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matrix valued operations speed up, inv and cholesky explictly added

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This commit is contained in:
Fabian Joswig 2021-10-22 14:24:51 +01:00
parent 60d2e25ed7
commit c3f8b24a21
2 changed files with 200 additions and 45 deletions
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33
tests/test_linalg.py
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@ -1,4 +1,5 @@
import autograd.numpy as np
import numpy as np
import autograd.numpy as anp
import math
import pyerrors as pe
import pytest
@ -6,6 +7,28 @@ import pytest
np.random.seed(0)
def test_matmul():
for dim in [4, 8]:
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
my_array = np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
my_array = np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
def test_matrix_inverse():
content = []
for t in range(9):
@ -14,7 +37,7 @@ def test_matrix_inverse():
content.append(1.0) # Add 1.0 as a float
matrix = np.diag(content)
inverse_matrix = pe.linalg.mat_mat_op(np.linalg.inv, matrix)
inverse_matrix = pe.linalg.inv(matrix)
assert all([o.is_zero() for o in np.diag(matrix) * np.diag(inverse_matrix) - 1])
@ -35,7 +58,7 @@ def test_complex_matrix_inverse():
matrix[n, m] = entry.real.value + 1j * entry.imag.value
inverse_matrix = np.linalg.inv(matrix)
inverse_obs_matrix = pe.linalg.mat_mat_op(np.linalg.inv, obs_matrix)
inverse_obs_matrix = pe.linalg.inv(obs_matrix)
for (n, m), entry in np.ndenumerate(inverse_matrix):
assert np.isclose(inverse_matrix[n, m].real, inverse_obs_matrix[n, m].real.value)
assert np.isclose(inverse_matrix[n, m].imag, inverse_obs_matrix[n, m].imag.value)
@ -53,7 +76,7 @@ def test_matrix_functions():
matrix = np.array(matrix) @ np.identity(dim)
# Check inverse of matrix
inv = pe.linalg.mat_mat_op(np.linalg.inv, matrix)
inv = pe.linalg.inv(matrix)
check_inv = matrix @ inv
for (i, j), entry in np.ndenumerate(check_inv):
@ -66,7 +89,7 @@ def test_matrix_functions():
# Check Cholesky decomposition
sym = np.dot(matrix, matrix.T)
cholesky = pe.linalg.mat_mat_op(np.linalg.cholesky, sym)
cholesky = pe.linalg.cholesky(sym)
check = cholesky @ cholesky.T
for (i, j), entry in np.ndenumerate(check):