884 lines
31 KiB
Python
884 lines
31 KiB
Python
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#
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# Created by: Pearu Peterson, September 2002
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#
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from __future__ import division, print_function, absolute_import
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import sys
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import subprocess
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import time
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from numpy.testing import (assert_equal, assert_array_almost_equal, assert_,
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assert_allclose, assert_almost_equal,
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assert_array_equal)
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import pytest
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from pytest import raises as assert_raises
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import numpy as np
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from numpy.random import rand, seed
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from scipy.linalg import _flapack as flapack
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from scipy.linalg import inv
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from scipy.linalg import svd
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from scipy.linalg.lapack import _compute_lwork
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try:
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from scipy.linalg import _clapack as clapack
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except ImportError:
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clapack = None
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from scipy.linalg.lapack import get_lapack_funcs
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from scipy.linalg.blas import get_blas_funcs
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REAL_DTYPES = [np.float32, np.float64]
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COMPLEX_DTYPES = [np.complex64, np.complex128]
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DTYPES = REAL_DTYPES + COMPLEX_DTYPES
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class TestFlapackSimple(object):
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def test_gebal(self):
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a = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
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a1 = [[1, 0, 0, 3e-4],
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[4, 0, 0, 2e-3],
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[7, 1, 0, 0],
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[0, 1, 0, 0]]
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for p in 'sdzc':
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f = getattr(flapack, p+'gebal', None)
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if f is None:
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continue
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ba, lo, hi, pivscale, info = f(a)
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assert_(not info, repr(info))
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assert_array_almost_equal(ba, a)
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assert_equal((lo, hi), (0, len(a[0])-1))
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assert_array_almost_equal(pivscale, np.ones(len(a)))
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ba, lo, hi, pivscale, info = f(a1, permute=1, scale=1)
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assert_(not info, repr(info))
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# print(a1)
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# print(ba, lo, hi, pivscale)
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def test_gehrd(self):
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a = [[-149, -50, -154],
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[537, 180, 546],
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[-27, -9, -25]]
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for p in 'd':
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f = getattr(flapack, p+'gehrd', None)
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if f is None:
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continue
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ht, tau, info = f(a)
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assert_(not info, repr(info))
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def test_trsyl(self):
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a = np.array([[1, 2], [0, 4]])
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b = np.array([[5, 6], [0, 8]])
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c = np.array([[9, 10], [11, 12]])
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trans = 'T'
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# Test single and double implementations, including most
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# of the options
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for dtype in 'fdFD':
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a1, b1, c1 = a.astype(dtype), b.astype(dtype), c.astype(dtype)
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trsyl, = get_lapack_funcs(('trsyl',), (a1,))
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if dtype.isupper(): # is complex dtype
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a1[0] += 1j
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trans = 'C'
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x, scale, info = trsyl(a1, b1, c1)
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assert_array_almost_equal(np.dot(a1, x) + np.dot(x, b1),
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scale * c1)
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x, scale, info = trsyl(a1, b1, c1, trana=trans, tranb=trans)
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assert_array_almost_equal(
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np.dot(a1.conjugate().T, x) + np.dot(x, b1.conjugate().T),
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scale * c1, decimal=4)
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x, scale, info = trsyl(a1, b1, c1, isgn=-1)
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assert_array_almost_equal(np.dot(a1, x) - np.dot(x, b1),
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scale * c1, decimal=4)
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def test_lange(self):
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a = np.array([
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[-149, -50, -154],
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[537, 180, 546],
