.. _op_ai_onnx_ml_Scaler:

Scaler
======

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

Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.

**Inputs**

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

**Outputs**

- **Y** (*tensor(float)*): Scaled output data.

**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_scaler_float**

.. code-block:: text

    Node:
      ai.onnx.ml.Scaler(x) -> (y)
      Attributes:
        offset = [0.5, 1.0, 1.5]
        scale = [2.0, 0.5, 1.0]

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=float32
        [[0., 1., 2.],
         [3., 4., 5.]]

    Outputs:
      y: shape=(2, 3), dtype=float32
        [[-1. ,  0. ,  0.5],
         [ 5. ,  1.5,  3.5]]

**test_cc_scaler_int64**

.. code-block:: text

    Node:
      ai.onnx.ml.Scaler(x) -> (y)
      Attributes:
        offset = [1.0]
        scale = [0.5]

.. code-block:: text

    Inputs:
      x: shape=(5,), dtype=int64
        [0, 1, 2, 3, 4]

    Outputs:
      y: shape=(5,), dtype=float32
        [-0.5,  0. ,  0.5,  1. ,  1.5]