KMeansConfiguration (OpenIMAJ master project 1.3.10 API)

java.lang.Object
- org.openimaj.ml.clustering.kmeans.KMeansConfiguration<NN,DATA>

Type Parameters:

NN - The type of NearestNeighbours to use

DATA - The type of data

All Implemented Interfaces:

Cloneable
```
public class KMeansConfiguration<NN extends NearestNeighbours<DATA,?,?>,DATA>
extends Object
implements Cloneable
```
Configuration for the K-Means algorithm.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`blockSize` The size of processing blocks for each thread
`static int`	`DEFAULT_BLOCK_SIZE` The default number of samples per parallel assignment instance.
`static int`	`DEFAULT_NUMBER_ITERATIONS` The default number of iterations.
`protected NearestNeighboursFactory<? extends NN,DATA>`	`factory` The factory for producing the `NearestNeighbours` objects used in assignment.
`protected int`	`K` The number of clusters
`protected int`	`niters` The max number of iterations
`protected ExecutorService`	`threadpool` The threadpool for parallel processing

Constructor Summary

Constructors
Constructor and Description
`KMeansConfiguration()` A completely default configuration used primarily as a convenience function for reading.
`KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory)` Create configuration for data that will create `K` clusters.
`KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory, int niters)` Create configuration for data that will create `K` clusters.
`KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory, int niters, ExecutorService threadpool)` Create configuration for data that will create `K` clusters.
`KMeansConfiguration(int K, NearestNeighboursFactory<? extends NN,DATA> nnFactory, int niters, int blockSize, ExecutorService threadpool)` Create configuration for data with `M` dimensions that will create `K` clusters.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`KMeansConfiguration<NN,DATA>`	`clone()`
`int`	`getBlockSize()` Get the number of samples processed in a batch by a thread.
`int`	`getK()` Get the number of clusters
`int`	`getMaxIterations()` Get the maximum allowed number of iterations.
`NearestNeighboursFactory<? extends NN,DATA>`	`getNearestNeighbourFactory()` Get the factory that produces the `NearestNeighbours` during clustering.
`int`	`numClusters()` Get the number of clusters
`void`	`setBlockSize(int blockSize)` Set the number of samples processed in a batch by a thread.
`void`	`setK(int k)` Set the number of clusters
`void`	`setMaxIterations(int niters)` Set the maximum allowed number of iterations.
`void`	`setNearestNeighbourFactory(NearestNeighboursFactory<? extends NN,DATA> factory)` Set the factory that produces the `NearestNeighbours` during clustering.
`void`	`setNumClusters(int k)` Set the number of clusters

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - DEFAULT_BLOCK_SIZE
```
public static final int DEFAULT_BLOCK_SIZE
```
    The default number of samples per parallel assignment instance.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_NUMBER_ITERATIONS
```
public static final int DEFAULT_NUMBER_ITERATIONS
```
    The default number of iterations.
    
    See Also:
    
    Constant Field Values
  - K
```
protected int K
```
    The number of clusters
  - factory
```
protected NearestNeighboursFactory<? extends NN extends NearestNeighbours<DATA,?,?>,DATA> factory
```
    The factory for producing the NearestNeighbours objects used in assignment.
  - blockSize
```
protected int blockSize
```
    The size of processing blocks for each thread
  - niters
```
protected int niters
```
    The max number of iterations
  - threadpool
```
protected ExecutorService threadpool
```
    The threadpool for parallel processing
- Constructor Detail
  - KMeansConfiguration
```
public KMeansConfiguration(int K,
                           NearestNeighboursFactory<? extends NN,DATA> nnFactory)
```
    Create configuration for data that will create K clusters. The algorithm will run for a maximum of DEFAULT_NUMBER_ITERATIONS iterations, and make use of all available processors, processing with blocks of DEFAULT_BLOCK_SIZE vectors.
    The specified NearestNeighboursFactory determines the actual type of k-means that will be performed; it could be exact nearest-neighbours, or it could be an approximate method, for example based on an ensemble of kd-trees.
    
    Parameters:
    
    K - number of clusters to be found
    
    nnFactory - the factory for producing the NearestNeighbours.
  - KMeansConfiguration
```
public KMeansConfiguration(int K,
                           NearestNeighboursFactory<? extends NN,DATA> nnFactory,
                           int niters)
```
    Create configuration for data that will create K clusters. The algorithm will run for a maximum of the given number of iterations, and will make use of all available processors, processing with blocks of DEFAULT_BLOCK_SIZE vectors.
    The specified NearestNeighboursFactory determines the actual type of k-means that will be performed; it could be exact nearest-neighbours, or it could be an approximate method, for example based on an ensemble of kd-trees.
    