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[-27, -9, -25]])
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for dtype in 'fdFD':
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for norm in 'Mm1OoIiFfEe':
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a1 = a.astype(dtype)
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if dtype.isupper():
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# is complex dtype
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a1[0, 0] += 1j
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lange, = get_lapack_funcs(('lange',), (a1,))
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value = lange(norm, a1)
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if norm in 'FfEe':
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if dtype in 'Ff':
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decimal = 3
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else:
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decimal = 7
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ref = np.sqrt(np.sum(np.square(np.abs(a1))))
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assert_almost_equal(value, ref, decimal)
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else:
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if norm in 'Mm':
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ref = np.max(np.abs(a1))
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elif norm in '1Oo':
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ref = np.max(np.sum(np.abs(a1), axis=0))
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elif norm in 'Ii':
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ref = np.max(np.sum(np.abs(a1), axis=1))
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assert_equal(value, ref)
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class TestLapack(object):
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def test_flapack(self):
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if hasattr(flapack, 'empty_module'):
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# flapack module is empty
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pass
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def test_clapack(self):
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if hasattr(clapack, 'empty_module'):
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# clapack module is empty
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pass
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class TestLeastSquaresSolvers(object):
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def test_gels(self):
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seed(1234)
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# Test fat/tall matrix argument handling - gh-issue #8329
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for ind, dtype in enumerate(DTYPES):
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m = 10
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n = 20
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nrhs = 1
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a1 = rand(m, n).astype(dtype)
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b1 = rand(n).astype(dtype)
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gls, glslw = get_lapack_funcs(('gels', 'gels_lwork'), dtype=dtype)
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# Request of sizes
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lwork = _compute_lwork(glslw, m, n, nrhs)
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_, _, info = gls(a1, b1, lwork=lwork)
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assert_(info >= 0)
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_, _, info = gls(a1, b1, trans='TTCC'[ind], lwork=lwork)
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assert_(info >= 0)
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for dtype in REAL_DTYPES:
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a1 = np.array([[1.0, 2.0],
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[4.0, 5.0],
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[7.0, 8.0]], dtype=dtype)
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b1 = np.array([16.0, 17.0, 20.0], dtype=dtype)
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gels, gels_lwork, geqrf = get_lapack_funcs(
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('gels', 'gels_lwork', 'geqrf'), (a1, b1))
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m, n = a1.shape
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if len(b1.shape) == 2:
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nrhs = b1.shape[1]
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else:
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nrhs = 1
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# Request of sizes
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lwork = _compute_lwork(gels_lwork, m, n, nrhs)
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lqr, x, info = gels(a1, b1, lwork=lwork)
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assert_allclose(x[:-1], np.array([-14.333333333333323,
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14.999999999999991],
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dtype=dtype),
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rtol=25*np.finfo(dtype).eps)
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lqr_truth, _, _, _ = geqrf(a1)
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assert_array_equal(lqr, lqr_truth)
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for dtype in COMPLEX_DTYPES:
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a1 = np.array([[1.0+4.0j, 2.0],
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[4.0+0.5j, 5.0-3.0j],
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[7.0-2.0j, 8.0+0.7j]], dtype=dtype)
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b1 = np.array([16.0, 17.0+2.0j, 20.0-4.0j], dtype=dtype)
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gels, gels_lwork, geqrf = get_lapack_funcs(
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('gels', 'gels_lwork', 'geqrf'), (a1, b1))
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m, n = a1.shape
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if len(b1.shape) == 2:
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nrhs = b1.shape[1]
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else:
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nrhs = 1
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# Request of sizes
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lwork = _compute_lwork(gels_lwork, m, n, nrhs)
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lqr, x, info = gels(a1, b1, lwork=lwork)
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assert_allclose(x[:-1],
