GaussianProcessRegressor - ~b-reg-NF-64 - rbf - cdist#

Fitted on a problem type ~b-reg-NF-64 (see find_suitable_problem), method predict matches output . Model was converted with additional parameter: <class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>={'optim': 'cdist'}.

GaussianProcessRegressor(alpha=100.0, kernel=RBF(length_scale=1),
                     random_state=42)

index

0

skl_nop

1

onx_size

363

onx_nnodes

3

onx_ninits

1

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_

15

onx_size_optim

363

onx_nnodes_optim

3

onx_ninits_optim

1

%0 X X double((0, 4)) Sh_Shape Shape (Sh_Shape) X->Sh_Shape GPmean GPmean double((0, 1)) Re_ReduceSumcst Re_ReduceSumcst int64((1,)) [1] Re_ReduceSum ReduceSum (Re_ReduceSum) keepdims=1 Re_ReduceSumcst->Re_ReduceSum Sh_shape0 Sh_shape0 Co_ConstantOfShape ConstantOfShape (Co_ConstantOfShape) value=[0.] Sh_shape0->Co_ConstantOfShape Sh_Shape->Sh_shape0 Co_output0 Co_output0 Co_output0->Re_ReduceSum Co_ConstantOfShape->Co_output0 Re_ReduceSum->GPmean