diff options
Diffstat (limited to 'numpy/core/defmatrix.py')
| -rw-r--r-- | numpy/core/defmatrix.py | 27 |
1 files changed, 13 insertions, 14 deletions
diff --git a/numpy/core/defmatrix.py b/numpy/core/defmatrix.py index becfa74c9..e64fd8c6d 100644 --- a/numpy/core/defmatrix.py +++ b/numpy/core/defmatrix.py @@ -1,4 +1,3 @@ - __all__ = ['matrix', 'bmat', 'mat', 'asmatrix'] import numeric as N @@ -135,7 +134,7 @@ class matrix(N.ndarray): if out.ndim == 0: return out[()] if out.ndim == 1: - sh = out.shape[0] + sh = out.shape[0] # Determine when we should have a column array try: n = len(index) @@ -146,14 +145,14 @@ class matrix(N.ndarray): else: out.shape = (1,sh) return out - + def _get_truendim(self): shp = self.shape truend = 0 for val in shp: if (val > 1): truend += 1 return truend - + def __mul__(self, other): if isinstance(other, N.ndarray) or N.isscalar(other) or \ @@ -249,28 +248,28 @@ class matrix(N.ndarray): def var(self, axis=None, dtype=None, out=None): return N.ndarray.var(self, axis, dtype, out)._align(axis) - + def prod(self, axis=None, dtype=None, out=None): return N.ndarray.prod(self, axis, dtype, out)._align(axis) - + def any(self, axis=None, out=None): - return N.ndarray.any(self, axis, out)._align(axis) + return N.ndarray.any(self, axis, out)._align(axis) def all(self, axis=None, out=None): return N.ndarray.all(self, axis, out)._align(axis) - + def max(self, axis=None, out=None): return N.ndarray.max(self, axis, out)._align(axis) def argmax(self, axis=None, out=None): return N.ndarray.argmax(self, axis, out)._align(axis) - + def min(self, axis=None, out=None): return N.ndarray.min(self, axis, out)._align(axis) - + def argmin(self, axis=None, out=None): return N.ndarray.argmin(self, axis, out)._align(axis) - + def ptp(self, axis=None, out=None): return N.ndarray.ptp(self, axis, out)._align(axis) @@ -289,10 +288,10 @@ class matrix(N.ndarray): def getA(self): return self.__array__() - + def getA1(self): return self.__array__().ravel() - + def getT(self): return self.transpose() @@ -300,7 +299,7 @@ class matrix(N.ndarray): if issubclass(self.dtype.type, N.complexfloating): return self.transpose().conjugate() else: - return self.transpose() + return self.transpose() T = property(getT, None, doc="transpose") A = property(getA, None, doc="base array") |