Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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767 lines
30 KiB
767 lines
30 KiB
4 years ago
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import os
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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import pytest
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from pytest import raises as assert_raises
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from scipy.linalg import solve_sylvester
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from scipy.linalg import solve_continuous_lyapunov, solve_discrete_lyapunov
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from scipy.linalg import solve_continuous_are, solve_discrete_are
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from scipy.linalg import block_diag, solve, LinAlgError
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from scipy.sparse.sputils import matrix
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def _load_data(name):
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"""
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Load npz data file under data/
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Returns a copy of the data, rather than keeping the npz file open.
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"""
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filename = os.path.join(os.path.abspath(os.path.dirname(__file__)),
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'data', name)
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with np.load(filename) as f:
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return dict(f.items())
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class TestSolveLyapunov(object):
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cases = [
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(np.array([[1, 2], [3, 4]]),
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np.array([[9, 10], [11, 12]])),
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# a, q all complex.
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(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
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np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
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# a real; q complex.
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(np.array([[1.0, 2.0], [3.0, 5.0]]),
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np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
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# a complex; q real.
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(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
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np.array([[2.0, 2.0], [-1.0, 2.0]])),
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# An example from Kitagawa, 1977
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(np.array([[3, 9, 5, 1, 4], [1, 2, 3, 8, 4], [4, 6, 6, 6, 3],
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[1, 5, 2, 0, 7], [5, 3, 3, 1, 5]]),
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np.array([[2, 4, 1, 0, 1], [4, 1, 0, 2, 0], [1, 0, 3, 0, 3],
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[0, 2, 0, 1, 0], [1, 0, 3, 0, 4]])),
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# Companion matrix example. a complex; q real; a.shape[0] = 11
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(np.array([[0.100+0.j, 0.091+0.j, 0.082+0.j, 0.073+0.j, 0.064+0.j,
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0.055+0.j, 0.046+0.j, 0.037+0.j, 0.028+0.j, 0.019+0.j,
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0.010+0.j],
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[1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j,
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0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j,
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0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j,
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0.000+0.j],
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[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
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0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j,
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0.000+0.j]]),
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np.eye(11)),
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# https://github.com/scipy/scipy/issues/4176
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(matrix([[0, 1], [-1/2, -1]]),
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(matrix([0, 3]).T @ matrix([0, 3]).T.T)),
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# https://github.com/scipy/scipy/issues/4176
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(matrix([[0, 1], [-1/2, -1]]),
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(np.array(matrix([0, 3]).T @ matrix([0, 3]).T.T))),
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]
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def test_continuous_squareness_and_shape(self):
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nsq = np.ones((3, 2))
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sq = np.eye(3)
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assert_raises(ValueError, solve_continuous_lyapunov, nsq, sq)
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assert_raises(ValueError, solve_continuous_lyapunov, sq, nsq)
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assert_raises(ValueError, solve_continuous_lyapunov, sq, np.eye(2))
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def check_continuous_case(self, a, q):
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x = solve_continuous_lyapunov(a, q)
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assert_array_almost_equal(
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np.dot(a, x) + np.dot(x, a.conj().transpose()), q)
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def check_discrete_case(self, a, q, method=None):
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x = solve_discrete_lyapunov(a, q, method=method)
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assert_array_almost_equal(
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np.dot(np.dot(a, x), a.conj().transpose()) - x, -1.0*q)
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def test_cases(self):
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for case in self.cases:
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self.check_continuous_case(case[0], case[1])
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self.check_discrete_case(case[0], case[1])
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self.check_discrete_case(case[0], case[1], method='direct')
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self.check_discrete_case(case[0], case[1], method='bilinear')
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def test_solve_continuous_are():
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mat6 = _load_data('carex_6_data.npz')
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mat15 = _load_data('carex_15_data.npz')
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mat18 = _load_data('carex_18_data.npz')
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mat19 = _load_data('carex_19_data.npz')
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mat20 = _load_data('carex_20_data.npz')
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cases = [
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# Carex examples taken from (with default parameters):
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# [1] P.BENNER, A.J. LAUB, V. MEHRMANN: 'A Collection of Benchmark
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# Examples for the Numerical Solution of Algebraic Riccati
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# Equations II: Continuous-Time Case', Tech. Report SPC 95_23,
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# Fak. f. Mathematik, TU Chemnitz-Zwickau (Germany), 1995.
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#
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# The format of the data is (a, b, q, r, knownfailure), where
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# knownfailure is None if the test passes or a string
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# indicating the reason for failure.
