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NumPy and cuNumeric behave differently for the following cases. Some of the cases need to be fixed while the others are expected divergences.
Case-1
res_num = num.array(object, ndmin=ndmin)
object is one of the following values:
0, [], [1, 2], [[1, 2], [3, 4.1]]
ndmin is one of the following values:
-1, 0, 1, 2, 3, 4
If dim of object is smaller than ndmin, In Numpy, it pass In cuNumeric, it raises TypeError: 'NoneType' object cannot be interpreted as an integer
The text was updated successfully, but these errors were encountered:
#910 is merged to fix this issue #885. Close this issue.
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Use - adding a workaround for NumPy's conversion from unit64 to float (…
35add03
…nv-legate#885)
Use adding a workaround for NumPy's conversion from unit64 to float (n…
e9bba87
…v-legate#885)
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robinwnv
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NumPy and cuNumeric behave differently for the following cases.
Some of the cases need to be fixed while the others are expected divergences.
Case-1
object is one of the following values:
ndmin is one of the following values:
If dim of object is smaller than ndmin,
In Numpy, it pass
In cuNumeric, it raises TypeError: 'NoneType' object cannot be interpreted as an integer
The text was updated successfully, but these errors were encountered: