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22:48:46.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 24276824 2019-03-09 22:48:46.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}]}, {"source1": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -3,21 +3,21 @@\n drwxr-xr-x 0 root (0) root (0) 0 2019-03-09 22:48:46.000000 ./usr/share/\n drwxr-xr-x 0 root (0) root (0) 0 2019-03-09 22:48:46.000000 ./usr/share/doc/\n drwxr-xr-x 0 root (0) root (0) 0 2019-03-09 22:48:46.000000 ./usr/share/doc/elki-dev/\n -rw-r--r-- 0 root (0) root (0) 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number of entries: 1\n--rw---- 2.0 fat 2668032 bl defN 24-Jan-06 12:43 member-search-index.json\n+-rw---- 2.0 fat 2668032 bl defN 25-Feb-07 19:11 member-search-index.json\n 1 file, 2668032 bytes uncompressed, 254495 bytes compressed: 90.5%\n"}]}, {"source1": "package-search-index.zip", "source2": "package-search-index.zip", "unified_diff": null, "details": [{"source1": "zipinfo {}", "source2": "zipinfo {}", "unified_diff": "@@ -1,3 +1,3 @@\n Zip file size: 1703 bytes, number of entries: 1\n--rw---- 2.0 fat 11965 bl defN 24-Jan-06 12:43 package-search-index.json\n+-rw---- 2.0 fat 11965 bl defN 25-Feb-07 19:11 package-search-index.json\n 1 file, 11965 bytes uncompressed, 1539 bytes compressed: 87.1%\n"}]}, {"source1": "parameters-byopt.html", "source2": "parameters-byopt.html", "unified_diff": null, "details": [{"source1": "parameters-byopt.html", "source2": "parameters-byopt.html", "comments": ["Ordering differences only"], "unified_diff": "@@ -3011,28 +3011,28 @@\n -comphist.positive <pattern>\n \n
\n

Class label for the 'positive' class.

\n

Parameter for:

\n \n
\n
\n \n -comphist.positive <pattern>\n
\n
\n

Class label for the 'positive' class.

\n

Parameter for:

\n \n
\n
\n \n -comphist.scaling <class|object>\n
\n@@ -3484,28 +3484,28 @@\n -cop.k <int>\n \n
\n

The number of nearest neighbors of an object to be considered for computing its COP_SCORE.

\n

Parameter for:

\n \n
\n
\n \n -cop.k <int>\n
\n
\n

The number of nearest neighbors of an object to be considered for computing its COP_SCORE.

\n

Parameter for:

\n \n
\n
\n \n -cop.models <|true|false>\n
\n@@ -3523,66 +3523,66 @@\n \n -cop.pcarunner <class|object>\n \n
\n

The class to compute (filtered) PCA.

\n

\n Class Restriction: extends\n- de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner\n+ de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner\n

\n

\n Default:\n- de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner\n+ de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner\n

\n

Known implementations:

\n \n

Parameter for:

\n \n
\n
\n \n -cop.pcarunner <class|object>\n
\n
\n

The class to compute (filtered) PCA.

\n

\n Class Restriction: extends\n- de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner\n+ de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner\n

\n

\n Default:\n- de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner\n+ de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner\n

\n

Known implementations:

\n \n

Parameter for:

\n \n
\n
\n \n -copac.knn <int>\n
\n@@ -4862,37 +4862,37 @@\n
  • \n de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.OnedimensionalDistanceFunction\n
    • \n \n \n
    \n \n- -dish.epsilon <double>\n+ -dish.epsilon <double_1,...,double_n>\n
    \n
    \n-

    The maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector.

    \n+

    A comma separated list of positive doubles specifying the maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector (default is 0.001 in each dimension). If only one value is specified, this value will be used for each dimension.

    \n

    Default: 0.001

    \n

    Parameter for:

    \n     \n
    \n
    \n \n- -dish.epsilon <double_1,...,double_n>\n+ -dish.epsilon <double>\n
    \n
    \n-

    A comma separated list of positive doubles specifying the maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector (default is 0.001 in each dimension). If only one value is specified, this value will be used for each dimension.

    \n+

    The maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector.

    \n

    Default: 0.001

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -dish.minpts <int>\n
    \n@@ -4986,34 +4986,34 @@\n -distance.matrix <file>\n \n
    \n

    The name of the file containing the distance matrix.

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -distance.matrix <file>\n
    \n
    \n

    The name of the file containing the distance matrix.

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -distance.parser <class|object>\n
    \n@@ -5330,28 +5330,28 @@\n -distribution.loggamma.shift <double>\n \n
    \n

    Shift offset parameter.

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -distribution.loggamma.shift <double>\n
    \n
    \n

    Shift offset parameter.

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -distribution.lognormal.logmean <double>\n
    \n@@ -7001,29 +7001,29 @@\n \n
    \n

    The minimum cluster size.

    \n

    Default: 1

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -hdbscan.minclsize <int>\n
    \n
    \n

    The minimum cluster size.

    \n

    Default: 1

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -hdbscan.minPts <int>\n
    \n@@ -8532,15 +8532,15 @@\n
  • \n RandomDoubleVectorDatabaseConnection\n
    • \n \n

    Parameter for:

    \n \n \n
    \n \n -join.sources <object_1|class_1,...,object_n|class_n>\n
    \n@@ -8585,15 +8585,15 @@\n
  • \n RandomDoubleVectorDatabaseConnection\n
    • \n \n

    Parameter for:

    \n \n \n
    \n \n -join.sources <object_1|class_1,...,object_n|class_n>\n
    \n@@ -8638,15 +8638,15 @@\n
  • \n RandomDoubleVectorDatabaseConnection\n
    • \n \n

    Parameter for:

    \n \n \n
    \n \n -json.port <int>\n
    \n@@ -9040,23 +9040,19 @@\n \n \n
    \n \n -kmeans.algorithm <class|object>\n
    \n
    \n-

    Clustering algorithm to use for detecting outliers.

