Crate ndarray_einsum_beta

The ndarray_einsum crate implements the einsum function, originally implemented for numpy by Mark Wiebe and subsequently reimplemented for other tensor libraries such as Tensorflow and PyTorch. einsum (Einstein summation) implements general multidimensional tensor contraction. Many linear algebra operations and generalizations of those operations can be expressed as special cases of tensor contraction. Examples include matrix multiplication, matrix trace, vector dot product, tensor Hadamard [element-wise] product, axis permutation, outer product, batch matrix multiplication, bilinear transformations, and many more.

Structs

Contraction	A Contraction contains the result of parsing an einsum-formatted string.
OperandNumPair
PathContraction
SizedContraction	A SizedContraction contains a Contraction as well as a HashMap<char, usize> specifying the axis lengths for each index in the contraction.
TensordotGeneral

Enums

ContractionOrder
OptimizationMethod

Traits

ArrayLike
PairContractor
PathContractor

Functions

einsum
einsum_path
einsum_sc
generate_optimized_order
slow_einsum
slow_einsum_given_sized_contraction
slow_einsum_with_flattened_operands_as_json_string_as_json
tensordot
validate	Wrapper around Contraction::new().
validate_and_optimize_order
validate_and_size	Wrapper around SizedContraction::new().
validate_and_size_from_shapes	Only included so the function can be called from WASM, i.e. without arguments that are already ndarray ArrayBases.
validate_and_size_from_shapes_as_string_as_json
validate_as_json