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#
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# Test Case 0: carex #1
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(np.diag([1.], 1),
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np.array([[0], [1]]),
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block_diag(1., 2.),
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1,
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None),
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# Test Case 1: carex #2
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(np.array([[4, 3], [-4.5, -3.5]]),
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np.array([[1], [-1]]),
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np.array([[9, 6], [6, 4.]]),
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1,
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None),
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# Test Case 2: carex #3
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(np.array([[0, 1, 0, 0],
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[0, -1.89, 0.39, -5.53],
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[0, -0.034, -2.98, 2.43],
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[0.034, -0.0011, -0.99, -0.21]]),
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np.array([[0, 0], [0.36, -1.6], [-0.95, -0.032], [0.03, 0]]),
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np.array([[2.313, 2.727, 0.688, 0.023],
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[2.727, 4.271, 1.148, 0.323],
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[0.688, 1.148, 0.313, 0.102],
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[0.023, 0.323, 0.102, 0.083]]),
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np.eye(2),
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None),
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# Test Case 3: carex #4
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(np.array([[-0.991, 0.529, 0, 0, 0, 0, 0, 0],
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[0.522, -1.051, 0.596, 0, 0, 0, 0, 0],
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[0, 0.522, -1.118, 0.596, 0, 0, 0, 0],
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[0, 0, 0.522, -1.548, 0.718, 0, 0, 0],
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[0, 0, 0, 0.922, -1.64, 0.799, 0, 0],
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[0, 0, 0, 0, 0.922, -1.721, 0.901, 0],
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[0, 0, 0, 0, 0, 0.922, -1.823, 1.021],
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[0, 0, 0, 0, 0, 0, 0.922, -1.943]]),
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np.array([[3.84, 4.00, 37.60, 3.08, 2.36, 2.88, 3.08, 3.00],
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[-2.88, -3.04, -2.80, -2.32, -3.32, -3.82, -4.12, -3.96]]
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).T * 0.001,
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np.array([[1.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.1],
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[0.0, 1.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0, 0.5, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
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[0.5, 0.1, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.5, 0.0, 0.0, 0.1, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0],
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[0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]]),
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np.eye(2),
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None),
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# Test Case 4: carex #5
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(np.array(
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[[-4.019, 5.120, 0., 0., -2.082, 0., 0., 0., 0.870],
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[-0.346, 0.986, 0., 0., -2.340, 0., 0., 0., 0.970],
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[-7.909, 15.407, -4.069, 0., -6.450, 0., 0., 0., 2.680],
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[-21.816, 35.606, -0.339, -3.870, -17.800, 0., 0., 0., 7.390],
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[-60.196, 98.188, -7.907, 0.340, -53.008, 0., 0., 0., 20.400],
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[0, 0, 0, 0, 94.000, -147.200, 0., 53.200, 0.],
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[0, 0, 0, 0, 0, 94.000, -147.200, 0, 0],
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[0, 0, 0, 0, 0, 12.800, 0.000, -31.600, 0],
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[0, 0, 0, 0, 12.800, 0.000, 0.000, 18.800, -31.600]]),
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np.array([[0.010, -0.011, -0.151],
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[0.003, -0.021, 0.000],
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[0.009, -0.059, 0.000],
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[0.024, -0.162, 0.000],
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[0.068, -0.445, 0.000],
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[0.000, 0.000, 0.000],
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[0.000, 0.000, 0.000],
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[0.000, 0.000, 0.000],
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[0.000, 0.000, 0.000]]),
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np.eye(9),
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np.eye(3),
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None),
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# Test Case 5: carex #6
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(mat6['A'], mat6['B'], mat6['Q'], mat6['R'], None),
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# Test Case 6: carex #7
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(np.array([[1, 0], [0, -2.]]),
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np.array([[1e-6], [0]]),
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np.ones((2, 2)),
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1.,
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'Bad residual accuracy'),
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# Test Case 7: carex #8
