diff options
Diffstat (limited to 'numpy/linalg/linalg.py')
| -rw-r--r-- | numpy/linalg/linalg.py | 60 |
1 files changed, 30 insertions, 30 deletions
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index 1e22f2b14..fac5775a9 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -68,7 +68,7 @@ _fastCT = fastCopyAndTranspose def _fastCopyAndTranspose(type, *arrays): cast_arrays = () for a in arrays: - if a.dtype.char == type: + if a.dtype is type: cast_arrays = cast_arrays + (_fastCT(a),) else: cast_arrays = cast_arrays + (_fastCT(a.astype(type)),) @@ -222,16 +222,10 @@ def eigvalsh(a, UPLO='L'): def _convertarray(a): if issubclass(a.dtype.type, complexfloating): - if a.dtype.char == 'D': - a = _fastCT(a) - else: - a = _fastCT(a.astype('D')) + a = _fastCT(asarray(a, dtype=cdouble)) else: - if a.dtype.char == 'd': - a = _fastCT(a) - else: - a = _fastCT(a.astype('d')) - return a, a.dtype.char + a = _fastCT(asarray(a, dtype=double)) + return a, a.dtype # Eigenvectors @@ -243,11 +237,12 @@ eigenvalue u[i]. Satisfies the equation dot(a, v[:,i]) = u[i]*v[:,i] a, wrap = _makearray(a) _assertRank2(a) _assertSquareness(a) - a,t = _convertarray(a) # convert to float_ or complex_ type - real_t = 'd' + a, t = _convertarray(a) # convert to float_ or complex_ type + real_t = _realType(t) n = a.shape[0] dummy = zeros((1,), t) - if t == 'D': # Complex routines take different arguments + if issubclass(t, complexfloating): + # Complex routines take different arguments lapack_routine = lapack_lite.zgeev w = zeros((n,), t) v = zeros((n,n), t) @@ -311,21 +306,25 @@ def eigh(a, UPLO='L'): work = zeros((lwork,), t) lrwork = 1 rwork = zeros((lrwork,),real_t) - results = lapack_routine('V', UPLO, n, a, n,w, work, -1, rwork, -1, iwork, liwork, 0) + results = lapack_routine('V', UPLO, n, a, n,w, work, -1, + rwork, -1, iwork, liwork, 0) lwork = int(abs(work[0])) work = zeros((lwork,), t) lrwork = int(rwork[0]) rwork = zeros((lrwork,),real_t) - results = lapack_routine('V', UPLO, n, a, n,w, work, lwork, rwork, lrwork, iwork, liwork, 0) + results = lapack_routine('V', UPLO, n, a, n,w, work, lwork, + rwork, lrwork, iwork, liwork, 0) else: lapack_routine = lapack_lite.dsyevd w = zeros((n,), t) lwork = 1 work = zeros((lwork,),t) - results = lapack_routine('V', UPLO, n, a, n,w, work, -1, iwork, liwork, 0) + results = lapack_routine('V', UPLO, n, a, n,w, work, -1, + iwork, liwork, 0) lwork = int(work[0]) work = zeros((lwork,),t) - results = lapack_routine('V', UPLO, n, a, n,w, work, lwork, iwork, liwork, 0) + results = lapack_routine('V', UPLO, n, a, n,w, work, lwork, + iwork, liwork, 0) if results['info'] > 0: raise LinAlgError, 'Eigenvalues did not converge' return w,wrap(a.transpose()) @@ -341,13 +340,13 @@ def svd(a, full_matrices=1, compute_uv=1): If a is an M x N array, then the svd produces a factoring of the array into two unitary (orthogonal) 2-d arrays u (MxM) and vh (NxN) and a min(M,N)-length array of singular values such that - - a == dot(u,dot(S,vh)) - - where S is an MxN array of zeros whose main diagonal is s. - + + a == dot(u,dot(S,vh)) + + where S is an MxN array of zeros whose main diagonal is s. + if compute_uv == 0, then return only the singular values - if full_matrices == 0, then only part of either u or vh is + if full_matrices == 0, then only part of either u or vh is returned so that it is MxN """ a, wrap = _makearray(a) @@ -372,8 +371,8 @@ def svd(a, full_matrices=1, compute_uv=1): option = 'N' nu = 1 nvt = 1 - u = empty((1,1),t) - vt = empty((1,1),t) + u = empty((1,1), t) + vt = empty((1,1), t) iwork = zeros((8*min(m,n),), fortran_int) if issubclass(t, complexfloating): @@ -421,10 +420,10 @@ def pinv(a, rcond = 1.e-10): This method computes the generalized inverse using the singular-value decomposition and all singular values larger than - rcond of the largest. + rcond of the largest. """ a, wrap = _makearray(a) - if a.dtype.char in typecodes['Complex']: + if issubclass(a.dtype.dtype, complexfloating): a = conjugate(a) u, s, vt = svd(a, 0) m = u.shape[0] @@ -445,7 +444,7 @@ def det(a): a = asarray(a) _assertRank2(a) _assertSquareness(a) - t =_commonType(a) + t = _commonType(a) a = _fastCopyAndTranspose(t, a) n = a.shape[0] if issubclass(t, complexfloating): @@ -546,7 +545,8 @@ def norm(x, ord=None): Comments: For arrays of any rank, if ord is None: - calculate the square norm (Euclidean norm for vectors, Frobenius norm for matrices) + calculate the square norm (Euclidean norm for vectors, + Frobenius norm for matrices) For vectors ord can be any real number including Inf or -Inf. ord = Inf, computes the maximum of the magnitudes @@ -566,7 +566,7 @@ def norm(x, ord=None): mathematical 'norm', but it may still be useful for numerical purposes. """ x = asarray(x) - nd = len(x.shape) + nd = len(x.shape) if ord is None: # check the default case first and handle it immediately return sqrt(add.reduce((x.conj() * x).ravel().real)) |