python-benchmarks/pairwise/pairwise_python.py at master · numfocus/python-benchmarks · GitHub

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# Authors: Jake Vanderplas, Alex Rubinsteyn, Olivier Grisel

# License: MIT

import numpy as np

def pairwise_python_nested_for_loops(data):

n_samples, n_features = data.shape

distances = np.empty((n_samples, n_samples), dtype=data.dtype)

#"omp parallel for private(j, d, k, tmp)"

for i in range(n_samples):

for j in range(n_samples):

d = 0.0

for k in range(n_features):

tmp = data[i, k] - data[j, k]

d += tmp * tmp

distances[i, j] = np.sqrt(d)

return distances

def pairwise_python_inner_numpy(data):

n_samples = data.shape[0]

result = np.empty((n_samples, n_samples), dtype=data.dtype)

for i in xrange(n_samples):

for j in xrange(n_samples):

result[i, j] = np.sqrt(np.sum((data[i, :] - data[j, :]) ** 2))

return result

def pairwise_python_broadcast_numpy(data):

return np.sqrt(((data[:, None, :] - data) ** 2).sum(axis=2))

def pairwise_python_numpy_dot(data):

X_norm_2 = (data ** 2).sum(axis=1)

dists = np.sqrt(2 * X_norm_2 - np.dot(data, data.T))

return dists

benchmarks = (

pairwise_python_nested_for_loops,

pairwise_python_inner_numpy,

pairwise_python_broadcast_numpy,

pairwise_python_numpy_dot,

)