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(block_diag(-0.1, -0.02),
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np.array([[0.100, 0.000], [0.001, 0.010]]),
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np.array([[100, 1000], [1000, 10000]]),
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np.ones((2, 2)) + block_diag(1e-6, 0),
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None),
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# Test Case 8: carex #9
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(np.array([[0, 1e6], [0, 0]]),
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np.array([[0], [1.]]),
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np.eye(2),
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1.,
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None),
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# Test Case 9: carex #10
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(np.array([[1.0000001, 1], [1., 1.0000001]]),
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np.eye(2),
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np.eye(2),
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np.eye(2),
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None),
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# Test Case 10: carex #11
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(np.array([[3, 1.], [4, 2]]),
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np.array([[1], [1]]),
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np.array([[-11, -5], [-5, -2.]]),
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1.,
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None),
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# Test Case 11: carex #12
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(np.array([[7000000., 2000000., -0.],
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[2000000., 6000000., -2000000.],
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[0., -2000000., 5000000.]]) / 3,
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np.eye(3),
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np.array([[1., -2., -2.], [-2., 1., -2.], [-2., -2., 1.]]).dot(
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np.diag([1e-6, 1, 1e6])).dot(
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np.array([[1., -2., -2.], [-2., 1., -2.], [-2., -2., 1.]])) / 9,
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np.eye(3) * 1e6,
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'Bad Residual Accuracy'),
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# Test Case 12: carex #13
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(np.array([[0, 0.4, 0, 0],
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[0, 0, 0.345, 0],
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[0, -0.524e6, -0.465e6, 0.262e6],
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[0, 0, 0, -1e6]]),
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np.array([[0, 0, 0, 1e6]]).T,
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np.diag([1, 0, 1, 0]),
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1.,
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None),
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# Test Case 13: carex #14
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(np.array([[-1e-6, 1, 0, 0],
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[-1, -1e-6, 0, 0],
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[0, 0, 1e-6, 1],
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[0, 0, -1, 1e-6]]),
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np.ones((4, 1)),
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np.ones((4, 4)),
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1.,
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None),
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# Test Case 14: carex #15
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(mat15['A'], mat15['B'], mat15['Q'], mat15['R'], None),
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# Test Case 15: carex #16
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(np.eye(64, 64, k=-1) + np.eye(64, 64)*(-2.) + np.rot90(
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block_diag(1, np.zeros((62, 62)), 1)) + np.eye(64, 64, k=1),
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np.eye(64),
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np.eye(64),
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np.eye(64),
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None),
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# Test Case 16: carex #17
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||
|
|
(np.diag(np.ones((20, )), 1),
|
||
|
|
np.flipud(np.eye(21, 1)),
|
||
|
|
np.eye(21, 1) * np.eye(21, 1).T,
|
||
|
|
1,
|
||
|
|
'Bad Residual Accuracy'),
|
||
|
|
# Test Case 17: carex #18
|
||
|
|
(mat18['A'], mat18['B'], mat18['Q'], mat18['R'], None),
|
||
|
|
# Test Case 18: carex #19
|
||
|
|
(mat19['A'], mat19['B'], mat19['Q'], mat19['R'],
|
||
|
|
'Bad Residual Accuracy'),
|
||
|
|
# Test Case 19: carex #20
|
||
|
|
(mat20['A'], mat20['B'], mat20['Q'], mat20['R'],
|
||
|
|
'Bad Residual Accuracy')
|
||
|
|
]
|
||
|
|
# Makes the minimum precision requirements customized to the test.
|
||
|
|
# Here numbers represent the number of decimals that agrees with zero
|
||
|
|
# matrix when the solution x is plugged in to the equation.
|
||
|
|
#
|
||
|
|
# res = array([[8e-3,1e-16],[1e-16,1e-20]]) --> min_decimal[k] = 2
|
||
|
|
#
|
||
|
|
# If the test is failing use "None" for that entry.
|
||
|
|
#
|
||
|
|
min_decimal = (14, 12, 13, 14, 11, 6, None, 5, 7, 14, 14,
|
||
|
|
None, 9, 14, 13, 14, None, 12, None, None)
|
||
|
|
|
||
|
|
def _test_factory(case, dec):
|
||
|
|
"""Checks if 0 = XA + A'X - XB(R)^{-1} B'X + Q is true"""
|
||
|
|
a, b, q, r, knownfailure = case
|
||
|
|
if knownfailure:
|
||
|
|
pytest.xfail(reason=knownfailure)
|
||
|
|
|
||
|
|
x = solve_continuous_are(a, b, q, r)
|
||
|
|
res = x.dot(a) + a.conj().T.dot(x) + q
|
||
|
|
out_fact = x.dot(b)
|
||
|
|
res -= out_fact.dot(solve(np.atleast_2d(r), out_fact.conj().T))
|
||
|
|
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
|
||
|
|
|
||
|
|
for ind, case in enumerate(cases):
|
||
|
|
_test_factory(case, min_decimal[ind])
|
||
|
|
|
||
|
|
|
||
|
|
def test_solve_discrete_are():
