Batched.BatchedArrayType
BatchedArray{T, NI, N, AT} <: AbstractBatchedArray{T, NI, N}

A concrete type for batched arrays. T is the element type, NI is the inner sample's dimension, N is the total dimension and AT is the array type that actually holds the value.

Batched.BatchedUniformScalingType
BatchedUniformScaling{T, N, ST <: AbstractArray{T, N}} <: AbstractBatchedArray{T, 0, N}

Scale a batch of arrays with a batch of scalars.

BatchedUniformScaling(scalars)

The shape of batch can be multidimentional, which means member BatchedScale.scalars can be a matrix or high dimentional array, the shape of this member is the shape of batch. dims defines the dimmension of each sample in the batch. It can be multidimentional as well.

Batched.batch_sizeFunction
batch_size(batched_array) -> Tuple

Returns a tuple of size of each batch dimension of the batched array.

Batched.inner_sizeFunction
inner_size(batched_array) -> Tuple

Returns a tuple of size of each inner dimension of the batched array.

Batched.merged_sizeFunction
merged_size(batched_array) -> Tuple

Returns the size of this batched array after merging all its batched dimension together.

Batched.AbstractBatchedArrayType
AbstractBatchedArray{T, NI, N}

Abstract type batched array. A batched array use its last N - NI dimension as batch dimension, it is a batch of array with dimension NI.

Batched.BatchedAdjointType
BatchedAdjoint{T, N, S <: AbstractBatchedMatrix} <: AbstractBatchedMatrix{T, N}

Batched ajoint. Transpose a batch of matrix.

Batched.BatchedTransposeType
BatchedTranspose{T, N, S} <: AbstractBatchedMatrix{T, N}

Batched transpose. Transpose a batch of matrix.

Batched.batched_gemmFunction
batched_gemm(A, B)
batched_gemm(tA, tB, A, B)
batched_gemm(tA, tB, alpha, A, B)

Batched version of BLAS.gemm.

Batched.batched_gemm!Function
batched_gemm!(transA, transB, alpha, A, B, beta, C)

Batched version of BLAS.gemm!.

Batched.batched_tr!Method
batched_tr!(B::AbstractVector{T}, A::AbstractArray{T, 3})

Perform batched matrix trace.