GloballyOptWithGrad

abstract val current_state: Map[String, Double]

A Map which stores the current state of the system.

abstract def energy(h: Map[String, Double], options: Map[String, String] = Map()): Double

Calculates the energy of the configuration, in most global optimization algorithms we aim to find an approximate value of the hyper-parameters such that this function is minimized.

abstract val hyper_parameters: List[String]

Stores the names of the hyper-parameters

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def _current_state: Map[String, Double]

def _hyper_parameters: List[String]

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 gradEnergy(h: Map[String, Double]): Map[String, Double]

Calculates the gradient energy of the configuration and subtracts this from the current value of h to yield a new hyper-parameter configuration.

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def persist(state: Map[String, Double]): Unit

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