Package org.apache.mahout.math.neighborhood

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

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    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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    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);
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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);
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    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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    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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    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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    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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