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elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java
GeneralizedExtremeValueDistribution.pdf
public static double pdf(double x, double mu, double sigma, double k) { if(x == Double.POSITIVE_INFINITY || x == Double.NEGATIVE_INFINITY) { return 0.; } x = (x - mu) / sigma; if(k > 0 || k < 0) { if(k * x > 1) { return 0.; } double t = FastMath.log(1 - k * x); retu...
java
public static double pdf(double x, double mu, double sigma, double k) { if(x == Double.POSITIVE_INFINITY || x == Double.NEGATIVE_INFINITY) { return 0.; } x = (x - mu) / sigma; if(k > 0 || k < 0) { if(k * x > 1) { return 0.; } double t = FastMath.log(1 - k * x); retu...
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PDF of GEV distribution @param x Value @param mu Location parameter mu @param sigma Scale parameter sigma @param k Shape parameter k @return PDF at position x.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java#L130-L147
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java
GeneralizedExtremeValueDistribution.cdf
public static double cdf(double val, double mu, double sigma, double k) { final double x = (val - mu) / sigma; if(k > 0 || k < 0) { if(k * x > 1) { return k > 0 ? 1 : 0; } return FastMath.exp(-FastMath.exp(FastMath.log(1 - k * x) / k)); } else { // Gumbel case: return Fas...
java
public static double cdf(double val, double mu, double sigma, double k) { final double x = (val - mu) / sigma; if(k > 0 || k < 0) { if(k * x > 1) { return k > 0 ? 1 : 0; } return FastMath.exp(-FastMath.exp(FastMath.log(1 - k * x) / k)); } else { // Gumbel case: return Fas...
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CDF of GEV distribution @param val Value @param mu Location parameter mu @param sigma Scale parameter sigma @param k Shape parameter k @return CDF at position x.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java#L196-L207
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java
GeneralizedExtremeValueDistribution.quantile
public static double quantile(double val, double mu, double sigma, double k) { if(val < 0.0 || val > 1.0) { return Double.NaN; } if(k < 0) { return mu + sigma * Math.max((1. - FastMath.pow(-FastMath.log(val), k)) / k, 1. / k); } else if(k > 0) { return mu + sigma * Math.min((1. - F...
java
public static double quantile(double val, double mu, double sigma, double k) { if(val < 0.0 || val > 1.0) { return Double.NaN; } if(k < 0) { return mu + sigma * Math.max((1. - FastMath.pow(-FastMath.log(val), k)) / k, 1. / k); } else if(k > 0) { return mu + sigma * Math.min((1. - F...
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Quantile function of GEV distribution @param val Value @param mu Location parameter mu @param sigma Scale parameter sigma @param k Shape parameter k @return Quantile function at position x.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java#L223-L236
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/RayleighDistribution.java
RayleighDistribution.cdf
public static double cdf(double x, double sigma) { if(x <= 0.) { return 0.; } final double xs = x / sigma; return 1. - FastMath.exp(-.5 * xs * xs); }
java
public static double cdf(double x, double sigma) { if(x <= 0.) { return 0.; } final double xs = x / sigma; return 1. - FastMath.exp(-.5 * xs * xs); }
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CDF of Rayleigh distribution @param x Value @param sigma Scale parameter @return CDF at position x.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/RayleighDistribution.java#L173-L179
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/RayleighDistribution.java
RayleighDistribution.quantile
public static double quantile(double val, double sigma) { if(!(val >= 0.) || !(val <= 1.)) { return Double.NaN; } if(val == 0.) { return 0.; } if(val == 1.) { return Double.POSITIVE_INFINITY; } return sigma * FastMath.sqrt(-2. * FastMath.log(1. - val)); }
java
public static double quantile(double val, double sigma) { if(!(val >= 0.) || !(val <= 1.)) { return Double.NaN; } if(val == 0.) { return 0.; } if(val == 1.) { return Double.POSITIVE_INFINITY; } return sigma * FastMath.sqrt(-2. * FastMath.log(1. - val)); }
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Quantile function of Rayleigh distribution @param val Value @param sigma Scale parameter @return Quantile function at position x.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/RayleighDistribution.java#L193-L204
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/anglebased/ABOD.java
ABOD.run
public OutlierResult run(Database db, Relation<V> relation) { ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs()); // Build a kernel matrix, to make O(n^3) slightly less bad. SimilarityQuery<V> sq = db.getSimilarityQuery(relation, kernelFunction); KernelMatrix kernelMatrix = new KernelMatrix(sq, ...
java
public OutlierResult run(Database db, Relation<V> relation) { ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs()); // Build a kernel matrix, to make O(n^3) slightly less bad. SimilarityQuery<V> sq = db.getSimilarityQuery(relation, kernelFunction); KernelMatrix kernelMatrix = new KernelMatrix(sq, ...
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Run ABOD on the data set. @param relation Relation to process @return Outlier detection result
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/anglebased/ABOD.java#L114-L135
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/anglebased/ABOD.java
ABOD.computeABOF
protected double computeABOF(KernelMatrix kernelMatrix, DBIDRef pA, DBIDArrayIter pB, DBIDArrayIter pC, MeanVariance s) { s.reset(); // Reused double simAA = kernelMatrix.getSimilarity(pA, pA); for(pB.seek(0); pB.valid(); pB.advance()) { if(DBIDUtil.equal(pB, pA)) { continue; } do...
java
protected double computeABOF(KernelMatrix kernelMatrix, DBIDRef pA, DBIDArrayIter pB, DBIDArrayIter pC, MeanVariance s) { s.reset(); // Reused double simAA = kernelMatrix.getSimilarity(pA, pA); for(pB.seek(0); pB.valid(); pB.advance()) { if(DBIDUtil.equal(pB, pA)) { continue; } do...
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Compute the exact ABOF value. @param kernelMatrix Kernel matrix @param pA Object A to compute ABOF for @param pB Iterator over objects B @param pC Iterator over objects C @param s Statistics tracker @return ABOF value
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/anglebased/ABOD.java#L147-L183
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/parallel/ParallelKNNWeightOutlier.java
ParallelKNNWeightOutlier.run
public OutlierResult run(Database database, Relation<O> relation) { DBIDs ids = relation.getDBIDs(); WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB); DistanceQuery<O> distq = database.getDistanceQuery(relation, getDistanceFunction()); KNNQuery<O> knnq = dat...
java
public OutlierResult run(Database database, Relation<O> relation) { DBIDs ids = relation.getDBIDs(); WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB); DistanceQuery<O> distq = database.getDistanceQuery(relation, getDistanceFunction()); KNNQuery<O> knnq = dat...
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Run the parallel kNN weight outlier detector. @param database Database to process @param relation Relation to analyze @return Outlier detection result
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/parallel/ParallelKNNWeightOutlier.java#L119-L147
train
elki-project/elki
addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java
UKMeans.run
public Clustering<?> run(final Database database, final Relation<DiscreteUncertainObject> relation) { if(relation.size() <= 0) { return new Clustering<>("Uk-Means Clustering", "ukmeans-clustering"); } // Choose initial means randomly DBIDs sampleids = DBIDUtil.randomSample(relation.getDBIDs(), k, ...
java
public Clustering<?> run(final Database database, final Relation<DiscreteUncertainObject> relation) { if(relation.size() <= 0) { return new Clustering<>("Uk-Means Clustering", "ukmeans-clustering"); } // Choose initial means randomly DBIDs sampleids = DBIDUtil.randomSample(relation.getDBIDs(), k, ...
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Run the clustering. @param database the Database @param relation the Relation @return Clustering result
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java#L132-L180
train
elki-project/elki
addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java
UKMeans.updateAssignment
protected boolean updateAssignment(DBIDIter iditer, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, int newA) { final int oldA = assignment.intValue(iditer); if(oldA == newA) { return false; } clusters.get(newA).add(iditer); assignment.putInt(iditer, newA); i...
java
protected boolean updateAssignment(DBIDIter iditer, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, int newA) { final int oldA = assignment.intValue(iditer); if(oldA == newA) { return false; } clusters.get(newA).add(iditer); assignment.putInt(iditer, newA); i...
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Update the cluster assignment. @param iditer Object id @param clusters Cluster list @param assignment Assignment storage @param newA New assignment. @return {@code true} if the assignment has changed.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java#L223-L234
train
elki-project/elki
addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java
UKMeans.getExpectedRepDistance
protected double getExpectedRepDistance(NumberVector rep, DiscreteUncertainObject uo) { SquaredEuclideanDistanceFunction euclidean = SquaredEuclideanDistanceFunction.STATIC; int counter = 0; double sum = 0.0; for(int i = 0; i < uo.getNumberSamples(); i++) { sum += euclidean.distance(rep, uo.getSam...
java
protected double getExpectedRepDistance(NumberVector rep, DiscreteUncertainObject uo) { SquaredEuclideanDistanceFunction euclidean = SquaredEuclideanDistanceFunction.STATIC; int counter = 0; double sum = 0.0; for(int i = 0; i < uo.getNumberSamples(); i++) { sum += euclidean.distance(rep, uo.getSam...
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Get expected distance between a Vector and an uncertain object @param rep A vector, e.g. a cluster representative @param uo A discrete uncertain object @return The distance
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java#L243-L252
train
elki-project/elki
addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java
UKMeans.logVarstat
protected void logVarstat(DoubleStatistic varstat, double[] varsum) { if(varstat != null) { double s = sum(varsum); getLogger().statistics(varstat.setDouble(s)); } }
java
protected void logVarstat(DoubleStatistic varstat, double[] varsum) { if(varstat != null) { double s = sum(varsum); getLogger().statistics(varstat.setDouble(s)); } }
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Log statistics on the variance sum. @param varstat Statistics log instance @param varsum Variance sum per cluster
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java#L306-L311
train
elki-project/elki
elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java
SavedSettingsFile.save
public void save() throws FileNotFoundException { PrintStream p = new PrintStream(file); p.println(COMMENT_PREFIX + "Saved ELKI settings. First line is title, remaining lines are parameters."); for (Pair<String, ArrayList<String>> settings : store) { p.println(settings.first); for (String str : ...
java
public void save() throws FileNotFoundException { PrintStream p = new PrintStream(file); p.println(COMMENT_PREFIX + "Saved ELKI settings. First line is title, remaining lines are parameters."); for (Pair<String, ArrayList<String>> settings : store) { p.println(settings.first); for (String str : ...
