case class OptParams(batchSize: Int = 512, regularization: Double = 0.0, alpha: Double = 0.5, maxIterations: Int = 1000, useL1: Boolean = false, tolerance: Double = 1E-5, useStochastic: Boolean = false, randomSeed: Int = 0) extends Product with Serializable

OptParams is a Configuration-compatible case class that can be used to select optimization routines at runtime.

Configurations: 1) useStochastic=false,useL1=false: LBFGS with L2 regularization 2) useStochastic=false,useL1=true: OWLQN with L1 regularization 3) useStochastic=true,useL1=false: AdaptiveGradientDescent with L2 regularization 3) useStochastic=true,useL1=true: AdaptiveGradientDescent with L1 regularization

batchSize

size of batches to use if useStochastic and you give a BatchDiffFunction

regularization

regularization constant to use.

alpha

rate of change to use, only applies to SGD.

useL1

if true, use L1 regularization. Otherwise, use L2.

tolerance

convergence tolerance, looking at both average improvement and the norm of the gradient.

useStochastic

if false, use LBFGS or OWLQN. If true, use some variant of Stochastic Gradient Descent.

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Instance Constructors

  1. new OptParams(batchSize: Int = 512, regularization: Double = 0.0, alpha: Double = 0.5, maxIterations: Int = 1000, useL1: Boolean = false, tolerance: Double = 1E-5, useStochastic: Boolean = false, randomSeed: Int = 0)

    batchSize

    size of batches to use if useStochastic and you give a BatchDiffFunction

    regularization

    regularization constant to use.

    alpha

    rate of change to use, only applies to SGD.

    useL1

    if true, use L1 regularization. Otherwise, use L2.

    tolerance

    convergence tolerance, looking at both average improvement and the norm of the gradient.

    useStochastic

    if false, use LBFGS or OWLQN. If true, use some variant of Stochastic Gradient Descent.

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. val alpha: Double
  5. final def asInstanceOf[T0]: T0
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  6. val batchSize: Int
  7. def clone(): AnyRef
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  10. final def isInstanceOf[T0]: Boolean
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  11. val maxIterations: Int
  12. final def ne(arg0: AnyRef): Boolean
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  13. final def notify(): Unit
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  14. final def notifyAll(): Unit
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  15. val randomSeed: Int
  16. val regularization: Double
  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. val tolerance: Double
  19. val useL1: Boolean
  20. val useStochastic: Boolean
  21. final def wait(arg0: Long, arg1: Int): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    @Deprecated @deprecated @throws( classOf[java.lang.Throwable] )
    Deprecated

    (Since version ) see corresponding Javadoc for more information.

  2. def iterations[T, K](f: DiffFunction[T], init: T)(implicit space: MutableEnumeratedCoordinateField[T, K, Double]): Iterator[State[T, Info, ApproximateInverseHessian[T]] forSome {val _1: LBFGS[T]}]
    Annotations
    @deprecated
    Deprecated

    (Since version 0.10) Use breeze.optimize.iterations(f, init, params) instead.

  3. def iterations[T](f: StochasticDiffFunction[T], init: T)(implicit space: MutableFiniteCoordinateField[T, _, Double]): Iterator[State[T, Info, FirstOrderMinimizer._1.type.History] forSome {val _1: FirstOrderMinimizer[T, StochasticDiffFunction[T]]}]
    Annotations
    @deprecated
    Deprecated

    (Since version 0.10) Use breeze.optimize.iterations(f, init, params) instead.

  4. def iterations[T](f: BatchDiffFunction[T], init: T)(implicit space: MutableFiniteCoordinateField[T, _, Double]): Iterator[State[T, Info, FirstOrderMinimizer._1.type.History] forSome {val _1: FirstOrderMinimizer[T, BatchDiffFunction[T]]}]
    Annotations
    @deprecated
    Deprecated

    (Since version 0.10) Use breeze.optimize.iterations(f, init, params) instead.

  5. def minimize[T](f: DiffFunction[T], init: T)(implicit space: MutableEnumeratedCoordinateField[T, _, Double]): T
    Annotations
    @deprecated
    Deprecated

    (Since version 0.10) Use breeze.optimize.minimize(f, init, params) instead.

  6. def minimize[T](f: BatchDiffFunction[T], init: T)(implicit space: MutableFiniteCoordinateField[T, _, Double]): T
    Annotations
    @deprecated
    Deprecated

    (Since version 0.10) Use breeze.optimize.minimize(f, init, params) instead.

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