diff options
Diffstat (limited to 'numpy/linalg/tests/test_linalg.py')
| -rw-r--r-- | numpy/linalg/tests/test_linalg.py | 128 |
1 files changed, 64 insertions, 64 deletions
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 7f102634e..2dd270521 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -39,32 +39,32 @@ def get_complex_dtype(dtype): class LinalgTestCase(object): def test_single(self): - a = array([[1.,2.], [3.,4.]], dtype=single) + a = array([[1., 2.], [3., 4.]], dtype=single) b = array([2., 1.], dtype=single) self.do(a, b) def test_double(self): - a = array([[1.,2.], [3.,4.]], dtype=double) + a = array([[1., 2.], [3., 4.]], dtype=double) b = array([2., 1.], dtype=double) self.do(a, b) def test_double_2(self): - a = array([[1.,2.], [3.,4.]], dtype=double) + a = array([[1., 2.], [3., 4.]], dtype=double) b = array([[2., 1., 4.], [3., 4., 6.]], dtype=double) self.do(a, b) def test_csingle(self): - a = array([[1.+2j,2+3j], [3+4j,4+5j]], dtype=csingle) + a = array([[1.+2j, 2+3j], [3+4j, 4+5j]], dtype=csingle) b = array([2.+1j, 1.+2j], dtype=csingle) self.do(a, b) def test_cdouble(self): - a = array([[1.+2j,2+3j], [3+4j,4+5j]], dtype=cdouble) + a = array([[1.+2j, 2+3j], [3+4j, 4+5j]], dtype=cdouble) b = array([2.+1j, 1.+2j], dtype=cdouble) self.do(a, b) def test_cdouble_2(self): - a = array([[1.+2j,2+3j], [3+4j,4+5j]], dtype=cdouble) + a = array([[1.+2j, 2+3j], [3+4j, 4+5j]], dtype=cdouble) b = array([[2.+1j, 1.+2j, 1+3j], [1-2j, 1-3j, 1-6j]], dtype=cdouble) self.do(a, b) @@ -78,71 +78,71 @@ class LinalgTestCase(object): pass def test_nonarray(self): - a = [[1,2], [3,4]] + a = [[1, 2], [3, 4]] b = [2, 1] - self.do(a,b) + self.do(a, b) def test_matrix_b_only(self): """Check that matrix type is preserved.""" - a = array([[1.,2.], [3.,4.]]) + a = array([[1., 2.], [3., 4.]]) b = matrix([2., 1.]).T self.do(a, b) def test_matrix_a_and_b(self): """Check that matrix type is preserved.""" - a = matrix([[1.,2.], [3.,4.]]) + a = matrix([[1., 2.], [3., 4.]]) b = matrix([2., 1.]).T self.do(a, b) class LinalgNonsquareTestCase(object): def test_single_nsq_1(self): - a = array([[1.,2.,3.], [3.,4.,6.]], dtype=single) + a = array([[1., 2., 3.], [3., 4., 6.]], dtype=single) b = array([2., 1.], dtype=single) self.do(a, b) def test_single_nsq_2(self): - a = array([[1.,2.], [3.,4.], [5.,6.]], dtype=single) + a = array([[1., 2.], [3., 4.], [5., 6.]], dtype=single) b = array([2., 1., 3.], dtype=single) self.do(a, b) def test_double_nsq_1(self): - a = array([[1.,2.,3.], [3.,4.,6.]], dtype=double) + a = array([[1., 2., 3.], [3., 4., 6.]], dtype=double) b = array([2., 1.], dtype=double) self.do(a, b) def test_double_nsq_2(self): - a = array([[1.,2.], [3.,4.], [5.,6.]], dtype=double) + a = array([[1., 2.], [3., 4.], [5., 6.]], dtype=double) b = array([2., 1., 3.], dtype=double) self.do(a, b) def test_csingle_nsq_1(self): - a = array([[1.+1j,2.+2j,3.-3j], [3.-5j,4.+9j,6.+2j]], dtype=csingle) + a = array([[1.+1j, 2.+2j, 3.-3j], [3.-5j, 4.+9j, 6.+2j]], dtype=csingle) b = array([2.+1j, 1.+2j], dtype=csingle) self.do(a, b) def test_csingle_nsq_2(self): - a = array([[1.+1j,2.+2j], [3.-3j,4.-9j], [5.-4j,6.+8j]], dtype=csingle) + a = array([[1.+1j, 2.+2j], [3.-3j, 4.-9j], [5.-4j, 6.+8j]], dtype=csingle) b = array([2.+1j, 1.+2j, 3.-3j], dtype=csingle) self.do(a, b) def test_cdouble_nsq_1(self): - a = array([[1.+1j,2.+2j,3.-3j], [3.-5j,4.+9j,6.+2j]], dtype=cdouble) + a = array([[1.+1j, 2.+2j, 3.-3j], [3.-5j, 4.+9j, 6.+2j]], dtype=cdouble) b = array([2.+1j, 1.+2j], dtype=cdouble) self.do(a, b) def test_cdouble_nsq_2(self): - a = array([[1.+1j,2.+2j], [3.-3j,4.-9j], [5.-4j,6.+8j]], dtype=cdouble) + a = array([[1.+1j, 2.+2j], [3.-3j, 4.-9j], [5.-4j, 6.+8j]], dtype=cdouble) b = array([2.+1j, 1.+2j, 3.-3j], dtype=cdouble) self.do(a, b) def test_cdouble_nsq_1_2(self): - a = array([[1.+1j,2.+2j,3.-3j], [3.-5j,4.+9j,6.+2j]], dtype=cdouble) + a = array([[1.+1j, 2.+2j, 3.-3j], [3.-5j, 4.+9j, 6.+2j]], dtype=cdouble) b = array([[2.+1j, 1.+2j], [1-1j, 2-2j]], dtype=cdouble) self.do(a, b) def test_cdouble_nsq_2_2(self): - a = array([[1.+1j,2.+2j], [3.-3j,4.-9j], [5.-4j,6.+8j]], dtype=cdouble) + a = array([[1.+1j, 2.+2j], [3.-3j, 4.-9j], [5.-4j, 6.+8j]], dtype=cdouble) b = array([[2.+1j, 1.+2j], [1-1j, 2-2j], [1-1j, 2-2j]], dtype=cdouble) self.do(a, b) @@ -211,14 +211,14 @@ class TestSolve(LinalgTestCase, LinalgGeneralizedTestCase, TestCase): a = np.arange(8).reshape(2, 2, 2) b = np.arange(6).reshape(1, 2, 3).view(ArraySubclass) - expected = linalg.solve(a, b)[:,0:0,:] - result = linalg.solve(a[:,0:0,0:0], b[:,0:0,:]) + expected = linalg.solve(a, b)[:, 0:0,:] + result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0,:]) assert_array_equal(result, expected) assert_(isinstance(result, ArraySubclass)) # Test errors for non-square and only b's dimension being 0 - assert_raises(linalg.LinAlgError, linalg.solve, a[:,0:0,0:1], b) - assert_raises(ValueError, linalg.solve, a, b[:,0:0,:]) + assert_raises(linalg.LinAlgError, linalg.solve, a[:, 0:0, 0:1], b) + assert_raises(ValueError, linalg.solve, a, b[:, 0:0,:]) # Test broadcasting error b = np.arange(6).reshape(1, 3, 2) # broadcasting error @@ -227,15 +227,15 @@ class TestSolve(LinalgTestCase, LinalgGeneralizedTestCase, TestCase): # Test zero "single equations" with 0x0 matrices. b = np.arange(2).reshape(1, 2).view(ArraySubclass) - expected = linalg.solve(a, b)[:,0:0] - result = linalg.solve(a[:,0:0,0:0], b[:,0:0]) + expected = linalg.solve(a, b)[:, 0:0] + result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0]) assert_array_equal(result, expected) assert_(isinstance(result, ArraySubclass)) b = np.arange(3).reshape(1, 3) assert_raises(ValueError, linalg.solve, a, b) assert_raises(ValueError, linalg.solve, a[0:0], b[0:0]) - assert_raises(ValueError, linalg.solve, a[:,0:0,0:0], b) + assert_raises(ValueError, linalg.solve, a[:, 0:0, 0:0], b) class TestInv(LinalgTestCase, LinalgGeneralizedTestCase, TestCase): @@ -256,13 +256,13 @@ class TestInv(LinalgTestCase, LinalgGeneralizedTestCase, TestCase): # Check that all kinds of 0-sized arrays work class ArraySubclass(np.ndarray): pass - a = np.zeros((0,1,1), dtype=np.int_).view(ArraySubclass) + a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass) res = linalg.inv(a) assert_(res.dtype.type is np.float64) assert_equal(a.shape, res.shape) assert_(isinstance(a, ArraySubclass)) - a = np.zeros((0,0), dtype=np.complex64).view(ArraySubclass) + a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass) res = linalg.inv(a) assert_(res.dtype.type is np.complex64) assert_equal(a.shape, res.shape) @@ -288,7 +288,7 @@ class TestEig(LinalgTestCase, LinalgGeneralizedTestCase, TestCase): def do(self, a, b): evalues, evectors = linalg.eig(a) if evectors.ndim == 3: - assert_almost_equal(dot_generalized(a, evectors), evectors * evalues[:,None,:]) + assert_almost_equal(dot_generalized(a, evectors), evectors * evalues[:, None,:]) else: assert_almost_equal(dot(a, evectors), multiply(evectors, evalues)) assert_(imply(isinstance(a, matrix), isinstance(evectors, matrix))) @@ -313,7 +313,7 @@ class TestSVD(LinalgTestCase, LinalgGeneralizedTestCase, TestCase): def do(self, a, b): u, s, vt = linalg.svd(a, 0) if u.ndim == 3: - assert_almost_equal(a, dot_generalized(u * s[:,None,:], vt)) + assert_almost_equal(a, dot_generalized(u * s[:, None,:], vt)) else: assert_almost_equal(a, dot(multiply(u, s), vt)) assert_(imply(isinstance(a, matrix), isinstance(u, matrix))) @@ -344,13 +344,13 @@ 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) + 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.) + A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]]) + assert_almost_equal(linalg.cond(A, inf), 3.) class TestPinv(LinalgTestCase, TestCase): @@ -426,10 +426,10 @@ class TestLstsq(LinalgTestCase, LinalgNonsquareTestCase, TestCase): class TestMatrixPower(object): - R90 = array([[0,1],[-1,0]]) - Arb22 = array([[4,-7],[-2,10]]) - noninv = array([[1,0],[0,0]]) - arbfloat = array([[0.1,3.2],[1.2,0.7]]) + R90 = array([[0, 1], [-1, 0]]) + Arb22 = array([[4, -7], [-2, 10]]) + noninv = array([[1, 0], [0, 0]]) + arbfloat = array([[0.1, 3.2], [1.2, 0.7]]) large = identity(10) t = large[1,:].copy() @@ -437,14 +437,14 @@ class TestMatrixPower(object): large[0,:] = t def test_large_power(self): - assert_equal(matrix_power(self.R90,2**100+2**10+2**5+1),self.R90) + assert_equal(matrix_power(self.R90, 2**100+2**10+2**5+1), self.R90) def test_large_power_trailing_zero(self): - assert_equal(matrix_power(self.R90,2**100+2**10+2**5),identity(2)) + assert_equal(matrix_power(self.R90, 2**100+2**10+2**5), identity(2)) def testip_zero(self): def tz(M): - mz = matrix_power(M,0) + mz = matrix_power(M, 0) assert_equal(mz, identity(M.shape[0])) assert_equal(mz.dtype, M.dtype) for M in [self.Arb22, self.arbfloat, self.large]: @@ -452,7 +452,7 @@ class TestMatrixPower(object): def testip_one(self): def tz(M): - mz = matrix_power(M,1) + mz = matrix_power(M, 1) assert_equal(mz, M) assert_equal(mz.dtype, M.dtype) for M in [self.Arb22, self.arbfloat, self.large]: @@ -460,46 +460,46 @@ class TestMatrixPower(object): def testip_two(self): def tz(M): - mz = matrix_power(M,2) - assert_equal(mz, dot(M,M)) + mz = matrix_power(M, 2) + assert_equal(mz, dot(M, M)) assert_equal(mz.dtype, M.dtype) for M in [self.Arb22, self.arbfloat, self.large]: yield tz, M def testip_invert(self): def tz(M): - mz = matrix_power(M,-1) - assert_almost_equal(identity(M.shape[0]), dot(mz,M)) + mz = matrix_power(M, -1) + assert_almost_equal(identity(M.shape[0]), dot(mz, M)) for M in [self.R90, self.Arb22, self.arbfloat, self.large]: yield tz, M def test_invert_noninvertible(self): import numpy.linalg