diff options
Diffstat (limited to 'elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java')
| -rw-r--r-- | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java index 1583ac99..92f0d7a9 100644 --- a/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java +++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java @@ -93,6 +93,7 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter; * </p> * * @author Erich Schubert + * @since 0.7.0 * * @apiviz.has KNNQuery * @apiviz.has KernelDensityFunction @@ -203,7 +204,7 @@ public class KDEOS<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult>im densities.put(iter, new double[knum]); } // Distribute densities: - FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Computing densities.", ids.size(), LOG) : null; + FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Computing densities", ids.size(), LOG) : null; double iminbw = (minBandwidth > 0.) ? 1. / (minBandwidth * scale) : Double.POSITIVE_INFINITY; for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) { KNNList neighbors = knnq.getKNNForDBID(iter, kmax + 1); @@ -269,7 +270,7 @@ public class KDEOS<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult>im */ protected void computeOutlierScores(KNNQuery<O> knnq, final DBIDs ids, WritableDataStore<double[]> densities, WritableDoubleDataStore kdeos, DoubleMinMax minmax) { final int knum = kmax + 1 - kmin; - FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Computing KDEOS scores.", ids.size(), LOG) : null; + FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Computing KDEOS scores", ids.size(), LOG) : null; double[][] scratch = new double[knum][kmax + 5]; MeanVariance mv = new MeanVariance(); @@ -339,32 +340,32 @@ public class KDEOS<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult>im /** * Parameter to specify the kernel density function. */ - private static final OptionID KERNEL_ID = new OptionID("kdeos.kernel", "Kernel density function to use."); + public static final OptionID KERNEL_ID = new OptionID("kdeos.kernel", "Kernel density function to use."); /** * Parameter to specify the minimum bandwidth. */ - private static final OptionID KERNEL_MIN_ID = new OptionID("kdeos.kernel.minbw", "Minimum bandwidth for kernel density estimation."); + public static final OptionID KERNEL_MIN_ID = new OptionID("kdeos.kernel.minbw", "Minimum bandwidth for kernel density estimation."); /** * Parameter to specify the kernel scaling factor. */ - private static final OptionID KERNEL_SCALE_ID = new OptionID("kdeos.kernel.scale", "Scaling factor for the kernel function."); + public static final OptionID KERNEL_SCALE_ID = new OptionID("kdeos.kernel.scale", "Scaling factor for the kernel function."); /** * Minimum value of k to analyze. */ - private static final OptionID KMIN_ID = new OptionID("kdeos.k.min", "Minimum value of k to analyze."); + public static final OptionID KMIN_ID = new OptionID("kdeos.k.min", "Minimum value of k to analyze."); /** * Maximum value of k to analyze. */ - private static final OptionID KMAX_ID = new OptionID("kdeos.k.max", "Maximum value of k to analyze."); + public static final OptionID KMAX_ID = new OptionID("kdeos.k.max", "Maximum value of k to analyze."); /** * Intrinsic dimensionality. */ - private static final OptionID IDIM_ID = new OptionID("kdeos.idim", "Intrinsic dimensionality of this data set. Use -1 for using the true data dimensionality, but values such as 0-2 often offer better performance."); + public static final OptionID IDIM_ID = new OptionID("kdeos.idim", "Intrinsic dimensionality of this data set. Use -1 for using the true data dimensionality, but values such as 0-2 often offer better performance."); /** * Kernel function to use for density estimation. |