More than 2 views

This will compare MCCA, GCCA, TCCA for linear models with more than 2 views

import numpy as np

from cca_zoo.data import generate_covariance_data
from cca_zoo.models import MCCA, GCCA, TCCA, KCCA, KGCCA, KTCCA, PMD

np.random.seed(42)
n = 30
p = 3
q = 3
r = 3
latent_dims = 1
cv = 3

(X, Y, Z), (tx, ty, tz) = generate_covariance_data(
    n, view_features=[p, q, r], latent_dims=latent_dims, correlation=[0.9]
)

Eigendecomposition-Based Methods

mcca = MCCA(latent_dims=latent_dims).fit((X, Y, X)).score((X, Y, Z))

gcca = GCCA(latent_dims=latent_dims).fit((X, Y, X)).score((X, Y, Z))

We can also use kernel versions of these methods

kcca = KCCA(latent_dims=latent_dims).fit((X, Y, X)).score((X, Y, Z))

kgcca = KGCCA(latent_dims=latent_dims).fit((X, Y, X)).score((X, Y, Z))

Higher order correlation methods

Tensor CCA finds higher order correlations so scores are not comparable (but TCCA is equivalent for 2 views)

tcca = TCCA(latent_dims=latent_dims).fit((X, Y, X)).score((X, Y, Z))

ktcca = KTCCA(latent_dims=latent_dims).fit((X, Y, X)).score((X, Y, Z))

Iterative Methods

Most of the iterative methods can also use multiple views e.g.

pmd = PMD(latent_dims=latent_dims, c=1).fit((X, Y, X)).score((X, Y, Z))