Generating Multiview Data from Gaussian Mixtures

In this example we show how to simulate multiview data from Gaussian mixtures and plot the views against each other using a crossviews plot.

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# License: MIT

import numpy as np
from mvlearn.datasets import make_gaussian_mixture
from mvlearn.plotting import crossviews_plot

# Latent variables are sampled from two multivariate Gaussians with equal
# prior probability. Then a polynomial transformation is applied and noise
# is added independently to both the transformed and untransformed latents.

n_samples = 100
centers = [[0, 1], [0, -1]]
covariances = [np.eye(2), np.eye(2)]
Xs, y, latent = make_gaussian_mixture(
    n_samples, centers, covariances, random_state=42, noise_dims=2,
    shuffle=True, shuffle_random_state=42, transform='poly',
    return_latents=True)


# The latent data is plotted against itself to reveal the underlying
# distribtution.


crossviews_plot([latent, latent], labels=y, title='Latent Variable',
                equal_axes=True)

# The noisy latent variable (view 1) is plotted against the transformed latent
# variable (view 2), an example of a dataset with two views.


crossviews_plot(Xs, labels=y,
                title='View 1 vs. View 2 (Polynomial Transform + noise)',
                equal_axes=True)