.. _op_ai_onnx_ml_SVMClassifier:

SVMClassifier
=============

- **Domain**: ``ai.onnx.ml``
- **Since version**: 1

Support Vector Machine classifier

**Inputs**

- **X** (*T1*): Data to be classified.

**Outputs**

- **Y** (*T2*): Classification outputs (one class per example).
- **Z** (*tensor(float)*): Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.

**Type Constraints**

- **T1**: The input must be a tensor of a numeric type, either [C] or [N,C].
  Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).
- **T2**: The output type will be a tensor of strings or integers, depending on which of the classlabels\* attributes is used. Its size will match the batch size of the input.
  Allowed types: tensor(int64), tensor(string).

Examples
--------

**test_cc_svmclassifier_int64_binary**

.. code-block:: text

    Node:
      ai.onnx.ml.SVMClassifier(x) -> (y, z)
      Attributes:
        kernel_type = "LINEAR"
        kernel_params = [0.0, 0.0, 0.0]
        vectors_per_class = [1, 1]
        support_vectors = [1.0, 0.0, 0.0, 1.0]
        coefficients = [1.0, -1.0]
        rho = [0.0]
        post_transform = "NONE"
        classlabels_ints = [0, 1]

.. code-block:: text

    Inputs:
      x: shape=(2, 2), dtype=float32
        [[2., 1.],
         [0., 3.]]

    Outputs:
      y: shape=(2,), dtype=int64
        [0, 1]
      z: shape=(2, 2), dtype=float32
        [[-1.,  1.],
         [ 3., -3.]]