IntRandomForest (The OpenIMAJ Clustering Library 1.3.9 API)

java.lang.Object
- org.openimaj.ml.clustering.rforest.IntRandomForest

All Implemented Interfaces:

Clusterer<int[][]>, Readable, ReadableASCII, ReadableBinary, ReadWriteable, Writeable, WriteableASCII, WriteableBinary, Assigner<int[]>, HardAssigner<int[],float[],IntFloatPair>, Clusters, SpatialClusterer<IntRandomForest,int[]>, SpatialClusters<int[]>
```
@Reference(type=Article,
           author={"Frank Moosmann","Eric Nowak","Fr{\'e}d{\'e}ric Jurie"},
           title="Randomized Clustering Forests for Image Classification",
           year="2008",
           journal="IEEE PAMI",
           url="http://dx.doi.org/10.1109/TPAMI.2007.70822")
public class IntRandomForest
extends Object
implements SpatialClusters<int[]>, SpatialClusterer<IntRandomForest,int[]>, HardAssigner<int[],float[],IntFloatPair>
```
An implementation of the RandomForest clustering algorithm proposed by Jurie et al.
In this implementation the training phase is used to identify the limits of the data (for which a very small subset may be provided). Once this is known N decision trees are constructed each with M decisions (see RandomDecisionTree). In the clustering phase each feature projected is assigned a letter for each decision tree.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk), Sina Samangooei (ss@ecs.soton.ac.uk)

Field Summary
- Fields inherited from interface org.openimaj.ml.clustering.Clusters
  CLUSTER_HEADER

Constructor Summary

Constructors
Constructor and Description
`IntRandomForest()` Makes a default random forest with 32 trees each with 32 decisions.
`IntRandomForest(int nTrees, int nDecisions)` Makes a random forest with nTrees each with nDecisions.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`String`	`asciiHeader()`
`int`	`assign(int[] data)` Uses the `assignWord(int[])` function to construct the word representing this data point.
`int[]`	`assign(int[][] data)` Assign data to a cluster.
`IntFloatPair`	`assignDistance(int[] data)` Assign a single point to a cluster.
`void`	`assignDistance(int[][] data, int[] indices, float[] distances)` Assign data to clusters.
`org.openimaj.ml.clustering.rforest.IntRandomForest.Word[]`	`assignLetters(int[][] data)` Push each data point provided to a set of letters, i.e.
`org.openimaj.ml.clustering.rforest.IntRandomForest.Word`	`assignWord(int[] data)` Push a single data point to a set of letters, return the letters as word.
`byte[]`	`binaryHeader()`
`IntRandomForest`	`cluster(DataSource<int[]> data)` Perform clustering with data from a data source.
`IntRandomForest`	`cluster(int[][] data)` Perform clustering on the given data.
`HardAssigner<int[],?,?>`	`defaultHardAssigner()` Get the default hard assigner for this clusterer.
`boolean`	`equals(Object r)`
`int`	`getNDecisions()`
`int`	`getNTrees()`
`List<RandomDecisionTree>`	`getTrees()`
`int`	`numClusters()` Get the number of clusters.
`int`	`numDimensions()` Get the data dimensionality
`int[][]`	`performClustering(int[][] data)`
`void`	`readASCII(Scanner br)`
`void`	`readBinary(DataInput dis)`
`void`	`setMinMax(int[] min, int[] max)` The maximum and minimum values for the various dimentions against which random decisions will be based.
`void`	`setRandomSeed(int random)`
`int`	`size()` The number of centroids or unique ids that can be generated.
`void`	`writeASCII(PrintWriter writer)`
`void`	`writeBinary(DataOutput o)`

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

- Constructor Detail
  - IntRandomForest
```
public IntRandomForest()
```
    Makes a default random forest with 32 trees each with 32 decisions. This results in 10^47 potential words.
  - IntRandomForest
```
public IntRandomForest(int nTrees,
                       int nDecisions)
```
    Makes a random forest with nTrees each with nDecisions. This will result in nTrees ^ nDecisions potential words
    
    Parameters:
    
    nTrees - number of trees
    
    nDecisions - number of decisions per tree
- Method Detail
  - setMinMax
```
public void setMinMax(int[] min,
                      int[] max)
```
    The maximum and minimum values for the various dimentions against which random decisions will be based.
    
    Parameters:
    
    min -
    
    max -
  - cluster
```
public IntRandomForest cluster(int[][] data)
```
    Description copied from interface: SpatialClusterer
    
    Perform clustering on the given data.
    
    Specified by:
    
    cluster in interface SpatialClusterer<IntRandomForest,int[]>
    
    Parameters:
    
    data - the data.
    
    Returns:
    
    the generated clusters.
  - cluster
```
public IntRandomForest cluster(DataSource<int[]> data)
```
    Description copied from interface: SpatialClusterer
    
    Perform clustering with data from a data source. The DataSource could potentially be backed by disk rather in memory.
    
    Specified by:
    
    cluster in interface SpatialClusterer<IntRandomForest,int[]>
    
    Parameters:
    
    data - the data.
    
    Returns:
    
    the generated clusters.
  - numClusters
```
public int numClusters()
```
    Description copied from interface: SpatialClusters
    
    Get the number of clusters.
    
    Specified by:
    
    numClusters in interface SpatialClusters<int[]>
    
    Returns:
    
    number of clusters.
  - numDimensions
```
public int numDimensions()
```
    Description copied from interface: SpatialClusters
    
    Get the data dimensionality
    
    Specified by:
    
    numDimensions in interface Assigner<int[]>
    
    Specified by:
    
    numDimensions in interface SpatialClusters<int[]>
    
    Returns:
    
    the data dimensionality.
  - assign
```
public int[] assign(int[][] data)
```
    Description copied from interface: HardAssigner
    
    Assign data to a cluster.
    
