.. _l-QuadraticDiscriminantAnalysis-b-cl-default--o17:

QuadraticDiscriminantAnalysis - b-cl - default - 
=================================================

Fitted on a problem type *b-cl*
(see :func:`find_suitable_problem
<mlprodict.onnxrt.validate.validate_problems.find_suitable_problem>`),
method `predict_proba` matches output .


::

        QuadraticDiscriminantAnalysis()

+----------------------+----------+
| index                | 0        |
+======================+==========+
| skl_nop              | 1        |
+----------------------+----------+
| onx_size             | 2450     |
+----------------------+----------+
| onx_nnodes           | 33       |
+----------------------+----------+
| onx_ninits           | 12       |
+----------------------+----------+
| 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_                 | 17       |
+----------------------+----------+
| onx_ai.onnx.ml       | 1        |
+----------------------+----------+
| onx_op_Cast          | 1        |
+----------------------+----------+
| onx_op_ZipMap        | 1        |
+----------------------+----------+
| onx_op_Reshape       | 1        |
+----------------------+----------+
| onx_size_optim       | 2450     |
+----------------------+----------+
| onx_nnodes_optim     | 33       |
+----------------------+----------+
| onx_ninits_optim     | 12       |
+----------------------+----------+
| fit_classes_.shape   | 2        |
+----------------------+----------+
| fit_priors_.shape    | 2        |
+----------------------+----------+
| fit_means_.shape     | (2, 4)   |
+----------------------+----------+


.. 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((1, 2))" fontsize=10];
      output_probability [shape=box color=green label="output_probability\n[{int64, {'kind': 'tensor', 'elem': 'float', 'shape': }}]" fontsize=10];

      const_n05 [shape=box label="const_n05\nfloat32(())\n-0.5" fontsize=10];
      const_p2 [shape=box label="const_p2\nfloat32(())\n2.0" fontsize=10];
      rotations [shape=box label="rotations\nfloat32((4, 4))\n[[ 0.68545145 -0.5605604   0.45274746  0.10463297]..." fontsize=10];
      scalings [shape=box label="scalings\nfloat32((4,))\n[0.27713665 0.14716321 0.12635745 0.06456038]" fontsize=10];
      means [shape=box label="means\nfloat32((4,))\n[5.0592794  3.3980393  1.3656081  0.36579275]" fontsize=10];
      ReduceSum_reducesum [shape=box label="ReduceSum_reducesum\nint64((1,))\n[1]" fontsize=10];
      rotations1 [shape=box label="rotations1\nfloat32((4, 4))\n[[ 0.56796074 -0.62302315  0.2226334  -0.48958874]..." fontsize=10];
      scalings1 [shape=box label="scalings1\nfloat32((4,))\n[1.2200791  0.20002407 0.1715445  0.10793532]" fontsize=10];
      means1 [shape=box label="means1\nfloat32((4,))\n[6.2244563 2.8159916 4.8482027 1.5622892]" fontsize=10];
      shape_tensor [shape=box label="shape_tensor\nint64((2,))\n[ 2 -1]" fontsize=10];
      priors [shape=box label="priors\nfloat32((2,))\n[0.3125 0.6875]" fontsize=10];
      classes [shape=box label="classes\nint64((2,))\n[0 1]" fontsize=10];

      log1 [shape=box label="log1" fontsize=10];
      Log1 [shape=box style="filled,rounded" color=orange label="Log\n(Log1)" fontsize=10];
      scalings1 -> Log1;
      Log1 -> log1;

      log_p [shape=box label="log_p" fontsize=10];
      Log2 [shape=box style="filled,rounded" color=orange label="Log\n(Log2)" fontsize=10];
      priors -> Log2;
      Log2 -> log_p;

      Xm1 [shape=box label="Xm1" fontsize=10];
      Sub1 [shape=box style="filled,rounded" color=orange label="Sub\n(Sub1)" fontsize=10];
      X -> Sub1;
      means1 -> Sub1;
      Sub1 -> Xm1;

      log [shape=box label="log" fontsize=10];
      Log [shape=box style="filled,rounded" color=orange label="Log\n(Log)" fontsize=10];
      scalings -> Log;
      Log -> log;

