distance-measures.py

import scipy.spatial.distance as dist
import numpy as np

# Prepare 2 vectors (data points) of 10 dimensions
A = np.random.uniform(0, 10, 10)
B = np.random.uniform(0, 10, 10)

print '\n2 10-dimensional vectors'
print '------------------------'
print A
print B

# Perform distance measurements 
print '\nDistance measurements with 10-dimensional vectors'
print '-------------------------------------------------'
print '\nEuclidean distance is', dist.euclidean(A, B)
print 'Manhattan distance is', dist.cityblock(A, B)
print 'Chebyshev distance is', dist.chebyshev(A, B)
print 'Canberra distance is', dist.canberra(A, B)
print 'Cosine distance is', dist.cosine(A, B)

# Prepare 2 vectors of 100 dimensions
AA = np.random.uniform(0, 10, 100)
BB = np.random.uniform(0, 10, 100)

# Perform distance measurements
print '\nDistance measurements with 100-dimensional vectors'
print '--------------------------------------------------'
print '\nEuclidean distance is', dist.euclidean(AA, BB)
print 'Manhattan distance is', dist.cityblock(AA, BB)
print 'Chebyshev distance is', dist.chebyshev(AA, BB)
print 'Canberra distance is', dist.canberra(AA, BB)
print 'Cosine distance is', dist.cosine(AA, BB)