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Save the current data to the given file. @throws FileNotFoundException thrown on output errors.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java#L73-L84
train
elki-project/elki
elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java
SavedSettingsFile.load
public void load() throws FileNotFoundException, IOException { BufferedReader is = new BufferedReader(new InputStreamReader(new FileInputStream(file))); ArrayList<String> buf = new ArrayList<>(); while (is.ready()) { String line = is.readLine(); // skip comments if (line.startsWith(COMMENT...
java
public void load() throws FileNotFoundException, IOException { BufferedReader is = new BufferedReader(new InputStreamReader(new FileInputStream(file))); ArrayList<String> buf = new ArrayList<>(); while (is.ready()) { String line = is.readLine(); // skip comments if (line.startsWith(COMMENT...
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Read the current file @throws FileNotFoundException thrown when file not found @throws IOException thrown on IO errprs
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java#L92-L115
train
elki-project/elki
elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java
SavedSettingsFile.remove
public void remove(String key) { Iterator<Pair<String, ArrayList<String>>> it = store.iterator(); while (it.hasNext()) { String thisKey = it.next().first; if (key.equals(thisKey)) { it.remove(); break; } } }
java
public void remove(String key) { Iterator<Pair<String, ArrayList<String>>> it = store.iterator(); while (it.hasNext()) { String thisKey = it.next().first; if (key.equals(thisKey)) { it.remove(); break; } } }
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Remove a given key from the file. @param key Key to remove
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java#L127-L136
train
elki-project/elki
elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java
SavedSettingsFile.get
public ArrayList<String> get(String key) { Iterator<Pair<String, ArrayList<String>>> it = store.iterator(); while (it.hasNext()) { Pair<String, ArrayList<String>> pair = it.next(); if (key.equals(pair.first)) { return pair.second; } } return null; }
java
public ArrayList<String> get(String key) { Iterator<Pair<String, ArrayList<String>>> it = store.iterator(); while (it.hasNext()) { Pair<String, ArrayList<String>> pair = it.next(); if (key.equals(pair.first)) { return pair.second; } } return null; }
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Find a saved setting by key. @param key Key to search for @return saved settings for this key
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java#L144-L153
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java
ORCLUS.run
public Clustering<Model> run(Database database, Relation<V> relation) { // current dimensionality associated with each seed int dim_c = RelationUtil.dimensionality(relation); if(dim_c < l) { throw new IllegalStateException("Dimensionality of data < parameter l! " + "(" + dim_c + " < " + l + ")"); ...
java
public Clustering<Model> run(Database database, Relation<V> relation) { // current dimensionality associated with each seed int dim_c = RelationUtil.dimensionality(relation); if(dim_c < l) { throw new IllegalStateException("Dimensionality of data < parameter l! " + "(" + dim_c + " < " + l + ")"); ...
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Performs the ORCLUS algorithm on the given database. @param database Database @param relation Relation
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java#L129-L177
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java
ORCLUS.initialSeeds
private List<ORCLUSCluster> initialSeeds(Relation<V> database, int k) { DBIDs randomSample = DBIDUtil.randomSample(database.getDBIDs(), k, rnd); List<ORCLUSCluster> seeds = new ArrayList<>(k); for(DBIDIter iter = randomSample.iter(); iter.valid(); iter.advance()) { seeds.add(new ORCLUSCluster(database...
java
private List<ORCLUSCluster> initialSeeds(Relation<V> database, int k) { DBIDs randomSample = DBIDUtil.randomSample(database.getDBIDs(), k, rnd); List<ORCLUSCluster> seeds = new ArrayList<>(k); for(DBIDIter iter = randomSample.iter(); iter.valid(); iter.advance()) { seeds.add(new ORCLUSCluster(database...
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Initializes the list of seeds wit a random sample of size k. @param database the database holding the objects @param k the size of the random sample @return the initial seed list
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java#L186-L193
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java
ORCLUS.assign
private void assign(Relation<V> database, List<ORCLUSCluster> clusters) { NumberVectorDistanceFunction<? super V> distFunc = SquaredEuclideanDistanceFunction.STATIC; // clear the current clusters for(ORCLUSCluster cluster : clusters) { cluster.objectIDs.clear(); } // projected centroids of th...
java
private void assign(Relation<V> database, List<ORCLUSCluster> clusters) { NumberVectorDistanceFunction<? super V> distFunc = SquaredEuclideanDistanceFunction.STATIC; // clear the current clusters for(ORCLUSCluster cluster : clusters) { cluster.objectIDs.clear(); } // projected centroids of th...
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Creates a partitioning of the database by assigning each object to its closest seed. @param database the database holding the objects @param clusters the array of clusters to which the objects should be assigned to
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java#L203-L243
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java
ORCLUS.merge
private void merge(Relation<V> relation, List<ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress) { ArrayList<ProjectedEnergy> projectedEnergies = new ArrayList<>((clusters.size() * (clusters.size() - 1)) >>> 1); for(int i = 0; i < clusters.size(); i++) { for(int j = i + 1; j < cl...
java
private void merge(Relation<V> relation, List<ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress) { ArrayList<ProjectedEnergy> projectedEnergies = new ArrayList<>((clusters.size() * (clusters.size() - 1)) >>> 1); for(int i = 0; i < clusters.size(); i++) { for(int j = i + 1; j < cl...
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Reduces the number of seeds to k_new @param relation the database holding the objects @param clusters the set of current seeds @param k_new the new number of seeds @param d_new the new dimensionality of the subspaces for each seed
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java#L268-L327
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java
ORCLUS.projectedEnergy
private ProjectedEnergy projectedEnergy(Relation<V> relation, ORCLUSCluster c_i, ORCLUSCluster c_j, int i, int j, int dim) { NumberVectorDistanceFunction<? super V> distFunc = SquaredEuclideanDistanceFunction.STATIC; // union of cluster c_i and c_j ORCLUSCluster c_ij = union(relation, c_i, c_j, dim); d...
java
private ProjectedEnergy projectedEnergy(Relation<V> relation, ORCLUSCluster c_i, ORCLUSCluster c_j, int i, int j, int dim) { NumberVectorDistanceFunction<? super V> distFunc = SquaredEuclideanDistanceFunction.STATIC; // union of cluster c_i and c_j ORCLUSCluster c_ij = union(relation, c_i, c_j, dim); d...
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Computes the projected energy of the specified clusters. The projected energy is given by the mean square distance of the points to the centroid of the union cluster c, when all points in c are projected to the subspace of c. @param relation the relation holding the objects @param c_i the first cluster @param c_j the ...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java#L343-L357
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java
ORCLUS.union
private ORCLUSCluster union(Relation<V> relation, ORCLUSCluster c1, ORCLUSCluster c2, int dim) { ORCLUSCluster c = new ORCLUSCluster(); c.objectIDs = DBIDUtil.newHashSet(c1.objectIDs); c.objectIDs.addDBIDs(c2.objectIDs); c.objectIDs = DBIDUtil.newArray(c.objectIDs); if(c.objectIDs.size() > 0) { ...
java
private ORCLUSCluster union(Relation<V> relation, ORCLUSCluster c1, ORCLUSCluster c2, int dim) { ORCLUSCluster c = new ORCLUSCluster(); c.objectIDs = DBIDUtil.newHashSet(c1.objectIDs); c.objectIDs.addDBIDs(c2.objectIDs); c.objectIDs = DBIDUtil.newArray(c.objectIDs); if(c.objectIDs.size() > 0) { ...
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Returns the union of the two specified clusters. @param relation the database holding the objects @param c1 the first cluster @param c2 the second cluster @param dim the dimensionality of the union cluster @return the union of the two specified clusters
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java#L368-L383
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java
AnderbergHierarchicalClustering.initializeNNCache
private static void initializeNNCache(double[] scratch, double[] bestd, int[] besti) { final int size = bestd.length; Arrays.fill(bestd, Double.POSITIVE_INFINITY); Arrays.fill(besti, -1); for(int x = 0, p = 0; x < size; x++) { assert (p == MatrixParadigm.triangleSize(x)); double bestdx = Dou...
java
private static void initializeNNCache(double[] scratch, double[] bestd, int[] besti) { final int size = bestd.length; Arrays.fill(bestd, Double.POSITIVE_INFINITY); Arrays.fill(besti, -1); for(int x = 0, p = 0; x < size; x++) { assert (p == MatrixParadigm.triangleSize(x)); double bestdx = Dou...
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Initialize the NN cache. @param scratch Scratch space @param bestd Best distance @param besti Best index
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java#L149-L171
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java
AnderbergHierarchicalClustering.findMerge
protected int findMerge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder) { double mindist = Double.POSITIVE_INFINITY; int x = -1, y = -1; // Find minimum: for(int cx = 0; cx < size; cx++) { // Skip if object has already joined a cluster: ...
java
protected int findMerge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder) { double mindist = Double.POSITIVE_INFINITY; int x = -1, y = -1; // Find minimum: for(int cx = 0; cx < size; cx++) { // Skip if object has already joined a cluster: ...
