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December 4, 2018 15:49
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Numpy benchmark over LA ops.
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from __future__ import print_function | |
import numpy as np | |
from time import time | |
# Let's take the randomness out of random numbers (for reproducibility) | |
np.random.seed(0) | |
# THIS SCRIPT ISN'T MINE AT ALL: I JUST FOUND IT ON REDDIT. | |
# Useful to benchmark. | |
size = 4096 | |
A, B = np.random.random((size, size)), np.random.random((size, size)) | |
C, D = np.random.random((size * 128,)), np.random.random((size * 128,)) | |
E = np.random.random((int(size / 2), int(size / 4))) | |
F = np.random.random((int(size / 2), int(size / 2))) | |
F = np.dot(F, F.T) | |
G = np.random.random((int(size / 2), int(size / 2))) | |
# Matrix multiplication | |
N = 20 | |
t = time() | |
for i in range(N): | |
np.dot(A, B) | |
delta = time() - t | |
print('Dotted two %dx%d matrices in %0.2f s.' % (size, size, delta / N)) | |
del A, B | |
# Vector multiplication | |
N = 5000 | |
t = time() | |
for i in range(N): | |
np.dot(C, D) | |
delta = time() - t | |
print('Dotted two vectors of length %d in %0.2f ms.' % (size * 128, 1e3 * delta / N)) | |
del C, D | |
# Singular Value Decomposition (SVD) | |
N = 3 | |
t = time() | |
for i in range(N): | |
np.linalg.svd(E, full_matrices = False) | |
delta = time() - t | |
print("SVD of a %dx%d matrix in %0.2f s." % (size / 2, size / 4, delta / N)) | |
del E | |
# Cholesky Decomposition | |
N = 3 | |
t = time() | |
for i in range(N): | |
np.linalg.cholesky(F) | |
delta = time() - t | |
print("Cholesky decomposition of a %dx%d matrix in %0.2f s." % (size / 2, size / 2, delta / N)) | |
# Eigendecomposition | |
t = time() | |
for i in range(N): | |
np.linalg.eig(G) | |
delta = time() - t | |
print("Eigendecomposition of a %dx%d matrix in %0.2f s." % (size / 2, size / 2, delta / N)) |
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