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asmeurer on add-code-of-conduct
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hameerabbasi on master
Fix test failures. (compare)
hameerabbasi on master
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asmeurer on add-code-of-conduct
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master
*[Unknown, Quansight]
Pull request opened by joaosferreira
Quansight-Labs/unumpy#67
This PR intends to expand unumpy's coverage of indexing routines. The multimethods added so far are the following:
Generating index arrays
• indices
• ix_
• ravel_multi_index
• unravel_index
• diag_indices
• diag_indices_from
• mask_indices
• tril_indices
• tril_indices_from
• triu_indices
• triu_indices_from
Some observations:
ones
creates a COO with a fill_value
of 1.0, the call to the next method fails since a zero fill_value
is necessary.ravel_multi_index
and unravel_index
. I'll push them as soon as I have a working prototype but tips on how to implement these are welcomed.c_
, r_
and s_
.*Note:* I'll be adding more multimethods that target these routines later.
master
*master
*master
*[Unknown, Quansight]
Pull request opened by sangyx
Quansight-Labs/udiff#31
I have added the diff function of np.stack
.
When we extend the code to higher-order derivatives, the situation becomes a bit more complicated. There are some places to improve:
• In some derivative functions, the program may enter an infinite loop. For example, np.sum
is used when registering the gradient of np.sum
. I plan to add depth attributes to DiffArray
or to()
to control the order of gradients.
• The function registered in nograd_functions will register the gradient when it is called in the form of ufunc.__call__
.
• Consider a cleaner way to control whether the calculation should derive.
Do you have any idea about the above questions?
[Unknown, Quansight]
Pull request opened by joaosferreira
Quansight-Labs/unumpy#70
This pull request adds multimethods for statistical functions. The multimethods added are the following:
Order statistics
• percentile
• nanpercentile
• quantile
• nanquantile
Averages and variances
• median
• average
• mean
• nanmedian
• nanmean
• nanstd
• nanvar
Correlating
• corrcoef
• correlate
• cov
Histograms
• histogram
• histogram2d
• histogramdd
• bincount
• histogram_bin_edges
• digitize
A few things to discuss:
dtype
not being dispatched in std
and var
? Other multimethods that use _reduce_argreplacer
also don't dispatch dtype
.min
and max
seem to be equal to amin
and amax
that this PR intends to add:In [2]: onp.amin
Out[2]: <function numpy.amin(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)>
In [3]: onp.min
Out[3]: <function numpy.amin(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)>
median
that reduce array slices along an axis might be the easier ones for now. As I understand these require a for loop over the given axis, so in terms of complexity it would be O(n) where n is the length of that dimension.master
*unumpy.linalg
as demonstrated by the fixed tests.
master
*master
*[Unknown, Quansight]
Pull request opened by peterbell10
Quansight-Labs/uarray#247
Should fix the segfaults in Quansight-Labs/udiff#31
Returning selected_backend.get()
instead of .release()
from determine_backend
meant the refcount was decreased without dropping the reference. The net result is decreasing the refcount of the backend once for each call to determine_backend
. Eventually it gets GC'd and the python interpreter accesses invalid memory causing the segfault.
The GC issues are unrelated but were spotted while debugging this. The context managers had tp_traverse
defined but the GC will only use these if the type has the Py_TPFLAGS_HAVE_GC
flag set.
Reviewers: @hameerabbasi
[Hameer Abbasi, Quansight] Hello, Kartik. Are you looking for GSoC? If so, the selections are already over and have been for some time.
If not, head on over to https://labs.quansight.org/blog/2019/07/uarray-update-api-changes-overhead-and-comparison-to-array_function/ and read the blog. Play around with it, explore, and come back to us with any specific questions you may have.
master
*master
*