tensor is a collection of tools to interact with multidimensional arrays
that are at the core of ML pipelines. It defines various ways to store data
and optimized mathematical operations. Contrary to other ndarray modules
such as numpy in Python or ndarray in Rust, this module allows full size
checking at compile time thanks to type level integers from the typenum
crate.
| allocation_policy | allocation_policy defines the traits StaticAllocationPolicy and
DynamicAllocationPolicy that both contain an associated Layout type
that should be used by operations when allocating. When an operation
involves more than one tensor, the policy of self should be applied and
passed to the new tensor. Policies are simply zero-sized structs that
implement both traits.
|
| core_ops | core_ops contains basic mathematical operations at the tensor level
that rely on the implementation of the underlying data type T of the
tensor. There are two families of operations: functionnal operations
and in-place operations.
|
| heap_layout | |
| layout | |
| linear_algebra | linear_algebra contains algebra-specific operations.
It is currently limited to vector/vector, matrix/vector,
and matrix/matrix dot products. It is entirely backed by
openblas through C bindings.
|
| prelude | |
| reduction | reductions contains mathematical operations that aply to one
tensor and output a tensor with the same shape except for one
dimension that is reduced to 1.
This covers sum, product, max and min over a certain axis.
|
| shape | shape contains all the tools to complement typenum
crate and efficiently use it for shape "arithmetics".
|
| slice_layout | |
| stack_layout | |
| static_heap_layout | |
| tensor | |
| transpose_policy | transpose_policy defines the trait BLASPolicy that dictates the behavior
of BLAS operations by passing BLAS_TRANSPOSE associated constant.
The trait TransposePolicy defines the relationship between transpose policies:
the associated type Transposed corresponds to the policy of the transposed tensor.
|