In NumPy for computing the covariance matrix of two given arrays with help of numpy.cov(). In this, we will pass the two arrays and it will return the covariance matrix of two given arrays.
Syntax: numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)
Example 1:
Python
import numpy as np array1 = np.array([0, 1, 1]) array2 = np.array([2, 2, 1]) # Original array1 print(array1) # Original array2 print(array2) # Covariance matrix print("\nCovariance matrix of the said arrays:\n", np.cov(array1, array2)) |
Output:
[0 1 1] [2 2 1] Covariance matrix of the said arrays: [[ 0.33333333 -0.16666667] [-0.16666667 0.33333333]]
Example 2:
Python
import numpy as np array1 = np.array([2, 1, 1, 4]) array2 = np.array([2, 2, 1, 1]) # Original array1 print(array1) # Original array2 print(array2) # Covariance matrix print("\nCovariance matrix of the said arrays:\n", np.cov(array1, array2)) |
Output:
[2 1 1 4] [2 2 1 1] Covariance matrix of the said arrays: [[ 2. -0.33333333] [-0.33333333 0.33333333]]
Example 3:
Python
import numpy as np array1 = np.array([1, 2]) array2 = np.array([1, 2]) # Original array1 print(array1) # Original array2 print(array2) # Covariance matrix print("\nCovariance matrix of the said arrays:\n", np.cov(array1, array2)) |
Output
[1 2] [1 2] Covariance matrix of the said arrays: [[0.5 0.5] [0.5 0.5]]
Example 4:
Python
import numpy as np x = [1.23, 2.12, 3.34, 4.5] y = [2.56, 2.89, 3.76, 3.95] # find out covariance with respect # rows cov_mat = np.stack((x, y), axis = 1) print("shape of matrix x and y:", np.shape(cov_mat)) print("shape of covariance matrix:", np.shape(np.cov(cov_mat))) print(np.cov(cov_mat)) |
Output
shape of matrix x and y: (4, 2) shape of covariance matrix: (4, 4) [[ 0.88445 0.51205 0.2793 -0.36575] [ 0.51205 0.29645 0.1617 -0.21175] [ 0.2793 0.1617 0.0882 -0.1155 ] [-0.36575 -0.21175 -0.1155 0.15125]]
