mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-28 08:21:45 +01:00
148 lines
5.5 KiB
Python
148 lines
5.5 KiB
Python
""" Test functions for linalg module
|
|
"""
|
|
import warnings
|
|
|
|
import numpy as np
|
|
from numpy import linalg, arange, float64, array, dot, transpose
|
|
from numpy.testing import (
|
|
assert_, assert_raises, assert_equal, assert_array_equal,
|
|
assert_array_almost_equal, assert_array_less
|
|
)
|
|
|
|
|
|
class TestRegression:
|
|
|
|
def test_eig_build(self):
|
|
# Ticket #652
|
|
rva = array([1.03221168e+02 + 0.j,
|
|
-1.91843603e+01 + 0.j,
|
|
-6.04004526e-01 + 15.84422474j,
|
|
-6.04004526e-01 - 15.84422474j,
|
|
-1.13692929e+01 + 0.j,
|
|
-6.57612485e-01 + 10.41755503j,
|
|
-6.57612485e-01 - 10.41755503j,
|
|
1.82126812e+01 + 0.j,
|
|
1.06011014e+01 + 0.j,
|
|
7.80732773e+00 + 0.j,
|
|
-7.65390898e-01 + 0.j,
|
|
1.51971555e-15 + 0.j,
|
|
-1.51308713e-15 + 0.j])
|
|
a = arange(13 * 13, dtype=float64)
|
|
a.shape = (13, 13)
|
|
a = a % 17
|
|
va, ve = linalg.eig(a)
|
|
va.sort()
|
|
rva.sort()
|
|
assert_array_almost_equal(va, rva)
|
|
|
|
def test_eigh_build(self):
|
|
# Ticket 662.
|
|
rvals = [68.60568999, 89.57756725, 106.67185574]
|
|
|
|
cov = array([[77.70273908, 3.51489954, 15.64602427],
|
|
[3.51489954, 88.97013878, -1.07431931],
|
|
[15.64602427, -1.07431931, 98.18223512]])
|
|
|
|
vals, vecs = linalg.eigh(cov)
|
|
assert_array_almost_equal(vals, rvals)
|
|
|
|
def test_svd_build(self):
|
|
# Ticket 627.
|
|
a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
|
|
m, n = a.shape
|
|
u, s, vh = linalg.svd(a)
|
|
|
|
b = dot(transpose(u[:, n:]), a)
|
|
|
|
assert_array_almost_equal(b, np.zeros((2, 2)))
|
|
|
|
def test_norm_vector_badarg(self):
|
|
# Regression for #786: Frobenius norm for vectors raises
|
|
# ValueError.
|
|
assert_raises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
|
|
|
|
def test_lapack_endian(self):
|
|
# For bug #1482
|
|
a = array([[5.7998084, -2.1825367],
|
|
[-2.1825367, 9.85910595]], dtype='>f8')
|
|
b = array(a, dtype='<f8')
|
|
|
|
ap = linalg.cholesky(a)
|
|
bp = linalg.cholesky(b)
|
|
assert_array_equal(ap, bp)
|
|
|
|
def test_large_svd_32bit(self):
|
|
# See gh-4442, 64bit would require very large/slow matrices.
|
|
x = np.eye(1000, 66)
|
|
np.linalg.svd(x)
|
|
|
|
def test_svd_no_uv(self):
|
|
# gh-4733
|
|
for shape in (3, 4), (4, 4), (4, 3):
|
|
for t in float, complex:
|
|
a = np.ones(shape, dtype=t)
|
|
w = linalg.svd(a, compute_uv=False)
|
|
c = np.count_nonzero(np.absolute(w) > 0.5)
|
|
assert_equal(c, 1)
|
|
assert_equal(np.linalg.matrix_rank(a), 1)
|
|
assert_array_less(1, np.linalg.norm(a, ord=2))
|
|
|
|
def test_norm_object_array(self):
|
|
# gh-7575
|
|
testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
|
|
|
|
norm = linalg.norm(testvector)
|
|
assert_array_equal(norm, [0, 1])
|
|
assert_(norm.dtype == np.dtype('float64'))
|
|
|
|
norm = linalg.norm(testvector, ord=1)
|
|
assert_array_equal(norm, [0, 1])
|
|
assert_(norm.dtype != np.dtype('float64'))
|
|
|
|
norm = linalg.norm(testvector, ord=2)
|
|
assert_array_equal(norm, [0, 1])
|
|
assert_(norm.dtype == np.dtype('float64'))
|
|
|
|
assert_raises(ValueError, linalg.norm, testvector, ord='fro')
|
|
assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
|
|
assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
|
|
assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("error", DeprecationWarning)
|
|
assert_raises((AttributeError, DeprecationWarning),
|
|
linalg.norm, testvector, ord=0)
|
|
assert_raises(ValueError, linalg.norm, testvector, ord=-1)
|
|
assert_raises(ValueError, linalg.norm, testvector, ord=-2)
|
|
|
|
testmatrix = np.array([[np.array([0, 1]), 0, 0],
|
|
[0, 0, 0]], dtype=object)
|
|
|
|
norm = linalg.norm(testmatrix)
|
|
assert_array_equal(norm, [0, 1])
|
|
assert_(norm.dtype == np.dtype('float64'))
|
|
|
|
norm = linalg.norm(testmatrix, ord='fro')
|
|
assert_array_equal(norm, [0, 1])
|
|
assert_(norm.dtype == np.dtype('float64'))
|
|
|
|
assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc')
|
|
assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf)
|
|
assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
|
|
assert_raises(ValueError, linalg.norm, testmatrix, ord=0)
|
|
assert_raises(ValueError, linalg.norm, testmatrix, ord=1)
|
|
assert_raises(ValueError, linalg.norm, testmatrix, ord=-1)
|
|
assert_raises(TypeError, linalg.norm, testmatrix, ord=2)
|
|
assert_raises(TypeError, linalg.norm, testmatrix, ord=-2)
|
|
assert_raises(ValueError, linalg.norm, testmatrix, ord=3)
|
|
|
|
def test_lstsq_complex_larger_rhs(self):
|
|
# gh-9891
|
|
size = 20
|
|
n_rhs = 70
|
|
G = np.random.randn(size, size) + 1j * np.random.randn(size, size)
|
|
u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs)
|
|
b = G.dot(u)
|
|
# This should work without segmentation fault.
|
|
u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None)
|
|
# check results just in case
|
|
assert_array_almost_equal(u_lstsq, u)
|