State

Tracks the information about the optimizer, including the current point, its value, gradient, and then any history. Also includes information for checking convergence.

x: the current point being considered
value: f(x)
grad: f.gradientAt(x)
adjustedValue: f(x) + r(x), where r is any regularization added to the objective. For LBFGS, this is f(x).
adjustedGradient: f'(x) + r'(x), where r is any regularization added to the objective. For LBFGS, this is f'(x).
iter: what iteration number we are on.
initialAdjVal: f(x_0) + r(x_0), used for checking convergence
history: any information needed by the optimizer to do updates.
fVals: the sequence of the last minImprovementWindow values, used for checking if the "value" isn't improving
numImprovementFailures: the number of times in a row the objective hasn't improved, mostly for SGD
searchFailed: did the line search fail?

Linear Supertypes

Serializable, Serializable, Product, Equals, AnyRef, Any

Instance Constructors

new 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)

x
the current point being considered
value
f(x)
grad
f.gradientAt(x)
adjustedValue
f(x) + r(x), where r is any regularization added to the objective. For LBFGS, this is f(x).
adjustedGradient
f'(x) + r'(x), where r is any regularization added to the objective. For LBFGS, this is f'(x).
iter
what iteration number we are on.
initialAdjVal
f(x_0) + r(x_0), used for checking convergence
history
any information needed by the optimizer to do updates.
fVals
the sequence of the last minImprovementWindow values, used for checking if the "value" isn't improving
numImprovementFailures
the number of times in a row the objective hasn't improved, mostly for SGD
searchFailed
did the line search fail?

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
def actuallyConverged: Boolean

true if the function value hasn't changed for several iterations or if the gradient's norm is near 0
true if the function value hasn't changed for several iterations or if the gradient's norm is near 0
returns
val adjustedGradient: T

f'(x) + r'(x), where r is any regularization added to the objective.
f'(x) + r'(x), where r is any regularization added to the objective. For LBFGS, this is f'(x).
val adjustedValue: Double

f(x) + r(x), where r is any regularization added to the objective.
f(x) + r(x), where r is any regularization added to the objective. For LBFGS, this is f(x).
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def converged: Boolean

True if the optimizer thinks it's done.
def convergedReason: Option[ConvergenceReason]
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val fVals: IndexedSeq[Double]

the sequence of the last minImprovementWindow values, used for checking if the "value" isn't improving
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
val grad: T

f.
f.gradientAt(x)
val history: History

any information needed by the optimizer to do updates.
val initialAdjVal: Double

f(x_0) + r(x_0), used for checking convergence
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val iter: Int

what iteration number we are on.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numImprovementFailures: Int

the number of times in a row the objective hasn't improved, mostly for SGD
val searchFailed: Boolean

did the line search fail?
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val value: Double

f(x)
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
val x: T

the current point being considered

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

Instance Constructors

new 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)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def actuallyConverged: Boolean

val adjustedGradient: T

val adjustedValue: Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def converged: Boolean

def convergedReason: Option[ConvergenceReason]

final def eq(arg0: AnyRef): Boolean

val fVals: IndexedSeq[Double]

def finalize(): Unit

final def getClass(): Class[_]

val grad: T

val history: History

val initialAdjVal: Double

final def isInstanceOf[T0]: Boolean

val iter: Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numImprovementFailures: Int

val searchFailed: Boolean

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

val value: Double

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val x: T

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

Ungrouped