# Plotting Across 2 Views¶

In many cases with multi-view data, especially after use of an embedding algorithm, one is interested in visualizing two views across dimensions. One use is assessing correlation between corresponding dimensions of views. Here, we use this function to display the relationship between two views simulated from transformations of multi-variant gaussians.

[1]:

from mvlearn.datasets import GaussianMixture
from mvlearn.plotting import crossviews_plot
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

[2]:

n_samples = 100
centers = [[0,1], [0,-1]]
covariances = [np.eye(2), np.eye(2)]
GM = GaussianMixture(n_samples, centers, covariances, shuffle=True)
GM = GM.sample_views(transform='poly', n_noise=2)


Below, we see that the first two dimensions are related by a degree 2 polynomial while the latter two dimensions are uncorrelated.

[3]:

crossviews_plot(GM.Xs_, labels=GM.y_, title='View 1 vs. View 2 (Polynomial Transform + noise)', equal_axes=True)