I have two vectors, let's say
y=[2,6,7,8] and I want to find the euclidean distance, or any other implemented distance (from scipy for example), between each corresponding pair. That will be
dist=[0, 2, 1, 1].
When I try
dist = scipy.spatial.distance.cdist(x,y, metric='sqeuclidean')
dist = [scipy.spatial.distance.cdist(x,y, metric='sqeuclidean') for x,y in zip(x,y)]
ValueError: XA must be a 2-dimensional array.
How am I supposed to calculate dist and why do I have to reshape data for that purpose?Answer1:
cdist does not compute the list of distances between corresponding pairs, but the matrix of distances between all pairs.
np.linalg.norm((np.asarray(x)-np.asarray(y))[:, None], axis=1)
Is how id typically write this for the Euclidian distance between n-dimensional points; but if you are only dealing with 1 dimensional points, the absolute difference, as suggested by elpres would be simpler.