Instance Constructors
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new
StochasticGradientDescent(defaultStepSize: Double, maxIter: Int, tolerance: Double = 1.0E-5, improvementTol: Double = 1.0E-4, minImprovementWindow: Int = 50)(implicit vspace: MutableCoordinateSpace[T, Double])
Type Members
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abstract
type
History
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case class
State(x: T, value: Double, grad: T, adjustedValue: Double, adjustedGradient: T, iter: Int, initialAdjVal: Double, history: History, fVals: IndexedSeq[Double] = ..., numImprovementFailures: Int = 0, searchFailed: Boolean = false) extends Product with Serializable
Concrete Value Members
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final
def
!=(arg0: AnyRef): Boolean
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: AnyRef): Boolean
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final
def
==(arg0: Any): Boolean
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def
adjust(newX: T, newGrad: T, newVal: Double): (Double, T)
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final
def
asInstanceOf[T0]: T0
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def
calculateObjective(f: StochasticDiffFunction[T], x: T, history: History): (Double, T)
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def
clone(): AnyRef
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val
defaultStepSize: Double
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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lazy val
logger: Logger
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val
maxIter: Int
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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val
numberOfImprovementFailures: Int
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
takeStep(state: State, dir: T, stepSize: Double): T
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def
toString(): String
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def
updateFValWindow(oldState: State, newAdjVal: Double): IndexedSeq[Double]
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
Inherited from Logging
Inherited from AnyRef
Inherited from Any
Minimizes a function using stochastic gradient descent