.. _op_ai_onnx_ml_LinearRegressor:

LinearRegressor
===============

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

Generalized linear regression evaluation. 
If targets is set to 1 (default) then univariate regression is performed. 
If targets is set to M then M sets of coefficients must be passed in as a sequence
and M results will be output for each input n in N. 
The coefficients array is of length n, and the coefficients for each target are contiguous.
Intercepts are optional but if provided must match the number of targets.

**Inputs**

- **X** (*T*): Data to be regressed.

**Outputs**

- **Y** (*tensor(float)*): Regression outputs (one per target, per example).

**Type Constraints**

- **T**: The input must be a tensor of a numeric type.
  Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).

Examples
--------

**test_cc_linearregressor_single_target**

.. code-block:: text

    Node:
      ai.onnx.ml.LinearRegressor(x) -> (y)
      Attributes:
        coefficients = [0.5, -1.0]
        intercepts = [0.25]
        targets = 1
        post_transform = "NONE"

.. code-block:: text

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

    Outputs:
      y: shape=(2, 1), dtype=float32
        [[ 0.25],
         [-2.75]]