laywerrobot/lib/python3.6/site-packages/scipy/linalg/tests/test_solve_toeplitz.py

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2020-08-27 21:55:39 +02:00
"""Test functions for linalg._solve_toeplitz module
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy.linalg._solve_toeplitz import levinson
from scipy.linalg import solve, toeplitz, solve_toeplitz
from numpy.testing import assert_equal, assert_allclose
import pytest
from pytest import raises as assert_raises
def test_solve_equivalence():
# For toeplitz matrices, solve_toeplitz() should be equivalent to solve().
random = np.random.RandomState(1234)
for n in (1, 2, 3, 10):
c = random.randn(n)
if random.rand() < 0.5:
c = c + 1j * random.randn(n)
r = random.randn(n)
if random.rand() < 0.5:
r = r + 1j * random.randn(n)
y = random.randn(n)
if random.rand() < 0.5:
y = y + 1j * random.randn(n)
# Check equivalence when both the column and row are provided.
actual = solve_toeplitz((c,r), y)
desired = solve(toeplitz(c, r=r), y)
assert_allclose(actual, desired)
# Check equivalence when the column is provided but not the row.
actual = solve_toeplitz(c, b=y)
desired = solve(toeplitz(c), y)
assert_allclose(actual, desired)
def test_multiple_rhs():
random = np.random.RandomState(1234)
c = random.randn(4)
r = random.randn(4)
for offset in [0, 1j]:
for yshape in ((4,), (4, 3), (4, 3, 2)):
y = random.randn(*yshape) + offset
actual = solve_toeplitz((c,r), b=y)
desired = solve(toeplitz(c, r=r), y)
assert_equal(actual.shape, yshape)
assert_equal(desired.shape, yshape)
assert_allclose(actual, desired)
def test_native_list_arguments():
c = [1,2,4,7]
r = [1,3,9,12]
y = [5,1,4,2]
actual = solve_toeplitz((c,r), y)
desired = solve(toeplitz(c, r=r), y)
assert_allclose(actual, desired)
def test_zero_diag_error():
# The Levinson-Durbin implementation fails when the diagonal is zero.
random = np.random.RandomState(1234)
n = 4
c = random.randn(n)
r = random.randn(n)
y = random.randn(n)
c[0] = 0
assert_raises(np.linalg.LinAlgError,
solve_toeplitz, (c, r), b=y)
def test_wikipedia_counterexample():
# The Levinson-Durbin implementation also fails in other cases.
# This example is from the talk page of the wikipedia article.
random = np.random.RandomState(1234)
c = [2, 2, 1]
y = random.randn(3)
assert_raises(np.linalg.LinAlgError, solve_toeplitz, c, b=y)
def test_reflection_coeffs():
# check that that the partial solutions are given by the reflection
# coefficients
random = np.random.RandomState(1234)
y_d = random.randn(10)
y_z = random.randn(10) + 1j
reflection_coeffs_d = [1]
reflection_coeffs_z = [1]
for i in range(2, 10):
reflection_coeffs_d.append(solve_toeplitz(y_d[:(i-1)], b=y_d[1:i])[-1])
reflection_coeffs_z.append(solve_toeplitz(y_z[:(i-1)], b=y_z[1:i])[-1])
y_d_concat = np.concatenate((y_d[-2:0:-1], y_d[:-1]))
y_z_concat = np.concatenate((y_z[-2:0:-1].conj(), y_z[:-1]))
_, ref_d = levinson(y_d_concat, b=y_d[1:])
_, ref_z = levinson(y_z_concat, b=y_z[1:])
assert_allclose(reflection_coeffs_d, ref_d[:-1])
assert_allclose(reflection_coeffs_z, ref_z[:-1])
@pytest.mark.xfail(reason='Instability of Levinson iteration')
def test_unstable():
# this is a "Gaussian Toeplitz matrix", as mentioned in Example 2 of
# I. Gohbert, T. Kailath and V. Olshevsky "Fast Gaussian Elimination with
# Partial Pivoting for Matrices with Displacement Structure"
# Mathematics of Computation, 64, 212 (1995), pp 1557-1576
# which can be unstable for levinson recursion.
# other fast toeplitz solvers such as GKO or Burg should be better.
random = np.random.RandomState(1234)
n = 100
c = 0.9 ** (np.arange(n)**2)
y = random.randn(n)
solution1 = solve_toeplitz(c, b=y)
solution2 = solve(toeplitz(c), y)
assert_allclose(solution1, solution2)