gov.sandia.cognition.statistics.distribution

Class ChineseRestaurantProcess

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<Vector>

gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDiscreteDistribution<Vector>, ClosedFormComputableDistribution<Vector>, ClosedFormDistribution<Vector>, ComputableDistribution<Vector>, DiscreteDistribution<Vector>, Distribution<Vector>, DistributionWithMean<Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

ChineseRestaurantProcess.PMF

@PublicationReference(author="Michael I. Jordan",title="Dirichlet Processes, Chinese Restaurant Processes and All That",year=2005,type=Conference,publication="NIPS Tutorial",url="http://www.cs.berkeley.edu/~jordan/nips-tutorial05.ps") @PublicationReference(author="Wikipedia",title="http://en.wikipedia.org/wiki/Chinese_restaurant_process",year=2010,type=WebPage,url="http://en.wikipedia.org/wiki/Chinese_restaurant_process",notes="Very poor, unclear description.")
public class ChineseRestaurantProcess
extends AbstractDistribution<Vector>
implements ClosedFormComputableDiscreteDistribution<Vector>

A Chinese Restaurant Process is a discrete stochastic processes that partitions data points to clusters. This is done by imagining a restaurant with an infinite number of tables. The first customer sits at an empty table. The next customer picks an existing table proportionate to how many customers are already sitting at the various tables, and a new table with some nonzero probability. This results in a Dirichlet distribution with a variable number of parameters, which grows approximately as O(log(n)), where n is the number of customers to assign to tables.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ChineseRestaurantProcess.PMF PMF of the Chinese Restaurant Process

Field Summary

Fields

Modifier and Type	Field and Description
protected double	alpha CRP concentration parameter, must be greater than zero.
static double	DEFAULT_ALPHA Default concentration parameter, 1.0.
static int	DEFAULT_NUM_CUSTOMERS Default number of customers, 2.
protected int	numCustomers Total number of customers that we will arrange around tables, must be greater than zero.

Constructor Summary

Constructors

Constructor and Description
ChineseRestaurantProcess() Creates a new instance of ChineseRestaurantProcess
ChineseRestaurantProcess(ChineseRestaurantProcess other) Default constructor
ChineseRestaurantProcess(double alpha, int numCustomers) Creates a new instance of ChineseRestaurantProcess

Method Summary

Modifier and Type	Method and Description
ChineseRestaurantProcess	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Converts the object from a Vector of parameters.
Vector	convertToVector() Converts the object to a vector.
double	getAlpha() Getter for alpha.
java.util.Set<Vector>	getDomain() Returns an object that allows an iteration through the domain (x-axis, independent variable) of the Distribution
int	getDomainSize() Gets the size of the domain.
Vector	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
int	getNumCustomers() Getter for numCustomers
ChineseRestaurantProcess.PMF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
Vector	sample(java.util.Random random) Draws a single random sample from the distribution.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super Vector> output) Draws multiple random samples from the distribution and puts the result into the given collection.
static int	sampleNextCustomer(java.util.Collection<java.lang.Integer> tables, int numCustomers, double alpha, java.util.Random random) Determines where the next customer sits, given the number of customers already sitting at the various tables and the concentration parameter alpha.
void	setAlpha(double alpha) Setter for alpha.
void	setNumCustomers(int numCustomers) Setter for numCustomers

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample

Field Detail

DEFAULT_ALPHA

public static final double DEFAULT_ALPHA

Default concentration parameter, 1.0.

See Also:

Constant Field Values

DEFAULT_NUM_CUSTOMERS

public static final int DEFAULT_NUM_CUSTOMERS

Default number of customers, 2.

See Also:

Constant Field Values

alpha

protected double alpha

CRP concentration parameter, must be greater than zero.

numCustomers

protected int numCustomers

Total number of customers that we will arrange around tables, must be greater than zero.

Constructor Detail

ChineseRestaurantProcess

public ChineseRestaurantProcess()

Creates a new instance of ChineseRestaurantProcess

ChineseRestaurantProcess

public ChineseRestaurantProcess(double alpha,
                                int numCustomers)

Creates a new instance of ChineseRestaurantProcess

Parameters:

alpha - CRP concentration parameter, must be greater than zero.

numCustomers - Total number of customers that we will arrange around tables, must be greater than zero.

ChineseRestaurantProcess

public ChineseRestaurantProcess(ChineseRestaurantProcess other)

Default constructor

Parameters:

other - CRP to copy

Method Detail

clone

public ChineseRestaurantProcess clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface Vectorizable

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractCloneableSerializable

Returns:

A clone of this object.

getMean

public Vector getMean()

Description copied from interface: DistributionWithMean

Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Specified by:

getMean in interface DistributionWithMean<Vector>

Returns:

Mean of the distribution.

sample

public Vector sample(java.util.Random random)

Description copied from interface: Distribution

Draws a single random sample from the distribution.

Specified by:

sample in interface Distribution<Vector>

Overrides:

sample in class AbstractDistribution<Vector>

Parameters:

random - Random-number generator to use in order to generate random numbers.

Returns:

Sample drawn according to this distribution.

sampleNextCustomer

public static int sampleNextCustomer(java.util.Collection<java.lang.Integer> tables,
                                     int numCustomers,
                                     double alpha,
                                     java.util.Random random)

Determines where the next customer sits, given the number of customers already sitting at the various tables and the concentration parameter alpha.

Parameters:

tables - Number of customers sitting at the various tables.

numCustomers - Number of customers already sitting, should equal the sum of "tables".

alpha - Concentration parameter.

random - Random number generator.

Returns:

Index of the table where the next customer sits, according to the Chinese Restaurant Process.

sampleInto

public void sampleInto(java.util.Random random,
                       int sampleCount,
                       java.util.Collection<? super Vector> output)

Description copied from interface: Distribution

Draws multiple random samples from the distribution and puts the result into the given collection.

Specified by:

sampleInto in interface Distribution<Vector>

Parameters:

random - Random number generator to use.

sampleCount - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.

getAlpha

public double getAlpha()

Getter for alpha.

Returns:

CRP concentration parameter, must be greater than zero.

setAlpha

public void setAlpha(double alpha)

Setter for alpha.

Parameters:

alpha - CRP concentration parameter, must be greater than zero.

getNumCustomers

public int getNumCustomers()

Getter for numCustomers

Returns:

Total number of customers that we will arrange around tables, must be greater than zero.

setNumCustomers

public void setNumCustomers(int numCustomers)

Setter for numCustomers

Parameters:

numCustomers - Total number of customers that we will arrange around tables, must be greater than zero.

getDomain

public java.util.Set<Vector> getDomain()

Description copied from interface: DiscreteDistribution

Returns an object that allows an iteration through the domain (x-axis, independent variable) of the Distribution

Specified by:

getDomain in interface DiscreteDistribution<Vector>

Returns:

Collection that enumerates each value that the domain can take

getDomainSize

public int getDomainSize()

Description copied from interface: DiscreteDistribution

Gets the size of the domain.

Specified by:

getDomainSize in interface DiscreteDistribution<Vector>

Returns:

The size of the domain.

getProbabilityFunction

public ChineseRestaurantProcess.PMF getProbabilityFunction()

Description copied from interface: ComputableDistribution

Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:

getProbabilityFunction in interface ComputableDistribution<Vector>

Specified by:

getProbabilityFunction in interface DiscreteDistribution<Vector>

Returns:

Distribution function associated with this Distribution.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.