GradientTester

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12. def logger: LazyLogger

    

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

    

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17. def test[K, T](f: DiffFunction[T], x: T, randFraction: Double = 0.01, skipZeros: Boolean = false, epsilon: Double = 1E-8, tolerance: Double = 1E-3, toString: (K) ⇒ String = _:K).toString)(implicit view2: <:<[T, NumericOps[T]], view: <:<[T, Tensor[K, Double]], copy: CanCopy[T], canNorm: linalg.norm.Impl[T, Double], opSub: linalg.operators.OpSub.Impl2[T, T, T]): T

    

    Tests a gradient by comparing the gradient to the empirically calculated gradient from finite differences, returning those that are bad, logging bad ones on WARN, ok ones on DEBUG, and overall statistics on INFO.

    Tests a gradient by comparing the gradient to the empirically calculated gradient from finite differences, returning those that are bad, logging bad ones on WARN, ok ones on DEBUG, and overall statistics on INFO.

    f

    the function to test

    x

    point to test from

    randFraction

    what percentage of x's domain to try.

    skipZeros

    should we skip components of x where the calculated gradient is 0. (Sometimes useful with sparse features. You might want to check that 0's are always 0's though!)

    epsilon

    Difference to try

    tolerance

    How big a relative difference before we start complaining.

    toString

    toString function for converting elements of x's domain to a string.

    returns

    differences in each component

18. def testIndices[T, K](f: DiffFunction[T], x: T, indices: Traversable[K], skipZeros: Boolean = false, toString: (K) ⇒ String = _:K).toString, epsilon: Double = 1e-8, tolerance: Double = 1E-3)(implicit view2: <:<[T, NumericOps[T]], view: <:<[T, Tensor[K, Double]], copy: CanCopy[T], canNorm: linalg.norm.Impl[T, Double], opSub: linalg.operators.OpSub.Impl2[T, T, T]): T

    

19. def toString(): String

    

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

    

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22. final def wait(arg0: Long): Unit

    

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