GaussianProcessRegressor - ~b-reg-64 - dotproduct -#

Fitted on a problem type ~b-reg-64 (see find_suitable_problem), method predict matches output .

GaussianProcessRegressor(alpha=100.0, kernel=DotProduct(sigma_0=1),
                     random_state=0)

index

0

skl_nop

1

onx_size

5032

onx_nnodes

5

onx_ninits

5

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_op_Reshape

1

onx_size_optim

5032

onx_nnodes_optim

5

onx_ninits_optim

5

fit_X_train_.shape

(112, 4)

fit_y_train_.shape

112

fit_log_marginal_likelihood_value_.shape

1

fit_L_.shape

(112, 112)

fit_alpha_.shape

112

%0 X X double((0, 4)) kgpd_MatMul MatMul (kgpd_MatMul) X->kgpd_MatMul GPmean GPmean double((0, 1)) kgpd_MatMulcst kgpd_MatMulcst float64((4, 112)) [[ 4.30175021e+00  5.16919873e+00  5.96835159e+00 ... kgpd_MatMulcst->kgpd_MatMul kgpd_Addcst kgpd_Addcst float64((1,)) [0.00017647] kgpd_Add Add (kgpd_Add) kgpd_Addcst->kgpd_Add gpr_MatMulcst gpr_MatMulcst float64((112,)) [-0.00250094 -0.00240356  0.01185891 -0.00383052 -... gpr_MatMul MatMul (gpr_MatMul) gpr_MatMulcst->gpr_MatMul gpr_Addcst gpr_Addcst float64((1, 1)) [[0.]] gpr_Add Add (gpr_Add) gpr_Addcst->gpr_Add Re_Reshapecst Re_Reshapecst int64((2,)) [-1  1] Re_Reshape Reshape (Re_Reshape) allowzero=0 Re_Reshapecst->Re_Reshape kgpd_Y0 kgpd_Y0 kgpd_Y0->kgpd_Add kgpd_MatMul->kgpd_Y0 kgpd_C0 kgpd_C0 kgpd_C0->gpr_MatMul kgpd_Add->kgpd_C0 gpr_Y0 gpr_Y0 gpr_Y0->gpr_Add gpr_MatMul->gpr_Y0 gpr_C0 gpr_C0 gpr_C0->Re_Reshape gpr_Add->gpr_C0 Re_Reshape->GPmean