List of destroyToNaiveMatrix() Examples

Examples of destroyToNaiveMatrix()

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

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

      V obj = database.get(it.next());
      double distance = weightDistance.distance(centroid, obj).doubleValue();
      double weight = weightfunction.getWeight(distance, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }


  /**
   * Compute Covariance Matrix for a QueryResult Collection
   *

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


      V obj = database.get(res.getDBID());
      double weight = weightfunction.getWeight(dist, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }


  /**
   * Parameterization class.
   *

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


    // Compute mean and covariance Matrix
    CovarianceMatrix temp = CovarianceMatrix.make(relation);
    V mean = temp.getMeanVector(relation);
    // debugFine(mean.toString());
    Matrix covarianceMatrix = temp.destroyToNaiveMatrix();
    // debugFine(covarianceMatrix.toString());
    Matrix covarianceTransposed = covarianceMatrix.cheatToAvoidSingularity(SINGULARITY_CHEAT).inverse();


    // Normalization factors for Gaussian PDF
    final double fakt = (1.0 / (Math.sqrt(Math.pow(MathUtil.TWOPI, DatabaseUtil.dimensionality(relation)) * covarianceMatrix.det())));

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

      V obj = database.get(it.next());
      double distance = weightDistance.distance(centroid, obj).doubleValue();
      double weight = weightfunction.getWeight(distance, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }


  /**
   * Compute Covariance Matrix for a QueryResult Collection
   *

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


      V obj = database.get(res.getDBID());
      double weight = weightfunction.getWeight(dist, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }


  /**
   * Parameterization class.
   *

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


    // Compute mean and covariance Matrix
    CovarianceMatrix temp = CovarianceMatrix.make(relation);
    Vector mean = temp.getMeanVector(relation).getColumnVector();
    // debugFine(mean.toString());
    Matrix covarianceMatrix = temp.destroyToNaiveMatrix();
    // debugFine(covarianceMatrix.toString());
    Matrix covarianceTransposed = covarianceMatrix.cheatToAvoidSingularity(SINGULARITY_CHEAT).inverse();


    // Normalization factors for Gaussian PDF
    final double fakt = (1.0 / (Math.sqrt(Math.pow(MathUtil.TWOPI, DatabaseUtil.dimensionality(relation)) * covarianceMatrix.det())));

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