StreamingKMeans (Spark 3.5.3 JavaDoc)

Object
- org.apache.spark.mllib.clustering.StreamingKMeans

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging
```
public class StreamingKMeans
extends Object
implements org.apache.spark.internal.Logging, scala.Serializable
```
StreamingKMeans provides methods for configuring a streaming k-means analysis, training the model on streaming, and using the model to make predictions on streaming data. See KMeansModel for details on algorithm and update rules.
Use a builder pattern to construct a streaming k-means analysis in an application, like:
```
  val model = new StreamingKMeans()
    .setDecayFactor(0.5)
    .setK(3)
    .setRandomCenters(5, 100.0)
    .trainOn(DStream)
 
```
See Also:

Serialized Form

Nested Class Summary
- Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
  org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors
Constructor and Description

StreamingKMeans()

StreamingKMeans(int k, double decayFactor, String timeUnit)

Constructors
Constructor and Description
`StreamingKMeans()`
`StreamingKMeans(int k, double decayFactor, String timeUnit)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static String`	`BATCHES()`
`double`	`decayFactor()`
`int`	`k()`
`StreamingKMeansModel`	`latestModel()` Return the latest model.
`static String`	`POINTS()`
`DStream<Object>`	`predictOn(DStream<Vector> data)` Use the clustering model to make predictions on batches of data from a DStream.
`JavaDStream<Integer>`	`predictOn(JavaDStream<Vector> data)` Java-friendly version of `predictOn`.
`<K> DStream<scala.Tuple2<K,Object>>`	`predictOnValues(DStream<scala.Tuple2<K,Vector>> data, scala.reflect.ClassTag<K> evidence$1)` Use the model to make predictions on the values of a DStream and carry over its keys.
`<K> JavaPairDStream<K,Integer>`	`predictOnValues(JavaPairDStream<K,Vector> data)` Java-friendly version of `predictOnValues`.
`StreamingKMeans`	`setDecayFactor(double a)` Set the forgetfulness of the previous centroids.
`StreamingKMeans`	`setHalfLife(double halfLife, String timeUnit)` Set the half life and time unit ("batches" or "points").
`StreamingKMeans`	`setInitialCenters(Vector[] centers, double[] weights)` Specify initial centers directly.
`StreamingKMeans`	`setK(int k)` Set the number of clusters.
`StreamingKMeans`	`setRandomCenters(int dim, double weight, long seed)` Initialize random centers, requiring only the number of dimensions.
`String`	`timeUnit()`
`void`	`trainOn(DStream<Vector> data)` Update the clustering model by training on batches of data from a DStream.
`void`	`trainOn(JavaDStream<Vector> data)` Java-friendly version of `trainOn`.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.internal.Logging
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

- Constructor Detail
  - StreamingKMeans
```
public StreamingKMeans(int k,
                       double decayFactor,
                       String timeUnit)
```
  - StreamingKMeans
```
public StreamingKMeans()
```
- Method Detail
  - BATCHES
```
public static final String BATCHES()
```
  - POINTS
```
public static final String POINTS()
```
  - k
```
public int k()
```
  - decayFactor
```
public double decayFactor()
```
  - timeUnit
```
public String timeUnit()
```
  - setK
```
public StreamingKMeans setK(int k)
```
    Set the number of clusters.
    
    Parameters:
    
    k - (undocumented)
    
    Returns:
    
    (undocumented)
  - setDecayFactor
```
public StreamingKMeans setDecayFactor(double a)
```
    Set the forgetfulness of the previous centroids.
    
    Parameters:
    
    a - (undocumented)
    
    Returns:
    
    (undocumented)
  - setHalfLife
```
public StreamingKMeans setHalfLife(double halfLife,
                                   String timeUnit)
```
    Set the half life and time unit ("batches" or "points"). If points, then the decay factor is raised to the power of number of new points and if batches, then decay factor will be used as is.
    
    Parameters:
    
    halfLife - (undocumented)
    
    timeUnit - (undocumented)
    
    Returns:
    
    (undocumented)
  - setInitialCenters
```
public StreamingKMeans setInitialCenters(Vector[] centers,
                                         double[] weights)
```
    Specify initial centers directly.
    
    Parameters:
    
    centers - (undocumented)
    
    weights - (undocumented)
    
    Returns:
    
    (undocumented)
  - setRandomCenters
```
public StreamingKMeans setRandomCenters(int dim,
                                        double weight,
                                        long seed)
```
    Initialize random centers, requiring only the number of dimensions.
    
    Parameters:
    
    dim - Number of dimensions
    
    weight - Weight for each center
    
    seed - Random seed
    
    Returns:
    
    (undocumented)
  - latestModel
```
public StreamingKMeansModel latestModel()
```
    Return the latest model.
    
    Returns:
    
    (undocumented)
  - trainOn
```
public void trainOn(DStream<Vector> data)
```
    Update the clustering model by training on batches of data from a DStream. This operation registers a DStream for training the model, checks whether the cluster centers have been initialized, and updates the model using each batch of data from the stream.
    
    Parameters:
    
    data - DStream containing vector data
  - trainOn
```
public void trainOn(JavaDStream<Vector> data)
```
    Java-friendly version of trainOn.
    
    Parameters:
    
    data - (undocumented)
  - predictOn
```
public DStream<Object> predictOn(DStream<Vector> data)
```
    Use the clustering model to make predictions on batches of data from a DStream.
    
    Parameters:
    
    data - DStream containing vector data
    
    Returns:
    
    DStream containing predictions
  - predictOn
```
public JavaDStream<Integer> predictOn(JavaDStream<Vector> data)
```
    Java-friendly version of predictOn.
    
    Parameters:
    
    data - (undocumented)
    
    Returns:
    
    (undocumented)
  - predictOnValues
```
public <K> DStream<scala.Tuple2<K,Object>> predictOnValues(DStream<scala.Tuple2<K,Vector>> data,
                                                           scala.reflect.ClassTag<K> evidence$1)
```
    Use the model to make predictions on the values of a DStream and carry over its keys.
    
    Parameters:
    
    data - DStream containing (key, feature vector) pairs
    
    evidence$1 - (undocumented)
    
    Returns:
    
    DStream containing the input keys and the predictions as values
  - predictOnValues
```
public <K> JavaPairDStream<K,Integer> predictOnValues(JavaPairDStream<K,Vector> data)
```
    Java-friendly version of predictOnValues.
    
    Parameters:
    
    data - (undocumented)
    
    Returns:
    
    (undocumented)

Class StreamingKMeans

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

Constructor Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.internal.Logging

Constructor Detail

StreamingKMeans

StreamingKMeans

Method Detail

BATCHES

POINTS

k

decayFactor

timeUnit

setK

setDecayFactor

setHalfLife

setInitialCenters

setRandomCenters

latestModel

trainOn

trainOn

predictOn

predictOn

predictOnValues

predictOnValues