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java.lang.Objectstatistics.Initialization
public abstract class Initialization
Collection of algorithms to initialize a codebook
Nested Class Summary | |
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static class |
Initialization.DensityRankingMethod
|
Constructor Summary | |
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Initialization()
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Method Summary | |
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static int |
assignToCluster(double[] x,
Density[] d)
Compute the ID of the nearest centroid. |
static int |
assignToCluster(double[] x,
java.util.List<Density> list)
Compute the ID of the nearest centroid. |
static MixtureDensity |
gMeansClustering(java.util.List<Sample> data,
double alpha,
int maxc,
boolean diagonalCovariances)
Perform a Gaussian-means (G-means) clustering on the given data set. |
static MixtureDensity |
hierarchicalGaussianClustering(java.util.List<Sample> data,
int maxc,
boolean diagonalCovariances,
Initialization.DensityRankingMethod rank)
Perform a hierarchical Gaussian clustering: Beginning with only one density, always split the cluster with highest variance in two parts, finding the new means by following the strongest eigen vector. |
static MixtureDensity |
kMeansClustering(java.util.List<Sample> data,
int nd,
boolean diagonalCovariances)
Perform a simple k-means clustering on the data. |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public Initialization()
Method Detail |
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public static int assignToCluster(double[] x, Density[] d)
x
- d
- Densities
public static int assignToCluster(double[] x, java.util.List<Density> list)
x
- list
-
public static MixtureDensity gMeansClustering(java.util.List<Sample> data, double alpha, int maxc, boolean diagonalCovariances) throws TrainingException
data
- alpha
- significance level in {0.1,0.05,0.025,0.01}maxc
- maximum number of clusters
TrainingException
public static MixtureDensity hierarchicalGaussianClustering(java.util.List<Sample> data, int maxc, boolean diagonalCovariances, Initialization.DensityRankingMethod rank) throws TrainingException
data
- List of data samplesmaxc
- Maximum number of clustersdiagonalCovariances
-
TrainingException
public static MixtureDensity kMeansClustering(java.util.List<Sample> data, int nd, boolean diagonalCovariances) throws TrainingException
data
- nd
- number of clusters
TrainingException
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