X *= 4
X **= 2
return X
def alloc_norm_mul_opt(A, B):
"""Take two matrices, A and B,
- normalize their rows by dividing with their sums
- do a polynomial transformation on each
- multiply them and return the result.
"""
A = A.copy()
B = B.copy()
A /= A.sum(axis=1)[:, np.newaxis]
B /= B.sum(axis=1)[: ,np.newaxis]
A = _poly_opt(A)
B = _poly_opt(B)
return A.dot(B)
if __name__ == '__main__':
from alloc_norm_mul import alloc_norm_mul
A = np.random.random((500, 20))
B = np.random.random((20, 300))
np.testing.assert_allclose(alloc_norm_mul(A, B),
alloc_norm_mul_opt(A, B))
X *= 4
X **= 2
return X
def alloc_norm_mul_opt(A, B):
"""Take two matrices, A and B,
- normalize their rows by dividing with their sums
- do a polynomial transformation on each
- multiply them and return the result.
"""
A = A.copy()
B = B.copy()
A /= A.sum(axis=1)[:, np.newaxis]
B /= B.sum(axis=1)[:, np.newaxis]
A = _poly_opt(A)
B = _poly_opt(B)
return A.dot(B)
if __name__ == '__main__':
from alloc_norm_mul import alloc_norm_mul
A = np.random.random((500, 20))
B = np.random.random((20, 300))
np.testing.assert_allclose(alloc_norm_mul(A, B), alloc_norm_mul_opt(A, B))
import numpy as np from alloc_norm_mul import alloc_norm_mul rng = np.random.RandomState(0) # Sorry, Josh, -1 doesn't work A = rng.uniform(size=((500, 100))) * 100 B = rng.uniform(size=((100, 60))) * 500 alloc_norm_mul(A, B)