    Parameters:
    
    K - number of clusters to be found
    
    nnFactory - the factory for producing the NearestNeighbours.
    
    niters - number of iterations
  - KMeansConfiguration
```
public KMeansConfiguration(int K,
                           NearestNeighboursFactory<? extends NN,DATA> nnFactory,
                           int niters,
                           ExecutorService threadpool)
```
    Create configuration for data that will create K clusters. The algorithm will run for a maximum of the given number of iterations, and will make use of the provided threadpool, processing with blocks of DEFAULT_BLOCK_SIZE vectors.
    The specified NearestNeighboursFactory determines the actual type of k-means that will be performed; it could be exact nearest-neighbours, or it could be an approximate method, for example based on an ensemble of kd-trees.
    
    Parameters:
    
    K - number of clusters to be found
    
    nnFactory - the factory for producing the NearestNeighbours.
    
    threadpool - threadpool to use for parallel processing
    
    niters - number of training iterations
  - KMeansConfiguration
```
public KMeansConfiguration(int K,
                           NearestNeighboursFactory<? extends NN,DATA> nnFactory,
                           int niters,
                           int blockSize,
                           ExecutorService threadpool)
```
    Create configuration for data with M dimensions that will create K clusters. The algorithm will run for a maximum of the given number of iterations, and will make use of nThreads processors, processing with blocks of DEFAULT_BLOCK_SIZE vectors.
    The specified NearestNeighboursFactory determines the actual type of k-means that will be performed; it could be exact nearest-neighbours, or it could be an approximate method, for example based on an ensemble of kd-trees.
    
    Parameters:
    
    K - number of clusters to be found
    
    nnFactory - the factory for producing the NearestNeighbours.
    
    threadpool - threadpool to use for parallel processing
    
    blockSize - number of samples per parallel thread
    
    niters - number of training iterations
  - KMeansConfiguration
```
public KMeansConfiguration()
```
    A completely default configuration used primarily as a convenience function for reading. The number of dimensions, number of clusters and nearest-neighbours factory must be set before the configuration is used.
- Method Detail
  - clone
```
public KMeansConfiguration<NN,DATA> clone()
```
    Overrides:
    
    clone in class Object
  - getK
```
public int getK()
```
    Get the number of clusters
    
    Returns:
    
    the number of clusters
  - setK
```
public void setK(int k)
```
    Set the number of clusters
    
    Parameters:
    
    k - the number of clusters
  - numClusters
```
public int numClusters()
```
    Get the number of clusters
    
    Returns:
    
    the number of clusters
  - setNumClusters
```
public void setNumClusters(int k)
```
    Set the number of clusters
    
    Parameters:
    
    k - the number of clusters
  - getBlockSize
```
public int getBlockSize()
```
    Get the number of samples processed in a batch by a thread. This needs to be small enough that that the memory isn't exhausted, but big enough for the thread to have enough data to work for a while.
    
    Returns:
    
    the the number of samples processed in a batch by a thread
  - setBlockSize
```
public void setBlockSize(int blockSize)
```
    Set the number of samples processed in a batch by a thread. This needs to be small enough that that the memory isn't exhausted, but big enough for the thread to have enough data to work for a while.
    
    Parameters:
    
    blockSize - the number of samples processed in a batch by a thread
  - getMaxIterations
```
public int getMaxIterations()
```
    Get the maximum allowed number of iterations.
    
    Returns:
    
    the maximum allowed number of iterations.
  - setMaxIterations
```
public void setMaxIterations(int niters)
```
    Set the maximum allowed number of iterations.
    
    Parameters:
    
    niters - the maximum allowed number of iterations.
  - getNearestNeighbourFactory
```
public NearestNeighboursFactory<? extends NN,DATA> getNearestNeighbourFactory()
```
    Get the factory that produces the NearestNeighbours during clustering.
    
    Returns:
    
    the factory
  - setNearestNeighbourFactory
```
public void setNearestNeighbourFactory(NearestNeighboursFactory<? extends NN,DATA> factory)
```
    Set the factory that produces the NearestNeighbours during clustering.
    
    Parameters:
    
    factory - the factory to set

Class KMeansConfiguration<NN extends NearestNeighbours<DATA,?,?>,DATA>

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_BLOCK_SIZE

DEFAULT_NUMBER_ITERATIONS

K

factory

blockSize

niters

threadpool

Constructor Detail

KMeansConfiguration

KMeansConfiguration

KMeansConfiguration

KMeansConfiguration

KMeansConfiguration

Method Detail

clone

getK

setK

numClusters

setNumClusters

getBlockSize

setBlockSize

getMaxIterations

setMaxIterations

getNearestNeighbourFactory

setNearestNeighbourFactory