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np.array([1.161753632288328-1.901075709391912j,
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1.735882340522193+1.521240901196909j],
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dtype=dtype), rtol=25*np.finfo(dtype).eps)
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lqr_truth, _, _, _ = geqrf(a1)
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assert_array_equal(lqr, lqr_truth)
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def test_gelsd(self):
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for dtype in REAL_DTYPES:
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a1 = np.array([[1.0, 2.0],
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[4.0, 5.0],
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[7.0, 8.0]], dtype=dtype)
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b1 = np.array([16.0, 17.0, 20.0], dtype=dtype)
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gelsd, gelsd_lwork = get_lapack_funcs(('gelsd', 'gelsd_lwork'),
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(a1, b1))
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m, n = a1.shape
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if len(b1.shape) == 2:
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nrhs = b1.shape[1]
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else:
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nrhs = 1
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# Request of sizes
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work, iwork, info = gelsd_lwork(m, n, nrhs, -1)
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lwork = int(np.real(work))
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iwork_size = iwork
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x, s, rank, info = gelsd(a1, b1, lwork, iwork_size,
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-1, False, False)
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assert_allclose(x[:-1], np.array([-14.333333333333323,
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14.999999999999991], dtype=dtype),
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rtol=25*np.finfo(dtype).eps)
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assert_allclose(s, np.array([12.596017180511966,
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0.583396253199685], dtype=dtype),
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rtol=25*np.finfo(dtype).eps)
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for dtype in COMPLEX_DTYPES:
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a1 = np.array([[1.0+4.0j, 2.0],
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[4.0+0.5j, 5.0-3.0j],
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[7.0-2.0j, 8.0+0.7j]], dtype=dtype)
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b1 = np.array([16.0, 17.0+2.0j, 20.0-4.0j], dtype=dtype)
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gelsd, gelsd_lwork = get_lapack_funcs(('gelsd', 'gelsd_lwork'),
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(a1, b1))
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m, n = a1.shape
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if len(b1.shape) == 2:
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nrhs = b1.shape[1]
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else:
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nrhs = 1
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# Request of sizes
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work, rwork, iwork, info = gelsd_lwork(m, n, nrhs, -1)
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lwork = int(np.real(work))
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rwork_size = int(rwork)
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iwork_size = iwork
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x, s, rank, info = gelsd(a1, b1, lwork, rwork_size, iwork_size,
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-1, False, False)
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assert_allclose(x[:-1],
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np.array([1.161753632288328-1.901075709391912j,
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1.735882340522193+1.521240901196909j],
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dtype=dtype), rtol=25*np.finfo(dtype).eps)
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assert_allclose(s,
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np.array([13.035514762572043, 4.337666985231382],
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dtype=dtype), rtol=25*np.finfo(dtype).eps)
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def test_gelss(self):
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for dtype in REAL_DTYPES:
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a1 = np.array([[1.0, 2.0],
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[4.0, 5.0],
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[7.0, 8.0]], dtype=dtype)
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b1 = np.array([16.0, 17.0, 20.0], dtype=dtype)
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gelss, gelss_lwork = get_lapack_funcs(('gelss', 'gelss_lwork'),
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(a1, b1))
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m, n = a1.shape
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if len(b1.shape) == 2:
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nrhs = b1.shape[1]
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else:
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nrhs = 1
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# Request of sizes
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work, info = gelss_lwork(m, n, nrhs, -1)
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lwork = int(np.real(work))
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v, x, s, rank, work, info = gelss(a1, b1, -1, lwork, False, False)
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assert_allclose(x[:-1], np.array([-14.333333333333323,
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14.999999999999991], dtype=dtype),
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rtol=25*np.finfo(dtype).eps)
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assert_allclose(s, np.array([12.596017180511966,
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0.583396253199685], dtype=dtype),
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rtol=25*np.finfo(dtype).eps)
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for dtype in COMPLEX_DTYPES:
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a1 = np.array([[1.0+4.0j, 2.0],
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[4.0+0.5j, 5.0-3.0j],
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[7.0-2.0j, 8.0+0.7j]], dtype=dtype)
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b1 = np.array([16.0, 17.0+2.0j, 20.0-4.0j], dtype=dtype)
