Compute the covariance matrix of two given NumPy arrays
Last Updated :
29 Aug, 2020
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 ])
print (array1)
print (array2)
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 ])
print (array1)
print (array2)
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 ])
print (array1)
print (array2)
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 ]
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]]
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