    Specified by:
    
    assign in interface HardAssigner<int[],float[],IntFloatPair>
    
    Parameters:
    
    data - the data.
    
    Returns:
    
    The cluster indices which the data was assigned to.
  - assignLetters
```
public org.openimaj.ml.clustering.rforest.IntRandomForest.Word[] assignLetters(int[][] data)
```
    Push each data point provided to a set of letters, i.e. a word. Each letter represents a set of decisions made in a single decision tree.
    
    Parameters:
    
    data -
    
    Returns:
    
    A word per data point
  - assignWord
```
public org.openimaj.ml.clustering.rforest.IntRandomForest.Word assignWord(int[] data)
```
    Push a single data point to a set of letters, return the letters as word. This is achieved by pushing the data point down each decision tree. This returns the result of the n decisions made for that tree, this is a single letter. As a letter is seen for the first time, it is assigned a number. The letters are combined in sequence to construct a word.
    
    Parameters:
    
    data - to be projected
    
    Returns:
    
    A single word containing the letters containing the decisions made on each tree
  - assign
```
public int assign(int[] data)
```
    Uses the assignWord(int[]) function to construct the word representing this data point. If this exact word has been seen before (i.e. these letters in this order) the same int is used. If not, a new int is assigned for this word.
    
    Specified by:
    
    assign in interface HardAssigner<int[],float[],IntFloatPair>
    
    Parameters:
    
    data - a data point to be clustered to a word
    
    Returns:
    
    a cluster centroid from a word
  - getNTrees
```
public int getNTrees()
```
    Returns:
    
    The number of decision trees
  - getNDecisions
```
public int getNDecisions()
```
    Returns:
    
    the number of decisions per tree
  - getTrees
```
public List<RandomDecisionTree> getTrees()
```
    Returns:
    
    the decision trees
  - equals
```
public boolean equals(Object r)
```
    Overrides:
    
    equals in class Object
  - asciiHeader
```
public String asciiHeader()
```
    Specified by:
    
    asciiHeader in interface ReadableASCII
    
    Specified by:
    
    asciiHeader in interface WriteableASCII
  - binaryHeader
```
public byte[] binaryHeader()
```
    Specified by:
    
    binaryHeader in interface ReadableBinary
    
    Specified by:
    
    binaryHeader in interface WriteableBinary
  - readASCII
```
public void readASCII(Scanner br)
               throws IOException
```
    Specified by:
    
    readASCII in interface ReadableASCII
    
    Throws:
    
    IOException
  - readBinary
```
public void readBinary(DataInput dis)
                throws IOException
```
    Specified by:
    
    readBinary in interface ReadableBinary
    
    Throws:
    
    IOException
  - writeASCII
```
public void writeASCII(PrintWriter writer)
                throws IOException
```
    Specified by:
    
    writeASCII in interface WriteableASCII
    
    Throws:
    
    IOException
  - writeBinary
```
public void writeBinary(DataOutput o)
                 throws IOException
```
    Specified by:
    
    writeBinary in interface WriteableBinary
    
    Throws:
    
    IOException
  - setRandomSeed
```
public void setRandomSeed(int random)
```
    Parameters:
    
    random - the seed of the java Random instance used by the decision trees
  - assignDistance
```
public void assignDistance(int[][] data,
                           int[] indices,
                           float[] distances)
```
    Description copied from interface: HardAssigner
    
    Assign data to clusters. The results are returned in the indices and distances arrays. The return arrays must have the same length as the data array.
    
    Specified by:
    
    assignDistance in interface HardAssigner<int[],float[],IntFloatPair>
    
    Parameters:
    
    data - the data.
    
    indices - the cluster index for each data point.
    
    distances - the distance to the closest cluster for each data point.
  - assignDistance
```
public IntFloatPair assignDistance(int[] data)
```
    Description copied from interface: HardAssigner
    
    Assign a single point to a cluster.
    
    Specified by:
    
    assignDistance in interface HardAssigner<int[],float[],IntFloatPair>
    
    Parameters:
    
    data - point to assign.
    
    Returns:
    
    the cluster index and distance.
  - defaultHardAssigner
```
public HardAssigner<int[],?,?> defaultHardAssigner()
```
    Description copied from interface: SpatialClusters
    
    Get the default hard assigner for this clusterer. This method is potentially expensive, so callers should only call it once, and hold on to the result (and reuse it).
    
    Specified by:
    
    defaultHardAssigner in interface SpatialClusters<int[]>
    
    Returns:
    
    a hard assigner.
  - size
```
public int size()
```
    Description copied from interface: HardAssigner
    
    The number of centroids or unique ids that can be generated.
    
    Specified by:
    
    size in interface HardAssigner<int[],float[],IntFloatPair>
    
    Returns:
    
    The number of centroids or unique ids that can be generated.
  - performClustering
```
public int[][] performClustering(int[][] data)
```
    Specified by:
    
    performClustering in interface Clusterer<int[][]>

Class IntRandomForest

Field Summary

Fields inherited from interface org.openimaj.ml.clustering.Clusters

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

IntRandomForest

IntRandomForest

Method Detail

setMinMax

cluster

cluster

numClusters

numDimensions

assign

assignLetters

assignWord

assign

getNTrees

getNDecisions

getTrees

equals

asciiHeader

binaryHeader

readASCII

readBinary

writeASCII

writeBinary

setRandomSeed

assignDistance

assignDistance

defaultHardAssigner

size

performClustering