List of destroyToSampleMatrix() Examples

Examples of destroyToSampleMatrix()

de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix.destroyToSampleMatrix()
Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom. This method doesn't require matrix duplication, but will not allow further updates, the object should be discarded. Use {@link #makeSampleMatrix()} ifyou want to perform further updates. @return New matrix

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix.destroyToSampleMatrix()

      deltas.put(id, delta);
      covmaker.put(delta);
    }
    // Finalize covariance matrix:
    Vector mean = covmaker.getMeanVector();
    Matrix cmati = covmaker.destroyToSampleMatrix().inverse();


    DoubleMinMax minmax = new DoubleMinMax();
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix.destroyToSampleMatrix()

      deltas.put(id, delta);
      covmaker.put(delta);
    }
    // Finalize covariance matrix:
    Vector mean = covmaker.getMeanVector();
    Matrix cmati = covmaker.destroyToSampleMatrix().inverse();


    DoubleMinMax minmax = new DoubleMinMax();
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix.destroyToSampleMatrix()

    }
    // Finalize covariance matrix, compute linear regression
    final double slope, inter;
    {
      double[] meanv = covm.getMeanVector().getArrayRef();
      Matrix fmat = covm.destroyToSampleMatrix();
      final double covxx = fmat.get(0, 0);
      final double covxy = fmat.get(0, 1);
      slope = covxy / covxx;
      inter = meanv[1] - slope * meanv[0];
    }

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix.destroyToSampleMatrix()

      deltas.put(id, delta);
      covmaker.put(delta);
    }
    // Finalize covariance matrix:
    Vector mean = covmaker.getMeanVector();
    Matrix cmati = covmaker.destroyToSampleMatrix().inverse();


    DoubleMinMax minmax = new DoubleMinMax();
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix.destroyToSampleMatrix()

    }
    // Finalize covariance matrix, compute linear regression
    final double slope, inter;
    {
      double[] meanv = covm.getMeanVector().getArrayRef();
      Matrix fmat = covm.destroyToSampleMatrix();
      final double covxx = fmat.get(0, 0);
      final double covxy = fmat.get(0, 1);
      slope = covxy / covxx;
      inter = meanv[1] - slope * meanv[0];
    }

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix.destroyToSampleMatrix()

      deltas.put(id, delta);
      covmaker.put(delta);
    }
    // Finalize covariance matrix:
    Vector mean = covmaker.getMeanVector();
    Matrix cmati = covmaker.destroyToSampleMatrix().inverse();


    DoubleMinMax minmax = new DoubleMinMax();
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);

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