import time
import numpy as np
from scipy.spatial.distance import cdist
from metrics.cbdm import cityblock_distance_matrix
nsamples, nfeat = (5000, 50)
x = 10. * np.random.random([nsamples, nfeat])
start = time.time()
# euclidean_distance_matrix(x.T, x.T, dist_matrix)
cbdm_f90 = cityblock_distance_matrix(x.T, x.T)
print("f90: %.2f seconds" % (time.time() - start))
start = time.time()
cbdm_np = cdist(x, x, 'cityblock')
print("scipy: %.2f seconds" % (time.time() - start))
print(np.abs(cbdm_f90 - cbdm_np).max())
def cityblock_numpy(x, y):
"""Cityblock distance matrix.
Inputs:
x: (N,) numpy array
y: (N,) numpy array
Ouput:
(N, N) Cityblock distance matrix:
r_ij = abs(x_ij - y_ij)
"""
return cdist(x, y, 'cityblock')
nsamples, nfeat = (5000, 500)
x = 10. * np.random.random([nsamples, nfeat])
cbdm_f90 = np.empty([nsamples, nsamples], order='F')
start = time.time()
# euclidean_distance_matrix(x.T, x.T, dist_matrix)
cityblock_distance_matrix(x.T, x.T, nsamples, nfeat, cbdm_f90)
print("f90: %.2f seconds" % (time.time() - start))
start = time.time()
cbdm_np = cityblock_numpy(x, x)
print("numpy: %.2f seconds" % (time.time() - start))
print(np.abs(cbdm_f90 - cbdm_np).max())