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gelss, gelss_lwork = get_lapack_funcs(('gelss', 'gelss_lwork'),
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(a1, b1))
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m, n = a1.shape
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if len(b1.shape) == 2:
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nrhs = b1.shape[1]
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else:
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nrhs = 1
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# Request of sizes
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work, info = gelss_lwork(m, n, nrhs, -1)
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lwork = int(np.real(work))
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v, x, s, rank, work, info = gelss(a1, b1, -1, lwork, False, False)
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assert_allclose(x[:-1],
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np.array([1.161753632288328-1.901075709391912j,
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1.735882340522193+1.521240901196909j],
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dtype=dtype),
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rtol=25*np.finfo(dtype).eps)
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assert_allclose(s, np.array([13.035514762572043,
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4.337666985231382], dtype=dtype),
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rtol=25*np.finfo(dtype).eps)
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def test_gelsy(self):
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for dtype in REAL_DTYPES:
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a1 = np.array([[1.0, 2.0],
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[4.0, 5.0],
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[7.0, 8.0]], dtype=dtype)
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b1 = np.array([16.0, 17.0, 20.0], dtype=dtype)
|
||
|
|
gelsy, gelsy_lwork = get_lapack_funcs(('gelsy', 'gelss_lwork'),
|
||
|
|
(a1, b1))
|
||
|
|
|
||
|
|
m, n = a1.shape
|
||
|
|
if len(b1.shape) == 2:
|
||
|
|
nrhs = b1.shape[1]
|
||
|
|
else:
|
||
|
|
nrhs = 1
|
||
|
|
|
||
|
|
# Request of sizes
|
||
|
|
work, info = gelsy_lwork(m, n, nrhs, 10*np.finfo(dtype).eps)
|
||
|
|
lwork = int(np.real(work))
|
||
|
|
|
||
|
|
jptv = np.zeros((a1.shape[1], 1), dtype=np.int32)
|
||
|
|
v, x, j, rank, info = gelsy(a1, b1, jptv, np.finfo(dtype).eps,
|
||
|
|
lwork, False, False)
|
||
|
|
assert_allclose(x[:-1], np.array([-14.333333333333323,
|
||
|
|
14.999999999999991], dtype=dtype),
|
||
|
|
rtol=25*np.finfo(dtype).eps)
|
||
|
|
|
||
|
|
for dtype in COMPLEX_DTYPES:
|
||
|
|
a1 = np.array([[1.0+4.0j, 2.0],
|
||
|
|
[4.0+0.5j, 5.0-3.0j],
|
||
|
|
[7.0-2.0j, 8.0+0.7j]], dtype=dtype)
|
||
|
|
b1 = np.array([16.0, 17.0+2.0j, 20.0-4.0j], dtype=dtype)
|
||
|
|
gelsy, gelsy_lwork = get_lapack_funcs(('gelsy', 'gelss_lwork'),
|
||
|
|
(a1, b1))
|
||
|
|
|
||
|
|
m, n = a1.shape
|
||
|
|
if len(b1.shape) == 2:
|
||
|
|
nrhs = b1.shape[1]
|
||
|
|
else:
|
||
|
|
nrhs = 1
|
||
|
|
|
||
|
|
# Request of sizes
|
||
|
|
work, info = gelsy_lwork(m, n, nrhs, 10*np.finfo(dtype).eps)
|
||
|
|
lwork = int(np.real(work))
|
||
|
|
|
||
|
|
jptv = np.zeros((a1.shape[1], 1), dtype=np.int32)
|
||
|
|
v, x, j, rank, info = gelsy(a1, b1, jptv, np.finfo(dtype).eps,
|
||
|
|
lwork, False, False)
|
||
|
|
assert_allclose(x[:-1],
|
||
|
|
np.array([1.161753632288328-1.901075709391912j,
|
||
|
|
1.735882340522193+1.521240901196909j],
|
||
|
|
dtype=dtype),
|
||
|
|
rtol=25*np.finfo(dtype).eps)
|
||
|
|
|
||
|
|
|
||
|
|
class TestRegression(object):
|
||
|
|
|
||
|
|
def test_ticket_1645(self):
|
||
|
|
# Check that RQ routines have correct lwork
|
||
|
|
for dtype in DTYPES:
|
||
|
|
a = np.zeros((300, 2), dtype=dtype)
|
||
|
|
|
||
|
|
gerqf, = get_lapack_funcs(['gerqf'], [a])
|
||
|
|
assert_raises(Exception, gerqf, a, lwork=2)
|
||
|
|
rq, tau, work, info = gerqf(a)
|
||
|
|
|
||
|
|
if dtype in REAL_DTYPES:
|
||
|
|
orgrq, = get_lapack_funcs(['orgrq'], [a])
|
||
|
|
assert_raises(Exception, orgrq, rq[-2:], tau, lwork=1)
|
||
|
|
orgrq(rq[-2:], tau, lwork=2)
|
||
|
|
elif dtype in COMPLEX_DTYPES:
|
||
|
|
ungrq, = get_lapack_funcs(['ungrq'], [a])
|
||
|
|
assert_raises(Exception, ungrq, rq[-2:], tau, lwork=1)
|
||
|
|
ungrq(rq[-2:], tau, lwork=2)
|
||
|
|
|
||
|
|
|
||
|
|
class TestDpotr(object):
|
||
|
|
def test_gh_2691(self):
|
||
|
|
# 'lower' argument of dportf/dpotri
|
||
|
|
for lower in [True, False]:
|
||
|
|
for clean in [True, False]:
|
||
|
|
np.random.seed(42)
|
||
|
|
x = np.random.normal(size=(3, 3))
|
||
|
|
a = x.dot(x.T)
|
||
|
|
|
||
|
|
dpotrf, dpotri = get_lapack_funcs(("potrf", "potri"), (a, ))
|
||
|
|
|
||
|
|
c, info = dpotrf(a, lower, clean=clean)
|
||
|
|
dpt = dpotri(c, lower)[0]
|
||
|
|
|
||
|
|
if lower:
|
||
|
|
assert_allclose(np.tril(dpt), np.tril(inv(a)))
|
||
|
|
else:
|
||
|
|
assert_allclose(np.triu(dpt), np.triu(inv(a)))
|
||
|
|
|
||
|
|
|
||
|
|
class TestDlasd4(object):
|
||
|
|
def test_sing_val_update(self):
|
||
|
|
|
||
|
|
sigmas = np.array([4., 3., 2., 0])
|
||
|
|
m_vec = np.array([3.12, 5.7, -4.8, -2.2])
|
||
|
|
|
||
|
|
M = np.hstack((np.vstack((np.diag(sigmas[0:-1]),
|
||
|
|
np.zeros((1, len(m_vec) - 1)))), m_vec[:, np.newaxis]))
|
||
|
|
SM = svd(M, full_matrices=False, compute_uv=False, overwrite_a=False,
|
||
|
|
check_finite=False)
|
||
|
|
|
||
|
|
it_len = len(sigmas)
|
||
|
|
sgm = np.concatenate((sigmas[::-1], (sigmas[0] +
|
||
|
|
it_len*np.sqrt(np.sum(np.power(m_vec, 2))),)))
|
||
|
|
mvc = np.concatenate((m_vec[::-1], (0,)))
|
||
|
|
|
||
|
|
lasd4 = get_lapack_funcs('lasd4', (sigmas,))
|
||
|
|
|
||
|
|
roots = []
|
||
|
|
for i in range(0, it_len):
|
||
|
|
res = lasd4(i, sgm, mvc)
|
||
|
|
roots.append(res[1])
|
||
|
|
|
||
|
|
assert_((res[3] <= 0), "LAPACK root finding dlasd4 failed to find \
|
||
|
|
the singular value %i" % i)
|
||
|
|
roots = np.array(roots)[::-1]
|
||
|
|
|
||
|
|
assert_((not np.any(np.isnan(roots)), "There are NaN roots"))
|
||
|
|
assert_allclose(SM, roots, atol=100*np.finfo(np.float64).eps,
|
||
|
|
rtol=100*np.finfo(np.float64).eps)
|
||
|
|
|
||
|
|
|
||
|
|
def test_lartg():
|
||
|
|
for dtype in 'fdFD':
|
||
|
|
lartg = get_lapack_funcs('lartg', dtype=dtype)
|
||
|
|
|
||
|
|
f = np.array(3, dtype)
|
||
|
|
g = np.array(4, dtype)
|
||
|
|
|
||
|
|
if np.iscomplexobj(g):
|
||
|
|
g *= 1j
|
||
|
|
|
||
|
|
cs, sn, r = lartg(f, g)
|
||
|
|
|
||
|
|
assert_allclose(cs, 3.0/5.0)
|
||
|
|
assert_allclose(r, 5.0)
|
||
|
|
|
||
|
|
if np.iscomplexobj(g):
|
||
|
|
assert_allclose(sn, -4.0j/5.0)
|
||
|
|
assert_(type(r) == complex)
|
||
|
|
assert_(type(cs) == float)
|
||
|
|
else:
|
||
|
|
assert_allclose(sn, 4.0/5.0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rot():
