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Calculating the Minkowski distance using SciPy
The Minkowski distance, a generalized form of Euclidean and Manhattan distance, is the distance between two points. It is mostly used for distance similarity of vectors. Below is the generalized formula to calculate Minkowski distance in n-dimensional space −
si and ri are data points.
n denotes the n-space.
p represents the order of the norm
SciPy provides us with a function named minkowski that returns the Minkowski Distance between two points. Let’s see how we can calculate the Minkowski distance between two points using SciPy library −
# Importing the SciPy library from scipy.spatial import distance # Defining the points A = (1, 2, 3, 4, 5, 6) B = (7, 8, 9, 10, 11, 12) A, B # Computing the Minkowski distance minkowski_distance = distance.minkowski(A, B, p=3) print('Minkowski Distance b/w', A, 'and', B, 'is: ', minkowski_distance)
((1, 2, 3, 4, 5, 6), (7, 8, 9, 10, 11, 12)) Minkowski Distance b/w (1, 2, 3, 4, 5, 6) and (7, 8, 9, 10, 11, 12) is: 10 .902723556992836
We have calculated the Minkowski distance with order(p) = 3. But when the order is 2, it will represent the Euclidean distance whereas when the order is 1, it will represent the Manhattan distance. Let’s understand it with the below given example −
# Importing the SciPy library from scipy.spatial import distance # Defining the points A = (1, 2, 3, 4, 5, 6) B = (7, 8, 9, 10, 11, 12) A, B # minkowski and manhattan distance minkowski_distance_with_order1 = distance.minkowski(A, B, p=1) print('Minkowski Distance of order(P)1:',minkowski_distance_with_order1,'\nManhattan Distance: ',manhattan_distance) # minkowski and euclidean distance minkowski_distance_with_order2 = distance.minkowski(A, B, p=2) print('Minkowski Distance of order(P)2:',minkowski_distance_order_2, '\nEuclidean Distance: ',euclidean_distance)
((1, 2, 3, 4, 5, 6), (7, 8, 9, 10, 11, 12)) Minkowski Distance of order(P)1: 36.0 Manhattan Distance: 36 Minkowski Distance of order(P)2: 14.696938456699069 Euclidean Distance: 14.696938456699069
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