import numpy as np
import scipy
import sklearn.metrics.pairwise

# create 64-bit vectors a and b that are very similar to each other
a_64 = np.array([61.221637725830078125, 71.60662841796875,    -65.7512664794921875],  dtype=np.float64)
b_64 = np.array([61.221637725830078125, 71.60894012451171875, -65.72847747802734375], dtype=np.float64)

# create 32-bit versions of a and b
a_32 = a_64.astype(np.float32)
b_32 = b_64.astype(np.float32)

# compute the distance from a to b using numpy, for both 64-bit and 32-bit
dist_64_np = np.array([np.linalg.norm(a_64 - b_64)], dtype=np.float64)
dist_32_np = np.array([np.linalg.norm(a_32 - b_32)], dtype=np.float32)

# compute the distance from a to b using sklearn, for both 64-bit and 32-bit
dist_64_sklearn = sklearn.metrics.pairwise.pairwise_distances([a_64], [b_64])
dist_32_sklearn = sklearn.metrics.pairwise.pairwise_distances([a_32], [b_32])

# note that the 64-bit sklearn results agree exactly with numpy, but the 32-bit results disagree
np.set_printoptions(precision=200)

print(dist_64_np)
print(dist_32_np)
print(dist_64_sklearn)
print(dist_32_sklearn)