    \n+

    KMeans variant to run multiple times.

    \n

    \n Class Restriction: implements\n de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans\n

    \n-

    \n- Default:\n- de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd\n-

    \n

    Known implementations:

    \n \n

    Parameter for:

    \n \n
    \n
    \n \n -kmeans.algorithm <class|object>\n
    \n@@ -9159,28 +9155,32 @@\n
  • \n parallel.ParallelLloydKMeans\n
    • \n \n

    Parameter for:

    \n \n \n
    \n \n -kmeans.algorithm <class|object>\n
    \n
    \n-

    KMeans variant to run multiple times.

    \n+

    Clustering algorithm to use for detecting outliers.

    \n

    \n Class Restriction: implements\n de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans\n

    \n+

    \n+ Default:\n+ de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd\n+

    \n

    Known implementations:

    \n \n

    Parameter for:

    \n \n
    \n
    \n \n -kmeans.blocks <int>\n
    \n@@ -9687,29 +9687,29 @@\n \n
    \n

    Compute the final clustering variance statistic. Needs an additional full pass over the data set.

    \n

    Default: false

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -kmeans.varstat <|true|false>\n
    \n
    \n

    Compute the final clustering variance statistic. Needs an additional full pass over the data set.

    \n

    Default: false

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -kml.autoopen <|true|false>\n
    \n@@ -10535,37 +10535,37 @@\n
  • \n de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex$Factory\n
    • \n \n \n
    \n \n- -loci.alpha <int>\n+ -loci.alpha <double>\n
    \n
    \n

    Scaling factor for averaging neighborhood

    \n-

    Default: 4

    \n+

    Default: 0.5

    \n

    Parameter for:

    \n     \n
    \n
    \n \n- -loci.alpha <double>\n+ -loci.alpha <int>\n
    \n
    \n

    Scaling factor for averaging neighborhood

    \n-

    Default: 0.5

    \n+

    Default: 4

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -loci.g <int>\n
    \n@@ -10585,29 +10585,29 @@\n \n
    \n

    Minimum neighborhood size to be considered.

    \n

    Default: 20

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -loci.nmin <int>\n
    \n
    \n

    Minimum neighborhood size to be considered.

    \n

    Default: 20

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -loci.rmax <double>\n
    \n@@ -13646,48 +13646,48 @@\n \n \n
    \n \n -nfold <int>\n
    \n
    \n-

    Number of folds for cross-validation

    \n+

    Number of folds for cross-validation.

    \n

    Default: 10

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -nfold <int>\n
    \n
    \n-

    positive number of folds for cross-validation

    \n+

    Number of folds for cross-validation

    \n

    Default: 10

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -nfold <int>\n
    \n
    \n-

    Number of folds for cross-validation.

    \n+

    positive number of folds for cross-validation

    \n

    Default: 10

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -normalization.max <double>\n
    \n@@ -15729,28 +15729,28 @@\n -predecon.lambda <int>\n \n
    \n

    Maximum dimensionality to consider for core points.

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -predecon.lambda <int>\n
    \n
    \n

    Maximum dimensionality to consider for core points.

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -proclus.mi <int>\n
    \n@@ -16930,33 +16930,33 @@\n \n
    \n \n -rankqual.bins <int>\n
    \n
    \n

    Number of bins to use in the histogram

    \n-

    Default: 100

    \n+

    Default: 20

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -rankqual.bins <int>\n
    \n
    \n

    Number of bins to use in the histogram

    \n-

    Default: 20

    \n+

    Default: 100

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -ransacpca.iterations <int>\n
    \n@@ -17854,15 +17854,15 @@\n
  • \n ZCurveSpatialSorter\n
    • \n \n

    Parameter for:

    \n \n \n
    \n \n -sfcknn.curves <object_1|class_1,...,object_n|class_n>\n
    \n@@ -17886,15 +17886,15 @@\n
  • \n ZCurveSpatialSorter\n
    • \n \n

    Parameter for:

    \n \n \n
    \n \n -sfcknn.dim <int>\n
    \n@@ -17976,57 +17976,57 @@\n \n
    \n

    Number of curve variants to generate.

    \n

    Default: 1

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -sfcknn.variants <int>\n
    \n
    \n

    Number of curve variants to generate.

    \n

    Default: 1

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -sfcknn.windowmult <double>\n
    \n
    \n

    Window size multiplicator.

    \n

    Default: 10.0

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -sfcknn.windowmult <double>\n
    \n
    \n

    Window size multiplicator.

    \n

    Default: 10.0

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -sharedNearestNeighbors <int>\n
    \n@@ -19905,28 +19905,28 @@\n -uncertain.dimensionality <int>\n \n
    \n

    Dimensionality of the data set (used for splitting).

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -uncertain.dimensionality <int>\n
    \n
    \n

    Dimensionality of the data set (used for splitting).

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -uncertain.probability.column <int>\n
    \n@@ -20143,28 +20143,28 @@\n -vafile.partitions <int>\n \n
    \n

    Number of partitions to use in each dimension.

    \n

    Parameter for:

    \n     \n
    \n
    \n \n -vafile.partitions <int>\n
    \n
    \n

    Number of partitions to use in each dimension.

    \n

    Parameter for:

    \n     \n
    \n
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