|
||
|
|
# srot, drot from blas and crot and zrot from lapack.
|
||
|
|
|
||
|
|
for dtype in 'fdFD':
|
||
|
|
c = 0.6
|
||
|
|
s = 0.8
|
||
|
|
|
||
|
|
u = np.ones(4, dtype) * 3
|
||
|
|
v = np.ones(4, dtype) * 4
|
||
|
|
atol = 10**-(np.finfo(dtype).precision-1)
|
||
|
|
|
||
|
|
if dtype in 'fd':
|
||
|
|
rot = get_blas_funcs('rot', dtype=dtype)
|
||
|
|
f = 4
|
||
|
|
else:
|
||
|
|
rot = get_lapack_funcs('rot', dtype=dtype)
|
||
|
|
s *= -1j
|
||
|
|
v *= 1j
|
||
|
|
f = 4j
|
||
|
|
|
||
|
|
assert_allclose(rot(u, v, c, s), [[5, 5, 5, 5],
|
||
|
|
[0, 0, 0, 0]], atol=atol)
|
||
|
|
assert_allclose(rot(u, v, c, s, n=2), [[5, 5, 3, 3],
|
||
|
|
[0, 0, f, f]], atol=atol)
|
||
|
|
assert_allclose(rot(u, v, c, s, offx=2, offy=2),
|
||
|
|
[[3, 3, 5, 5], [f, f, 0, 0]], atol=atol)
|
||
|
|
assert_allclose(rot(u, v, c, s, incx=2, offy=2, n=2),
|
||
|
|
[[5, 3, 5, 3], [f, f, 0, 0]], atol=atol)
|
||
|
|
assert_allclose(rot(u, v, c, s, offx=2, incy=2, n=2),
|
||
|
|
[[3, 3, 5, 5], [0, f, 0, f]], atol=atol)
|
||
|
|
assert_allclose(rot(u, v, c, s, offx=2, incx=2, offy=2, incy=2, n=1),
|
||
|
|
[[3, 3, 5, 3], [f, f, 0, f]], atol=atol)
|
||
|
|
assert_allclose(rot(u, v, c, s, incx=-2, incy=-2, n=2),
|
||
|
|
[[5, 3, 5, 3], [0, f, 0, f]], atol=atol)
|
||
|
|
|
||
|
|
a, b = rot(u, v, c, s, overwrite_x=1, overwrite_y=1)
|
||
|
|
assert_(a is u)
|
||
|
|
assert_(b is v)
|
||
|
|
assert_allclose(a, [5, 5, 5, 5], atol=atol)
|
||
|
|
assert_allclose(b, [0, 0, 0, 0], atol=atol)
|
||
|
|
|
||
|
|
|
||
|
|
def test_larfg_larf():
|
||
|
|
np.random.seed(1234)
|
||
|
|
a0 = np.random.random((4, 4))
|
||
|
|
a0 = a0.T.dot(a0)
|
||
|
|
|
||
|
|
a0j = np.random.random((4, 4)) + 1j*np.random.random((4, 4))
|
||
|
|
a0j = a0j.T.conj().dot(a0j)
|
||
|
|
|
||
|
|
# our test here will be to do one step of reducing a hermetian matrix to
|
||
|
|
# tridiagonal form using householder transforms.
|
||
|
|
|
||
|
|
for dtype in 'fdFD':
|
||
|
|
larfg, larf = get_lapack_funcs(['larfg', 'larf'], dtype=dtype)
|
||
|
|
|
||
|
|
if dtype in 'FD':
|
||
|
|
a = a0j.copy()
|
||
|
|
else:
|
||
|
|
a = a0.copy()
|
||
|
|
|
||
|
|
# generate a householder transform to clear a[2:,0]
|
||
|
|
alpha, x, tau = larfg(a.shape[0]-1, a[1, 0], a[2:, 0])
|
||
|
|
|
||
|
|
# create expected output
|
||
|
|
expected = np.zeros_like(a[:, 0])
|
||
|
|
expected[0] = a[0, 0]
|
||
|
|
expected[1] = alpha
|
||
|
|
|
||
|
|
# assemble householder vector
|
||
|
|
v = np.zeros_like(a[1:, 0])
|
||
|
|
v[0] = 1.0
|
||
|
|
v[1:] = x
|
||
|
|
|
||
|
|
# apply transform from the left
|
||
|
|
a[1:, :] = larf(v, tau.conjugate(), a[1:, :], np.zeros(a.shape[1]))
|
||
|
|
|
||
|
|
# apply transform from the right
|
||
|
|
a[:, 1:] = larf(v, tau, a[:,1:], np.zeros(a.shape[0]), side='R')
|
||
|
|
|
||
|
|
assert_allclose(a[:, 0], expected, atol=1e-5)
|
||
|
|
assert_allclose(a[0, :], expected, atol=1e-5)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.xslow
|
||
|
|
def test_sgesdd_lwork_bug_workaround():
|
||
|
|
# Test that SGESDD lwork is sufficiently large for LAPACK.
|
||
|
|
#
|
||
|
|
# This checks that workaround around an apparent LAPACK bug
|
||
|
|
# actually works. cf. gh-5401
|
||
|
|
#
|
||
|
|
# xslow: requires 1GB+ of memory
|
||
|
|
|
||
|
|
p = subprocess.Popen([sys.executable, '-c',
|
||
|
|
'import numpy as np; '
|
||
|
|
'from scipy.linalg import svd; '
|
||
|
|
'a = np.zeros([9537, 9537], dtype=np.float32); '
|
||
|
|
'svd(a)'],
|
||
|
|
stdout=subprocess.PIPE,
|
||
|
|
stderr=subprocess.STDOUT)
|
||
|
|
|
||
|
|
# Check if it an error occurred within 5 sec; the computation can
|
||
|
|
# take substantially longer, and we will not wait for it to finish
|
||
|
|
for j in range(50):
|
||
|
|
time.sleep(0.1)
|
||
|
|
if p.poll() is not None:
|
||
|
|
returncode = p.returncode
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
# Didn't exit in time -- probably entered computation. The
|
||
|
|
# error is raised before entering computation, so things are
|
||
|
|
# probably OK.
|
||
|
|
returncode = 0
|
||
|
|
p.terminate()
|
||
|
|
|
||
|
|
assert_equal(returncode, 0,
|
||
|
|
"Code apparently failed: " + p.stdout.read())
|
||
|
|
|
||
|
|
|
||
|
|
class TestSytrd(object):
|
||
|
|
def test_sytrd(self):
|
||
|
|
for dtype in REAL_DTYPES:
|
||
|
|
# Assert that a 0x0 matrix raises an error
|
||
|
|
A = np.zeros((0, 0), dtype=dtype)
|
||
|
|
sytrd, sytrd_lwork = \
|
||
|
|
get_lapack_funcs(('sytrd', 'sytrd_lwork'), (A,))
|
||
|
|
assert_raises(ValueError, sytrd, A)
|
||
|
|
|
||
|
|
# Tests for n = 1 currently fail with
|
||
|
|
# ```
|
||
|
|
# ValueError: failed to create intent(cache|hide)|optional array--
|
||
|
|
# must have defined dimensions but got (0,)
|
||
|
|
# ```
|
||
|
|
# This is a NumPy issue
|
||
|
|
# <https://github.com/numpy/numpy/issues/9617>.
|
||
|
|
# TODO once the issue has been resolved, test for n=1
|
||
|
|
|
||
|
|
# some upper triangular array
|
||
|
|
n = 3
|
||
|
|
A = np.zeros((n, n), dtype=dtype)
|
||
|
|
A[np.triu_indices_from(A)] = \
|
||
|
|
np.arange(1, n*(n+1)//2+1, dtype=dtype)
|
||
|
|
|
||
|
|
# query lwork
|
||
|
|
lwork, info = sytrd_lwork(n)
|
||
|
|
assert_equal(info, 0)
|
||
|
|
|
||
|
|
# check lower=1 behavior (shouldn't do much since the matrix is
|
||
|
|
# upper triangular)
|
||
|
|
data, d, e, tau, info = sytrd(A, lower=1, lwork=lwork)
|
||
|
|
assert_equal(info, 0)
|
||
|
|
|
||
|
|
assert_allclose(data, A, atol=5*np.finfo(dtype).eps, rtol=1.0)
|
||
|
|
assert_allclose(d, np.diag(A))
|
||
|
|
assert_allclose(e, 0.0)
|
||
|
|
assert_allclose(tau, 0.0)
|
||
|
|
|
||
|
|
# and now for the proper test (lower=0 is the default)
|
||
|
|
data, d, e, tau, info = sytrd(A, lwork=lwork)
|
||
|
|
assert_equal(info, 0)
|
||
|
|
|
||
|
|
# assert Q^T*A*Q = tridiag(e, d, e)
|
||
|
|
|
||
|
|
# build tridiagonal matrix
|
||
|
|
T = np.zeros_like(A, dtype=dtype)
|
||
|
|
k = np.arange(A.shape[0])
|
||
|
|
T[k, k] = d
|
||
|
|
k2 = np.arange(A.shape[0]-1)
|
||
|
|
T[k2+1, k2] = e
|
||
|
|
T[k2, k2+1] = e
|
||
|
|
|
||
|
|
# build Q
|
||
|
|
Q = np.eye(n, n, dtype=dtype)
|
||
|
|
for i in range(n-1):
|
||
|
|
v = np.zeros(n, dtype=dtype)
|
||
|
|
v[:i] = data[:i, i+1]
|
||
|
|
v[i] = 1.0
|
||
|
|
H = np.eye(n, n, dtype=dtype) - tau[i] * np.outer(v, v)
|
||
|
|
Q = np.dot(H, Q)
|
||
|
|
|
||
|
|
# Make matrix fully symmetric
|
||
|
|
i_lower = np.tril_indices(n, -1)
|
||
|
|
A[i_lower] = A.T[i_lower]
|
||
|
|
|
||
|
|
QTAQ = np.dot(Q.T, np.dot(A, Q))
|
||
|
|
|
||
|
|
# disable rtol here since some values in QTAQ and T are very close
|
||
|
|
# to 0.
|
||
|
|
assert_allclose(QTAQ, T, atol=5*np.finfo(dtype).eps, rtol=1.0)
|
||
|
|
|
||
|
|
|
||
|
|
class TestHetrd(object):
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def test_hetrd(self):
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for real_dtype, complex_dtype in zip(REAL_DTYPES, COMPLEX_DTYPES):
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# Assert that a 0x0 matrix raises an error
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A = np.zeros((0, 0), dtype=complex_dtype)
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hetrd, hetrd_lwork = \
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get_lapack_funcs(('hetrd', 'hetrd_lwork'), (A,))
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assert_raises(ValueError, hetrd, A)
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# Tests for n = 1 currently fail with
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# ```
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# ValueError: failed to create intent(cache|hide)|optional array--
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# must have defined dimensions but got (0,)
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# ```
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# This is a NumPy issue
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# <https://github.com/numpy/numpy/issues/9617>.