|
||
|
|
|
||
|
|
cases = [
|
||
|
|
# Darex examples taken from (with default parameters):
|
||
|
|
# [1] P.BENNER, A.J. LAUB, V. MEHRMANN: 'A Collection of Benchmark
|
||
|
|
# Examples for the Numerical Solution of Algebraic Riccati
|
||
|
|
# Equations II: Discrete-Time Case', Tech. Report SPC 95_23,
|
||
|
|
# Fak. f. Mathematik, TU Chemnitz-Zwickau (Germany), 1995.
|
||
|
|
# [2] T. GUDMUNDSSON, C. KENNEY, A.J. LAUB: 'Scaling of the
|
||
|
|
# Discrete-Time Algebraic Riccati Equation to Enhance Stability
|
||
|
|
# of the Schur Solution Method', IEEE Trans.Aut.Cont., vol.37(4)
|
||
|
|
#
|
||
|
|
# The format of the data is (a, b, q, r, knownfailure), where
|
||
|
|
# knownfailure is None if the test passes or a string
|
||
|
|
# indicating the reason for failure.
|
||
|
|
#
|
||
|
|
# TEST CASE 0 : Complex a; real b, q, r
|
||
|
|
(np.array([[2, 1-2j], [0, -3j]]),
|
||
|
|
np.array([[0], [1]]),
|
||
|
|
np.array([[1, 0], [0, 2]]),
|
||
|
|
np.array([[1]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 1 :Real a, q, r; complex b
|
||
|
|
(np.array([[2, 1], [0, -1]]),
|
||
|
|
np.array([[-2j], [1j]]),
|
||
|
|
np.array([[1, 0], [0, 2]]),
|
||
|
|
np.array([[1]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 2 : Real a, b; complex q, r
|
||
|
|
(np.array([[3, 1], [0, -1]]),
|
||
|
|
np.array([[1, 2], [1, 3]]),
|
||
|
|
np.array([[1, 1+1j], [1-1j, 2]]),
|
||
|
|
np.array([[2, -2j], [2j, 3]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 3 : User-reported gh-2251 (Trac #1732)
|
||
|
|
(np.array([[0.63399379, 0.54906824, 0.76253406],
|
||
|
|
[0.5404729, 0.53745766, 0.08731853],
|
||
|
|
[0.27524045, 0.84922129, 0.4681622]]),
|
||
|
|
np.array([[0.96861695], [0.05532739], [0.78934047]]),
|
||
|
|
np.eye(3),
|
||
|
|
np.eye(1),
|
||
|
|
None),
|
||
|
|
# TEST CASE 4 : darex #1
|
||
|
|
(np.array([[4, 3], [-4.5, -3.5]]),
|
||
|
|
np.array([[1], [-1]]),
|
||
|
|
np.array([[9, 6], [6, 4]]),
|
||
|
|
np.array([[1]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 5 : darex #2
|
||
|
|
(np.array([[0.9512, 0], [0, 0.9048]]),
|
||
|
|
np.array([[4.877, 4.877], [-1.1895, 3.569]]),
|
||
|
|
np.array([[0.005, 0], [0, 0.02]]),
|
||
|
|
np.array([[1/3, 0], [0, 3]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 6 : darex #3
|
||
|
|
(np.array([[2, -1], [1, 0]]),
|
||
|
|
np.array([[1], [0]]),
|
||
|
|
np.array([[0, 0], [0, 1]]),
|
||
|
|
np.array([[0]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 7 : darex #4 (skipped the gen. Ric. term S)
|
||
|
|
(np.array([[0, 1], [0, -1]]),
|
||
|
|
np.array([[1, 0], [2, 1]]),
|
||
|
|
np.array([[-4, -4], [-4, 7]]) * (1/11),
|
||
|
|
np.array([[9, 3], [3, 1]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 8 : darex #5
|
||
|
|
(np.array([[0, 1], [0, 0]]),
|
||
|
|
np.array([[0], [1]]),
|
||
|
|
np.array([[1, 2], [2, 4]]),
|
||
|
|
np.array([[1]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 9 : darex #6
|
||
|
|
(np.array([[0.998, 0.067, 0, 0],
|
||
|
|
[-.067, 0.998, 0, 0],
|
||
|
|
[0, 0, 0.998, 0.153],
|
||
|
|
[0, 0, -.153, 0.998]]),
|
||
|
|
np.array([[0.0033, 0.0200],
|
||
|
|
[0.1000, -.0007],
|
||
|
|
[0.0400, 0.0073],
|
||
|
|
[-.0028, 0.1000]]),
|
||
|
|
np.array([[1.87, 0, 0, -0.244],
|
||
|
|
[0, 0.744, 0.205, 0],
|
||
|
|
[0, 0.205, 0.589, 0],
|
||
|
|
[-0.244, 0, 0, 1.048]]),
|
||
|
|
np.eye(2),
|
||
|
|
None),
|
||
|
|
# TEST CASE 10 : darex #7
|
||
|
|
(np.array([[0.984750, -.079903, 0.0009054, -.0010765],
|
||
|
|
[0.041588, 0.998990, -.0358550, 0.0126840],
|
||
|
|
[-.546620, 0.044916, -.3299100, 0.1931800],
|
||
|
|
[2.662400, -.100450, -.9245500, -.2632500]]),
|
||
|
|
np.array([[0.0037112, 0.0007361],
|
||
|
|
[-.0870510, 9.3411e-6],
