class StreamingKMeansModel extends KMeansModel with Logging
StreamingKMeansModel extends MLlib's KMeansModel for streaming algorithms, so it can keep track of a continuously updated weight associated with each cluster, and also update the model by doing a single iteration of the standard k-means algorithm.
The update algorithm uses the "mini-batch" KMeans rule, generalized to incorporate forgetfulness (i.e. decay). The update rule (for each cluster) is:
$$ \begin{align} c_{t+1} &= [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] \\ n_{t+1} &= n_t * a + m_t \end{align} $$
Where c_t is the previously estimated centroid for that cluster, n_t is the number of points assigned to it thus far, x_t is the centroid estimated on the current batch, and m_t is the number of points assigned to that centroid in the current batch.
The decay factor 'a' scales the contribution of the clusters as estimated thus far, by applying a as a discount weighting on the current point when evaluating new incoming data. If a=1, all batches are weighted equally. If a=0, new centroids are determined entirely by recent data. Lower values correspond to more forgetting.
Decay can optionally be specified by a half life and associated time unit. The time unit can either be a batch of data or a single data point. Considering data arrived at time t, the half life h is defined such that at time t + h the discount applied to the data from t is 0.5. The definition remains the same whether the time unit is given as batches or points.
- Annotations
- @Since("1.2.0")
- Alphabetic
- By Inheritance
- StreamingKMeansModel
- Logging
- KMeansModel
- PMMLExportable
- Serializable
- Saveable
- AnyRef
- Any
- Hide All
- Show All
- Public
- Protected
Instance Constructors
Type Members
- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- val clusterCenters: Array[Vector]
- Definition Classes
- StreamingKMeansModel → KMeansModel
- Annotations
- @Since("1.2.0")
- val clusterWeights: Array[Double]
- Annotations
- @Since("1.2.0")
- def computeCost(data: RDD[Vector]): Double
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
- Definition Classes
- KMeansModel
- Annotations
- @Since("0.8.0")
- val distanceMeasure: String
- Definition Classes
- KMeansModel
- Annotations
- @Since("2.4.0")
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- def initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- def k: Int
Total number of clusters.
Total number of clusters.
- Definition Classes
- KMeansModel
- Annotations
- @Since("0.8.0")
- def log: Logger
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logName: String
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- def predict(points: JavaRDD[Vector]): JavaRDD[Integer]
Maps given points to their cluster indices.
Maps given points to their cluster indices.
- Definition Classes
- KMeansModel
- Annotations
- @Since("1.0.0")
- def predict(points: RDD[Vector]): RDD[Int]
Maps given points to their cluster indices.
Maps given points to their cluster indices.
- Definition Classes
- KMeansModel
- Annotations
- @Since("1.0.0")
- def predict(point: Vector): Int
Returns the cluster index that a given point belongs to.
Returns the cluster index that a given point belongs to.
- Definition Classes
- KMeansModel
- Annotations
- @Since("0.8.0")
- def save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using
Loader.load.- sc
Spark context used to save model data.
- path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.
- Definition Classes
- KMeansModel → Saveable
- Annotations
- @Since("1.4.0")
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toPMML(): String
Export the model to a String in PMML format
Export the model to a String in PMML format
- Definition Classes
- PMMLExportable
- Annotations
- @Since("1.4.0")
- def toPMML(outputStream: OutputStream): Unit
Export the model to the OutputStream in PMML format
Export the model to the OutputStream in PMML format
- Definition Classes
- PMMLExportable
- Annotations
- @Since("1.4.0")
- def toPMML(sc: SparkContext, path: String): Unit
Export the model to a directory on a distributed file system in PMML format
Export the model to a directory on a distributed file system in PMML format
- Definition Classes
- PMMLExportable
- Annotations
- @Since("1.4.0")
- def toPMML(localPath: String): Unit
Export the model to a local file in PMML format
Export the model to a local file in PMML format
- Definition Classes
- PMMLExportable
- Annotations
- @Since("1.4.0")
- def toString(): String
- Definition Classes
- AnyRef → Any
- val trainingCost: Double
- Definition Classes
- KMeansModel
- Annotations
- @Since("2.4.0")
- def update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel
Perform a k-means update on a batch of data.
Perform a k-means update on a batch of data.
- Annotations
- @Since("1.2.0")
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
- Attributes
- protected
- Definition Classes
- Logging
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)