      Xm [shape=box label="Xm" fontsize=10];
      Sub [shape=box style="filled,rounded" color=orange label="Sub\n(Sub)" fontsize=10];
      X -> Sub;
      means -> Sub;
      Sub -> Xm;

      s_pow_n05 [shape=box label="s_pow_n05" fontsize=10];
      Pow [shape=box style="filled,rounded" color=orange label="Pow\n(Pow)" fontsize=10];
      scalings -> Pow;
      const_n05 -> Pow;
      Pow -> s_pow_n05;

      s_pow_n051 [shape=box label="s_pow_n051" fontsize=10];
      Pow2 [shape=box style="filled,rounded" color=orange label="Pow\n(Pow2)" fontsize=10];
      scalings1 -> Pow2;
      const_n05 -> Pow2;
      Pow2 -> s_pow_n051;

      sum_log1 [shape=box label="sum_log1" fontsize=10];
      ReduceSum3 [shape=box style="filled,rounded" color=orange label="ReduceSum\n(ReduceSum3)\nkeepdims=1" fontsize=10];
      log1 -> ReduceSum3;
      ReduceSum3 -> sum_log1;

      mul1 [shape=box label="mul1" fontsize=10];
      Mul1 [shape=box style="filled,rounded" color=orange label="Mul\n(Mul1)" fontsize=10];
      rotations1 -> Mul1;
      s_pow_n051 -> Mul1;
      Mul1 -> mul1;

      sum_log [shape=box label="sum_log" fontsize=10];
      ReduceSum1 [shape=box style="filled,rounded" color=orange label="ReduceSum\n(ReduceSum1)\nkeepdims=1" fontsize=10];
      log -> ReduceSum1;
      ReduceSum1 -> sum_log;

      mul [shape=box label="mul" fontsize=10];
      Mul [shape=box style="filled,rounded" color=orange label="Mul\n(Mul)" fontsize=10];
      rotations -> Mul;
      s_pow_n05 -> Mul;
      Mul -> mul;

      concat_logsum [shape=box label="concat_logsum" fontsize=10];
      Concat1 [shape=box style="filled,rounded" color=orange label="Concat\n(Concat1)\naxis=0" fontsize=10];
      sum_log -> Concat1;
      sum_log1 -> Concat1;
      Concat1 -> concat_logsum;

      matmul [shape=box label="matmul" fontsize=10];
      MatMul [shape=box style="filled,rounded" color=orange label="MatMul\n(MatMul)" fontsize=10];
      Xm -> MatMul;
      mul -> MatMul;
      MatMul -> matmul;

      matmul1 [shape=box label="matmul1" fontsize=10];
      MatMul1 [shape=box style="filled,rounded" color=orange label="MatMul\n(MatMul1)" fontsize=10];
      Xm1 -> MatMul1;
      mul1 -> MatMul1;
      MatMul1 -> matmul1;

      pow_x21 [shape=box label="pow_x21" fontsize=10];
      Pow3 [shape=box style="filled,rounded" color=orange label="Pow\n(Pow3)" fontsize=10];
      matmul1 -> Pow3;
      const_p2 -> Pow3;
      Pow3 -> pow_x21;

      pow_x2 [shape=box label="pow_x2" fontsize=10];
      Pow1 [shape=box style="filled,rounded" color=orange label="Pow\n(Pow1)" fontsize=10];
      matmul -> Pow1;
      const_p2 -> Pow1;
      Pow1 -> pow_x2;

      sum1 [shape=box label="sum1" fontsize=10];
      ReduceSum2 [shape=box style="filled,rounded" color=orange label="ReduceSum\n(ReduceSum2)\nkeepdims=1" fontsize=10];
      pow_x21 -> ReduceSum2;
      ReduceSum_reducesum -> ReduceSum2;
      ReduceSum2 -> sum1;

      sum [shape=box label="sum" fontsize=10];
      ReduceSum [shape=box style="filled,rounded" color=orange label="ReduceSum\n(ReduceSum)\nkeepdims=1" fontsize=10];
      pow_x2 -> ReduceSum;
      ReduceSum_reducesum -> ReduceSum;
      ReduceSum -> sum;

