.. _op_ai_onnx_ml_LinearClassifier:

LinearClassifier
================

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

Linear classifier

**Inputs**

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

**Outputs**

- **Y** (*T2*): Classification outputs (one class per example).
- **Z** (*tensor(float)*): Classification scores ([N,E] - one score for each class and example

**Type Constraints**

- **T1**: The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]
  Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).
- **T2**: The output will be a tensor of strings or integers.
  Allowed types: tensor(int64), tensor(string).

Examples
--------

**test_cc_linearclassifier_int64_binary**

.. code-block:: text

    Node:
      ai.onnx.ml.LinearClassifier(x) -> (y, z)
      Attributes:
        coefficients = [1.0, -1.0]
        intercepts = [0.0]
        multi_class = 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
        [1, 0]
      z: shape=(2, 2), dtype=float32
        [[-1.,  1.],
         [ 3., -3.]]