Package de.lmu.ifi.dbs.elki.math

Examples of de.lmu.ifi.dbs.elki.math.MeanVariance

58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
  double Bfinal;

  @Override
  public void prepare(OutlierResult or) {
    // Initial parameters - are these defaults sounds?
    MeanVariance mv = new MeanVariance();
    for(DBID id : or.getScores().iterDBIDs()) {
      double val = or.getScores().get(id);
      mv.put(val);
    }
    double a = 1.0;
    double b = - mv.getMean();
    int iter = 0;

    ArrayDBIDs ids = DBIDUtil.ensureArray(or.getScores().getDBIDs());
    BitSet t = new BitSet(ids.size());
    boolean changing = true;
View Full Code Here
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
  }

  @Override
  public void prepare(OutlierResult or) {
    if(usemean) {
      MeanVariance mv = new MeanVariance();
      DoubleMinMax mm = (max == null) ? new DoubleMinMax() : null;
      boolean skippedzeros = false;
      for(DBID id : or.getScores().iterDBIDs()) {
        double val = or.getScores().get(id);
        if(nozeros && val == 0.0) {
          skippedzeros = true;
          continue;
        }
        if(!Double.isNaN(val) && !Double.isInfinite(val)) {
          mv.put(val);
        }
        if(max == null) {
          mm.put(val);
        }
      }
      if(skippedzeros && mm.getMin() == mm.getMax()) {
        mm.put(0.0);
        mv.put(0.0);
      }
      min = mv.getMean();
      if(max == null) {
        max = mm.getMax();
      }
    }
    else {
View Full Code Here
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
   */
  double scaledmax;

  @Override
  public void prepare(OutlierResult or) {
    MeanVariance mv = new MeanVariance();
    DoubleMinMax minmax = new DoubleMinMax();

    for(DBID id : or.getScores().iterDBIDs()) {
      double val = or.getScores().get(id);
      if(!Double.isNaN(val) && !Double.isInfinite(val)) {
        mv.put(val);
        minmax.put(val);
      }
    }

    mean = mv.getMean();
    stddev = mv.getSampleStddev();
    scaledmax = getScaled(minmax.getMax());
    scaledmin = getScaled(minmax.getMin());
  }
View Full Code Here
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
  }

  @Override
  public void prepare(OutlierResult or) {
    meta = or.getOutlierMeta();
    MeanVariance mv = new MeanVariance();
    for(DBID id : or.getScores().iterDBIDs()) {
      double score = or.getScores().get(id);
      score = preScale(score);
      if(!Double.isNaN(score) && !Double.isInfinite(score)) {
        mv.put(score);
      }
    }
    final double mean = mv.getMean();
    final double var = mv.getSampleVariance();
    k = (mean * mean) / var;
    theta = var / mean;
    atmean = MathUtil.regularizedGammaP(k, mean / theta);
    // logger.warning("Mean:"+mean+" Var:"+var+" Theta: "+theta+" k: "+k+" valatmean"+atmean);
  }
View Full Code Here
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
  }

  @Override
  public void prepare(OutlierResult or) {
    // Initial parameters - are these defaults sounds?
    MeanVariance mv = new MeanVariance();
    for(DBID id : or.getScores().iterDBIDs()) {
      double val = or.getScores().get(id);
      if(!Double.isNaN(val) && !Double.isInfinite(val)) {
        mv.put(val);
      }
    }
    double curMu = mv.getMean() * 2;
    if(curMu == 0) {
      curMu = Double.MIN_NORMAL;
    }
    double curSigma = Math.max(mv.getSampleStddev(), Double.MIN_NORMAL);
    double curLambda = Math.min(1.0 / curMu, Double.MAX_VALUE);
    double curAlpha = 0.05;

    ArrayDBIDs ids = DBIDUtil.ensureArray(or.getScores().getDBIDs());
    // TODO: stop condition!
View Full Code Here
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
   */
  public OutlierResult run(Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    MeanVariance mv = new MeanVariance();
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      final double median;
      {
        double[] fi = new double[neighbors.size()];
        // calculate and store Median of neighborhood
        int c = 0;
        for(DBID n : neighbors) {
          if(id.equals(n)) {
            continue;
          }
          fi[c] = relation.get(n).doubleValue(1);
          c++;
        }

        if(c > 0) {
          // Note: only use up to c-1, since we may have used a too big array
          median = QuickSelect.median(fi, 0, c - 1);
        }
        else {
          median = relation.get(id).doubleValue(1);
        }
      }
      double h = relation.get(id).doubleValue(1) - median;
      scores.put(id, h);
      mv.put(h);
    }

