numpy.correlate() function defines the cross-correlation of two 1-dimensional sequences. This function computes the correlation as generally defined in signal processing texts: c_{av}[k] = sum_n a[n+k] * conj(v[n])
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.
Code #1 :
# Python program explaining # numpy.correlate() function # importing numpy as geek import numpy as geek a = [2, 5, 7] v = [0, 1, 0.5] gfg = geek.correlate(a, v) print (gfg) |
Output :
[8.5]
Code #2 :
# Python program explaining # numpy.correlate() function # importing numpy as geek import numpy as geek a = [2, 5, 7] v = [0, 1, 0.5] gfg = geek.correlate(a, v, "same") print (gfg) |
Output :
[4.5 8.5 7. ]
