.. _l-GaussianNB-b-cl-default--o17: GaussianNB - b-cl - default - ============================== Fitted on a problem type *b-cl* (see :func:`find_suitable_problem `), method `predict_proba` matches output . :: GaussianNB() +------------------------+----------+ | index | 0 | +========================+==========+ | skl_nop | 1 | +------------------------+----------+ | onx_size | 1762 | +------------------------+----------+ | 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 | 1.13.1 | +------------------------+----------+ | onx_ | 13 | +------------------------+----------+ | onx_ai.onnx.ml | 1 | +------------------------+----------+ | onx_op_Cast | 3 | +------------------------+----------+ | onx_op_ZipMap | 1 | +------------------------+----------+ | onx_op_Reshape | 3 | +------------------------+----------+ | onx_size_optim | 1762 | +------------------------+----------+ | onx_nnodes_optim | 19 | +------------------------+----------+ | onx_ninits_optim | 11 | +------------------------+----------+ | fit_classes_.shape | 2 | +------------------------+----------+ | fit_epsilon_.shape | 1 | +------------------------+----------+ | fit_theta_.shape | (2, 4) | +------------------------+----------+ | fit_var_.shape | (2, 4) | +------------------------+----------+ | fit_class_count_.shape | 2 | +------------------------+----------+ | fit_class_prior_.shape | 2 | +------------------------+----------+ .. gdot:: digraph{ size=7; ranksep=0.25; nodesep=0.05; orientation=portrait; X [shape=box color=red label="X\nfloat((0, 4))" fontsize=10]; output_label [shape=box color=green label="output_label\nint64((0,))" fontsize=10]; output_probability [shape=box color=green label="output_probability\n[{int64, {'kind': 'tensor', 'elem': 'float', 'shape': }}]" fontsize=10]; classes [shape=box label="classes\nint32((2,))\n[0 1]" fontsize=10]; theta [shape=box label="theta\nfloat32((1, 2, 4))\n[[[5.0592794 3.3980393 1.3656081 0.36579275]\n [6.2244563 2.8159916 4.8482027 1.5622892 ]]]" fontsize=10]; sigma [shape=box label="sigma\nfloat32((1, 2, 4))\n[[[0.19725956 0.2030348 0.12801166 0.06933402]\n [0.49902156 0.20583251 0.7204392 0.2522171 ]]]" fontsize=10]; jointi [shape=box label="jointi\nfloat32((1, 2))\n[[-1.1631508 -0.37469345]]" fontsize=10]; sigma_sum_log [shape=box label="sigma_sum_log\nfloat32((1, 2))\n[[ 0.2952789 -1.6851753]]" fontsize=10]; exponent [shape=box label="exponent\nfloat32(())\n2.0" fontsize=10]; prod_operand [shape=box label="prod_operand\nfloat32(())\n0.5" fontsize=10]; shape_tensor [shape=box label="shape_tensor\nint64((3,))\n[-1 1 4]" fontsize=10]; axis [shape=box label="axis\nint64((1,))\n[2]" fontsize=10]; shape_tensor1 [shape=box label="shape_tensor1\nint64((2,))\n[-1 1]" fontsize=10]; shape_tensor2 [shape=box label="shape_tensor2\nint64((1,))\n[-1]" fontsize=10]; reshaped_input [shape=box label="reshaped_input" fontsize=10]; Reshape [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape)" fontsize=10]; X -> Reshape; shape_tensor -> Reshape; Reshape -> reshaped_input; subtracted_input [shape=box label="subtracted_input" fontsize=10]; Sub [shape=box style="filled,rounded" color=orange label="Sub\n(Sub)" fontsize=10]; reshaped_input -> Sub; theta -> Sub; Sub -> subtracted_input; pow_result [shape=box label="pow_result" fontsize=10]; Pow [shape=box style="filled,rounded" color=orange label="Pow\n(Pow)" fontsize=10]; subtracted_input -> Pow; exponent -> Pow; Pow -> pow_result; div_result [shape=box label="div_result" fontsize=10]; Div [shape=box style="filled,rounded" color=orange label="Div\n(Div)" fontsize=10]; pow_result -> Div; sigma -> Div; Div -> div_result; reduced_sum [shape=box label="reduced_sum" fontsize=10]; ReduceSum [shape=box style="filled,rounded" color=orange label="ReduceSum\n(ReduceSum)\nkeepdims=0" fontsize=10]; div_result -> ReduceSum; axis -> ReduceSum; ReduceSum -> reduced_sum; mul_result [shape=box label="mul_result" fontsize=10]; Mul [shape=box style="filled,rounded" color=orange label="Mul\n(Mul)" fontsize=10]; reduced_sum -> Mul; prod_operand -> Mul; Mul -> mul_result; part_log_likelihood [shape=box label="part_log_likelihood" fontsize=10]; Sub1 [shape=box style="filled,rounded" color=orange label="Sub\n(Sub1)" fontsize=10]; sigma_sum_log -> Sub1; mul_result -> Sub1; Sub1 -> part_log_likelihood; sum_result [shape=box label="sum_result" fontsize=10]; Add [shape=box style="filled,rounded" color=orange label="Add\n(Add)" fontsize=10]; jointi -> Add; part_log_likelihood -> Add; Add -> sum_result; reduce_log_sum_exp_result [shape=box label="reduce_log_sum_exp_result" fontsize=10]; ReduceLogSumExp [shape=box style="filled,rounded" color=orange label="ReduceLogSumExp\n(ReduceLogSumExp)\naxes=[1]\nkeepdims=0" fontsize=10]; sum_result -> ReduceLogSumExp; ReduceLogSumExp -> reduce_log_sum_exp_result; argmax_output [shape=box label="argmax_output" fontsize=10]; ArgMax [shape=box style="filled,rounded" color=orange label="ArgMax\n(ArgMax)\naxis=1" fontsize=10]; sum_result -> ArgMax; ArgMax -> argmax_output; reshaped_log_prob [shape=box label="reshaped_log_prob" fontsize=10]; Reshape1 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape1)" fontsize=10]; reduce_log_sum_exp_result -> Reshape1; shape_tensor1 -> Reshape1; Reshape1 -> reshaped_log_prob; array_feature_extractor_result [shape=box label="array_feature_extractor_result" fontsize=10]; ArrayFeatureExtractor [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor)" fontsize=10]; classes -> ArrayFeatureExtractor; argmax_output -> ArrayFeatureExtractor; ArrayFeatureExtractor -> array_feature_extractor_result; log_prob [shape=box label="log_prob" fontsize=10]; Sub2 [shape=box style="filled,rounded" color=orange label="Sub\n(Sub2)" fontsize=10]; sum_result -> Sub2; reshaped_log_prob -> Sub2; Sub2 -> log_prob; cast2_result [shape=box label="cast2_result" fontsize=10]; Cast [shape=box style="filled,rounded" color=orange label="Cast\n(Cast)\nto=1" fontsize=10]; array_feature_extractor_result -> Cast; Cast -> cast2_result; reshaped_result [shape=box label="reshaped_result" fontsize=10]; Reshape2 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape2)" fontsize=10]; cast2_result -> Reshape2; shape_tensor2 -> Reshape2; Reshape2 -> reshaped_result; probabilities [shape=box label="probabilities" fontsize=10]; Exp [shape=box style="filled,rounded" color=orange label="Exp\n(Exp)" fontsize=10]; log_prob -> Exp; Exp -> probabilities; label [shape=box label="label" fontsize=10]; Cast1 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast1)\nto=7" fontsize=10]; reshaped_result -> Cast1; Cast1 -> label; ZipMap [shape=box style="filled,rounded" color=orange label="ZipMap\n(ZipMap)\nclasslabels_int64s=[0 1]" fontsize=10]; probabilities -> ZipMap; ZipMap -> output_probability; Cast2 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast2)\nto=7" fontsize=10]; label -> Cast2; Cast2 -> output_label; }