package modelkit

You can search for identifiers within the package.

in-package search v0.2.0

Portable classical machine learning workflows for OCaml

Install

dune-project

Dependency

github.com Readme Changelog Edit opam file Versions (5)

Authors

A

Asara

Maintainers

A

Asara developers <devs@asara.io>

Sources

modelkit-0.4.1.tbz

sha256=7a7af032790248b5d1d5392bbf29f8c8163df2e7f50ab5801cdaed5fd2c543e1

sha512=185bc224afc9b141d54b89a564694438a268429ba9c58dd3a402da31cb29f3e1eefd3643a76766ce9170c5f6c927f307873d51e4e3ce08aece00884cebbe0e2a

doc/modelkit/Modelkit/Reference_backend/index.html

Module `Modelkit.Reference_backend`Source

Native OCaml float64 kernels with stable fixed-order reductions.

val name : string

val sum : Vector.t -> float

val dot : Vector.t -> Vector.t -> (float, Error.t) result

val matrix_vector_product : Matrix.t -> Vector.t -> (Vector.t, Error.t) result

val transposed_matrix_vector_product : 
  Matrix.t ->
  Vector.t ->
  (Vector.t, Error.t) result

val feature_matrix_vector_product : 
  Feature_matrix.t ->
  Vector.t ->
  (Vector.t, Error.t) result

Dispatches to dense or CSR storage without densifying sparse input. CSR kernels visit stored entries only; dense and CSR results agree for finite operands representing the same matrix.

val transposed_feature_matrix_vector_product : 
  Feature_matrix.t ->
  Vector.t ->
  (Vector.t, Error.t) result