GaussianProcessRegressor - ~b-reg-NF-std-64 - dotproduct - cdist#

Fitted on a problem type ~b-reg-NF-std-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=DotProduct(sigma_0=1),
                     random_state=42)

index

0

skl_nop

1

onx_size

632

onx_nnodes

7

onx_ninits

2

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

632

onx_nnodes_optim

7

onx_ninits_optim

2
%0 X X double((0, 4)) Sh_Shape Shape (Sh_Shape) X->Sh_Shape Re_ReduceSumSquare ReduceSumSquare (Re_ReduceSumSquare) axes=[1] X->Re_ReduceSumSquare GPmean GPmean double((0, 1)) GPcovstd GPcovstd double(('?',)) Re_ReduceSumcst Re_ReduceSumcst int64((1,)) [1] Re_ReduceSum ReduceSum (Re_ReduceSum) keepdims=1 Re_ReduceSumcst->Re_ReduceSum Sq_Squeeze Squeeze (Sq_Squeeze) Re_ReduceSumcst->Sq_Squeeze Ad_Addcst Ad_Addcst float64((1,)) [1.] Ad_Add Add (Ad_Add) Ad_Addcst->Ad_Add Sh_shape0 Sh_shape0 Co_ConstantOfShape ConstantOfShape (Co_ConstantOfShape) value=[0.] Sh_shape0->Co_ConstantOfShape Sh_Shape->Sh_shape0 Re_reduced0 Re_reduced0 Re_reduced0->Ad_Add Re_ReduceSumSquare->Re_reduced0 Co_output0 Co_output0 Co_output0->Re_ReduceSum Co_ConstantOfShape->Co_output0 Ad_C0 Ad_C0 Ad_C0->Sq_Squeeze Ad_Add->Ad_C0 Re_ReduceSum->GPmean Sq_squeezed0 Sq_squeezed0 Sq_Sqrt Sqrt (Sq_Sqrt) Sq_squeezed0->Sq_Sqrt Sq_Squeeze->Sq_squeezed0 Sq_Sqrt->GPcovstd

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