diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 75 |
1 files changed, 48 insertions, 27 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index fb5dd6fdd..a0c94114a 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -516,7 +516,8 @@ def average(a, axis=None, weights=None, returned=False, *, if weights is None: avg = a.mean(axis, **keepdims_kw) - scl = avg.dtype.type(a.size/avg.size) + avg_as_array = np.asanyarray(avg) + scl = avg_as_array.dtype.type(a.size/avg_as_array.size) else: wgt = np.asanyarray(weights) @@ -547,12 +548,12 @@ def average(a, axis=None, weights=None, returned=False, *, raise ZeroDivisionError( "Weights sum to zero, can't be normalized") - avg = np.multiply(a, wgt, + avg = avg_as_array = np.multiply(a, wgt, dtype=result_dtype).sum(axis, **keepdims_kw) / scl if returned: - if scl.shape != avg.shape: - scl = np.broadcast_to(scl, avg.shape).copy() + if scl.shape != avg_as_array.shape: + scl = np.broadcast_to(scl, avg_as_array.shape).copy() return avg, scl else: return avg @@ -3665,6 +3666,13 @@ def msort(a): ----- ``np.msort(a)`` is equivalent to ``np.sort(a, axis=0)``. + Examples + -------- + >>> a = np.array([[1, 4], [3, 1]]) + >>> np.msort(a) # sort along the first axis + array([[1, 1], + [3, 4]]) + """ b = array(a, subok=True, copy=True) b.sort(0) @@ -3934,7 +3942,7 @@ def percentile(a, 8. 'median_unbiased' 9. 'normal_unbiased' - The first three methods are discontiuous. NumPy further defines the + The first three methods are discontinuous. NumPy further defines the following discontinuous variations of the default 'linear' (7.) option: * 'lower' @@ -3979,7 +3987,7 @@ def percentile(a, Notes ----- - Given a vector ``V`` of length ``N``, the q-th percentile of ``V`` is + Given a vector ``V`` of length ``n``, the q-th percentile of ``V`` is the value ``q/100`` of the way from the minimum to the maximum in a sorted copy of ``V``. The values and distances of the two nearest neighbors as well as the `method` parameter will determine the @@ -3988,19 +3996,22 @@ def percentile(a, same as the minimum if ``q=0`` and the same as the maximum if ``q=100``. - This optional `method` parameter specifies the method to use when the - desired quantile lies between two data points ``i < j``. - If ``g`` is the fractional part of the index surrounded by ``i`` and - alpha and beta are correction constants modifying i and j. - - Below, 'q' is the quantile value, 'n' is the sample size and - alpha and beta are constants. - The following formula gives an interpolation "i + g" of where the quantile - would be in the sorted sample. - With 'i' being the floor and 'g' the fractional part of the result. + The optional `method` parameter specifies the method to use when the + desired percentile lies between two indexes ``i`` and ``j = i + 1``. + In that case, we first determine ``i + g``, a virtual index that lies + between ``i`` and ``j``, where ``i`` is the floor and ``g`` is the + fractional part of the index. The final result is, then, an interpolation + of ``a[i]`` and ``a[j]`` based on ``g``. During the computation of ``g``, + ``i`` and ``j`` are modified using correction constants ``alpha`` and + ``beta`` whose choices depend on the ``method`` used. Finally, note that + since Python uses 0-based indexing, the code subtracts another 1 from the + index internally. + + The following formula determines the virtual index ``i + g``, the location + of the percentile in the sorted sample: .. math:: - i + g = q * ( n - alpha - beta + 1 ) + alpha + i + g = (q / 100) * ( n - alpha - beta + 1 ) + alpha The different methods then work as follows @@ -4265,18 +4276,28 @@ def quantile(a, Notes ----- - Given a vector ``V`` of length ``N``, the q-th quantile of ``V`` is the - value ``q`` of the way from the minimum to the maximum in a sorted copy of - ``V``. The values and distances of the two nearest neighbors as well as the - `method` parameter will determine the quantile if the normalized - ranking does not match the location of ``q`` exactly. This function is the - same as the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and - the same as the maximum if ``q=1.0``. + Given a vector ``V`` of length ``n``, the q-th quantile of ``V`` is + the value ``q`` of the way from the minimum to the maximum in a + sorted copy of ``V``. The values and distances of the two nearest + neighbors as well as the `method` parameter will determine the + quantile if the normalized ranking does not match the location of + ``q`` exactly. This function is the same as the median if ``q=0.5``, the + same as the minimum if ``q=0.0`` and the same as the maximum if + ``q=1.0``. The optional `method` parameter specifies the method to use when the - desired quantile lies between two data points ``i < j``. - If ``g`` is the fractional part of the index surrounded by ``i`` and ``j``, - and alpha and beta are correction constants modifying i and j: + desired quantile lies between two indexes ``i`` and ``j = i + 1``. + In that case, we first determine ``i + g``, a virtual index that lies + between ``i`` and ``j``, where ``i`` is the floor and ``g`` is the + fractional part of the index. The final result is, then, an interpolation + of ``a[i]`` and ``a[j]`` based on ``g``. During the computation of ``g``, + ``i`` and ``j`` are modified using correction constants ``alpha`` and + ``beta`` whose choices depend on the ``method`` used. Finally, note that + since Python uses 0-based indexing, the code subtracts another 1 from the + index internally. + + The following formula determines the virtual index ``i + g``, the location + of the quantile in the sorted sample: .. math:: i + g = q * ( n - alpha - beta + 1 ) + alpha |