GaussianNB - m-cl - default -#

Fitted on a problem type m-cl (see find_suitable_problem), method predict_proba matches output .

GaussianNB()

index

0

skl_nop

1

onx_size

1805

onx_nnodes

19

onx_ninits

11

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_

13

onx_ai.onnx.ml

1

onx_op_Cast

3

onx_op_ZipMap

1

onx_op_Reshape

3

onx_size_optim

1805

onx_nnodes_optim

19

onx_ninits_optim

11

fit_classes_.shape

3

fit_epsilon_.shape

1

fit_theta_.shape

(3, 4)

fit_var_.shape

(3, 4)

fit_class_count_.shape

3

fit_class_prior_.shape

3
%0 X X float((0, 4)) Reshape Reshape (Reshape) X->Reshape output_label output_label int64((0,)) output_probability output_probability [{int64, {'kind': 'tensor', 'elem': 'float', 'shape': }}] classes classes int32((3,)) [0 1 2] ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) classes->ArrayFeatureExtractor theta theta float32((1, 3, 4)) [[[5.0592794  3.3980393  1.3656081  0.36579275]  ... Sub Sub (Sub) theta->Sub sigma sigma float32((1, 3, 4)) [[[0.19725956 0.2030348  0.12801166 0.06933402]  ... Div Div (Div) sigma->Div jointi jointi float32((1, 3)) [[-1.1631508 -1.0549372 -1.0809127]] Add Add (Add) jointi->Add sigma_sum_log sigma_sum_log float32((1, 3)) [[ 0.2952789  -0.88625735 -0.82548994]] Sub1 Sub (Sub1) sigma_sum_log->Sub1 exponent exponent float32(()) 2.0 Pow Pow (Pow) exponent->Pow prod_operand prod_operand float32(()) 0.5 Mul Mul (Mul) prod_operand->Mul shape_tensor shape_tensor int64((3,)) [-1  1  4] shape_tensor->Reshape axis axis int64((1,)) [2] ReduceSum ReduceSum (ReduceSum) keepdims=0 axis->ReduceSum shape_tensor1 shape_tensor1 int64((2,)) [-1  1] Reshape1 Reshape (Reshape1) shape_tensor1->Reshape1 shape_tensor2 shape_tensor2 int64((1,)) [-1] Reshape2 Reshape (Reshape2) shape_tensor2->Reshape2 reshaped_input reshaped_input reshaped_input->Sub Reshape->reshaped_input subtracted_input subtracted_input subtracted_input->Pow Sub->subtracted_input pow_result pow_result pow_result->Div Pow->pow_result div_result div_result div_result->ReduceSum Div->div_result reduced_sum reduced_sum reduced_sum->Mul ReduceSum->reduced_sum mul_result mul_result mul_result->Sub1 Mul->mul_result part_log_likelihood part_log_likelihood part_log_likelihood->Add Sub1->part_log_likelihood sum_result sum_result ArgMax ArgMax (ArgMax) axis=1 sum_result->ArgMax ReduceLogSumExp ReduceLogSumExp (ReduceLogSumExp) axes=[1] keepdims=0 sum_result->ReduceLogSumExp Sub2 Sub (Sub2) sum_result->Sub2 Add->sum_result argmax_output argmax_output argmax_output->ArrayFeatureExtractor ArgMax->argmax_output reduce_log_sum_exp_result reduce_log_sum_exp_result reduce_log_sum_exp_result->Reshape1 ReduceLogSumExp->reduce_log_sum_exp_result reshaped_log_prob reshaped_log_prob reshaped_log_prob->Sub2 Reshape1->reshaped_log_prob array_feature_extractor_result array_feature_extractor_result Cast Cast (Cast) to=1 array_feature_extractor_result->Cast ArrayFeatureExtractor->array_feature_extractor_result cast2_result cast2_result cast2_result->Reshape2 Cast->cast2_result log_prob log_prob Exp Exp (Exp) log_prob->Exp Sub2->log_prob probabilities probabilities ZipMap ZipMap (ZipMap) classlabels_int64s=[0 1 2] probabilities->ZipMap Exp->probabilities reshaped_result reshaped_result Cast1 Cast (Cast1) to=7 reshaped_result->Cast1 Reshape2->reshaped_result label label Cast2 Cast (Cast2) to=7 label->Cast2 Cast1->label ZipMap->output_probability Cast2->output_label

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