|
||
|
|
[-1.198440, -4.1378e-4],
|
||
|
|
[-3.192700, 9.2535e-4]]),
|
||
|
|
np.eye(4)*1e-2,
|
||
|
|
np.eye(2),
|
||
|
|
None),
|
||
|
|
# TEST CASE 11 : darex #8
|
||
|
|
(np.array([[-0.6000000, -2.2000000, -3.6000000, -5.4000180],
|
||
|
|
[1.0000000, 0.6000000, 0.8000000, 3.3999820],
|
||
|
|
[0.0000000, 1.0000000, 1.8000000, 3.7999820],
|
||
|
|
[0.0000000, 0.0000000, 0.0000000, -0.9999820]]),
|
||
|
|
np.array([[1.0, -1.0, -1.0, -1.0],
|
||
|
|
[0.0, 1.0, -1.0, -1.0],
|
||
|
|
[0.0, 0.0, 1.0, -1.0],
|
||
|
|
[0.0, 0.0, 0.0, 1.0]]),
|
||
|
|
np.array([[2, 1, 3, 6],
|
||
|
|
[1, 2, 2, 5],
|
||
|
|
[3, 2, 6, 11],
|
||
|
|
[6, 5, 11, 22]]),
|
||
|
|
np.eye(4),
|
||
|
|
None),
|
||
|
|
# TEST CASE 12 : darex #9
|
||
|
|
(np.array([[95.4070, 1.9643, 0.3597, 0.0673, 0.0190],
|
||
|
|
[40.8490, 41.3170, 16.0840, 4.4679, 1.1971],
|
||
|
|
[12.2170, 26.3260, 36.1490, 15.9300, 12.3830],
|
||
|
|
[4.1118, 12.8580, 27.2090, 21.4420, 40.9760],
|
||
|
|
[0.1305, 0.5808, 1.8750, 3.6162, 94.2800]]) * 0.01,
|
||
|
|
np.array([[0.0434, -0.0122],
|
||
|
|
[2.6606, -1.0453],
|
||
|
|
[3.7530, -5.5100],
|
||
|
|
[3.6076, -6.6000],
|
||
|
|
[0.4617, -0.9148]]) * 0.01,
|
||
|
|
np.eye(5),
|
||
|
|
np.eye(2),
|
||
|
|
None),
|
||
|
|
# TEST CASE 13 : darex #10
|
||
|
|
(np.kron(np.eye(2), np.diag([1, 1], k=1)),
|
||
|
|
np.kron(np.eye(2), np.array([[0], [0], [1]])),
|
||
|
|
np.array([[1, 1, 0, 0, 0, 0],
|
||
|
|
[1, 1, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, -1, 0],
|
||
|
|
[0, 0, 0, -1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0]]),
|
||
|
|
np.array([[3, 0], [0, 1]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 14 : darex #11
|
||
|
|
(0.001 * np.array(
|
||
|
|
[[870.1, 135.0, 11.59, .5014, -37.22, .3484, 0, 4.242, 7.249],
|
||
|
|
[76.55, 897.4, 12.72, 0.5504, -40.16, .3743, 0, 4.53, 7.499],
|
||
|
|
[-127.2, 357.5, 817, 1.455, -102.8, .987, 0, 11.85, 18.72],
|
||
|
|
[-363.5, 633.9, 74.91, 796.6, -273.5, 2.653, 0, 31.72, 48.82],
|
||
|
|
[-960, 1645.9, -128.9, -5.597, 71.42, 7.108, 0, 84.52, 125.9],
|
||
|
|
[-664.4, 112.96, -88.89, -3.854, 84.47, 13.6, 0, 144.3, 101.6],
|
||
|
|
[-410.2, 693, -54.71, -2.371, 66.49, 12.49, .1063, 99.97, 69.67],
|
||
|
|
[-179.9, 301.7, -23.93, -1.035, 60.59, 22.16, 0, 213.9, 35.54],
|
||
|
|
[-345.1, 580.4, -45.96, -1.989, 105.6, 19.86, 0, 219.1, 215.2]]),
|
||
|
|
np.array([[4.7600, -0.5701, -83.6800],
|
||
|
|
[0.8790, -4.7730, -2.7300],
|
||
|
|
[1.4820, -13.1200, 8.8760],
|
||
|
|
[3.8920, -35.1300, 24.8000],
|
||
|
|
[10.3400, -92.7500, 66.8000],
|
||
|
|
[7.2030, -61.5900, 38.3400],
|
||
|
|
[4.4540, -36.8300, 20.2900],
|
||
|
|
[1.9710, -15.5400, 6.9370],
|
||
|
|
[3.7730, -30.2800, 14.6900]]) * 0.001,
|
||
|
|
np.diag([50, 0, 0, 0, 50, 0, 0, 0, 0]),
|
||
|
|
np.eye(3),
|
||
|
|
None),
|
||
|
|
# TEST CASE 15 : darex #12 - numerically least accurate example
|
||
|
|
(np.array([[0, 1e6], [0, 0]]),
|
||
|
|
np.array([[0], [1]]),
|
||
|
|
np.eye(2),
|
||
|
|
np.array([[1]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 16 : darex #13
|
||
|
|
(np.array([[16, 10, -2],
|
||
|
|
[10, 13, -8],
|
||
|
|
[-2, -8, 7]]) * (1/9),
|
||
|
|
np.eye(3),
|
||
|
|
1e6 * np.eye(3),
|
||
|
|
1e6 * np.eye(3),
|
||
|
|
None),
|
||
|
|
# TEST CASE 17 : darex #14
|
||
|
|
(np.array([[1 - 1/1e8, 0, 0, 0],
|
||
|
|
[1, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0]]),
|
||
|
|
np.array([[1e-08], [0], [0], [0]]),
|
||
|
|
np.diag([0, 0, 0, 1]),
|
||
|
|
np.array([[0.25]]),
|
||
|
|
None),
|
||
|
|
# TEST CASE 18 : darex #15
|
||
|
|
(np.eye(100, k=1),
|
||
|
|
np.flipud(np.eye(100, 1)),
|
||
|
|
np.eye(100),
|
||
|
|
np.array([[1]]),
|
||
|
|
None)
|
||
|
|
]
