P2LAlgorithm

abstract def predict(model: M, query: Q): P

Implement this method to produce a prediction from a query and trained model.

abstract def train(pd: PD): M

Implement this method to produce a model from prepared data.

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def batchPredict(model: M, queries: Iterator[(Long, Q)]): Iterator[(Long, P)]

def batchPredictBase(baseModel: Any, baseQueries: RDD[(Long, Q)]): RDD[(Long, P)]

def batchPredictWrapper(model: Iterator[M], queries: Iterator[(Long, Q)]): Iterator[(Long, P)]

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getModel(baseModel: Any): RDD[Any]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isJava: Boolean

def isParallel: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predictBase(localBaseModel: Any, query: Q): P

def queryManifest(): Manifest[Q]

lazy val querySerializer: Formats

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def trainBase(sc: SparkContext, pd: PD): RDD[M]

Do not use directly or override this method, as this is called by PredictionIO workflow to train a model.

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit