pmlayer.torch.layers.HLattice
==================================

Hierarchical lattice layer with partially monotone constraints for PyTorch.

.. code-block:: python

    class HLattice(
        num_input_dims,
        lattice_sizes,
        indices_increasing,
        neural_network=None)


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

=================== =============== ===================================================================================
Args                Type            Description
=================== =============== ===================================================================================
num_input_dims      int             The length of input feature vectors.
lattice_sizes       Tensor (long)   The latice sizes of monotonically increasing features.  Each entry corresponds to a monotonically increasing feature, and each entry must be at least 2.
indices_increasing  list of int     The list of indices of monotonically increasing features.
neural_network      neural network  Default: ``None``.  By setting this parameter, the sub-network internally used in this layer can be replaced.
=================== =============== ===================================================================================


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

==================== ==============================================================
I/O                  Shape
==================== ==============================================================
Input                (N, num_input_dims)
Output               (N, 1)
==================== ==============================================================

N usually corresponds the batch size.

.. note::
    Each value in the input tensor must be between 0 and 1.


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

The following code transforms input tensor x into output tensor y, and the output (the shape of y) is ``(128,1)``.

.. code-block:: python

    sizes = torch.tensor([4,4], dtype=torch.long)
    l = HLattice(10, sizes, [2,3])
    x = torch.randn(128, 10)
    y = l(x)
    print(y.shape)