|
||
|
|
|
||
|
|
# Makes the minimum precision requirements customized to the test.
|
||
|
|
# Here numbers represent the number of decimals that agrees with zero
|
||
|
|
# matrix when the solution x is plugged in to the equation.
|
||
|
|
#
|
||
|
|
# res = array([[8e-3,1e-16],[1e-16,1e-20]]) --> min_decimal[k] = 2
|
||
|
|
#
|
||
|
|
# If the test is failing use "None" for that entry.
|
||
|
|
#
|
||
|
|
min_decimal = (12, 14, 13, 14, 13, 16, 18, 14, 14, 13,
|
||
|
|
14, 13, 13, 14, 12, 2, 5, 6, 10)
|
||
|
|
|
||
|
|
def _test_factory(case, dec):
|
||
|
|
"""Checks if X = A'XA-(A'XB)(R+B'XB)^-1(B'XA)+Q) is true"""
|
||
|
|
a, b, q, r, knownfailure = case
|
||
|
|
if knownfailure:
|
||
|
|
pytest.xfail(reason=knownfailure)
|
||
|
|
|
||
|
|
x = solve_discrete_are(a, b, q, r)
|
||
|
|
res = a.conj().T.dot(x.dot(a)) - x + q
|
||
|
|
res -= a.conj().T.dot(x.dot(b)).dot(
|
||
|
|
solve(r+b.conj().T.dot(x.dot(b)), b.conj().T).dot(x.dot(a))
|
||
|
|
)
|
||
|
|
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
|
||
|
|
|
||
|
|
for ind, case in enumerate(cases):
|
||
|
|
_test_factory(case, min_decimal[ind])
|
||
|
|
|
||
|
|
# An infeasible example taken from https://arxiv.org/abs/1505.04861v1
|
||
|
|
A = np.triu(np.ones((3, 3)))
|
||
|
|
A[0, 1] = -1
|
||
|
|
B = np.array([[1, 1, 0], [0, 0, 1]]).T
|
||
|
|
Q = np.full_like(A, -2) + np.diag([8, -1, -1.9])
|
||
|
|
R = np.diag([-10, 0.1])
|
||
|
|
assert_raises(LinAlgError, solve_continuous_are, A, B, Q, R)
|
||
|
|
|
||
|
|
|
||
|
|
def test_solve_generalized_continuous_are():
|
||
|
|
cases = [
|
||
|
|
# Two random examples differ by s term
|
||
|
|
# in the absence of any literature for demanding examples.
|
||
|
|
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
|
||
|
|
[4.617139e-02, 6.948286e-01, 3.444608e-02],
|
||
|
|
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
|
||
|
|
np.array([[3.815585e-01, 1.868726e-01],
|
||
|
|
[7.655168e-01, 4.897644e-01],
|
||
|
|
[7.951999e-01, 4.455862e-01]]),
|
||
|
|
np.eye(3),
|
||
|
|
np.eye(2),
|
||
|
|
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
|
||
|
|
[7.093648e-01, 6.797027e-01, 1.189977e-01],
|
||
|
|
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
|
||
|
|
np.zeros((3, 2)),
|
||
|
|
None),
|
||
|
|
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
|
||
|
|
[4.617139e-02, 6.948286e-01, 3.444608e-02],
|
||
|
|
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
|
||
|
|
np.array([[3.815585e-01, 1.868726e-01],
|
||
|
|
[7.655168e-01, 4.897644e-01],
|
||
|
|
[7.951999e-01, 4.455862e-01]]),
|
||
|
|
np.eye(3),
|
||
|
|
np.eye(2),
|
||
|
|
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
|
||
|
|
[7.093648e-01, 6.797027e-01, 1.189977e-01],
|
||
|
|
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
|
||
|
|
np.ones((3, 2)),
|
||
|
|
None)
|
||
|
|
]
|
||
|
|
|
||
|
|
min_decimal = (10, 10)
|
||
|
|
|
||
|
|
def _test_factory(case, dec):
|
||
|
|
"""Checks if X = A'XA-(A'XB)(R+B'XB)^-1(B'XA)+Q) is true"""
|
||
|
|
a, b, q, r, e, s, knownfailure = case
|
||
|
|
if knownfailure:
|
||
|
|
pytest.xfail(reason=knownfailure)
|
||
|
|
|
||
|
|
x = solve_continuous_are(a, b, q, r, e, s)
|
||
|
|
res = a.conj().T.dot(x.dot(e)) + e.conj().T.dot(x.dot(a)) + q
|
||
|
|
out_fact = e.conj().T.dot(x).dot(b) + s
|
||
|
|
res -= out_fact.dot(solve(np.atleast_2d(r), out_fact.conj().T))
|
||
|
|
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
|
||
|
|
|
||
|
|
for ind, case in enumerate(cases):
|
||
|
|
_test_factory(case, min_decimal[ind])
|
||
|
|
|
||
|
|
|
||
|
|
def test_solve_generalized_discrete_are():
|
||
|
|
mat20170120 = _load_data('gendare_20170120_data.npz')