assert_raises(numpy.linalg.linalg.LinAlgError, - lambda: matrix_power(self.noninv,-1)) + lambda: matrix_power(self.noninv, -1)) class TestBoolPower(TestCase): def test_square(self): - A = array([[True,False],[True,True]]) - assert_equal(matrix_power(A,2),A) + A = array([[True, False], [True, True]]) + assert_equal(matrix_power(A, 2), A) class HermitianTestCase(object): def test_single(self): - a = array([[1.,2.], [2.,1.]], dtype=single) + a = array([[1., 2.], [2., 1.]], dtype=single) self.do(a, None) def test_double(self): - a = array([[1.,2.], [2.,1.]], dtype=double) + a = array([[1., 2.], [2., 1.]], dtype=double) self.do(a, None) def test_csingle(self): - a = array([[1.,2+3j], [2-3j,1]], dtype=csingle) + a = array([[1., 2+3j], [2-3j, 1]], dtype=csingle) self.do(a, None) def test_cdouble(self): - a = array([[1.,2+3j], [2-3j,1]], dtype=cdouble) + a = array([[1., 2+3j], [2-3j, 1]], dtype=cdouble) self.do(a, None) def test_empty(self): @@ -507,17 +507,17 @@ class HermitianTestCase(object): assert_raises(linalg.LinAlgError, self.do, a, None) def test_nonarray(self): - a = [[1,2], [2,1]] + a = [[1, 2], [2, 1]] self.do(a, None) def test_matrix_b_only(self): """Check that matrix type is preserved.""" - a = array([[1.,2.], [2.,1.]]) + a = array([[1., 2.], [2., 1.]]) self.do(a, None) def test_matrix_a_and_b(self): """Check that matrix type is preserved.""" - a = matrix([[1.,2.], [2.,1.]]) + a = matrix([[1., 2.], [2., 1.]]) self.do(a, None) @@ -623,7 +623,7 @@ class _TestNorm(TestCase): # or column separately. A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]: - expected0 = [norm(A[:,k], ord=order) for k in range(A.shape[1])] + expected0 = [norm(A[:, k], ord=order) for k in range(A.shape[1])] assert_almost_equal(norm(A, ord=order, axis=0), expected0) expected1 = [norm(A[k,:], ord=order) for k in range(A.shape[0])] assert_almost_equal(norm(A, ord=order, axis=1), expected1) @@ -644,11 +644,11 @@ class _TestNorm(TestCase): assert_almost_equal(n, expected) n = norm(B, ord=order, axis=(0, 2)) - expected = [norm(B[:,k,:], ord=order) for k in range(B.shape[1])] + expected = [norm(B[:, k,:], ord=order) for k in range(B.shape[1])] assert_almost_equal(n, expected) n = norm(B, ord=order, axis=(0, 1)) - expected = [norm(B[:,:,k], ord=order) for k in range(B.shape[2])] + expected = [norm(B[:,:, k], ord=order) for k in range(B.shape[2])] assert_almost_equal(n, expected) def test_bad_args(self): @@ -696,17 +696,17 @@ class TestMatrixRank(object): # Full rank matrix yield assert_equal, 4, matrix_rank(np.eye(4)) # rank deficient matrix - I=np.eye(4); I[-1,-1] = 0. + 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 + 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)) + yield assert_raises, TypeError, matrix_rank, np.zeros((2, 2, 2)) # works on scalar yield assert_equal, matrix_rank(1), 1 @@ -769,7 +769,7 @@ class TestQR(TestCase): def test_qr_empty(self): - a = np.zeros((0,2)) + a = np.zeros((0, 2)) self.assertRaises(linalg.LinAlgError, linalg.qr, a) @@ -864,7 +864,7 @@ def test_generalized_raise_multiloop(): x = np.zeros([4, 4, 2, 2])[1::2] x[...] = invertible - x[0,0] = non_invertible + x[0, 0] = non_invertible assert_raises(np.linalg.LinAlgError, np.linalg.inv, x) |