Distance Functions in Machine Learning

data_hamming.py

# defining two strings
string_1 = 'euclidean'
string_2 = 'manhattan'

data_hamming_2.py

# strings of different shapes
new_string_1 = 'data'
new_string_2 = 'science'
len(new_string_1), len(new_string_2)

euclidean_distance.py

# computing the euclidean distance
euclidean_distance = distance.euclidean(point_1, point_2)
print('Euclidean Distance b/w', point_1, 'and', point_2, 'is: ', euclidean_distance)

hamming_different_shape.py

# computing the hamming distance
hamming_distance = distance.hamming(list(new_string_1), list(new_string_2))

hamming_distance.py

# computing the hamming distance
hamming_distance = distance.hamming(list(string_1), list(string_2))*len(string_1)
print('Hamming Distance b/w', string_1, 'and', string_2, 'is: ', hamming_distance)

library_and_dataset.py

# importing the library
from scipy.spatial import distance

# defining the points
point_1 = (1, 2, 3)
point_2 = (4, 5, 6)
point_1, point_2

manhattan_distance.py

# computing the manhattan distance
manhattan_distance = distance.cityblock(point_1, point_2)
print('Manhattan Distance b/w', point_1, 'and', point_2, 'is: ', manhattan_distance)

minkowski_distance.py

# computing the minkowski distance
minkowski_distance = distance.minkowski(point_1, point_2, p=3)
print('Minkowski Distance b/w', point_1, 'and', point_2, 'is: ', minkowski_distance)

minkowski_distance_vs_euclidean_distance.py

# minkowski and euclidean distance
minkowski_distance_order_2 = distance.minkowski(point_1, point_2, p=2)
print('Minkowski Distance of order 2:',minkowski_distance_order_2, '\nEuclidean Distance: ',euclidean_distance)

minkowski_distance_vs_manhattan_distance.py

# minkowski and manhattan distance
minkowski_distance_order_1 = distance.minkowski(point_1, point_2, p=1)
print('Minkowski Distance of order 1:',minkowski_distance_order_1, '\nManhattan Distance: ',manhattan_distance)

Thanksss!