector> closestColumnCentroid = columnSearcher.search(vector, 1).get(0);
      int row = ((Centroid) closestRowCentroid.getValue()).getIndex();
      int column = ((Centroid) closestColumnCentroid.getValue()).getIndex();
      double vectorWeight;
      if (vector instanceof WeightedVector) {
        vectorWeight = ((WeightedVector) vector).getWeight();

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    for (org.apache.hadoop.mrunit.types.Pair<IntWritable, CentroidWritable> result : results) {
      resultSearcher.add(result.getSecond().getCentroid());
    }
    System.out.printf("Clustered the data into %d clusters\n", results.size());
    for (Vector mean : syntheticData.getSecond()) {
      WeightedThing<Vector> closest = resultSearcher.search(mean, 1).get(0);
      assertTrue("Weight " + closest.getWeight() + " not less than 0.5", closest.getWeight() < 0.5);
    }
  }


  @Test

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    // Verify that each corner of the cube has a centroid very nearby.
    // This is probably FALSE for large-dimensional spaces!
    OnlineSummarizer summarizer = new OnlineSummarizer();
    for (Vector mean : syntheticData.getSecond()) {
      WeightedThing<Vector> v = searcher.search(mean, 1).get(0);
      summarizer.add(v.getWeight());
    }
    assertTrue(String.format("Median weight [%f] too large [>%f]", summarizer.getMedian(),
        DISTRIBUTION_RADIUS), summarizer.getMedian() < DISTRIBUTION_RADIUS);

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    Searcher trueFinder = new BruteSearch(new EuclideanDistanceMeasure());
    for (Vector trueCluster : syntheticData.getSecond()) {
      trueFinder.add(trueCluster);
    }
    for (Centroid centroid : clusterer) {
      WeightedThing<Vector> closest = trueFinder.search(centroid, 1).get(0);
      cornerWeights[((Centroid)closest.getValue()).getIndex()] += centroid.getWeight();
    }
    int expectedNumPoints = NUM_DATA_POINTS / (1 << NUM_DIMENSIONS);
    for (double v : cornerWeights) {
      System.out.printf("%f ", v);

View Full Code Here

    Searcher trueFinder = new BruteSearch(new EuclideanDistanceMeasure());
    for (Vector trueCluster : syntheticData.getSecond()) {
      trueFinder.add(trueCluster);
    }
    for (Centroid centroid : clusterer) {
      WeightedThing<Vector> closest = trueFinder.search(centroid, 1).get(0);
      cornerWeights[((Centroid)closest.getValue()).getIndex()] += centroid.getWeight();
    }
    int expectedNumPoints = NUM_DATA_POINTS / (1 << NUM_DIMENSIONS);
    for (double v : cornerWeights) {
      System.out.printf("%f ", v);

View Full Code Here

    int numCols = columnCentroids.size();
    Matrix confusionMatrix = new DenseMatrix(numRows, numCols);


    for (Vector vector : datapoints) {
      WeightedThing<Vector> closestRowCentroid = rowSearcher.search(vector, 1).get(0);
      WeightedThing<Vector> closestColumnCentroid = columnSearcher.search(vector, 1).get(0);
      int row = ((Centroid) closestRowCentroid.getValue()).getIndex();
      int column = ((Centroid) closestColumnCentroid.getValue()).getIndex();
      double vectorWeight;
      if (vector instanceof WeightedVector) {
        vectorWeight = ((WeightedVector) vector).getWeight();

View Full Code Here


    // Verify that each corner of the cube has a centroid very nearby.
    // This is probably FALSE for large-dimensional spaces!
    OnlineSummarizer summarizer = new OnlineSummarizer();
    for (Vector mean : syntheticData.getSecond()) {
      WeightedThing<Vector> v = searcher.search(mean, 1).get(0);
      summarizer.add(v.getWeight());
    }
    assertTrue(String.format("Median weight [%f] too large [>%f]", summarizer.getMedian(),
        DISTRIBUTION_RADIUS), summarizer.getMedian() < DISTRIBUTION_RADIUS);

View Full Code Here

    Searcher trueFinder = new BruteSearch(new EuclideanDistanceMeasure());
    for (Vector trueCluster : syntheticData.getSecond()) {
      trueFinder.add(trueCluster);
    }
    for (Centroid centroid : clusterer) {
      WeightedThing<Vector> closest = trueFinder.search(centroid, 1).get(0);
      cornerWeights[((Centroid)closest.getValue()).getIndex()] += centroid.getWeight();
    }
    int expectedNumPoints = NUM_DATA_POINTS / (1 << NUM_DIMENSIONS);
    for (double v : cornerWeights) {
      System.out.printf("%f ", v);

View Full Code Here

    for (org.apache.hadoop.mrunit.types.Pair<IntWritable, CentroidWritable> result : results) {
      resultSearcher.add(result.getSecond().getCentroid());
    }
    System.out.printf("Clustered the data into %d clusters\n", results.size());
    for (Vector mean : syntheticData.getSecond()) {
      WeightedThing<Vector> closest = resultSearcher.search(mean, 1).get(0);
      assertTrue("Weight " + closest.getWeight() + " not less than 0.5", closest.getWeight() < 0.5);
    }
  }


  @Test

View Full Code Here

    Searcher trueFinder = new BruteSearch(new EuclideanDistanceMeasure());
    for (Vector trueCluster : syntheticData.getSecond()) {
      trueFinder.add(trueCluster);
    }
    for (Centroid centroid : clusterer) {
      WeightedThing<Vector> closest = trueFinder.search(centroid, 1).get(0);
      cornerWeights[((Centroid)closest.getValue()).getIndex()] += centroid.getWeight();
    }
    int expectedNumPoints = NUM_DATA_POINTS / (1 << NUM_DIMENSIONS);
    for (double v : cornerWeights) {
      System.out.printf("%f ", v);

View Full Code Here

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