    // Normalize scores
    final double mean = mv.getMean();
    final double stddev = mv.getNaiveStddev();
    DoubleMinMax minmax = new DoubleMinMax();
    for(DBID id : relation.iterDBIDs()) {
      double score = Math.abs((scores.get(id) - mean) / stddev);
      minmax.put(score);
      scores.put(id, score);
View Full Code Here
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
      }
    }
    max = mm.getMax();
    mlogmax = -Math.log(mm.getMin() / max);
    // with the prescaling, do Gamma Scaling.
    MeanVariance mv = new MeanVariance();
    for(DBID id : or.getScores().iterDBIDs()) {
      double score = or.getScores().get(id);
      score = preScale(score);
      if(!Double.isNaN(score) && !Double.isInfinite(score)) {
        mv.put(score);
      }
    }
    final double mean = mv.getMean();
    final double var = mv.getSampleVariance();
    k = (mean * mean) / var;
    theta = var / mean;
    atmean = MathUtil.regularizedGammaP(k, mean / theta);
    // logger.warning("Mean:"+mean+" Var:"+var+" Theta: "+theta+" k: "+k+" valatmean"+atmean);
  }
View Full Code Here
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
  }

  @Override
  public void prepare(OutlierResult or) {
    if(fixedmean == null) {
      MeanVariance mv = new MeanVariance();
      for(DBID id : or.getScores().iterDBIDs()) {
        double val = -Math.log(or.getScores().get(id));
        if(!Double.isNaN(val) && !Double.isInfinite(val)) {
          mv.put(val);
        }
      }
      mean = mv.getMean();
      factor = lambda * mv.getSampleStddev() * MathUtil.SQRT2;
    }
    else {
      mean = fixedmean;
      double sqsum = 0;
      int cnt = 0;
View Full Code Here
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
  double Bfinal;

  @Override
  public void prepare(OutlierResult or) {
    // Initial parameters - are these defaults sounds?
    MeanVariance mv = new MeanVariance();
    for(DBID id : or.getScores().iterDBIDs()) {
      double val = or.getScores().get(id);
      mv.put(val);
    }
    double a = 1.0;
    double b = - mv.getMean();
    int iter = 0;

    ArrayDBIDs ids = DBIDUtil.ensureArray(or.getScores().getDBIDs());
    BitSet t = new BitSet(ids.size());
    boolean changing = true;
View Full Code Here
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
    // Euclidean
    {
      DistanceQuery<NumberVector<?, ?>, DoubleDistance> dq = db.getDistanceQuery(relation, EuclideanDistanceFunction.STATIC);
      KNNQuery<NumberVector<?, ?>, DoubleDistance> knnq = QueryUtil.getLinearScanKNNQuery(dq);

      MeanVariance meansize = new MeanVariance();
      for(DBID id : relation.iterDBIDs()) {
        KNNResult<DoubleDistance> knnlist = knnq.getKNNForDBID(id, 2);
        meansize.put(knnlist.size());
      }
      org.junit.Assert.assertEquals("Euclidean mean 2NN", mean2nnEuclid, meansize.getMean(), 0.00001);
      org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
    }
    // Manhattan
    {
      DistanceQuery<NumberVector<?, ?>, DoubleDistance> dq = db.getDistanceQuery(relation, ManhattanDistanceFunction.STATIC);
      KNNQuery<NumberVector<?, ?>, DoubleDistance> knnq = QueryUtil.getLinearScanKNNQuery(dq);

      MeanVariance meansize = new MeanVariance();
      for(DBID id : relation.iterDBIDs()) {
        KNNResult<DoubleDistance> knnlist = knnq.getKNNForDBID(id, 2);
        meansize.put(knnlist.size());
      }
      org.junit.Assert.assertEquals("Manhattan mean 2NN", mean2nnManhattan, meansize.getMean(), 0.00001);
      org.junit.Assert.assertEquals("Manhattan variance 2NN", var2nnManhattan, meansize.getSampleVariance(), 0.00001);
    }
  }
View Full Code Here
TOP

Related Classes of de.lmu.ifi.dbs.elki.math.MeanVariance

  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
  • de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD
  • de.lmu.ifi.dbs.elki.algorithm.outlier.LOCI
  • de.lmu.ifi.dbs.elki.algorithm.outlier.LoOP
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianAlgorithm
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMoranScatterplotOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuScatterplotOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuZTestOutlier
  • de.lmu.ifi.dbs.elki.algorithm.statistics.DistanceStatisticsWithClasses
  • de.lmu.ifi.dbs.elki.algorithm.statistics.RankingQualityHistogram
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.