I am trying to understand the mode "same" in the function np.correlate(). I saw the definition in some website, but I can't understand how it does the computation to get the final array.
How from this: np.correlate([2, 1], [1, 1], 'same') we get this: array([2, 3])
Can someone make a computation example?
You can refer to the numpy manual，https://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html，
Here is the example of numpy.convolve from the manual (the "mode" in numpy.correlate is the same as numpy.convolve)：
>>>np.convolve([1, 2, 3], [0, 1, 0.5])
array([ 0. , 1. , 2.5, 4. , 1.5])
#Only return the middle values of the convolution. Contains boundary effects, where zeros are taken into account:
>>>np.convolve([1,2,3],[0,1,0.5], 'same')
array([ 1. , 2.5, 4. ])
#The two arrays are of the same length, so there is only one position where they completely overlap:
>>>np.convolve([1,2,3],[0,1,0.5], 'valid')
array([ 2.5])
