Correlation matrix


Let’s create two datasets, \(\mu_1\) and \(\mu_2\) such that

\[ \mu_i = \{x_1,\dots,x_n\} \sim \{\mathcal{U}_1(-1,1),\dots,\mathcal{U}_n(-1,1)\} \]

We will use \(N=100\) observations for a vector sized \(n=10\).

import numpy as np
import pandas as pd
import scipy.stats as stats
from scipy.spatial.distance import squareform

n = 10
N = 100

mu_1 = np.random.normal(loc=0, scale=1, size=(N, n))
mu_2 = np.random.normal(loc=0, scale=1, size=(N, n))

We now add some noise, \(\epsilon=0.6\), to \(\mu_2\) such that

\[ \mu_2 = \epsilon \mu_2 + (1-\epsilon)*\mu_1 \]

epsilon = 0.6
mu_2 = epsilon*mu_2 + (1-epsilon)*mu_1

We use Pandas to calculate the correlation matrix:

C1 = pd.DataFrame(mu_1).corr()
C2 = pd.DataFrame(mu_2).corr()

And we plot the correlation matrices.

Spearman correlation

Calculate similarity using Spearman correlation between the top triangle of the covariance matrices \(C_1\) and \(C_2\).

indices = np.triu_indices(C1.shape[0], k=1)