|
||
|
|
|
||
|
|
cases = [
|
||
|
|
# Two random examples differ by s term
|
||
|
|
# in the absence of any literature for demanding examples.
|
||
|
|
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
|
||
|
|
[4.617139e-02, 6.948286e-01, 3.444608e-02],
|
||
|
|
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
|
||
|
|
np.array([[3.815585e-01, 1.868726e-01],
|
||
|
|
[7.655168e-01, 4.897644e-01],
|
||
|
|
[7.951999e-01, 4.455862e-01]]),
|
||
|
|
np.eye(3),
|
||
|
|
np.eye(2),
|
||
|
|
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
|
||
|
|
[7.093648e-01, 6.797027e-01, 1.189977e-01],
|
||
|
|
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
|
||
|
|
np.zeros((3, 2)),
|
||
|
|
None),
|
||
|
|
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
|
||
|
|
[4.617139e-02, 6.948286e-01, 3.444608e-02],
|
||
|
|
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
|
||
|
|
np.array([[3.815585e-01, 1.868726e-01],
|
||
|
|
[7.655168e-01, 4.897644e-01],
|
||
|
|
[7.951999e-01, 4.455862e-01]]),
|
||
|
|
np.eye(3),
|
||
|
|
np.eye(2),
|
||
|
|
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
|
||
|
|
[7.093648e-01, 6.797027e-01, 1.189977e-01],
|
||
|
|
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
|
||
|
|
np.ones((3, 2)),
|
||
|
|
None),
|
||
|
|
# user-reported (under PR-6616) 20-Jan-2017
|
||
|
|
# tests against the case where E is None but S is provided
|
||
|
|
(mat20170120['A'],
|
||
|
|
mat20170120['B'],
|
||
|
|
mat20170120['Q'],
|
||
|
|
mat20170120['R'],
|
||
|
|
None,
|
||
|
|
mat20170120['S'],
|
||
|
|
None),
|
||
|
|
]
|
||
|
|
|
||
|
|
min_decimal = (11, 11, 16)
|
||
|
|
|
||
|
|
def _test_factory(case, dec):
|
||
|
|
"""Checks if X = A'XA-(A'XB)(R+B'XB)^-1(B'XA)+Q) is true"""
|
||
|
|
a, b, q, r, e, s, knownfailure = case
|
||
|
|
if knownfailure:
|
||
|
|
pytest.xfail(reason=knownfailure)
|
||
|
|
|
||
|
|
x = solve_discrete_are(a, b, q, r, e, s)
|
||
|
|
if e is None:
|
||
|
|
e = np.eye(a.shape[0])
|
||
|
|
if s is None:
|
||
|
|
s = np.zeros_like(b)
|
||
|
|
res = a.conj().T.dot(x.dot(a)) - e.conj().T.dot(x.dot(e)) + q
|
||
|
|
res -= (a.conj().T.dot(x.dot(b)) + s).dot(
|
||
|
|
solve(r+b.conj().T.dot(x.dot(b)),
|
||
|
|
(b.conj().T.dot(x.dot(a)) + s.conj().T)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
|
||
|
|
|
||
|
|
for ind, case in enumerate(cases):
|
||
|
|
_test_factory(case, min_decimal[ind])
|
||
|
|
|
||
|
|
|
||
|
|
def test_are_validate_args():
|
||
|
|
|
||
|
|
def test_square_shape():
|
||
|
|
nsq = np.ones((3, 2))
|
||
|
|
sq = np.eye(3)
|
||
|
|
for x in (solve_continuous_are, solve_discrete_are):
|
||
|
|
assert_raises(ValueError, x, nsq, 1, 1, 1)
|
||
|
|
assert_raises(ValueError, x, sq, sq, nsq, 1)
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, nsq)
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, sq, nsq)
|
||
|
|
|
||
|
|
def test_compatible_sizes():
|
||
|
|
nsq = np.ones((3, 2))
|
||
|
|
sq = np.eye(4)
|
||
|
|
for x in (solve_continuous_are, solve_discrete_are):
|
||
|
|
assert_raises(ValueError, x, sq, nsq, 1, 1)
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, sq, sq, nsq)
|
||
|
|
assert_raises(ValueError, x, sq, sq, np.eye(3), sq)
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, np.eye(3))
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, sq, np.eye(3))
|
||
|
|
|
||
|
|
def test_symmetry():
|
||
|
|
nsym = np.arange(9).reshape(3, 3)
|
||
|
|
sym = np.eye(3)
|
||
|
|
for x in (solve_continuous_are, solve_discrete_are):
|
||
|
|
assert_raises(ValueError, x, sym, sym, nsym, sym)
|
||
|
|
assert_raises(ValueError, x, sym, sym, sym, nsym)
|
||
|
|
|
||
|
|
def test_singularity():
|
||
|
|
sing = np.full((3, 3), 1e12)
|
||
|
|
sing[2, 2] -= 1
|
||
|
|
sq = np.eye(3)
|
||
|
|
for x in (solve_continuous_are, solve_discrete_are):
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, sq, sing)
|
||
|
|
|
||
|
|
assert_raises(ValueError, solve_continuous_are, sq, sq, sq, sing)
|
||
|
|
|
||
|
|
def test_finiteness():
|
||
|
|
nm = np.full((2, 2), np.nan)
|
||
|
|
sq = np.eye(2)
|
||
|
|
for x in (solve_continuous_are, solve_discrete_are):
|
||
|
|
assert_raises(ValueError, x, nm, sq, sq, sq)
|
||
|
|
assert_raises(ValueError, x, sq, nm, sq, sq)
|
||
|
|
assert_raises(ValueError, x, sq, sq, nm, sq)
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, nm)
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, sq, nm)
|
||
|
|
assert_raises(ValueError, x, sq, sq, sq, sq, sq, nm)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSolveSylvester(object):
