diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 66 |
1 files changed, 31 insertions, 35 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index d473c99cb..c85aad451 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -359,7 +359,7 @@ def average(a, axis=None, weights=None, returned=False): Returns the average along the specified axis by default. When returned is True, the returns a tuple with the average as the first element and the sum of the weights as the second element. The return type is - double if a is of integer type, otherwise it is of the same type as a. + Float if a is of integer type, otherwise it is of the same type as a. When returned, sum_of_weights is a scalar with the same type as the average. @@ -368,55 +368,51 @@ def average(a, axis=None, weights=None, returned=False): ZeroDivisionError Results when all weights are zero.if appropriate. The version in MA does not, it returns masked values. + ValueError + Results when both an axis and weights are specified and the weights are + not an 1D array. Notes ----- - The default behavior is equivalent to - a.sum(axis) / size(a, axis) . + a.mean(axis). If weights are given, and axis=None, then the result is equivalent to - sum(a * weights) / sum(weights), + sum(a * weights) / (a.size/weights.size)*sum(weights)), In the case when the axis is not the default, then the result is equivalent - to - - tensordot(weights, a, (0,axis))/weights.sum() - - size of a in the given axis. Integer weights are converted to Float. Not - specifying weights is equivalent to specifying weights that are all 1. + to weights broadcast over the specified axis, then - If 'returned' is True, return a tuple: the result and the sum of the weights - or count of values. These are both scalars. + sum(a * weights)/sum(weights) """ - # convert a to array and compatible floating type. - a = np.asarray(a) + 0.0 - if axis is None : - if weights is None: - scl = a.dtype.type(a.size) - tot = a.sum() - else : - wgt = np.asarray(weights, dtype=a.dtype) - scl = wgt.sum() - tot = np.multiply(a,wgt).sum() - else: - if weights is None: - scl = a.dtype.type(a.shape[axis]) - tot = a.sum(axis) - else : - wgt = np.array(weights, copy=False, dtype=a.dtype, ndmin=1) - scl = wgt.sum() - tot = np.tensordot(wgt, a, (0,axis)) - - if scl == 0.0: - raise ZeroDivisionError, 'zero denominator in average()' + if not isinstance(a, np.matrix) : + a = np.asarray(a) + + if weights is None : + avg = a.mean(axis) + scl = avg.dtype.type(a.size/avg.size) + else : + a = a + 0.0 + wgt = np.array(weights, dtype=a.dtype, copy=0) + scl = wgt.sum() + if axis is not None and wgt.ndim != 1 : + raise ValueError, 'Weights must be 1D when axis is specified' + if scl == 0.0: + raise ZeroDivisionError, "Weights sum to zero, can't be normalized" + + if axis is None : + scl = scl*(a.size/wgt.size) + else: + wgt = np.array(wgt, copy=0, ndmin=a.ndim).swapaxes(-1,axis) + avg = np.multiply(a,wgt).sum(axis)/scl + if returned: - return tot/scl, scl + return avg, scl else: - return tot/scl + return avg def asarray_chkfinite(a): """Like asarray, but check that no NaNs or Infs are present. |