In the Numpy program, we can compute cross-correlation of two given arrays with the help of correlate(). In this first parameter and second parameter pass the given arrays it will return the cross-correlation of two given arrays.
Syntax : numpy.correlate(a, v, mode = ‘valid’)
Parameters :
a, v : [array_like] Input sequences.
mode : [{‘valid’, ‘same’, ‘full’}, optional] Refer to the convolve docstring. Default is ‘valid’.Return : [ndarray] Discrete cross-correlation of a and v.
Example 1:
In this example, we will create two NumPy arrays and the task is to compute cross-correlation using correlate().
import numpy as np
array1 = np.array([ 0 , 1 , 2 ])
array2 = np.array([ 3 , 4 , 5 ])
# Original array1 print (array1)
# Original array2 print (array2)
# ross-correlation of the arrays print ( "\nCross-correlation:\n" ,
np.correlate(array1, array2))
|
Output:
[0 1 2] [3 4 5] Cross-correlation: [14]
Example 2:
import numpy as np
array1 = np.array([ 1 , 2 ])
array2 = np.array([ 1 , 2 ])
# Original array1 print (array1)
# Original array2 print (array2)
# Cross-correlation of the arrays print ( "\nCross-correlation:\n" ,
np.correlate(array1, array2))
|
Output:
[1 2] [1 2] Cross-correlation: [5]