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Perform the next merge step. Due to the cache, this is now O(n) each time, instead of O(n*n). @param size Data set size @param mat Matrix paradigm @param bestd Best distance @param besti Index of best distance @param builder Hierarchy builder @return x, for shrinking the working set.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java#L185-L206
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java
AnderbergHierarchicalClustering.merge
protected void merge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder, double mindist, int x, int y) { // Avoid allocating memory, by reusing existing iterators: final DBIDArrayIter ix = mat.ix.seek(x), iy = mat.iy.seek(y); if(LOG.isDebuggingFine()) { ...
java
protected void merge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder, double mindist, int x, int y) { // Avoid allocating memory, by reusing existing iterators: final DBIDArrayIter ix = mat.ix.seek(x), iy = mat.iy.seek(y); if(LOG.isDebuggingFine()) { ...
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Execute the cluster merge. @param size Data set size @param mat Matrix paradigm @param bestd Best distance @param besti Index of best distance @param builder Hierarchy builder @param mindist Distance that was used for merging @param x First matrix position @param y Second matrix position
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java#L220-L242
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java
AnderbergHierarchicalClustering.updateCache
private void updateCache(int size, double[] scratch, double[] bestd, int[] besti, int x, int y, int j, double d) { // New best if(d <= bestd[j]) { bestd[j] = d; besti[j] = y; return; } // Needs slow update. if(besti[j] == x || besti[j] == y) { findBest(size, scratch, bestd, b...
java
private void updateCache(int size, double[] scratch, double[] bestd, int[] besti, int x, int y, int j, double d) { // New best if(d <= bestd[j]) { bestd[j] = d; besti[j] = y; return; } // Needs slow update. if(besti[j] == x || besti[j] == y) { findBest(size, scratch, bestd, b...
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Update the cache. @param size Working set size @param scratch Scratch matrix @param bestd Best distance @param besti Best index @param x First cluster @param y Second cluster, {@code y < x} @param j Updated value d(y, j) @param d New distance
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java#L312-L323
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java
VisualizerParameterizer.newContext
public VisualizerContext newContext(ResultHierarchy hier, Result start) { Collection<Relation<?>> rels = ResultUtil.filterResults(hier, Relation.class); for(Relation<?> rel : rels) { if(samplesize == 0) { continue; } if(!ResultUtil.filterResults(hier, rel, SamplingResult.class).isEmpty...
java
public VisualizerContext newContext(ResultHierarchy hier, Result start) { Collection<Relation<?>> rels = ResultUtil.filterResults(hier, Relation.class); for(Relation<?> rel : rels) { if(samplesize == 0) { continue; } if(!ResultUtil.filterResults(hier, rel, SamplingResult.class).isEmpty...
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Make a new visualization context @param hier Result hierarchy @param start Starting result @return New context
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java#L128-L144
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java
VisualizerParameterizer.getTitle
public static String getTitle(Database db, Result result) { List<TrackedParameter> settings = new ArrayList<>(); for(SettingsResult sr : SettingsResult.getSettingsResults(result)) { settings.addAll(sr.getSettings()); } String algorithm = null; String distance = null; String dataset = null;...
java
public static String getTitle(Database db, Result result) { List<TrackedParameter> settings = new ArrayList<>(); for(SettingsResult sr : SettingsResult.getSettingsResults(result)) { settings.addAll(sr.getSettings()); } String algorithm = null; String distance = null; String dataset = null;...
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Try to automatically generate a title for this. @param db Database @param result Result object @return generated title
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java#L153-L196
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java
VisualizerParameterizer.shortenClassname
protected static String shortenClassname(String nam, char c) { final int lastdot = nam.lastIndexOf(c); if(lastdot >= 0) { nam = nam.substring(lastdot + 1); } return nam; }
java
protected static String shortenClassname(String nam, char c) { final int lastdot = nam.lastIndexOf(c); if(lastdot >= 0) { nam = nam.substring(lastdot + 1); } return nam; }
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Shorten the class name. @param nam Class name @param c Splitting character @return Shortened name
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java#L205-L211
train
elki-project/elki
elki-docutil/src/main/java/de/lmu/ifi/dbs/elki/application/internal/DocumentParameters.java
DocumentParameters.getRestrictionClass
private static Class<?> getRestrictionClass(OptionID oid, final Parameter<?> firstopt, Map<OptionID, List<Pair<Parameter<?>, Class<?>>>> byopt) { Class<?> superclass = getRestrictionClass(firstopt); // Also look for more general restrictions: for(Pair<Parameter<?>, Class<?>> clinst : byopt.get(oid)) { ...
java
private static Class<?> getRestrictionClass(OptionID oid, final Parameter<?> firstopt, Map<OptionID, List<Pair<Parameter<?>, Class<?>>>> byopt) { Class<?> superclass = getRestrictionClass(firstopt); // Also look for more general restrictions: for(Pair<Parameter<?>, Class<?>> clinst : byopt.get(oid)) { ...
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Get the restriction class of an option. @param oid Option ID @param firstopt Parameter @param byopt Option to parameter map @return Restriction class
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-docutil/src/main/java/de/lmu/ifi/dbs/elki/application/internal/DocumentParameters.java#L791-L809
train
elki-project/elki
elki-docutil/src/main/java/de/lmu/ifi/dbs/elki/application/internal/DocumentParameters.java
DocumentParameters.sorted
private static <T> ArrayList<T> sorted(Collection<T> cls, Comparator<? super T> c) { ArrayList<T> sorted = new ArrayList<>(cls); sorted.sort(c); return sorted; }
java
private static <T> ArrayList<T> sorted(Collection<T> cls, Comparator<? super T> c) { ArrayList<T> sorted = new ArrayList<>(cls); sorted.sort(c); return sorted; }
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Sort a collection of classes. @param cls Classes to sort @return Sorted list
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-docutil/src/main/java/de/lmu/ifi/dbs/elki/application/internal/DocumentParameters.java#L831-L835
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractTooltipVisualization.java
AbstractTooltipVisualization.handleHoverEvent
protected void handleHoverEvent(Event evt) { if(evt.getTarget() instanceof Element) { Element e = (Element) evt.getTarget(); Node next = e.getNextSibling(); if(next instanceof Element) { toggleTooltip((Element) next, evt.getType()); } else { LoggingUtil.warning("Tooltip...
java
protected void handleHoverEvent(Event evt) { if(evt.getTarget() instanceof Element) { Element e = (Element) evt.getTarget(); Node next = e.getNextSibling(); if(next instanceof Element) { toggleTooltip((Element) next, evt.getType()); } else { LoggingUtil.warning("Tooltip...
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Handle the hover events. @param evt Event.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractTooltipVisualization.java#L140-L154
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractTooltipVisualization.java
AbstractTooltipVisualization.toggleTooltip
protected void toggleTooltip(Element elem, String type) { String csscls = elem.getAttribute(SVGConstants.SVG_CLASS_ATTRIBUTE); if(SVGConstants.SVG_MOUSEOVER_EVENT_TYPE.equals(type)) { if(TOOLTIP_HIDDEN.equals(csscls)) { SVGUtil.setAtt(elem, SVGConstants.SVG_CLASS_ATTRIBUTE, TOOLTIP_VISIBLE); ...
java
protected void toggleTooltip(Element elem, String type) { String csscls = elem.getAttribute(SVGConstants.SVG_CLASS_ATTRIBUTE); if(SVGConstants.SVG_MOUSEOVER_EVENT_TYPE.equals(type)) { if(TOOLTIP_HIDDEN.equals(csscls)) { SVGUtil.setAtt(elem, SVGConstants.SVG_CLASS_ATTRIBUTE, TOOLTIP_VISIBLE); ...
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Toggle the Tooltip of an element. @param elem Element @param type Event type
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractTooltipVisualization.java#L162-L182
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java
MkAppTree.reverseKNNQuery
@Override public DoubleDBIDList reverseKNNQuery(DBIDRef id, int k) { ModifiableDoubleDBIDList result = DBIDUtil.newDistanceDBIDList(); final Heap<MTreeSearchCandidate> pq = new UpdatableHeap<>(); // push root pq.add(new MTreeSearchCandidate(0., getRootID(), null, Double.NaN)); // search in tree ...
java
@Override public DoubleDBIDList reverseKNNQuery(DBIDRef id, int k) { ModifiableDoubleDBIDList result = DBIDUtil.newDistanceDBIDList(); final Heap<MTreeSearchCandidate> pq = new UpdatableHeap<>(); // push root pq.add(new MTreeSearchCandidate(0., getRootID(), null, Double.NaN)); // search in tree ...
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Performs a reverse k-nearest neighbor query for the given object ID. The query result is in ascending order to the distance to the query object. @param id the query object id @param k the number of nearest neighbors to be returned @return a List of the query results
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java#L142-L191
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java
MkAppTree.leafEntryIDs
private void leafEntryIDs(MkAppTreeNode<O> node, ModifiableDBIDs result) { if(node.isLeaf()) { for(int i = 0; i < node.getNumEntries(); i++) { MkAppEntry entry = node.getEntry(i); result.add(((LeafEntry) entry).getDBID()); } } else { for(int i = 0; i < node.getNumEntries();...
java
private void leafEntryIDs(MkAppTreeNode<O> node, ModifiableDBIDs result) { if(node.isLeaf()) { for(int i = 0; i < node.getNumEntries(); i++) { MkAppEntry entry = node.getEntry(i); result.add(((LeafEntry) entry).getDBID()); } } else { for(int i = 0; i < node.getNumEntries();...
