com.gpudb.protocol

Class AggregateKMeansRequest

java.lang.Object

com.gpudb.protocol.AggregateKMeansRequest

All Implemented Interfaces:

org.apache.avro.generic.GenericContainer, org.apache.avro.generic.IndexedRecord

public class AggregateKMeansRequest
extends Object
implements org.apache.avro.generic.IndexedRecord

A set of parameters for GPUdb.aggregateKMeans(AggregateKMeansRequest).

This endpoint runs the k-means algorithm - a heuristic algorithm that attempts to do k-means clustering. An ideal k-means clustering algorithm selects k points such that the sum of the mean squared distances of each member of the set to the nearest of the k points is minimized. The k-means algorithm however does not necessarily produce such an ideal cluster. It begins with a randomly selected set of k points and then refines the location of the points iteratively and settles to a local minimum. Various parameters and options are provided to control the heuristic search.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	AggregateKMeansRequest.Options Optional parameters.

Constructor Summary

Constructors

Constructor and Description
AggregateKMeansRequest() Constructs an AggregateKMeansRequest object with default parameters.
AggregateKMeansRequest(String tableName, List<String> columnNames, int k, double tolerance, Map<String,String> options) Constructs an AggregateKMeansRequest object with the specified parameters.

Method Summary

Methods

Modifier and Type	Method and Description
boolean	equals(Object obj)
Object	get(int index) This method supports the Avro framework and is not intended to be called directly by the user.
static org.apache.avro.Schema	getClassSchema() This method supports the Avro framework and is not intended to be called directly by the user.
List<String>	getColumnNames()
int	getK()
Map<String,String>	getOptions()
org.apache.avro.Schema	getSchema() This method supports the Avro framework and is not intended to be called directly by the user.
String	getTableName()
double	getTolerance()
int	hashCode()
void	put(int index, Object value) This method supports the Avro framework and is not intended to be called directly by the user.
AggregateKMeansRequest	setColumnNames(List<String> columnNames)
AggregateKMeansRequest	setK(int k)
AggregateKMeansRequest	setOptions(Map<String,String> options)
AggregateKMeansRequest	setTableName(String tableName)
AggregateKMeansRequest	setTolerance(double tolerance)
String	toString()

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AggregateKMeansRequest

public AggregateKMeansRequest()

Constructs an AggregateKMeansRequest object with default parameters.

AggregateKMeansRequest

public AggregateKMeansRequest(String tableName,
                      List<String> columnNames,
                      int k,
                      double tolerance,
                      Map<String,String> options)

Constructs an AggregateKMeansRequest object with the specified parameters.

Parameters:

tableName - Name of the table on which the operation will be performed. Must be an existing table or collection.

columnNames - List of column names on which the operation would be performed. If n columns are provided then each of the k result points will have n dimensions corresponding to the n columns.

k - The number of mean points to be determined by the algorithm.

tolerance - Stop iterating when the distances between successive points is less than the given tolerance.

options - Optional parameters.

• WHITEN: When set to 1 each of the columns is first normalized by its stdv - default is not to whiten.
• MAX_ITERS: Number of times to try to hit the tolerance limit before giving up - default is 10.
• NUM_TRIES: Number of times to run the k-means algorithm with a different randomly selected starting points - helps avoid local minimum. Default is 1.

Method Detail

getClassSchema

public static org.apache.avro.Schema getClassSchema()

This method supports the Avro framework and is not intended to be called directly by the user.

Returns:

the schema for the class.

getTableName

public String getTableName()

Returns:

Name of the table on which the operation will be performed. Must be an existing table or collection.

setTableName

public AggregateKMeansRequest setTableName(String tableName)

Parameters:

tableName - Name of the table on which the operation will be performed. Must be an existing table or collection.

Returns:

this to mimic the builder pattern.

getColumnNames

public List<String> getColumnNames()

Returns:

List of column names on which the operation would be performed. If n columns are provided then each of the k result points will have n dimensions corresponding to the n columns.

setColumnNames

public AggregateKMeansRequest setColumnNames(List<String> columnNames)

Parameters:

columnNames - List of column names on which the operation would be performed. If n columns are provided then each of the k result points will have n dimensions corresponding to the n columns.

Returns:

this to mimic the builder pattern.

getK

public int getK()

Returns:

The number of mean points to be determined by the algorithm.

setK

public AggregateKMeansRequest setK(int k)

Parameters:

k - The number of mean points to be determined by the algorithm.

Returns:

this to mimic the builder pattern.

getTolerance

public double getTolerance()

Returns:

Stop iterating when the distances between successive points is less than the given tolerance.

setTolerance

public AggregateKMeansRequest setTolerance(double tolerance)

Parameters:

tolerance - Stop iterating when the distances between successive points is less than the given tolerance.

Returns:

this to mimic the builder pattern.

getOptions

public Map<String,String> getOptions()

Returns:

Optional parameters.

• WHITEN: When set to 1 each of the columns is first normalized by its stdv - default is not to whiten.
• MAX_ITERS: Number of times to try to hit the tolerance limit before giving up - default is 10.
• NUM_TRIES: Number of times to run the k-means algorithm with a different randomly selected starting points - helps avoid local minimum. Default is 1.

setOptions

public AggregateKMeansRequest setOptions(Map<String,String> options)

Parameters:

options - Optional parameters.

• WHITEN: When set to 1 each of the columns is first normalized by its stdv - default is not to whiten.
• MAX_ITERS: Number of times to try to hit the tolerance limit before giving up - default is 10.
• NUM_TRIES: Number of times to run the k-means algorithm with a different randomly selected starting points - helps avoid local minimum. Default is 1.

Returns:

this to mimic the builder pattern.

getSchema

public org.apache.avro.Schema getSchema()

This method supports the Avro framework and is not intended to be called directly by the user.

Specified by:

getSchema in interface org.apache.avro.generic.GenericContainer

Returns:

the schema object describing this class.

get

public Object get(int index)

This method supports the Avro framework and is not intended to be called directly by the user.

Specified by:

get in interface org.apache.avro.generic.IndexedRecord

Parameters:

index - the position of the field to get

Returns:

value of the field with the given index.

Throws:

IndexOutOfBoundsException

put

public void put(int index,
       Object value)

This method supports the Avro framework and is not intended to be called directly by the user.

Specified by:

put in interface org.apache.avro.generic.IndexedRecord

Parameters:

index - the position of the field to set

value - the value to set

Throws:

IndexOutOfBoundsException

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

toString

public String toString()

Overrides:

toString in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object