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().
Python3
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:
Python3
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]