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# TODO once the issue has been resolved, test for n=1
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# some upper triangular array
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n = 3
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A = np.zeros((n, n), dtype=complex_dtype)
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A[np.triu_indices_from(A)] = (
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np.arange(1, n*(n+1)//2+1, dtype=real_dtype)
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+ 1j * np.arange(1, n*(n+1)//2+1, dtype=real_dtype)
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)
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np.fill_diagonal(A, np.real(np.diag(A)))
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# query lwork
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lwork, info = hetrd_lwork(n)
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assert_equal(info, 0)
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# check lower=1 behavior (shouldn't do much since the matrix is
|
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# upper triangular)
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data, d, e, tau, info = hetrd(A, lower=1, lwork=lwork)
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assert_equal(info, 0)
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assert_allclose(data, A, atol=5*np.finfo(real_dtype).eps, rtol=1.0)
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assert_allclose(d, np.real(np.diag(A)))
|
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assert_allclose(e, 0.0)
|
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assert_allclose(tau, 0.0)
|
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|
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# and now for the proper test (lower=0 is the default)
|
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data, d, e, tau, info = hetrd(A, lwork=lwork)
|
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assert_equal(info, 0)
|
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|
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# assert Q^T*A*Q = tridiag(e, d, e)
|
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# build tridiagonal matrix
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T = np.zeros_like(A, dtype=real_dtype)
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k = np.arange(A.shape[0], dtype=int)
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T[k, k] = d
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k2 = np.arange(A.shape[0]-1, dtype=int)
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T[k2+1, k2] = e
|
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T[k2, k2+1] = e
|
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|
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|
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# build Q
|
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Q = np.eye(n, n, dtype=complex_dtype)
|
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for i in range(n-1):
|
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v = np.zeros(n, dtype=complex_dtype)
|
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v[:i] = data[:i, i+1]
|
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|
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v[i] = 1.0
|
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|
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H = np.eye(n, n, dtype=complex_dtype) \
|
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- tau[i] * np.outer(v, np.conj(v))
|
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|
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Q = np.dot(H, Q)
|
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|
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|
||
|
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# Make matrix fully Hermetian
|
||
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i_lower = np.tril_indices(n, -1)
|
||
|
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A[i_lower] = np.conj(A.T[i_lower])
|
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|
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|
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QHAQ = np.dot(np.conj(Q.T), np.dot(A, Q))
|
||
|
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|
||
|
|
# disable rtol here since some values in QTAQ and T are very close
|
||
|
|
# to 0.
|
||
|
|
assert_allclose(
|
||
|
|
QHAQ, T, atol=10*np.finfo(real_dtype).eps, rtol=1.0
|
||
|
|
)
|
||
|
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|
||
|
|
|
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|
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def test_gglse():
|
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|
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# Example data taken from NAG manual
|
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|
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for ind, dtype in enumerate(DTYPES):
|
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|
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# DTYPES = <s,d,c,z> gglse
|
||
|