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Determines the ids of the leaf entries stored in the specified subtree. @param node the root of the subtree @param result the result list containing the ids of the leaf entries stored in the specified subtree
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java#L304-L317
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java
MkAppTree.approximateKnnDistances
private PolynomialApproximation approximateKnnDistances(double[] knnDistances) { StringBuilder msg = new StringBuilder(); // count the zero distances (necessary of log-log space is used) int k_0 = 0; if(settings.log) { for(int i = 0; i < settings.kmax; i++) { double dist = knnDistances[i]...
java
private PolynomialApproximation approximateKnnDistances(double[] knnDistances) { StringBuilder msg = new StringBuilder(); // count the zero distances (necessary of log-log space is used) int k_0 = 0; if(settings.log) { for(int i = 0; i < settings.kmax; i++) { double dist = knnDistances[i]...
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Computes the polynomial approximation of the specified knn-distances. @param knnDistances the knn-distances of the leaf entry @return the polynomial approximation of the specified knn-distances.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java#L325-L365
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java
GrahamScanConvexHull2D.isLeft
protected final int isLeft(double[] a, double[] b, double[] o) { final double cross = getRX(a, o) * getRY(b, o) - getRY(a, o) * getRX(b, o); if(cross == 0) { // Compare manhattan distances - same angle! final double dista = Math.abs(getRX(a, o)) + Math.abs(getRY(a, o)); final double distb = Ma...
java
protected final int isLeft(double[] a, double[] b, double[] o) { final double cross = getRX(a, o) * getRY(b, o) - getRY(a, o) * getRX(b, o); if(cross == 0) { // Compare manhattan distances - same angle! final double dista = Math.abs(getRX(a, o)) + Math.abs(getRY(a, o)); final double distb = Ma...
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Test whether a point is left of the other wrt. the origin. @param a double[] A @param b double[] B @param o Origin double[] @return +1 when left, 0 when same, -1 when right
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java#L193-L202
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java
GrahamScanConvexHull2D.mdist
private double mdist(double[] a, double[] b) { return Math.abs(a[0] - b[0]) + Math.abs(a[1] - b[1]); }
java
private double mdist(double[] a, double[] b) { return Math.abs(a[0] - b[0]) + Math.abs(a[1] - b[1]); }
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Manhattan distance. @param a double[] A @param b double[] B @return Manhattan distance
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java#L211-L213
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java
GrahamScanConvexHull2D.isConvex
private boolean isConvex(double[] a, double[] b, double[] c) { // We're using factor to improve numerical contrast for small polygons. double area = (b[0] - a[0]) * factor * (c[1] - a[1]) - (c[0] - a[0]) * factor * (b[1] - a[1]); return (-1e-13 < area && area < 1e-13) ? (mdist(b, c) > mdist(a, b) + mdist(a,...
java
private boolean isConvex(double[] a, double[] b, double[] c) { // We're using factor to improve numerical contrast for small polygons. double area = (b[0] - a[0]) * factor * (c[1] - a[1]) - (c[0] - a[0]) * factor * (b[1] - a[1]); return (-1e-13 < area && area < 1e-13) ? (mdist(b, c) > mdist(a, b) + mdist(a,...
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Simple convexity test. @param a double[] A @param b double[] B @param c double[] C @return convexity
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java#L223-L227
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java
GrahamScanConvexHull2D.grahamScan
private void grahamScan() { if(points.size() < 3) { return; } Iterator<double[]> iter = points.iterator(); Stack<double[]> stack = new Stack<>(); // Start with the first two points on the stack final double[] first = iter.next(); stack.add(first); while(iter.hasNext()) { doub...
java
private void grahamScan() { if(points.size() < 3) { return; } Iterator<double[]> iter = points.iterator(); Stack<double[]> stack = new Stack<>(); // Start with the first two points on the stack final double[] first = iter.next(); stack.add(first); while(iter.hasNext()) { doub...
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The actual graham scan main loop.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java#L232-L260
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java
GrahamScanConvexHull2D.getHull
public Polygon getHull() { if(!ok) { computeConvexHull(); } return new Polygon(points, minmaxX.getMin(), minmaxX.getMax(), minmaxY.getMin(), minmaxY.getMax()); }
java
public Polygon getHull() { if(!ok) { computeConvexHull(); } return new Polygon(points, minmaxX.getMin(), minmaxX.getMax(), minmaxY.getMin(), minmaxY.getMax()); }
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Compute the convex hull, and return the resulting polygon. @return Polygon of the hull
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java#L267-L272
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java
MSTSplit.coverRadius
private static double coverRadius(double[][] matrix, int[] idx, int i) { final int idx_i = idx[i]; final double[] row_i = matrix[i]; double m = 0; for(int j = 0; j < row_i.length; j++) { if(i != j && idx_i == idx[j]) { final double d = row_i[j]; m = d > m ? d : m; } } ...
java
private static double coverRadius(double[][] matrix, int[] idx, int i) { final int idx_i = idx[i]; final double[] row_i = matrix[i]; double m = 0; for(int j = 0; j < row_i.length; j++) { if(i != j && idx_i == idx[j]) { final double d = row_i[j]; m = d > m ? d : m; } } ...
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Find the cover radius of a partition. @param matrix Distance matrix @param idx Partition keys @param i Candidate index @return max(d(i,j)) for all j with the same index
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java#L108-L119
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java
MSTSplit.mstPartition
private static int[] mstPartition(double[][] matrix) { final int n = matrix.length; int[] edges = PrimsMinimumSpanningTree.processDense(matrix); // Note: Prims does *not* yield edges sorted by edge length! double meanlength = thresholdLength(matrix, edges); int[] idx = new int[n], best = new int[n],...
java
private static int[] mstPartition(double[][] matrix) { final int n = matrix.length; int[] edges = PrimsMinimumSpanningTree.processDense(matrix); // Note: Prims does *not* yield edges sorted by edge length! double meanlength = thresholdLength(matrix, edges); int[] idx = new int[n], best = new int[n],...
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Partition the data using the minimu spanning tree. @param matrix @return partition assignments
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java#L127-L156
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java
MSTSplit.thresholdLength
private static double thresholdLength(double[][] matrix, int[] edges) { double[] lengths = new double[edges.length >> 1]; for(int i = 0, e = edges.length - 1; i < e; i += 2) { lengths[i >> 1] = matrix[edges[i]][edges[i + 1]]; } Arrays.sort(lengths); final int pos = (lengths.length >> 1); // 50...
java
private static double thresholdLength(double[][] matrix, int[] edges) { double[] lengths = new double[edges.length >> 1]; for(int i = 0, e = edges.length - 1; i < e; i += 2) { lengths[i >> 1] = matrix[edges[i]][edges[i + 1]]; } Arrays.sort(lengths); final int pos = (lengths.length >> 1); // 50...
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Choose the threshold length of edges to consider omittig. @param matrix Distance matrix @param edges Edges @return Distance threshold
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java#L165-L173
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java
MSTSplit.edgelength
private static double edgelength(double[][] matrix, int[] edges, int i) { i <<= 1; return matrix[edges[i]][edges[i + 1]]; }
java
private static double edgelength(double[][] matrix, int[] edges, int i) { i <<= 1; return matrix[edges[i]][edges[i + 1]]; }
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Length of edge i. @param matrix Distance matrix @param edges Edge list @param i Edge number @return Edge length
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java#L183-L186
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java
MSTSplit.omitEdge
private static void omitEdge(int[] edges, int[] idx, int[] sizes, int omit) { for(int i = 0; i < idx.length; i++) { idx[i] = i; } Arrays.fill(sizes, 1); for(int i = 0, j = 0, e = edges.length - 1; j < e; i++, j += 2) { if(i == omit) { continue; } int ea = edges[j + 1], eb...
java
private static void omitEdge(int[] edges, int[] idx, int[] sizes, int omit) { for(int i = 0; i < idx.length; i++) { idx[i] = i; } Arrays.fill(sizes, 1); for(int i = 0, j = 0, e = edges.length - 1; j < e; i++, j += 2) { if(i == omit) { continue; } int ea = edges[j + 1], eb...
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Partition the data by omitting one edge. @param edges Edges list @param idx Partition index storage @param sizes Partition sizes @param omit Edge number to omit
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java#L196-L216
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java
MSTSplit.follow
private static int follow(int i, int[] partitions) { int next = partitions[i], tmp; while(i != next) { tmp = next; next = partitions[i] = partitions[next]; i = tmp; } return i; }
java
private static int follow(int i, int[] partitions) { int next = partitions[i], tmp; while(i != next) { tmp = next; next = partitions[i] = partitions[next]; i = tmp; } return i; }
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Union-find with simple path compression. @param i Start @param partitions Partitions array @return Partition
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java#L225-L233
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/COP.java
COP.computeCentroid
private static void computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids) { Arrays.fill(centroid, 0); int dim = centroid.length; for(DBIDIter it = ids.iter(); it.valid(); it.advance()) { NumberVector v = relation.get(it); for(int i = 0; i < dim; i++) { ...
java
private static void computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids) { Arrays.fill(centroid, 0); int dim = centroid.length; for(DBIDIter it = ids.iter(); it.valid(); it.advance()) { NumberVector v = relation.get(it); for(int i = 0; i < dim; i++) { ...
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Recompute the centroid of a set. @param centroid Scratch buffer @param relation Input data @param ids IDs to include
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/COP.java#L285-L295
train
elki-project/elki
elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java
QueryUtil.getDistanceQuery
public static <O> DistanceQuery<O> getDistanceQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) { final Relation<O> objectQuery = database.getRelation(distanceFunction.getInputTypeRestriction(), hints); return database.getDistanceQuery(objectQuery, distanceFunction, hints);...
java
public static <O> DistanceQuery<O> getDistanceQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) { final Relation<O> objectQuery = database.getRelation(distanceFunction.getInputTypeRestriction(), hints); return database.getDistanceQuery(objectQuery, distanceFunction, hints);...
