Package com.gpudb.protocol

Class AggregateKMeansResponse

java.lang.Object

com.gpudb.protocol.AggregateKMeansResponse

All Implemented Interfaces:

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

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

A set of results returned by GPUdb.aggregateKMeans.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	AggregateKMeansResponse.Info	A set of string constants for the AggregateKMeansResponse parameter info.

Constructor Summary

Constructors

Constructor	Description
AggregateKMeansResponse()	Constructs an AggregateKMeansResponse object with default parameters.

Method Summary

Modifier and Type	Method	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.
long	getCount()	The total count of all the clusters - will be the size of the input table.
List<Long>	getCounts()	The number of elements in the cluster closest the corresponding k-means values.
Map<String,String>	getInfo()	Additional information.
List<List<Double>>	getMeans()	The k-mean values found.
int	getNumIters()	The number of iterations the algorithm executed before it quit.
double	getRmsDist()	The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.
List<Double>	getRmsDists()	The root mean squared distance of the elements in the cluster for each of the k-means values.
org.apache.avro.Schema	getSchema()	This method supports the Avro framework and is not intended to be called directly by the user.
double	getTolerance()	The distance between the last two iterations of the algorithm before it quit.
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.
AggregateKMeansResponse	setCount(long count)	The total count of all the clusters - will be the size of the input table.
AggregateKMeansResponse	setCounts(List<Long> counts)	The number of elements in the cluster closest the corresponding k-means values.
AggregateKMeansResponse	setInfo(Map<String,String> info)	Additional information.
AggregateKMeansResponse	setMeans(List<List<Double>> means)	The k-mean values found.
AggregateKMeansResponse	setNumIters(int numIters)	The number of iterations the algorithm executed before it quit.
AggregateKMeansResponse	setRmsDist(double rmsDist)	The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.
AggregateKMeansResponse	setRmsDists(List<Double> rmsDists)	The root mean squared distance of the elements in the cluster for each of the k-means values.
AggregateKMeansResponse	setTolerance(double tolerance)	The distance between the last two iterations of the algorithm before it quit.
String	toString()

Methods inherited from class java.lang.Object

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

Constructor Detail

AggregateKMeansResponse

public AggregateKMeansResponse()

Constructs an AggregateKMeansResponse object with default parameters.

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.

getMeans

public List<List<Double>> getMeans()

The k-mean values found.

Returns:

The current value of means.

setMeans

public AggregateKMeansResponse setMeans(List<List<Double>> means)

The k-mean values found.

Parameters:

means - The new value for means.

Returns:

this to mimic the builder pattern.

getCounts

public List<Long> getCounts()

The number of elements in the cluster closest the corresponding k-means values.

Returns:

The current value of counts.

setCounts

public AggregateKMeansResponse setCounts(List<Long> counts)

The number of elements in the cluster closest the corresponding k-means values.

Parameters:

counts - The new value for counts.

Returns:

this to mimic the builder pattern.

getRmsDists

public List<Double> getRmsDists()

The root mean squared distance of the elements in the cluster for each of the k-means values.

Returns:

The current value of rmsDists.

setRmsDists

public AggregateKMeansResponse setRmsDists(List<Double> rmsDists)

The root mean squared distance of the elements in the cluster for each of the k-means values.

Parameters:

rmsDists - The new value for rmsDists.

Returns:

this to mimic the builder pattern.

getCount

public long getCount()

The total count of all the clusters - will be the size of the input table.

Returns:

The current value of count.

setCount

public AggregateKMeansResponse setCount(long count)

The total count of all the clusters - will be the size of the input table.

Parameters:

count - The new value for count.

Returns:

this to mimic the builder pattern.

getRmsDist

public double getRmsDist()

The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.

Returns:

The current value of rmsDist.

setRmsDist

public AggregateKMeansResponse setRmsDist(double rmsDist)

The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.

Parameters:

rmsDist - The new value for rmsDist.

Returns:

this to mimic the builder pattern.

getTolerance

public double getTolerance()

The distance between the last two iterations of the algorithm before it quit.

Returns:

The current value of tolerance.

setTolerance

public AggregateKMeansResponse setTolerance(double tolerance)

The distance between the last two iterations of the algorithm before it quit.

Parameters:

tolerance - The new value for tolerance.

Returns:

this to mimic the builder pattern.

getNumIters

public int getNumIters()

The number of iterations the algorithm executed before it quit.

Returns:

The current value of numIters.

setNumIters

public AggregateKMeansResponse setNumIters(int numIters)

The number of iterations the algorithm executed before it quit.

Parameters:

numIters - The new value for numIters.

Returns:

this to mimic the builder pattern.

getInfo

public Map<String,String> getInfo()

Additional information.

• QUALIFIED_RESULT_TABLE_NAME: The fully qualified name of the result table (i.e. including the schema) used to store the results.

The default value is an empty Map.

Returns:

The current value of info.

setInfo

public AggregateKMeansResponse setInfo(Map<String,String> info)

Additional information.

• QUALIFIED_RESULT_TABLE_NAME: The fully qualified name of the result table (i.e. including the schema) used to store the results.

The default value is an empty Map.

Parameters:

info - The new value for info.

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