def __init__(self, *shape):
if len(shape) == 1 and isinstance(shape[0], tuple):
shape = shape[0]
x = as_strided(_nx.zeros(1), shape=shape,
strides=_nx.zeros_like(shape))
self._it = _nx.nditer(x, flags=['multi_index', 'zerosize_ok'],
order='C')
def __init__(self, *shape):
if len(shape) == 1 and isinstance(shape[0], tuple):
shape = shape[0]
x = as_strided(_nx.zeros(1), shape=shape,
strides=_nx.zeros_like(shape))
self._it = _nx.nditer(x, flags=['multi_index', 'zerosize_ok'],
order='C')
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis)
# Make this warning show up on top.
if ddof >= rcount:
warnings.warn("Degrees of freedom <= 0 for slice",
RuntimeWarning,
stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
# Compute the mean.
# Note that if dtype is not of inexact type then arraymean will
# not be either.
arrmean = umr_sum(arr, axis, dtype, keepdims=True)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean,
rcount,
out=arrmean,
casting='unsafe',
subok=False)
else:
arrmean = arrmean.dtype.type(arrmean / rcount)
# Compute sum of squared deviations from mean
ret = 0
for i in nm.nditer(arr, order='F'):
if issubclass(arr.dtype.type, nt.complexfloating):
ret += (i - arrmean * um.conjugate(i - arrmean)).real
else:
ret += (i - arrmean)**2
# Compute degrees of freedom and make sure it is not negative.
rcount = max([rcount - ddof, 0])
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret,
rcount,
out=ret,
casting='unsafe',
subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def __init__(self, *shape):
x = as_strided(_nx.zeros(1),
shape=shape,
strides=_nx.zeros_like(shape))
self._it = _nx.nditer(x, flags=['multi_index'], order='C')
def __init__(self, *shape):
x = as_strided(_nx.zeros(1), shape=shape, strides=_nx.zeros_like(shape))
self._it = _nx.nditer(x, flags=['multi_index'], order='C')