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Get a distance query for a given distance function, automatically choosing a relation. @param <O> Object type @param database Database @param distanceFunction Distance function @param hints Optimizer hints @return Distance query
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java#L78-L81
train
elki-project/elki
elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java
QueryUtil.getSimilarityQuery
public static <O> SimilarityQuery<O> getSimilarityQuery(Database database, SimilarityFunction<? super O> similarityFunction, Object... hints) { final Relation<O> objectQuery = database.getRelation(similarityFunction.getInputTypeRestriction(), hints); return database.getSimilarityQuery(objectQuery, similarityFun...
java
public static <O> SimilarityQuery<O> getSimilarityQuery(Database database, SimilarityFunction<? super O> similarityFunction, Object... hints) { final Relation<O> objectQuery = database.getRelation(similarityFunction.getInputTypeRestriction(), hints); return database.getSimilarityQuery(objectQuery, similarityFun...
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Get a similarity query, automatically choosing a relation. @param <O> Object type @param database Database @param similarityFunction Similarity function @param hints Optimizer hints @return Similarity Query
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java#L92-L95
train
elki-project/elki
elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java
QueryUtil.getRKNNQuery
public static <O> RKNNQuery<O> getRKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, Object... hints) { final DistanceQuery<O> distanceQuery = relation.getDistanceQuery(distanceFunction, hints); return relation.getRKNNQuery(distanceQuery, hints); }
java
public static <O> RKNNQuery<O> getRKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, Object... hints) { final DistanceQuery<O> distanceQuery = relation.getDistanceQuery(distanceFunction, hints); return relation.getRKNNQuery(distanceQuery, hints); }
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Get a rKNN query object for the given distance function. When possible, this will use an index, but it may default to an expensive linear scan. Hints include: <ul> <li>Integer: maximum value for k needed</li> <li>{@link de.lmu.ifi.dbs.elki.database.query.DatabaseQuery#HINT_BULK} bulk query needed</li> </ul> @param r...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java#L217-L220
train
elki-project/elki
elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java
QueryUtil.getLinearScanSimilarityRangeQuery
public static <O> RangeQuery<O> getLinearScanSimilarityRangeQuery(SimilarityQuery<O> simQuery) { // Slight optimizations of linear scans if(simQuery instanceof PrimitiveSimilarityQuery) { final PrimitiveSimilarityQuery<O> pdq = (PrimitiveSimilarityQuery<O>) simQuery; return new LinearScanPrimitiveSi...
java
public static <O> RangeQuery<O> getLinearScanSimilarityRangeQuery(SimilarityQuery<O> simQuery) { // Slight optimizations of linear scans if(simQuery instanceof PrimitiveSimilarityQuery) { final PrimitiveSimilarityQuery<O> pdq = (PrimitiveSimilarityQuery<O>) simQuery; return new LinearScanPrimitiveSi...
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Get a linear scan query for the given similarity query. @param <O> Object type @param simQuery similarity query @return Range query
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java#L271-L278
train
elki-project/elki
elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java
ELKIServiceRegistry.register
protected static void register(Class<?> parent, String cname) { Entry e = data.get(parent); if(e == null) { data.put(parent, e = new Entry()); } e.addName(cname); }
java
protected static void register(Class<?> parent, String cname) { Entry e = data.get(parent); if(e == null) { data.put(parent, e = new Entry()); } e.addName(cname); }
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Register a class with the registry. @param parent Parent class @param cname Class name
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java#L160-L166
train
elki-project/elki
elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java
ELKIServiceRegistry.register
protected static void register(Class<?> parent, Class<?> clazz) { Entry e = data.get(parent); if(e == null) { data.put(parent, e = new Entry()); } final String cname = clazz.getCanonicalName(); e.addHit(cname, clazz); if(clazz.isAnnotationPresent(Alias.class)) { Alias aliases = clazz...
java
protected static void register(Class<?> parent, Class<?> clazz) { Entry e = data.get(parent); if(e == null) { data.put(parent, e = new Entry()); } final String cname = clazz.getCanonicalName(); e.addHit(cname, clazz); if(clazz.isAnnotationPresent(Alias.class)) { Alias aliases = clazz...
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Register a class in the registry. Careful: do not use this from your code before first making sure this has been fully initialized. Otherwise, other implementations will not be found. Therefore, avoid calling this from your own Java code! @param parent Class @param clazz Implementation
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java#L178-L191
train
elki-project/elki
elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java
ELKIServiceRegistry.registerAlias
protected static void registerAlias(Class<?> parent, String alias, String cname) { Entry e = data.get(parent); assert (e != null); e.addAlias(alias, cname); }
java
protected static void registerAlias(Class<?> parent, String alias, String cname) { Entry e = data.get(parent); assert (e != null); e.addAlias(alias, cname); }
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Register a class alias with the registry. @param parent Parent class @param alias Alias name @param cname Class name
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java#L200-L204
train
elki-project/elki
elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java
ELKIServiceRegistry.tryLoadClass
private static Class<?> tryLoadClass(String value) { try { return CLASSLOADER.loadClass(value); } catch(ClassNotFoundException e) { return null; } }
java
private static Class<?> tryLoadClass(String value) { try { return CLASSLOADER.loadClass(value); } catch(ClassNotFoundException e) { return null; } }
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Attempt to load a class @param value Class name to try. @return Class, or {@code null}.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java#L212-L219
train
elki-project/elki
elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java
ELKIServiceRegistry.findAllImplementations
public static List<Class<?>> findAllImplementations(Class<?> restrictionClass) { if(restrictionClass == null) { return Collections.emptyList(); } if(!contains(restrictionClass)) { ELKIServiceLoader.load(restrictionClass); ELKIServiceScanner.load(restrictionClass); } Entry e = data....
java
public static List<Class<?>> findAllImplementations(Class<?> restrictionClass) { if(restrictionClass == null) { return Collections.emptyList(); } if(!contains(restrictionClass)) { ELKIServiceLoader.load(restrictionClass); ELKIServiceScanner.load(restrictionClass); } Entry e = data....
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Find all implementations of a particular interface. @param restrictionClass Class to scan for @return Found implementations
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java#L237-L270
train
elki-project/elki
elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java
ELKIServiceRegistry.findAllImplementations
public static List<Class<?>> findAllImplementations(Class<?> c, boolean everything, boolean parameterizable) { if(c == null) { return Collections.emptyList(); } // Default is served from the registry if(!everything && parameterizable) { return findAllImplementations(c); } // This cod...
java
public static List<Class<?>> findAllImplementations(Class<?> c, boolean everything, boolean parameterizable) { if(c == null) { return Collections.emptyList(); } // Default is served from the registry if(!everything && parameterizable) { return findAllImplementations(c); } // This cod...
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Find all implementations of a given class in the classpath. Note: returned classes may be abstract. @param c Class restriction @param everything include interfaces, abstract and private classes @param parameterizable only return classes instantiable by the parameterizable API @return List of found classes.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java#L283-L330
train
elki-project/elki
elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java
ELKIServiceRegistry.tryAlternateNames
private static <C> Class<?> tryAlternateNames(Class<? super C> restrictionClass, String value, Entry e) { StringBuilder buf = new StringBuilder(value.length() + 100); // Try with FACTORY_POSTFIX first: Class<?> clazz = tryLoadClass(buf.append(value).append(FACTORY_POSTFIX).toString()); if(clazz != null)...
java
private static <C> Class<?> tryAlternateNames(Class<? super C> restrictionClass, String value, Entry e) { StringBuilder buf = new StringBuilder(value.length() + 100); // Try with FACTORY_POSTFIX first: Class<?> clazz = tryLoadClass(buf.append(value).append(FACTORY_POSTFIX).toString()); if(clazz != null)...
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Try loading alternative names. @param restrictionClass Context class, for prepending a package name. @param value Class name requested @param e Cache entry, may be null @param <C> Generic type @return Class, or null
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java#L404-L438
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractScatterplotVisualization.java
AbstractScatterplotVisualization.setupCanvas
protected Element setupCanvas() { final double margin = context.getStyleLibrary().getSize(StyleLibrary.MARGIN); this.layer = setupCanvas(svgp, this.proj, margin, getWidth(), getHeight()); return layer; }
java
protected Element setupCanvas() { final double margin = context.getStyleLibrary().getSize(StyleLibrary.MARGIN); this.layer = setupCanvas(svgp, this.proj, margin, getWidth(), getHeight()); return layer; }
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Setup our canvas. @return Canvas
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractScatterplotVisualization.java#L89-L93
train
elki-project/elki
elki-input/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/transform/AbstractSupervisedProjectionVectorFilter.java
AbstractSupervisedProjectionVectorFilter.convertedType
protected SimpleTypeInformation<?> convertedType(SimpleTypeInformation<?> in, NumberVector.Factory<V> factory) { return new VectorFieldTypeInformation<>(factory, tdim); }
java
protected SimpleTypeInformation<?> convertedType(SimpleTypeInformation<?> in, NumberVector.Factory<V> factory) { return new VectorFieldTypeInformation<>(factory, tdim); }
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Get the output type from the input type after conversion. @param in input type restriction @param factory Vector factory @return output type restriction
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-input/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/transform/AbstractSupervisedProjectionVectorFilter.java#L153-L155
train
elki-project/elki
elki-input/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/transform/AbstractSupervisedProjectionVectorFilter.java
AbstractSupervisedProjectionVectorFilter.partition
protected <O> Map<O, IntList> partition(List<? extends O> classcolumn) { Map<O, IntList> classes = new HashMap<>(); Iterator<? extends O> iter = classcolumn.iterator(); for(int i = 0; iter.hasNext(); i++) { O lbl = iter.next(); IntList ids = classes.get(lbl); if(ids == null) { ids ...
java
protected <O> Map<O, IntList> partition(List<? extends O> classcolumn) { Map<O, IntList> classes = new HashMap<>(); Iterator<? extends O> iter = classcolumn.iterator(); for(int i = 0; iter.hasNext(); i++) { O lbl = iter.next(); IntList ids = classes.get(lbl); if(ids == null) { ids ...
