A simple example of covariance matrix

covariance_matrix.py

import numpy as np

data = np.array([[40, 35, 80],
                 [80, 50, 90],
                 [20, 55, 40],
                 [94, 80, 88],
                 [90, 30, 100]])
print('Input data (N, featuers):', data, data.shape)
N = data.shape[0]
n_dim = data.shape[1]
cov = np.zeros((n_dim, n_dim))

# E(X)
avg = []
for i in range(n_dim):
    avg.append(sum([d[i] for d in data]) / N)
print('avg', avg)

# covariance matrix
for i in range(n_dim):
    for j in range(n_dim):
        var = 0
        for d in data:
            var += (d[i] - avg[i]) * (d[j] - avg[j])
        var /= (N - 1)
        cov[i, j] = var
print('cov', cov)
print('numpy cov', np.cov(data, rowvar=False, bias=False))