Revision d0a99151704ed9575dbe9d8422ed25f86972bbc3 authored by Marge Bot on 19 January 2024, 11:29:07 UTC, committed by Marge Bot on 19 January 2024, 11:29:07 UTC
Co-authored-by: Eugen Zalinescu <eugen.zalinescu@nomadic-labs.com> Approved-by: Raphaƫl Cauderlier <raphael.cauderlier@nomadic-labs.com> Approved-by: Mohamed IGUERNLALA <iguer@functori.com> See merge request https://gitlab.com/tezos/tezos/-/merge_requests/11544
pyinference.mli
(*****************************************************************************)
(* *)
(* Open Source License *)
(* Copyright (c) 2018 Dynamic Ledger Solutions, Inc. <contact@tezos.com> *)
(* Copyright (c) 2022 Nomadic Labs. <contact@nomadic-labs.com> *)
(* *)
(* Permission is hereby granted, free of charge, to any person obtaining a *)
(* copy of this software and associated documentation files (the "Software"),*)
(* to deal in the Software without restriction, including without limitation *)
(* the rights to use, copy, modify, merge, publish, distribute, sublicense, *)
(* and/or sell copies of the Software, and to permit persons to whom the *)
(* Software is furnished to do so, subject to the following conditions: *)
(* *)
(* The above copyright notice and this permission notice shall be included *)
(* in all copies or substantial portions of the Software. *)
(* *)
(* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR*)
(* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, *)
(* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL *)
(* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER*)
(* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING *)
(* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER *)
(* DEALINGS IN THE SOFTWARE. *)
(* *)
(*****************************************************************************)
module LinearModel : sig
val ridge :
alpha:float ->
?fit_intercept:bool ->
input:Scikit_matrix.t ->
output:Scikit_matrix.t ->
unit ->
Scikit_matrix.t
val lasso :
alpha:float ->
?fit_intercept:bool ->
?positive:bool ->
input:Scikit_matrix.t ->
output:Scikit_matrix.t ->
unit ->
Scikit_matrix.t
val nnls : input:Scikit_matrix.t -> output:Scikit_matrix.t -> Scikit_matrix.t
end
val predict_output :
input:Scikit_matrix.t -> weights:Scikit_matrix.t -> Pytypes.pyobject
val r2_score :
output:Scikit_matrix.t -> prediction:Pytypes.pyobject -> float option
val rmse_score : output:Scikit_matrix.t -> prediction:Pytypes.pyobject -> float
val benchmark_score :
input:Scikit_matrix.t ->
output:(float, Bigarray.float64_elt, Bigarray.c_layout) Bigarray.Array1.t ->
Scikit_matrix.t * Scikit_matrix.t
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