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Partition the bundle based on the class label. @param classcolumn @return Partitioned data set.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-input/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/transform/AbstractSupervisedProjectionVectorFilter.java#L180-L193
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/XYPlot.java
XYPlot.makeCurve
public Curve makeCurve() { Curve c = new Curve(curves.size()); curves.add(c); return c; }
java
public Curve makeCurve() { Curve c = new Curve(curves.size()); curves.add(c); return c; }
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Make a new curve. @return Curve
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/XYPlot.java#L282-L286
train
elki-project/elki
elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/LogPane.java
LogPane.publish
public void publish(String message, Level level) { try { publish(new LogRecord(level, message)); } catch(BadLocationException e) { throw new RuntimeException("Error writing a log-like message.", e); } }
java
public void publish(String message, Level level) { try { publish(new LogRecord(level, message)); } catch(BadLocationException e) { throw new RuntimeException("Error writing a log-like message.", e); } }
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Print a message as if it were logged, without going through the full logger. @param message Message text @param level Message level
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/LogPane.java#L125-L132
train
elki-project/elki
elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/LogPane.java
LogPane.publish
protected synchronized void publish(LogRecord record) throws BadLocationException { // choose an appropriate formatter final Formatter fmt; final Style style; // always format progress messages using the progress formatter. if(record.getLevel().intValue() >= Level.WARNING.intValue()) { // form...
java
protected synchronized void publish(LogRecord record) throws BadLocationException { // choose an appropriate formatter final Formatter fmt; final Style style; // always format progress messages using the progress formatter. if(record.getLevel().intValue() >= Level.WARNING.intValue()) { // form...
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Publish a log record to the logging pane. @param record Log record @throws Exception
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/LogPane.java#L141-L188
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java
SNE.optimizeSNE
protected void optimizeSNE(AffinityMatrix pij, double[][] sol) { final int size = pij.size(); if(size * 3L * dim > 0x7FFF_FFFAL) { throw new AbortException("Memory exceeds Java array size limit."); } // Meta information on each point; joined for memory locality. // Gradient, Momentum, and lear...
java
protected void optimizeSNE(AffinityMatrix pij, double[][] sol) { final int size = pij.size(); if(size * 3L * dim > 0x7FFF_FFFAL) { throw new AbortException("Memory exceeds Java array size limit."); } // Meta information on each point; joined for memory locality. // Gradient, Momentum, and lear...
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Perform the actual tSNE optimization. @param pij Initial affinity matrix @param sol Solution output array (preinitialized)
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java#L219-L248
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java
SNE.computeQij
protected double computeQij(double[][] qij, double[][] solution) { double qij_sum = 0; for(int i = 1; i < qij.length; i++) { final double[] qij_i = qij[i], vi = solution[i]; for(int j = 0; j < i; j++) { qij_sum += qij_i[j] = qij[j][i] = MathUtil.exp(-sqDist(vi, solution[j])); } } ...
java
protected double computeQij(double[][] qij, double[][] solution) { double qij_sum = 0; for(int i = 1; i < qij.length; i++) { final double[] qij_i = qij[i], vi = solution[i]; for(int j = 0; j < i; j++) { qij_sum += qij_i[j] = qij[j][i] = MathUtil.exp(-sqDist(vi, solution[j])); } } ...
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Compute the qij of the solution, and the sum. @param qij Qij matrix (output) @param solution Solution matrix (input) @return qij sum
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java#L257-L266
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java
SNE.computeGradient
protected void computeGradient(AffinityMatrix pij, double[][] qij, double qij_isum, double[][] sol, double[] meta) { final int dim3 = dim * 3; int size = pij.size(); for(int i = 0, off = 0; i < size; i++, off += dim3) { final double[] sol_i = sol[i], qij_i = qij[i]; Arrays.fill(meta, off, off + ...
java
protected void computeGradient(AffinityMatrix pij, double[][] qij, double qij_isum, double[][] sol, double[] meta) { final int dim3 = dim * 3; int size = pij.size(); for(int i = 0, off = 0; i < size; i++, off += dim3) { final double[] sol_i = sol[i], qij_i = qij[i]; Arrays.fill(meta, off, off + ...
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Compute the gradients. @param pij Desired affinity matrix @param qij Projected affinity matrix @param qij_isum Normalization factor @param sol Current solution coordinates @param meta Point metadata
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java#L295-L315
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/clustering/KMeansOutlierDetection.java
KMeansOutlierDetection.run
public OutlierResult run(Database database, Relation<O> relation) { DistanceFunction<? super O> df = clusterer.getDistanceFunction(); DistanceQuery<O> dq = database.getDistanceQuery(relation, df); // TODO: improve ELKI api to ensure we're using the same DBIDs! Clustering<?> c = clusterer.run(database, ...
java
public OutlierResult run(Database database, Relation<O> relation) { DistanceFunction<? super O> df = clusterer.getDistanceFunction(); DistanceQuery<O> dq = database.getDistanceQuery(relation, df); // TODO: improve ELKI api to ensure we're using the same DBIDs! Clustering<?> c = clusterer.run(database, ...
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Run the outlier detection algorithm. @param database Database @param relation Relation @return Outlier detection result
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/clustering/KMeansOutlierDetection.java#L102-L129
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/linearalgebra/fitting/GaussianFittingFunction.java
GaussianFittingFunction.eval
@Override public FittingFunctionResult eval(double x, double[] params) { final int len = params.length; // We always need triples: (mean, stddev, scaling) assert (len % 3) == 0; double y = 0.0; double[] gradients = new double[len]; // Loosely based on the book: // Numerical Recipes in C:...
java
@Override public FittingFunctionResult eval(double x, double[] params) { final int len = params.length; // We always need triples: (mean, stddev, scaling) assert (len % 3) == 0; double y = 0.0; double[] gradients = new double[len]; // Loosely based on the book: // Numerical Recipes in C:...
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Compute the mixture of Gaussians at the given position
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/linearalgebra/fitting/GaussianFittingFunction.java#L61-L90
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/application/greedyensemble/VisualizePairwiseGainMatrix.java
VisualizePairwiseGainMatrix.showVisualization
private void showVisualization(VisualizerContext context, SimilarityMatrixVisualizer factory, VisualizationTask task) { VisualizationPlot plot = new VisualizationPlot(); Visualization vis = factory.makeVisualization(context, task, plot, 1.0, 1.0, null); plot.getRoot().appendChild(vis.getLayer()); plot.g...
java
private void showVisualization(VisualizerContext context, SimilarityMatrixVisualizer factory, VisualizationTask task) { VisualizationPlot plot = new VisualizationPlot(); Visualization vis = factory.makeVisualization(context, task, plot, 1.0, 1.0, null); plot.getRoot().appendChild(vis.getLayer()); plot.g...
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Show a single visualization. @param context Visualizer context @param factory Visualizer factory @param task Visualization task
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/application/greedyensemble/VisualizePairwiseGainMatrix.java#L265-L275
train
elki-project/elki
elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/IntegerMinMax.java
IntegerMinMax.put
public void put(int[] data) { final int l = data.length; for(int i = 0; i < l; i++) { put(data[i]); } }
java
public void put(int[] data) { final int l = data.length; for(int i = 0; i < l; i++) { put(data[i]); } }
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Process a whole array of int values. If any of the values is smaller than the current minimum, it will become the new minimum. If any of the values is larger than the current maximum, it will become the new maximum. @param data Data to process
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/IntegerMinMax.java#L90-L95
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java
KDEOS.run
public OutlierResult run(Database database, Relation<O> rel) { final DBIDs ids = rel.getDBIDs(); LOG.verbose("Running kNN preprocessor."); KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, rel, getDistanceFunction(), kmax + 1); // Initialize store for densities WritableDataStore<double...
java
public OutlierResult run(Database database, Relation<O> rel) { final DBIDs ids = rel.getDBIDs(); LOG.verbose("Running kNN preprocessor."); KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, rel, getDistanceFunction(), kmax + 1); // Initialize store for densities WritableDataStore<double...
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Run the KDEOS outlier detection algorithm. @param database Database to query @param rel Relation to process @return Outlier detection result
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java#L169-L187
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java
KDEOS.estimateDensities
protected void estimateDensities(Relation<O> rel, KNNQuery<O> knnq, final DBIDs ids, WritableDataStore<double[]> densities) { final int dim = dimensionality(rel); final int knum = kmax + 1 - kmin; // Initialize storage: for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) { densities.put(...
java
protected void estimateDensities(Relation<O> rel, KNNQuery<O> knnq, final DBIDs ids, WritableDataStore<double[]> densities) { final int dim = dimensionality(rel); final int knum = kmax + 1 - kmin; // Initialize storage: for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) { densities.put(...
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Perform the kernel density estimation step. @param rel Relation to query @param knnq kNN query @param ids IDs to process @param densities Density storage
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java#L197-L236
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java
KDEOS.dimensionality
private int dimensionality(Relation<O> rel) { // Explicit: if(idim >= 0) { return idim; } // Cast to vector field relation. @SuppressWarnings("unchecked") final Relation<NumberVector> frel = (Relation<NumberVector>) rel; int dim = RelationUtil.dimensionality(frel); if(dim < 1) { ...
java
private int dimensionality(Relation<O> rel) { // Explicit: if(idim >= 0) { return idim; } // Cast to vector field relation. @SuppressWarnings("unchecked") final Relation<NumberVector> frel = (Relation<NumberVector>) rel; int dim = RelationUtil.dimensionality(frel); if(dim < 1) { ...
