我们从Python开源项目中，提取了以下16个代码示例，用于说明如何使用numpy.common_type()。
def dot_generalized(a, b): a = asarray(a) if a.ndim >= 3: if a.ndim == b.ndim: # matrix x matrix new_shape = a.shape[:-1] + b.shape[-1:] elif a.ndim == b.ndim + 1: # matrix x vector new_shape = a.shape[:-1] else: raise ValueError("Not implemented...") r = np.empty(new_shape, dtype=np.common_type(a, b)) for c in itertools.product(*map(range, a.shape[:-2])): r[c] = dot(a[c], b[c]) return r else: return dot(a, b)
def dtype(self): """Returns the dtype that should be returned by ``to_array``""" return np.common_type(*tuple(self._lt))
def nulp_diff(x, y, dtype=None): """For each item in x and y, return the number of representable floating points between them. Parameters ---------- x : array_like first input array y : array_like second input array dtype : dtype, optional Data-type to convert `x` and `y` to if given. Default is None. Returns ------- nulp : array_like number of representable floating point numbers between each item in x and y. Examples -------- # By definition, epsilon is the smallest number such as 1 + eps != 1, so # there should be exactly one ULP between 1 and 1 + eps >>> nulp_diff(1, 1 + np.finfo(x.dtype).eps) 1.0 """ import numpy as np if dtype: x = np.array(x, dtype=dtype) y = np.array(y, dtype=dtype) else: x = np.array(x) y = np.array(y) t = np.common_type(x, y) if np.iscomplexobj(x) or np.iscomplexobj(y): raise NotImplementedError("_nulp not implemented for complex array") x = np.array(x, dtype=t) y = np.array(y, dtype=t) if not x.shape == y.shape: raise ValueError("x and y do not have the same shape: %s - %s" % (x.shape, y.shape)) def _diff(rx, ry, vdt): diff = np.array(rx-ry, dtype=vdt) return np.abs(diff) rx = integer_repr(x) ry = integer_repr(y) return _diff(rx, ry, t)
def transform(self, func, *args, **kwargs): """ Call function producing a like-indexed Series on each group and return a Series with the transformed values Parameters ---------- func : function To apply to each group. Should return a Series with the same index Examples -------- >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) Returns ------- transformed : Series """ func = self._is_cython_func(func) or func # if string function if isinstance(func, compat.string_types): if func in _cython_transforms: # cythonized transform return getattr(self, func)(*args, **kwargs) else: # cythonized aggregation and merge return self._transform_fast( lambda: getattr(self, func)(*args, **kwargs)) # reg transform dtype = self._selected_obj.dtype result = self._selected_obj.values.copy() wrapper = lambda x: func(x, *args, **kwargs) for i, (name, group) in enumerate(self): object.__setattr__(group, 'name', name) res = wrapper(group) if hasattr(res, 'values'): res = res.values # may need to astype try: common_type = np.common_type(np.array(res), result) if common_type != result.dtype: result = result.astype(common_type) except: pass indexer = self._get_index(name) result[indexer] = res result = _possibly_downcast_to_dtype(result, dtype) return self._selected_obj.__class__(result, index=self._selected_obj.index, name=self._selected_obj.name)
