Spark 3.5.5 ScalaDoc - org.apache.spark.mllib.optimization.LBFGS (original) (raw)
class LBFGS extends Optimizer with Logging
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- final def !=(arg0: Any): Boolean
- final def ##(): Int
- final def ==(arg0: Any): Boolean
- final def asInstanceOf[T0]: T0
- def clone(): AnyRef
- final def eq(arg0: AnyRef): Boolean
- def equals(arg0: Any): Boolean
- def finalize(): Unit
- final def getClass(): Class[_]
- def hashCode(): Int
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- def initializeLogIfNecessary(isInterpreter: Boolean): Unit
- final def isInstanceOf[T0]: Boolean
- def isTraceEnabled(): Boolean
- def log: Logger
- def logDebug(msg: ⇒ String, throwable: Throwable): Unit
- def logDebug(msg: ⇒ String): Unit
- def logError(msg: ⇒ String, throwable: Throwable): Unit
- def logError(msg: ⇒ String): Unit
- def logInfo(msg: ⇒ String, throwable: Throwable): Unit
- def logInfo(msg: ⇒ String): Unit
- def logName: String
- def logTrace(msg: ⇒ String, throwable: Throwable): Unit
- def logTrace(msg: ⇒ String): Unit
- def logWarning(msg: ⇒ String, throwable: Throwable): Unit
- def logWarning(msg: ⇒ String): Unit
- final def ne(arg0: AnyRef): Boolean
- final def notify(): Unit
- final def notifyAll(): Unit
- def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector
- def optimizeWithLossReturned(data: RDD[(Double, Vector)], initialWeights: Vector): (Vector, Array[Double])
- def setConvergenceTol(tolerance: Double): LBFGS.this.type
- def setGradient(gradient: Gradient): LBFGS.this.type
- def setNumCorrections(corrections: Int): LBFGS.this.type
- def setNumIterations(iters: Int): LBFGS.this.type
- def setRegParam(regParam: Double): LBFGS.this.type
- def setUpdater(updater: Updater): LBFGS.this.type
- final def synchronized[T0](arg0: ⇒ T0): T0
- def toString(): String
- final def wait(): Unit
- final def wait(arg0: Long, arg1: Int): Unit
- final def wait(arg0: Long): Unit