def nanmean(a, axis=None, dtype=None, keepdims=None):
a = array_ops.array(a)
if np.issubdtype(a.dtype, np.bool_) or np.issubdtype(a.dtype, np.integer):
return array_ops.mean(a, axis=axis, dtype=dtype, keepdims=keepdims)
nan_mask = logical_not(isnan(a))
normalizer = array_ops.sum(nan_mask,
axis=axis,
dtype=np.int64,
keepdims=keepdims)
return nansum(a, axis=axis, dtype=dtype, keepdims=keepdims) / normalizer
def run_test(arr, *args, **kwargs):
axis = kwargs.pop('axis', None)
for fn1 in self.array_transforms:
for fn2 in self.array_transforms:
arr_arg = fn1(arr)
axis_arg = fn2(axis) if axis is not None else None
self.match(
array_ops.mean(arr_arg, axis=axis_arg, *args,
**kwargs),
np.mean(arr_arg, axis=axis, *args, **kwargs))
def nanmean(a, axis=None, dtype=None, keepdims=None): # pylint: disable=missing-docstring
a = array_ops.array(a)
if np.issubdtype(a.dtype, np.bool_) or np.issubdtype(a.dtype, np.integer):
return array_ops.mean(a, axis=axis, dtype=dtype, keepdims=keepdims)
nan_mask = logical_not(isnan(a))
if dtype is None:
dtype = a.dtype
normalizer = array_ops.sum(
nan_mask, axis=axis, dtype=dtype, keepdims=keepdims)
return nansum(a, axis=axis, dtype=dtype, keepdims=keepdims) / normalizer