diff options
Diffstat (limited to 'numpy/linalg')
| -rw-r--r-- | numpy/linalg/tests/test_linalg.py | 64 |
1 files changed, 42 insertions, 22 deletions
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 1f135fd4b..3e832b62f 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -22,6 +22,7 @@ def assert_almost_equal(a, b, **kw): decimal = 12 old_assert_almost_equal(a, b, decimal=decimal, **kw) + class LinalgTestCase(object): def test_single(self): a = array([[1.,2.], [3.,4.]], dtype=single) @@ -138,24 +139,28 @@ class TestSolve(LinalgTestCase, TestCase): assert_almost_equal(b, dot(a, x)) assert_(imply(isinstance(b, matrix), isinstance(x, matrix))) + class TestInv(LinalgTestCase, TestCase): def do(self, a, b): a_inv = linalg.inv(a) assert_almost_equal(dot(a, a_inv), identity(asarray(a).shape[0])) assert_(imply(isinstance(a, matrix), isinstance(a_inv, matrix))) + class TestEigvals(LinalgTestCase, TestCase): def do(self, a, b): ev = linalg.eigvals(a) evalues, evectors = linalg.eig(a) assert_almost_equal(ev, evalues) + class TestEig(LinalgTestCase, TestCase): def do(self, a, b): evalues, evectors = linalg.eig(a) assert_almost_equal(dot(a, evectors), multiply(evectors, evalues)) assert_(imply(isinstance(a, matrix), isinstance(evectors, matrix))) + class TestSVD(LinalgTestCase, TestCase): def do(self, a, b): u, s, vt = linalg.svd(a, 0) @@ -163,29 +168,34 @@ class TestSVD(LinalgTestCase, TestCase): assert_(imply(isinstance(a, matrix), isinstance(u, matrix))) assert_(imply(isinstance(a, matrix), isinstance(vt, matrix))) + class TestCondSVD(LinalgTestCase, TestCase): def do(self, a, b): c = asarray(a) # a might be a matrix s = linalg.svd(c, compute_uv=False) old_assert_almost_equal(s[0]/s[-1], linalg.cond(a), decimal=5) + class TestCond2(LinalgTestCase, TestCase): def do(self, a, b): c = asarray(a) # a might be a matrix s = linalg.svd(c, compute_uv=False) old_assert_almost_equal(s[0]/s[-1], linalg.cond(a,2), decimal=5) + class TestCondInf(TestCase): def test(self): A = array([[1.,0,0],[0,-2.,0],[0,0,3.]]) assert_almost_equal(linalg.cond(A,inf),3.) + class TestPinv(LinalgTestCase, TestCase): def do(self, a, b): a_ginv = linalg.pinv(a) assert_almost_equal(dot(a, a_ginv), identity(asarray(a).shape[0])) assert_(imply(isinstance(a, matrix), isinstance(a_ginv, matrix))) + class TestDet(LinalgTestCase, TestCase): def do(self, a, b): d = linalg.det(a) @@ -215,6 +225,7 @@ class TestDet(LinalgTestCase, TestCase): assert_equal(type(linalg.slogdet([[0.0j]])[0]), cdouble) assert_equal(type(linalg.slogdet([[0.0j]])[1]), double) + class TestLstsq(LinalgTestCase, LinalgNonsquareTestCase, TestCase): def do(self, a, b): arr = np.asarray(a) @@ -240,6 +251,7 @@ class TestLstsq(LinalgTestCase, LinalgNonsquareTestCase, TestCase): assert_(imply(isinstance(b, matrix), isinstance(x, matrix))) assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) + class TestMatrixPower(object): R90 = array([[0,1],[-1,0]]) Arb22 = array([[4,-7],[-2,10]]) @@ -293,6 +305,7 @@ class TestMatrixPower(object): assert_raises(numpy.linalg.linalg.LinAlgError, lambda: matrix_power(self.noninv,-1)) + class TestBoolPower(TestCase): def test_square(self): A = array([[True,False],[True,True]]) @@ -334,6 +347,7 @@ class HermitianTestCase(object): a = matrix([[1.,2.], [2.,1.]]) self.do(a) + class TestEigvalsh(HermitianTestCase, TestCase): def do(self, a): # note that eigenvalue arrays must be sorted since @@ -344,6 +358,7 @@ class TestEigvalsh(HermitianTestCase, TestCase): evalues.sort() assert_almost_equal(ev, evalues) + class TestEigh(HermitianTestCase, TestCase): def do(self, a): # note that eigenvalue arrays must be sorted since @@ -354,6 +369,7 @@ class TestEigh(HermitianTestCase, TestCase): evalues.sort() assert_almost_equal(ev, evalues) + class _TestNorm(TestCase): dt = None dec = None @@ -403,37 +419,41 @@ class _TestNorm(TestCase): self.assertRaises(ValueError, norm, A, -3) self.assertRaises(ValueError, norm, A, 0) + class TestNormDouble(_TestNorm): dt = np.double dec= 12 + class TestNormSingle(_TestNorm): dt = np.float32 dec = 6 -def test_matrix_rank(): - # Full rank matrix - yield assert_equal, 4, matrix_rank(np.eye(4)) - # rank deficient matrix - I=np.eye(4); I[-1,-1] = 0. - yield assert_equal, matrix_rank(I), 3 - # All zeros - zero rank - yield assert_equal, matrix_rank(np.zeros((4,4))), 0 - # 1 dimension - rank 1 unless all 0 - yield assert_equal, matrix_rank([1, 0, 0, 0]), 1 - yield assert_equal, matrix_rank(np.zeros((4,))), 0 - # accepts array-like - yield assert_equal, matrix_rank([1]), 1 - # greater than 2 dimensions raises error - yield assert_raises, TypeError, matrix_rank, np.zeros((2,2,2)) - # works on scalar - yield assert_equal, matrix_rank(1), 1 - -@raises(linalg.LinAlgError) -def test_qr_empty(): - a = np.zeros((0,2)) - linalg.qr(a) +class TestMatrixRank(object): + def test_matrix_rank(self): + # Full rank matrix + yield assert_equal, 4, matrix_rank(np.eye(4)) + # rank deficient matrix + I=np.eye(4); I[-1,-1] = 0. + yield assert_equal, matrix_rank(I), 3 + # All zeros - zero rank + yield assert_equal, matrix_rank(np.zeros((4,4))), 0 + # 1 dimension - rank 1 unless all 0 + yield assert_equal, matrix_rank([1, 0, 0, 0]), 1 + yield assert_equal, matrix_rank(np.zeros((4,))), 0 + # accepts array-like + yield assert_equal, matrix_rank([1]), 1 + # greater than 2 dimensions raises error + yield assert_raises, TypeError, matrix_rank, np.zeros((2,2,2)) + # works on scalar + yield assert_equal, matrix_rank(1), 1 + + +class TestQR(TestCase): + def test_qr_empty(self): + a = np.zeros((0,2)) + self.assertRaises(linalg.LinAlgError, linalg.qr, a) if __name__ == "__main__": |