pmlayer.torch.layers.PMLinear
==================================

Linear layer with partially monotone constraints for PyTorch.

.. code-block:: python

    class PMLinear(
        num_input_dims,
        num_output_dims=1,
        indices_increasing=[],
        indices_decreasing=[],
        use_bias=True)

This layer applies a linear transformation to the incoming data :math:`y=Ax+b`.
The output of this layer is monotonically increasing with respect to the input features specified by ``indices_increasing``, and
the output of this layer is monotonically decreasing with respect to the input features specified by ``indices_decreasing``.


Parameters
---------------------------------

=================== =============== ===================================================================================
Args                Type            Description
=================== =============== ===================================================================================
num_input_dims      int             The number of input features.
num_output_dims     int             The number of output features.  Default: ``1``.
indices_increasing  list of int     | The list of indices of monotonically increasing features.
                                    | Default: ``[]``.
indices_decreasing  list of int     | The list of indices of monotonically decreasing features.
                                    | Default: ``[]``.
use_bias            bool            | If set to ``False``, the layer will not learn an additive bias.
                                    | Default: ``True``.
=================== =============== ===================================================================================


Tensor Shape
---------------------------------

==================== ==============================================================
I/O                  Shape
==================== ==============================================================
Input                (N, num_input_dims)
Output               (N, num_output_dims)
==================== ==============================================================

N usually represents the batch size.


Example
---------------------------------

The following code transforms input tensor x into output tensor y, and the output (the shape of y) is ``(128,5)``.

.. code-block:: python

    l = PMLinear(10, 5, indices_increasing=[2,3])
    x = torch.randn(128, 10)
    y = l(x)
    print(y.shape)