      concat_norm [shape=box label="concat_norm" fontsize=10];
      Concat [shape=box style="filled,rounded" color=orange label="Concat\n(Concat)\naxis=0" fontsize=10];
      sum -> Concat;
      sum1 -> Concat;
      Concat -> concat_norm;

      reshape_concat_norm [shape=box label="reshape_concat_norm" fontsize=10];
      Reshape [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape)" fontsize=10];
      concat_norm -> Reshape;
      shape_tensor -> Reshape;
      Reshape -> reshape_concat_norm;

      transpose_norm [shape=box label="transpose_norm" fontsize=10];
      Transpose [shape=box style="filled,rounded" color=orange label="Transpose\n(Transpose)\nperm=[1 0]" fontsize=10];
      reshape_concat_norm -> Transpose;
      Transpose -> transpose_norm;

      add_norm2_u [shape=box label="add_norm2_u" fontsize=10];
      Add [shape=box style="filled,rounded" color=orange label="Add\n(Add)" fontsize=10];
      transpose_norm -> Add;
      concat_logsum -> Add;
      Add -> add_norm2_u;

      norm2_u_n05 [shape=box label="norm2_u_n05" fontsize=10];
      Mul2 [shape=box style="filled,rounded" color=orange label="Mul\n(Mul2)" fontsize=10];
      const_n05 -> Mul2;
      add_norm2_u -> Mul2;
      Mul2 -> norm2_u_n05;

      decision_fun [shape=box label="decision_fun" fontsize=10];
      Add1 [shape=box style="filled,rounded" color=orange label="Add\n(Add1)" fontsize=10];
      norm2_u_n05 -> Add1;
      log_p -> Add1;
      Add1 -> decision_fun;

      argmax_out [shape=box label="argmax_out" fontsize=10];
      ArgMax [shape=box style="filled,rounded" color=orange label="ArgMax\n(ArgMax)\naxis=1\nkeepdims=1\nselect_last_index=0" fontsize=10];
      decision_fun -> ArgMax;
      ArgMax -> argmax_out;

      df_max [shape=box label="df_max" fontsize=10];
      N27 [shape=box style="filled,rounded" color=orange label="ReduceMax\n(N27)\naxes=[1]" fontsize=10];
      decision_fun -> N27;
      N27 -> df_max;

      df_sub_max [shape=box label="df_sub_max" fontsize=10];
      Sub2 [shape=box style="filled,rounded" color=orange label="Sub\n(Sub2)" fontsize=10];
      decision_fun -> Sub2;
      df_max -> Sub2;
      Sub2 -> df_sub_max;

      label [shape=box label="label" fontsize=10];
      N26 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(N26)" fontsize=10];
      classes -> N26;
      argmax_out -> N26;
      N26 -> label;

      likelihood [shape=box label="likelihood" fontsize=10];
      Exp [shape=box style="filled,rounded" color=orange label="Exp\n(Exp)" fontsize=10];
      df_sub_max -> Exp;
      Exp -> likelihood;

      Cast [shape=box style="filled,rounded" color=orange label="Cast\n(Cast)\nto=7" fontsize=10];
      label -> Cast;
      Cast -> output_label;

      likelihood_sum [shape=box label="likelihood_sum" fontsize=10];
      ReduceSum4 [shape=box style="filled,rounded" color=orange label="ReduceSum\n(ReduceSum4)\nkeepdims=1" fontsize=10];
      likelihood -> ReduceSum4;
      ReduceSum_reducesum -> ReduceSum4;
      ReduceSum4 -> likelihood_sum;

      probabilities [shape=box label="probabilities" fontsize=10];
      Div [shape=box style="filled,rounded" color=orange label="Div\n(Div)" fontsize=10];
      likelihood -> Div;
      likelihood_sum -> Div;
      Div -> probabilities;

      ZipMap [shape=box style="filled,rounded" color=orange label="ZipMap\n(ZipMap)\nclasslabels_int64s=[0 1]" fontsize=10];
      probabilities -> ZipMap;
      ZipMap -> output_probability;
    }