Data.Tensor.Utils

Definitions

Scalar : Type -> Type

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Vector : Axis -> Type -> Type

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Matrix : (row : Axis) -> (col : Axis) -> ConsistentWith row [col] => Type -> Type

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fill : Num a => AllC TensorMonoid shape => a -> Tensor shape a

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zeros : Num a => AllC TensorMonoid shape => Tensor shape a

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ones : Num a => AllC TensorMonoid shape => Tensor shape a

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identityBool : IsCubical c => Tensor [c, c] Bool

  An identity matrix with True on the diagonal and False elsewhere

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identity : IsCubical c => Num a => Tensor [c, c] a

  An identity matrix with ones on the diagonal and zeros elsewhere
  Analogous to numpy.eye

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arange : IsCubical stop => Cast Nat a => Tensor [stop] a

  A range of numbers [0, stop>

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arangeFromTo : {default (TTInternalName ~~> 0) start : Axis} -> {auto cStart : IsCubical start} -> {auto cStop : IsCubical stop} -> Cast Nat a => Tensor [(stop .name ~~> minus (dim stop) (dim start))] a

  A range of numbers [start, stop>

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flip : (axis : Fin rank) -> IsCubical (index axis (toVect shape)) => Tensor shape a -> Tensor shape a

  Reverse a tensor along a given axis

concat : {auto {conArg:12566} : IsCubical x} -> {auto {conArg:12569} : IsCubical y} -> {auto {conArg:12572} : ConsistentWith (l ~~> (dim x + dim y)) shape} -> {auto {conArg:12581} : ConsistentWith x shape} -> {auto {conArg:12585} : ConsistentWith y shape} -> Tensor (x :: shape) a -> Tensor (y :: shape) a -> Tensor ((l ~~> (dim x + dim y)) :: shape) a

  Concatenate two tensors along an existing axis, the first one
  TODO extend to allow concatenation along an arbitrary/named axis

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size : Tensor shape a -> Nat

  Number of elements in a non-cubical tensor

size : All IsCubical (toVect shape) => (0 _ : Tensor shape a) -> Nat

  Number of elements in a cubical tensor

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flatten : Foldable (Tensor shape) => Tensor shape a -> List a

  Flatten a non-cubical tensor into a list
  Requires that we have Foldable on all the components
  In general we won't know the number of elements of a non-cubical tensor at compile time

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max : Foldable (Tensor shape) => Ord a => Tensor shape a -> Maybe a

  Maximum value in a tensor
  Returns Nothing if the tensor is empty

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oneHot : {auto {conArg:12840} : IsCubical c} -> Fin (dim c) -> Num a => Tensor [c] a

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cTriBool : InterfaceOnPositions (c .cont) MOrd => TensorMonoid (c .cont) => (c .cont) .Shp -> Tensor [c, c] Bool

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triBool : IsCubical c => Tensor [c, c] Bool

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tri : IsCubical c => Num a => Tensor [c, c] a

  A matrix with ones on and below the diagonal, and zeros elsewhere
  Analogous to numpy.tri

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lowerTriangular : IsCubical c => Num a => Tensor [c, c] a -> Tensor [c, c] a

  Lower triangular part of a matrix. Elements above the diagonal are set to
  zero. Analogous to numpy.tril

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upperTriangular : IsCubical c => Num a => Tensor [c, c] a -> Tensor [c, c] a

  Upper triangular part of a matrix. Elements below the diagonal are set to
  zero. Analogous to numpy.triu(.., k=1)

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maskedFill : Num a => AllC TensorMonoid shape => Tensor shape a -> Tensor shape Bool -> a -> Tensor shape a

  Fill the elements of a tensor `t` with `fill` where `mask` is True

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sum : Algebra (Tensor shape) a => Tensor shape a -> a

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mean : All IsCubical (toVect shape) => Cast Nat a => Fractional a => Algebra (Tensor shape) a => Tensor shape a -> a

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variance : IsCubical c => Neg a => Fractional a => Cast Nat a => Tensor [c] a -> a

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cumulativeSum : Num a => IsCubical c => Tensor [c] a -> Tensor [c] a

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inorder : Tensor [(b ~> BinTreeNode)] a -> Tensor [(l ~> List)] a

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random : Num a => Random a => HasIO io => (shape : TensorShape rank) -> All IsCubical (toVect shape) => Applicative (Tensor shape) => Traversable (Tensor shape) => io (Tensor shape a)

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exMatrix : Ext (Vect 3 >< Vect 3) Double

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applMap : Ext (Vect n >< Vect n) Double -> Ext (Vect n) Double

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