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func, func_lwork = get_lapack_funcs(('gglse', 'gglse_lwork'),
|
||
|
|
dtype=dtype)
|
||
|
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lwork = _compute_lwork(func_lwork, m=6, n=4, p=2)
|
||
|
|
# For <s,d>gglse
|
||
|
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if ind < 2:
|
||
|
|
a = np.array([[-0.57, -1.28, -0.39, 0.25],
|
||
|
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[-1.93, 1.08, -0.31, -2.14],
|
||
|
|
[2.30, 0.24, 0.40, -0.35],
|
||
|
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[-1.93, 0.64, -0.66, 0.08],
|
||
|
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[0.15, 0.30, 0.15, -2.13],
|
||
|
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[-0.02, 1.03, -1.43, 0.50]], dtype=dtype)
|
||
|
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c = np.array([-1.50, -2.14, 1.23, -0.54, -1.68, 0.82], dtype=dtype)
|
||
|
|
d = np.array([0., 0.], dtype=dtype)
|
||
|
|
# For <s,d>gglse
|
||
|
|
else:
|
||
|
|
a = np.array([[0.96-0.81j, -0.03+0.96j, -0.91+2.06j, -0.05+0.41j],
|
||
|
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[-0.98+1.98j, -1.20+0.19j, -0.66+0.42j, -0.81+0.56j],
|
||
|
|
[0.62-0.46j, 1.01+0.02j, 0.63-0.17j, -1.11+0.60j],
|
||
|
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[0.37+0.38j, 0.19-0.54j, -0.98-0.36j, 0.22-0.20j],
|
||
|
|
[0.83+0.51j, 0.20+0.01j, -0.17-0.46j, 1.47+1.59j],
|
||
|
|
[1.08-0.28j, 0.20-0.12j, -0.07+1.23j, 0.26+0.26j]])
|
||
|
|
c = np.array([[-2.54+0.09j],
|
||
|
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[1.65-2.26j],
|
||
|
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[-2.11-3.96j],
|
||
|
|
[1.82+3.30j],
|
||
|
|
[-6.41+3.77j],
|
||
|
|
[2.07+0.66j]])
|
||
|
|
d = np.zeros(2, dtype=dtype)
|
||
|
|
|
||
|
|
b = np.array([[1., 0., -1., 0.], [0., 1., 0., -1.]], dtype=dtype)
|
||
|
|
|
||
|
|
_, _, _, result, _ = func(a, b, c, d, lwork=lwork)
|
||
|
|
if ind < 2:
|
||
|
|
expected = np.array([0.48904455,
|
||
|
|
0.99754786,
|
||
|
|
0.48904455,
|
||
|
|
0.99754786])
|
||
|
|
else:
|
||
|
|
expected = np.array([1.08742917-1.96205783j,
|
||
|
|
-0.74093902+3.72973919j,
|
||
|
|
1.08742917-1.96205759j,
|
||
|
|
-0.74093896+3.72973895j])
|
||
|
|
assert_array_almost_equal(result, expected, decimal=4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_sycon_hecon():
|
||
|
|
seed(1234)
|
||
|
|
for ind, dtype in enumerate(DTYPES+COMPLEX_DTYPES):
|
||
|
|
# DTYPES + COMPLEX DTYPES = <s,d,c,z> sycon + <c,z>hecon
|
||
|
|
n = 10
|
||
|
|
# For <s,d,c,z>sycon
|
||
|
|
if ind < 4:
|
||
|
|
func_lwork = get_lapack_funcs('sytrf_lwork', dtype=dtype)
|
||
|
|
funcon, functrf = get_lapack_funcs(('sycon', 'sytrf'), dtype=dtype)
|
||
|
|
A = (rand(n, n)).astype(dtype)
|
||
|
|
# For <c,z>hecon
|
||
|
|
else:
|
||
|
|
func_lwork = get_lapack_funcs('hetrf_lwork', dtype=dtype)
|
||
|
|
funcon, functrf = get_lapack_funcs(('hecon', 'hetrf'), dtype=dtype)
|
||
|
|
A = (rand(n, n) + rand(n, n)*1j).astype(dtype)
|
||
|
|
|
||
|
|
# Since sycon only refers to upper/lower part, conj() is safe here.
|
||
|
|
A = (A + A.conj().T)/2 + 2*np.eye(n, dtype=dtype)
|
||
|
|
|
||
|
|
anorm = np.linalg.norm(A, 1)
|
||
|
|
lwork = _compute_lwork(func_lwork, n)
|
||
|
|
ldu, ipiv, _ = functrf(A, lwork=lwork, lower=1)
|
||
|
|
rcond, _ = funcon(a=ldu, ipiv=ipiv, anorm=anorm, lower=1)
|
||
|
|
# The error is at most 1-fold
|
||
|
|
assert_(abs(1/rcond - np.linalg.cond(A, p=1))*rcond < 1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_sygst():
|
||
|
|
seed(1234)
|
||
|
|
for ind, dtype in enumerate(REAL_DTYPES):
|
||
|
|
# DTYPES = <s,d> sygst
|
||
|
|
n = 10
|
||
|
|
|
||
|
|
potrf, sygst, syevd, sygvd = get_lapack_funcs(('potrf', 'sygst', 'syevd', 'sygvd'), dtype=dtype)
|
||
|
|
|
||
|
|
A = rand(n, n).astype(dtype)
|
||
|
|
A = (A + A.T)/2
|
||
|
|
# B must be positive definite
|
||
|
|
B = rand(n, n).astype(dtype)
|
||
|
|
B = (B + B.T)/2 + 2 * np.eye(n, dtype=dtype)
|
||
|
|
|
||
|
|
# Perform eig (sygvd)
|
||
|
|
_, eig_gvd, info = sygvd(A, B)
|
||
|
|
assert_(info == 0)
|
||
|
|
|
||
|
|
# Convert to std problem potrf
|
||
|
|
b, info = potrf(B)
|
||
|
|
assert_(info == 0)
|
||
|
|
a, info = sygst(A, b)
|
||
|
|
assert_(info == 0)
|
||
|
|
|
||
|
|
eig, _, info = syevd(a)
|
||
|
|
assert_(info == 0)
|
||
|
|
assert_allclose(eig, eig_gvd, rtol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_hegst():
|
||
|
|
seed(1234)
|
||
|
|
for ind, dtype in enumerate(COMPLEX_DTYPES):
|
||
|
|
# DTYPES = <c,z> hegst
|
||
|
|
n = 10
|
||
|
|
|
||
|
|
potrf, hegst, heevd, hegvd = get_lapack_funcs(('potrf', 'hegst', 'heevd', 'hegvd'), dtype=dtype)
|
||
|
|
|
||
|
|
A = rand(n, n).astype(dtype) + 1j * rand(n, n).astype(dtype)
|
||
|
|
A = (A + A.conj().T)/2
|
||
|
|
# B must be positive definite
|
||
|
|
B = rand(n, n).astype(dtype) + 1j * rand(n, n).astype(dtype)
|
||
|
|
B = (B + B.conj().T)/2 + 2 * np.eye(n, dtype=dtype)
|
||
|
|
|
||
|
|
# Perform eig (hegvd)
|
||
|
|
_, eig_gvd, info = hegvd(A, B)
|
||
|
|
assert_(info == 0)
|
||
|
|
|
||
|
|
# Convert to std problem potrf
|
||
|
|
b, info = potrf(B)
|
||
|
|
assert_(info == 0)
|
||
|
|
a, info = hegst(A, b)
|
||
|
|
assert_(info == 0)
|
||
|
|
|
||
|
|
eig, _, info = heevd(a)
|
||
|
|
assert_(info == 0)
|
||
|
|
assert_allclose(eig, eig_gvd, rtol=1e-4)
|