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Ugly hack to allow using this implementation without having a well-defined dimensionality. @param rel Data relation @return Dimensionality
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java#L245-L258
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java
KDEOS.computeOutlierScores
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; ...
java
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; ...
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Compute the final KDEOS scores. @param knnq kNN query @param ids IDs to process @param densities Density estimates @param kdeos Score outputs @param minmax Minimum and maximum scores
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java#L269-L312
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.run
public Clustering<Model> run(Relation<V> rel) { fulldatabase = preprocess(rel); processedIDs = DBIDUtil.newHashSet(fulldatabase.size()); noiseDim = dimensionality(fulldatabase); FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("CASH Clustering", fulldatabase.size(), LOG) : null; Clust...
java
public Clustering<Model> run(Relation<V> rel) { fulldatabase = preprocess(rel); processedIDs = DBIDUtil.newHashSet(fulldatabase.size()); noiseDim = dimensionality(fulldatabase); FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("CASH Clustering", fulldatabase.size(), LOG) : null; Clust...
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Run CASH on the relation. @param rel Relation @return Clustering result
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L188-L211
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.preprocess
private Relation<ParameterizationFunction> preprocess(Relation<V> vrel) { DBIDs ids = vrel.getDBIDs(); SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class); WritableDataStore<ParameterizationFunction> prep = DataStoreUtil.makeStorage(ids, DataSto...
java
private Relation<ParameterizationFunction> preprocess(Relation<V> vrel) { DBIDs ids = vrel.getDBIDs(); SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class); WritableDataStore<ParameterizationFunction> prep = DataStoreUtil.makeStorage(ids, DataSto...
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Preprocess the dataset, precomputing the parameterization functions. @param vrel Vector relation @return Preprocessed relation
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L219-L229
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.initHeap
private void initHeap(ObjectHeap<CASHInterval> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids) { CASHIntervalSplit split = new CASHIntervalSplit(relation, minPts); // determine minimum and maximum function value of all functions double[] minMax = determineMinMaxDistance(relation, dim...
java
private void initHeap(ObjectHeap<CASHInterval> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids) { CASHIntervalSplit split = new CASHIntervalSplit(relation, minPts); // determine minimum and maximum function value of all functions double[] minMax = determineMinMaxDistance(relation, dim...
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Initializes the heap with the root intervals. @param heap the heap to be initialized @param relation the database storing the parameterization functions @param dim the dimensionality of the database @param ids the ids of the database
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L376-L414
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.buildDB
private MaterializedRelation<ParameterizationFunction> buildDB(int dim, double[][] basis, DBIDs ids, Relation<ParameterizationFunction> relation) { ProxyDatabase proxy = new ProxyDatabase(ids); SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class); ...
java
private MaterializedRelation<ParameterizationFunction> buildDB(int dim, double[][] basis, DBIDs ids, Relation<ParameterizationFunction> relation) { ProxyDatabase proxy = new ProxyDatabase(ids); SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class); ...
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Builds a dim-1 dimensional database where the objects are projected into the specified subspace. @param dim the dimensionality of the database @param basis the basis defining the subspace @param ids the ids for the new database @param relation the database storing the parameterization functions @return a dim-1 dimensi...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L427-L443
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.project
private ParameterizationFunction project(double[][] basis, ParameterizationFunction f) { // Matrix m = new Matrix(new // double[][]{f.getPointCoordinates()}).times(basis); double[] m = transposeTimes(basis, f.getColumnVector()); return new ParameterizationFunction(DoubleVector.wrap(m)); }
java
private ParameterizationFunction project(double[][] basis, ParameterizationFunction f) { // Matrix m = new Matrix(new // double[][]{f.getPointCoordinates()}).times(basis); double[] m = transposeTimes(basis, f.getColumnVector()); return new ParameterizationFunction(DoubleVector.wrap(m)); }
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Projects the specified parameterization function into the subspace described by the given basis. @param basis the basis defining he subspace @param f the parameterization function to be projected @return the projected parameterization function
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L453-L458
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.determineBasis
private double[][] determineBasis(double[] alpha) { final int dim = alpha.length; // Primary vector: double[] nn = new double[dim + 1]; for(int i = 0; i < nn.length; i++) { double alpha_i = i == alpha.length ? 0 : alpha[i]; nn[i] = ParameterizationFunction.sinusProduct(0, i, alpha) * FastMat...
java
private double[][] determineBasis(double[] alpha) { final int dim = alpha.length; // Primary vector: double[] nn = new double[dim + 1]; for(int i = 0; i < nn.length; i++) { double alpha_i = i == alpha.length ? 0 : alpha[i]; nn[i] = ParameterizationFunction.sinusProduct(0, i, alpha) * FastMat...
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Determines a basis defining a subspace described by the specified alpha values. @param alpha the alpha values @return a basis defining a subspace described by the specified alpha values
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L467-L506
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.determineNextIntervalAtMaxLevel
private CASHInterval determineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) { CASHInterval next = doDetermineNextIntervalAtMaxLevel(heap); // noise path was chosen while(next == null) { if(heap.isEmpty()) { return null; } next = doDetermineNextIntervalAtMaxLevel(heap); ...
java
private CASHInterval determineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) { CASHInterval next = doDetermineNextIntervalAtMaxLevel(heap); // noise path was chosen while(next == null) { if(heap.isEmpty()) { return null; } next = doDetermineNextIntervalAtMaxLevel(heap); ...
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Determines the next ''best'' interval at maximum level, i.e. the next interval containing the most unprocessed objects. @param heap the heap storing the intervals @return the next ''best'' interval at maximum level
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L515-L525
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.doDetermineNextIntervalAtMaxLevel
private CASHInterval doDetermineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) { CASHInterval interval = heap.poll(); int dim = interval.getDimensionality(); while(true) { // max level is reached if(interval.getLevel() >= maxLevel && interval.getMaxSplitDimension() == (dim - 1)) { ...
java
private CASHInterval doDetermineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) { CASHInterval interval = heap.poll(); int dim = interval.getDimensionality(); while(true) { // max level is reached if(interval.getLevel() >= maxLevel && interval.getMaxSplitDimension() == (dim - 1)) { ...
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Recursive helper method to determine the next ''best'' interval at maximum level, i.e. the next interval containing the most unprocessed objects @param heap the heap storing the intervals @return the next ''best'' interval at maximum level
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L534-L584
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
CASH.determineMinMaxDistance
private double[] determineMinMaxDistance(Relation<ParameterizationFunction> relation, int dimensionality) { double[] min = new double[dimensionality - 1]; double[] max = new double[dimensionality - 1]; Arrays.fill(max, Math.PI); HyperBoundingBox box = new HyperBoundingBox(min, max); double d_min = ...
java
private double[] determineMinMaxDistance(Relation<ParameterizationFunction> relation, int dimensionality) { double[] min = new double[dimensionality - 1]; double[] max = new double[dimensionality - 1]; Arrays.fill(max, Math.PI); HyperBoundingBox box = new HyperBoundingBox(min, max); double d_min = ...
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Determines the minimum and maximum function value of all parameterization functions stored in the specified database. @param relation the database containing the parameterization functions. @param dimensionality the dimensionality of the database @return an array containing the minimum and maximum function value of al...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java#L595-L612
train
elki-project/elki
elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/statistics/RankingQualityHistogram.java
RankingQualityHistogram.run
public HistogramResult run(Database database, Relation<O> relation) { final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction()); final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, relation.size()); if(LOG.isVerbose()) { LOG.verbose("Preprocessing ...
java
public HistogramResult run(Database database, Relation<O> relation) { final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction()); final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, relation.size()); if(LOG.isVerbose()) { LOG.verbose("Preprocessing ...
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Process a database @param database Database to process @param relation Relation to process @return Histogram of ranking qualities
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/statistics/RankingQualityHistogram.java#L99-L140
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java
EM.run
public Clustering<M> run(Database database, Relation<V> relation) { if(relation.size() == 0) { throw new IllegalArgumentException("database empty: must contain elements"); } // initial models List<? extends EMClusterModel<M>> models = mfactory.buildInitialModels(database, relation, k, SquaredEucli...
java
public Clustering<M> run(Database database, Relation<V> relation) { if(relation.size() == 0) { throw new IllegalArgumentException("database empty: must contain elements"); } // initial models List<? extends EMClusterModel<M>> models = mfactory.buildInitialModels(database, relation, k, SquaredEucli...
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Performs the EM clustering algorithm on the given database. Finally a hard clustering is provided where each clusters gets assigned the points exhibiting the highest probability to belong to this cluster. But still, the database objects hold associated the complete probability-vector for all models. @param database D...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java#L213-L272
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java
EM.recomputeCovarianceMatrices
public static void recomputeCovarianceMatrices(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, List<? extends EMClusterModel<?>> models, double prior) { final int k = models.size(); boolean needsTwoPass = false; for(EMClusterModel<?> m : models) { m.beginESte...
java
public static void recomputeCovarianceMatrices(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, List<? extends EMClusterModel<?>> models, double prior) { final int k = models.size(); boolean needsTwoPass = false; for(EMClusterModel<?> m : models) { m.beginESte...
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Recompute the covariance matrixes. @param relation Vector data @param probClusterIGivenX Object probabilities @param models Cluster models to update @param prior MAP prior (use 0 for MLE)
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java#L282-L322
train
elki-project/elki
elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java
EM.assignProbabilitiesToInstances
public static double assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, List<? extends EMClusterModel<?>> models, WritableDataStore<double[]> probClusterIGivenX) { final int k = models.size(); double emSum = 0.; for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advan...
java
public static double assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, List<? extends EMClusterModel<?>> models, WritableDataStore<double[]> probClusterIGivenX) { final int k = models.size(); double emSum = 0.; for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advan...
