diff options
Diffstat (limited to 'numpy/linalg/linalg.py')
| -rw-r--r-- | numpy/linalg/linalg.py | 34 |
1 files changed, 17 insertions, 17 deletions
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index 87be0e8a3..043deb24f 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -57,7 +57,7 @@ class LinAlgError(Exception): in inv return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) File "...linalg.py", line 249, in solve - raise LinAlgError, 'Singular matrix' + raise LinAlgError('Singular matrix') numpy.linalg.linalg.LinAlgError: Singular matrix """ @@ -157,12 +157,12 @@ def _assertRank2(*arrays): def _assertSquareness(*arrays): for a in arrays: if max(a.shape) != min(a.shape): - raise LinAlgError, 'Array must be square' + raise LinAlgError('Array must be square') def _assertFinite(*arrays): for a in arrays: if not (isfinite(a).all()): - raise LinAlgError, "Array must not contain infs or NaNs" + raise LinAlgError("Array must not contain infs or NaNs") def _assertNonEmpty(*arrays): for a in arrays: @@ -313,7 +313,7 @@ def solve(a, b): n_eq = a.shape[0] n_rhs = b.shape[1] if n_eq != b.shape[0]: - raise LinAlgError, 'Incompatible dimensions' + raise LinAlgError('Incompatible dimensions') t, result_t = _commonType(a, b) # lapack_routine = _findLapackRoutine('gesv', t) if isComplexType(t): @@ -325,7 +325,7 @@ def solve(a, b): pivots = zeros(n_eq, fortran_int) results = lapack_routine(n_eq, n_rhs, a, n_eq, pivots, b, n_eq, 0) if results['info'] > 0: - raise LinAlgError, 'Singular matrix' + raise LinAlgError('Singular matrix') if one_eq: return wrap(b.ravel().astype(result_t)) else: @@ -393,7 +393,7 @@ def tensorinv(a, ind=2): for k in oldshape[ind:]: prod *= k else: - raise ValueError, "Invalid ind argument." + raise ValueError("Invalid ind argument.") a = a.reshape(prod, -1) ia = inv(a) return ia.reshape(*invshape) @@ -802,7 +802,7 @@ def eigvals(a): w = wr+1j*wi result_t = _complexType(result_t) if results['info'] > 0: - raise LinAlgError, 'Eigenvalues did not converge' + raise LinAlgError('Eigenvalues did not converge') return w.astype(result_t) @@ -891,7 +891,7 @@ def eigvalsh(a, UPLO='L'): results = lapack_routine(_N, UPLO, n, a, n, w, work, lwork, iwork, liwork, 0) if results['info'] > 0: - raise LinAlgError, 'Eigenvalues did not converge' + raise LinAlgError('Eigenvalues did not converge') return w.astype(result_t) def _convertarray(a): @@ -1061,7 +1061,7 @@ def eig(a): result_t = _complexType(result_t) if results['info'] > 0: - raise LinAlgError, 'Eigenvalues did not converge' + raise LinAlgError('Eigenvalues did not converge') vt = v.transpose().astype(result_t) return w.astype(result_t), wrap(vt) @@ -1184,7 +1184,7 @@ def eigh(a, UPLO='L'): results = lapack_routine(_V, UPLO, n, a, n, w, work, lwork, iwork, liwork, 0) if results['info'] > 0: - raise LinAlgError, 'Eigenvalues did not converge' + raise LinAlgError('Eigenvalues did not converge') at = a.transpose().astype(result_t) return w.astype(_realType(result_t)), wrap(at) @@ -1317,7 +1317,7 @@ def svd(a, full_matrices=1, compute_uv=1): results = lapack_routine(option, m, n, a, m, s, u, m, vt, nvt, work, lwork, iwork, 0) if results['info'] > 0: - raise LinAlgError, 'SVD did not converge' + raise LinAlgError('SVD did not converge') s = s.astype(_realType(result_t)) if compute_uv: u = u.transpose().astype(result_t) @@ -1627,7 +1627,7 @@ def slogdet(a): results = lapack_routine(n, n, a, n, pivots, 0) info = results['info'] if (info < 0): - raise TypeError, "Illegal input to Fortran routine" + raise TypeError("Illegal input to Fortran routine") elif (info > 0): return (t(0.0), _realType(t)(-Inf)) sign = 1. - 2. * (add.reduce(pivots != arange(1, n + 1)) % 2) @@ -1771,7 +1771,7 @@ def lstsq(a, b, rcond=-1): n_rhs = b.shape[1] ldb = max(n, m) if m != b.shape[0]: - raise LinAlgError, 'Incompatible dimensions' + raise LinAlgError('Incompatible dimensions') t, result_t = _commonType(a, b) result_real_t = _realType(result_t) real_t = _linalgRealType(t) @@ -1812,7 +1812,7 @@ def lstsq(a, b, rcond=-1): results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond, 0, work, lwork, iwork, 0) if results['info'] > 0: - raise LinAlgError, 'SVD did not converge in Linear Least Squares' + raise LinAlgError('SVD did not converge in Linear Least Squares') resids = array([], result_real_t) if is_1d: x = array(ravel(bstar)[:n], dtype=result_t, copy=True) @@ -1959,7 +1959,7 @@ def norm(x, ord=None): try: ord + 1 except TypeError: - raise ValueError, "Invalid norm order for vectors." + raise ValueError("Invalid norm order for vectors.") return ((abs(x)**ord).sum())**(1.0/ord) elif nd == 2: if ord == 2: @@ -1977,6 +1977,6 @@ def norm(x, ord=None): elif ord in ['fro','f']: return sqrt(add.reduce((x.conj() * x).real.ravel())) else: - raise ValueError, "Invalid norm order for matrices." + raise ValueError("Invalid norm order for matrices.") else: - raise ValueError, "Improper number of dimensions to norm." + raise ValueError("Improper number of dimensions to norm.") |