"""
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))