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Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions. Computed as the sum of the logarithms of the prior probability of each instance. @param relation the database used for assignment to instances @param models Cluster models...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java#L336-L355
train
elki-project/elki
addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/gui/ResultWindow.java
DynamicMenu.updateVisualizerMenus
protected synchronized void updateVisualizerMenus() { Projection proj = null; if(svgCanvas.getPlot() instanceof DetailView) { PlotItem item = ((DetailView) svgCanvas.getPlot()).getPlotItem(); proj = item.proj; } menubar.removeAll(); menubar.add(filemenu); ResultHierar...
java
protected synchronized void updateVisualizerMenus() { Projection proj = null; if(svgCanvas.getPlot() instanceof DetailView) { PlotItem item = ((DetailView) svgCanvas.getPlot()).getPlotItem(); proj = item.proj; } menubar.removeAll(); menubar.add(filemenu); ResultHierar...
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Update the visualizer menus.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/gui/ResultWindow.java#L204-L234
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/meta/HiCS.java
HiCS.run
public OutlierResult run(Relation<V> relation) { final DBIDs ids = relation.getDBIDs(); ArrayList<ArrayDBIDs> subspaceIndex = buildOneDimIndexes(relation); Set<HiCSSubspace> subspaces = calculateSubspaces(relation, subspaceIndex, rnd.getSingleThreadedRandom()); if(LOG.isVerbose()) { LOG.verbose(...
java
public OutlierResult run(Relation<V> relation) { final DBIDs ids = relation.getDBIDs(); ArrayList<ArrayDBIDs> subspaceIndex = buildOneDimIndexes(relation); Set<HiCSSubspace> subspaces = calculateSubspaces(relation, subspaceIndex, rnd.getSingleThreadedRandom()); if(LOG.isVerbose()) { LOG.verbose(...
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Perform HiCS on a given database. @param relation the database @return The aggregated resulting scores that were assigned by the given outlier detection algorithm
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/meta/HiCS.java#L176-L224
train
elki-project/elki
elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/meta/HiCS.java
HiCS.buildOneDimIndexes
private ArrayList<ArrayDBIDs> buildOneDimIndexes(Relation<? extends NumberVector> relation) { final int dim = RelationUtil.dimensionality(relation); ArrayList<ArrayDBIDs> subspaceIndex = new ArrayList<>(dim + 1); SortDBIDsBySingleDimension comp = new VectorUtil.SortDBIDsBySingleDimension(relation); for...
java
private ArrayList<ArrayDBIDs> buildOneDimIndexes(Relation<? extends NumberVector> relation) { final int dim = RelationUtil.dimensionality(relation); ArrayList<ArrayDBIDs> subspaceIndex = new ArrayList<>(dim + 1); SortDBIDsBySingleDimension comp = new VectorUtil.SortDBIDsBySingleDimension(relation); for...
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Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list @param relation Relation to index @return List of sorted objects
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/meta/HiCS.java#L234-L247
train
elki-project/elki
elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mktab/MkTabTree.java
MkTabTree.max
private double[] max(double[] distances1, double[] distances2) { if(distances1.length != distances2.length) { throw new RuntimeException("different lengths!"); } double[] result = new double[distances1.length]; for(int i = 0; i < distances1.length; i++) { result[i] = Math.max(distances1[i],...
java
private double[] max(double[] distances1, double[] distances2) { if(distances1.length != distances2.length) { throw new RuntimeException("different lengths!"); } double[] result = new double[distances1.length]; for(int i = 0; i < distances1.length; i++) { result[i] = Math.max(distances1[i],...
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Returns an array that holds the maximum values of the both specified arrays in each index. @param distances1 the first array @param distances2 the second array @return an array that holds the maximum values of the both specified arrays in each index
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mktab/MkTabTree.java#L252-L262
train
elki-project/elki
addons/joglvis/src/main/java/de/lmu/ifi/dbs/elki/joglvis/ShaderUtil.java
ShaderUtil.compileShader
public static int compileShader(Class<?> context, GL2 gl, int type, String name) throws ShaderCompilationException { int prog = -1; try (InputStream in = context.getResourceAsStream(name)) { int[] error = new int[1]; String shaderdata = FileUtil.slurp(in); prog = gl.glCreateShader(type); ...
java
public static int compileShader(Class<?> context, GL2 gl, int type, String name) throws ShaderCompilationException { int prog = -1; try (InputStream in = context.getResourceAsStream(name)) { int[] error = new int[1]; String shaderdata = FileUtil.slurp(in); prog = gl.glCreateShader(type); ...
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Compile a shader from a file. @param context Class context for loading the resource file. @param gl GL context @param type @param name @return Shader program number. @throws ShaderCompilationException When compilation failed.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/joglvis/src/main/java/de/lmu/ifi/dbs/elki/joglvis/ShaderUtil.java#L54-L81
train
elki-project/elki
elki-core-distance/src/main/java/de/lmu/ifi/dbs/elki/distance/distancefunction/timeseries/AbstractEditDistanceFunction.java
AbstractEditDistanceFunction.effectiveBandSize
protected int effectiveBandSize(final int dim1, final int dim2) { if(bandSize == Double.POSITIVE_INFINITY) { return (dim1 > dim2) ? dim1 : dim2; } if(bandSize >= 1.) { return (int) bandSize; } // Max * bandSize: return (int) Math.ceil((dim1 >= dim2 ? dim1 : dim2) * bandSize); }
java
protected int effectiveBandSize(final int dim1, final int dim2) { if(bandSize == Double.POSITIVE_INFINITY) { return (dim1 > dim2) ? dim1 : dim2; } if(bandSize >= 1.) { return (int) bandSize; } // Max * bandSize: return (int) Math.ceil((dim1 >= dim2 ? dim1 : dim2) * bandSize); }
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Compute the effective band size. @param dim1 First dimensionality @param dim2 Second dimensionality @return Effective bandsize
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-distance/src/main/java/de/lmu/ifi/dbs/elki/distance/distancefunction/timeseries/AbstractEditDistanceFunction.java#L61-L70
train
elki-project/elki
elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java
AbstractNode.addLeafEntry
@Override public final int addLeafEntry(E entry) { // entry is not a leaf entry if(!(entry instanceof LeafEntry)) { throw new UnsupportedOperationException("Entry is not a leaf entry!"); } // this is a not a leaf node if(!isLeaf()) { throw new UnsupportedOperationException("Node is not...
java
@Override public final int addLeafEntry(E entry) { // entry is not a leaf entry if(!(entry instanceof LeafEntry)) { throw new UnsupportedOperationException("Entry is not a leaf entry!"); } // this is a not a leaf node if(!isLeaf()) { throw new UnsupportedOperationException("Node is not...
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Adds a new leaf entry to this node's children and returns the index of the entry in this node's children array. An UnsupportedOperationException will be thrown if the entry is not a leaf entry or this node is not a leaf node. @param entry the leaf entry to be added @return the index of the entry in this node's childre...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L165-L178
train
elki-project/elki
elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java
AbstractNode.addDirectoryEntry
@Override public final int addDirectoryEntry(E entry) { // entry is not a directory entry if(entry instanceof LeafEntry) { throw new UnsupportedOperationException("Entry is not a directory entry!"); } // this is a not a directory node if(isLeaf()) { throw new UnsupportedOperationExcept...
java
@Override public final int addDirectoryEntry(E entry) { // entry is not a directory entry if(entry instanceof LeafEntry) { throw new UnsupportedOperationException("Entry is not a directory entry!"); } // this is a not a directory node if(isLeaf()) { throw new UnsupportedOperationExcept...
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Adds a new directory entry to this node's children and returns the index of the entry in this node's children array. An UnsupportedOperationException will be thrown if the entry is not a directory entry or this node is not a directory node. @param entry the directory entry to be added @return the index of the entry in...
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L191-L203
train
elki-project/elki
elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java
AbstractNode.deleteEntry
public boolean deleteEntry(int index) { System.arraycopy(entries, index + 1, entries, index, numEntries - index - 1); entries[--numEntries] = null; return true; }
java
public boolean deleteEntry(int index) { System.arraycopy(entries, index + 1, entries, index, numEntries - index - 1); entries[--numEntries] = null; return true; }
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Deletes the entry at the specified index and shifts all entries after the index to left. @param index the index at which the entry is to be deleted @return true id deletion was successful
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L212-L216
train
elki-project/elki
elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java
AbstractNode.getEntries
@SuppressWarnings("unchecked") @Deprecated public final List<E> getEntries() { List<E> result = new ArrayList<>(numEntries); for(Entry entry : entries) { if(entry != null) { result.add((E) entry); } } return result; }
java
@SuppressWarnings("unchecked") @Deprecated public final List<E> getEntries() { List<E> result = new ArrayList<>(numEntries); for(Entry entry : entries) { if(entry != null) { result.add((E) entry); } } return result; }
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Returns a list of the entries. @return a list of the entries @deprecated Using this method means an extra copy - usually at the cost of performance.
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L245-L255
train
elki-project/elki
elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java
AbstractNode.removeMask
public void removeMask(long[] mask) { int dest = BitsUtil.nextSetBit(mask, 0); if(dest < 0) { return; } int src = BitsUtil.nextSetBit(mask, dest); while(src < numEntries) { if(!BitsUtil.get(mask, src)) { entries[dest] = entries[src]; dest++; } src++; } ...
java
public void removeMask(long[] mask) { int dest = BitsUtil.nextSetBit(mask, 0); if(dest < 0) { return; } int src = BitsUtil.nextSetBit(mask, dest); while(src < numEntries) { if(!BitsUtil.get(mask, src)) { entries[dest] = entries[src]; dest++; } src++; } ...
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Remove entries according to the given mask. @param mask Mask to remove
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b54673327e76198ecd4c8a2a901021f1a9174498
https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L274-L293
train