from typing import Iterable
import numpy as np
from cca_zoo.deepmodels import objectives
from cca_zoo.deepmodels.dcca import DCCA
from cca_zoo.models import TCCA
[docs]class DTCCA(DCCA):
"""
A class used to fit a DTCCA model.
Is just a thin wrapper round DCCA with the DTCCA objective and a TCCA post-processing
:Citation:
Wong, Hok Shing, et al. "Deep Tensor CCA for Multi-view Learning." IEEE Transactions on Big Data (2021).
"""
def __init__(
self, latent_dims: int, encoders=None, r: float = 0, eps: float = 1e-5
):
"""
:param latent_dims: # latent dimensions
:param encoders: list of encoder networks
:param r: regularisation parameter of tracenorm CCA like ridge CCA. Needs to be VERY SMALL. If you get errors make this smaller
:param eps: epsilon used throughout. Needs to be VERY SMALL. If you get errors make this smaller
"""
super().__init__(
latent_dims=latent_dims,
objective=objectives.TCCA,
encoders=encoders,
r=r,
eps=eps,
)
[docs] def post_transform(self, z_list, train=False) -> Iterable[np.ndarray]:
if train:
self.cca = TCCA(latent_dims=self.latent_dims)
z_list = self.cca.fit_transform(z_list)
else:
z_list = self.cca.transform(z_list)
return z_list