| Package | Description |
|---|---|
| net.returnvoid.analytics |
A package for analysing things (e.g. images).
|
| Modifier and Type | Method and Description |
|---|---|
static ColorCluster[] |
ClusteringHelper.addColorsToClusters(ColorCluster[] clusters,
RColor[] colors)
Assigns each color from given set of colors to one of the cluster
of the given set clusters.
|
ColorCluster |
ColorCluster.addElement(RColor newMember)
Adds an element to the cluster.
|
static ColorCluster[] |
ClusteringHelper.colorsToClusters(RColor[] means,
ColorDifferenceMeasure m)
Builds new ColorClusters from a given set of colors.
|
ColorCluster |
ColorClustering.get(int index)
Returns the cluster with the given index.
|
ColorCluster |
ColorClustering.getCluster(int c)
Returns the cluster which is the most similar to the given color.
|
ColorCluster |
ColorClustering.getCluster(java.lang.Object c) |
ColorCluster |
ColorClustering.getCluster(RColor c)
Returns the cluster which is the most similar to the given color.
|
ColorCluster[] |
ColorClustering.getClusters() |
ColorCluster |
ColorCluster.removeElement(RColor member)
Removes an element from the cluster (if the given element is a member).
|
ColorCluster |
ColorCluster.setColorDifferenceMeasure(ColorDifferenceMeasure m)
Sets a new ColorDifferenceMeasure.
|
ColorCluster |
ColorCluster.updateMean() |
| Modifier and Type | Method and Description |
|---|---|
static ColorCluster[] |
ClusteringHelper.addColorsToClusters(ColorCluster[] clusters,
RColor[] colors)
Assigns each color from given set of colors to one of the cluster
of the given set clusters.
|
static ColorClustering |
ClusteringHelper.kmeans(RColor[] colors,
java.lang.Integer k,
ColorDifferenceMeasure m,
ColorCluster[] existingClusters)
Applies the k-means algorithm to colors for a given k.
|
boolean |
ColorCluster.switchElement(RColor member,
ColorCluster newCluster)
The given element member switches to another cluster under certain
circumstances.
|
| Constructor and Description |
|---|
ColorClustering(ColorCluster[] clusters,
int k)
Builds a new clustering from a given set of clusters.
|