import torch
import torch.nn as nn
from .helper import _check_torch_tensor, _check_dimension
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class RecLayer(nn.Module):
"""Reconstruction Matrix Layer
A matrix X (n times m) is projected to
a smaller matrix XV,
and then reconstructed such as XVV^T,
where the size of V is n times k (k << m).
Attributes:
x (torch.Tensor): A matrix X (n times m)
n_components (int): The number of lower dimensions (k)
Example:
>>> import torchdecomp as td
>>> import torch
>>> torch.manual_seed(123456)
>>> x = torch.randn(10, 6) # Test datasets
>>> rec_layer = td.RecLayer(x, 3) # Instantiation
"""
def __init__(self, x, n_components):
"""Initialization function
"""
super(RecLayer, self).__init__()
_check_torch_tensor(x)
size = x.size(1)
_check_dimension(size, n_components)
V = torch.nn.init.orthogonal_(torch.randn(size, n_components), gain=1)
self.V = nn.Parameter(V)
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def forward(self, x):
"""Forward propagation function
"""
return torch.mm(torch.mm(x, self.V), self.V.t())