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| author | Srimukh Sripada <git@srimukh.com> | 2022-06-27 05:06:00 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-06-26 23:06:00 -0400 |
| commit | e67fe9f1a86013dbfd4a5ad2bd69aa6e7f60e1fb (patch) | |
| tree | c63192e3b1c5eeede2bd042f4531f44568256d5e /numpy/lib | |
| parent | b65f0b7b8ba7e80b65773e06aae22a8369678868 (diff) | |
| download | numpy-e67fe9f1a86013dbfd4a5ad2bd69aa6e7f60e1fb.tar.gz | |
BUG: Use `keepdims` during normalization in `np.average` and `np.ma.average` (#21851)
The keepdims flag needs to be applied during the calculation of the sum of
the weights in np.average and np.ma.average. Not passing it causes weights
to broadcast incorrectly.
Fixes #21850
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/function_base.py | 2 | ||||
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 12 |
2 files changed, 13 insertions, 1 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 843e1b85a..d90c23bfe 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -542,7 +542,7 @@ def average(a, axis=None, weights=None, returned=False, *, wgt = np.broadcast_to(wgt, (a.ndim-1)*(1,) + wgt.shape) wgt = wgt.swapaxes(-1, axis) - scl = wgt.sum(axis=axis, dtype=result_dtype) + scl = wgt.sum(axis=axis, dtype=result_dtype, **keepdims_kw) if np.any(scl == 0.0): raise ZeroDivisionError( "Weights sum to zero, can't be normalized") diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 64318255b..9cc18a5e4 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -360,6 +360,18 @@ class TestAverage: assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3)) + # test weights with `keepdims=False` and `keepdims=True` + x = np.array([2, 3, 4]).reshape(3, 1) + w = np.array([4, 5, 6]).reshape(3, 1) + + actual = np.average(x, weights=w, axis=1, keepdims=False) + desired = np.array([2., 3., 4.]) + assert_array_equal(actual, desired) + + actual = np.average(x, weights=w, axis=1, keepdims=True) + desired = np.array([[2.], [3.], [4.]]) + assert_array_equal(actual, desired) + def test_returned(self): y = np.array([[1, 2, 3], [4, 5, 6]]) |