public class KMeans
extends java.lang.Object
| 限定符和类型 | 字段和说明 |
|---|---|
protected Cluster[] |
clusters |
protected int |
countAllFeatures |
protected java.util.ArrayList<double[]> |
features |
protected int |
length |
protected int |
numClusters |
| 构造器和说明 |
|---|
KMeans(int numClusters) |
| 限定符和类型 | 方法和说明 |
|---|---|
void |
addFeature(double[] feature) |
double |
clusteringStep()
Do one step and return the overall stress (squared error).
|
Cluster[] |
getClusters() |
int |
getFeatureCount() |
int |
getNumClusters()
Get the number of desired clusters.
|
protected boolean |
hasNaNs(double[] histogram) |
void |
init() |
protected double |
overallStress()
Squared error in classification.
|
protected void |
recomputeMeans()
Computes the mean per cluster (averaged vector)
|
protected void |
reOrganizeFeatures()
Re-shuffle all features.
|
protected java.util.Set<java.lang.Integer> |
selectInitialMedians(int numClusters) |
protected int countAllFeatures
protected int numClusters
protected int length
protected java.util.ArrayList<double[]> features
protected Cluster[] clusters
public void addFeature(double[] feature)
public void init()
protected java.util.Set<java.lang.Integer> selectInitialMedians(int numClusters)
public double clusteringStep()
protected boolean hasNaNs(double[] histogram)
protected void reOrganizeFeatures()
protected void recomputeMeans()
protected double overallStress()
public int getNumClusters()
public int getFeatureCount()
public Cluster[] getClusters()