|
||
|
|
|
||
|
|
cases = [
|
||
|
|
# a, b, c all real.
|
||
|
|
(np.array([[1, 2], [0, 4]]),
|
||
|
|
np.array([[5, 6], [0, 8]]),
|
||
|
|
np.array([[9, 10], [11, 12]])),
|
||
|
|
# a, b, c all real, 4x4. a and b have non-trival 2x2 blocks in their
|
||
|
|
# quasi-triangular form.
|
||
|
|
(np.array([[1.0, 0, 0, 0],
|
||
|
|
[0, 1.0, 2.0, 0.0],
|
||
|
|
[0, 0, 3.0, -4],
|
||
|
|
[0, 0, 2, 5]]),
|
||
|
|
np.array([[2.0, 0, 0, 1.0],
|
||
|
|
[0, 1.0, 0.0, 0.0],
|
||
|
|
[0, 0, 1.0, -1],
|
||
|
|
[0, 0, 1, 1]]),
|
||
|
|
np.array([[1.0, 0, 0, 0],
|
||
|
|
[0, 1.0, 0, 0],
|
||
|
|
[0, 0, 1.0, 0],
|
||
|
|
[0, 0, 0, 1.0]])),
|
||
|
|
# a, b, c all complex.
|
||
|
|
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
|
||
|
|
np.array([[-1.0, 2j], [3.0, 4.0]]),
|
||
|
|
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
|
||
|
|
# a and b real; c complex.
|
||
|
|
(np.array([[1.0, 2.0], [3.0, 5.0]]),
|
||
|
|
np.array([[-1.0, 0], [3.0, 4.0]]),
|
||
|
|
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
|
||
|
|
# a and c complex; b real.
|
||
|
|
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
|
||
|
|
np.array([[-1.0, 0], [3.0, 4.0]]),
|
||
|
|
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
|
||
|
|
# a complex; b and c real.
|
||
|
|
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
|
||
|
|
np.array([[-1.0, 0], [3.0, 4.0]]),
|
||
|
|
np.array([[2.0, 2.0], [-1.0, 2.0]])),
|
||
|
|
# not square matrices, real
|
||
|
|
(np.array([[8, 1, 6], [3, 5, 7], [4, 9, 2]]),
|
||
|
|
np.array([[2, 3], [4, 5]]),
|
||
|
|
np.array([[1, 2], [3, 4], [5, 6]])),
|
||
|
|
# not square matrices, complex
|
||
|
|
(np.array([[8, 1j, 6+2j], [3, 5, 7], [4, 9, 2]]),
|
||
|
|
np.array([[2, 3], [4, 5-1j]]),
|
||
|
|
np.array([[1, 2j], [3, 4j], [5j, 6+7j]])),
|
||
|
|
]
|
||
|
|
|
||
|
|
def check_case(self, a, b, c):
|
||
|
|
x = solve_sylvester(a, b, c)
|
||
|
|
assert_array_almost_equal(np.dot(a, x) + np.dot(x, b), c)
|
||
|
|
|
||
|
|
def test_cases(self):
|
||
|
|
for case in self.cases:
|
||
|
|
self.check_case(case[0], case[1], case[2])
|
||
|
|
|
||
|
|
def test_trivial(self):
|
||
|
|
a = np.array([[1.0, 0.0], [0.0, 1.0]])
|
||
|
|
b = np.array([[1.0]])
|
||
|
|
c = np.array([2.0, 2.0]).reshape(-1, 1)
|
||
|
|
x = solve_sylvester(a, b, c)
|
||
|
|
assert_array_almost_equal(x, np.array([1.0, 1.0]).reshape(-1, 1))
|