diff options
| -rw-r--r-- | numpy/core/_methods.py | 52 |
1 files changed, 29 insertions, 23 deletions
diff --git a/numpy/core/_methods.py b/numpy/core/_methods.py index ccf02ce36..27e445a54 100644 --- a/numpy/core/_methods.py +++ b/numpy/core/_methods.py @@ -5,6 +5,8 @@ and the Python code for the NumPy-namespace function """ from __future__ import division, absolute_import, print_function +import warnings + from numpy.core import multiarray as mu from numpy.core import umath as um from numpy.core.numeric import asanyarray @@ -47,15 +49,17 @@ def _count_reduce_items(arr, axis): def _mean(a, axis=None, dtype=None, out=None, keepdims=False): arr = asanyarray(a) - # Cast bool, unsigned int, and int to float64 + rcount = _count_reduce_items(arr, axis) + # Make this warning show up first + if rcount == 0: + warnings.warn("Mean of empty slice.", RuntimeWarning) + + + # Cast bool, unsigned int, and int to float64 by default if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)): - ret = um.add.reduce(arr, axis=axis, dtype='f8', - out=out, keepdims=keepdims) - else: - ret = um.add.reduce(arr, axis=axis, dtype=dtype, - out=out, keepdims=keepdims) + dtype = mu.dtype('f8') - rcount = _count_reduce_items(arr, axis) + ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) if isinstance(ret, mu.ndarray): ret = um.true_divide( ret, rcount, out=ret, casting='unsafe', subok=False) @@ -64,39 +68,41 @@ def _mean(a, axis=None, dtype=None, out=None, keepdims=False): return ret -def _var(a, axis=None, dtype=None, out=None, ddof=0, - keepdims=False): +def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): arr = asanyarray(a) - # First compute the mean, saving 'rcount' for reuse later + 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) + + # Cast bool, unsigned int, and int to float64 by default if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)): - arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True) - else: - arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True) + dtype = mu.dtype('f8') - rcount = _count_reduce_items(arr, axis) + # Compute the mean. + # Note that if dtype is not of inexact type then arraymean will + # not be either. + arrmean = um.add.reduce(arr, axis=axis, dtype=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) - # arr - arrmean + # Compute sum of squared deviations from mean + # Note that x may not be inexact x = arr - arrmean - - # (arr - arrmean) ** 2 if issubclass(arr.dtype.type, nt.complexfloating): x = um.multiply(x, um.conjugate(x), out=x).real else: x = um.multiply(x, x, out=x) - - # add.reduce((arr - arrmean) ** 2, axis) ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out, keepdims=keepdims) - # add.reduce((arr - arrmean) ** 2, axis) / (n - ddof) - if not keepdims and isinstance(rcount, mu.ndarray): - rcount = rcount.squeeze(axis=axis) - rcount -= ddof + # 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) |