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Commits on Nov 6, 2019
Update README.md
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Commits on Nov 5, 2019
Update README.md
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Commits on Nov 4, 2019
NDArray.FromMultiDimArray<T>: Added guards.
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NDArray.FromMultiDimArray<T>: Fixed #367
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Commits on Oct 13, 2019
NDArray: added GetIndicesFromSlice which is needed for indexing special cases
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Commits on Oct 12, 2019
Shape: better doc strings for GetOffset, GetCoordinates and GetCoordinatesFromAbsoluteIndex
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GetCoordinatesFromAbsoluteIndex calls GetCoordinates for unsliced shape
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Shape.GetCoordinates: cleaned up and moved absolute offset conversion into own method
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np.math.Test: separated unit-tests.
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np.minimum issue #359
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Shape: Fixed shape cloning when unbroadcasting involved
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Merge branch 'master' of https://github.com/SciSharp/NumSharp
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np.minimum, np.maximum: fixed return shape resolving.
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Broadcasting: Prevent throw NotSupportedException when shapes are already broadcasted but actually doen't require broadcasting.
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Maximum_Slice test is not fixed yet, correct Python output added
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Shape: added GetCoordinates test which shows shortcomings of that method for complicated shapes
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shold contains 6 elements. #359
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Merge pull request #364 from henon/master
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Merge remote-tracking branch 'upstream/master' into select_and_mask
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marked all failing tests [Ignore("TODO: fix this test")] for better breaking-change-awareness
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Added np.all, np.any (temporarly without axis support)
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Merge remote-tracking branch 'upstream/select_and_mask' into select_and_mask
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NDArray.Indexing: moved to object[] impl.
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np.array: Added IEnumerable<T> overloads
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UnmanagedStorage: Added GetData that accepts (int* dims, int len)
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Added NDArray`1.Reshape
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Shape.Unmanaged: Added GetOffset, GetSubshape, InferNegativeCoordinates that support unmanaged int array.
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Merge branch 'master' into select_and_mask
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NDArray: additional constructors
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fixed bug revealed by Maximum_Slice test https://github.com/SciSharp/NumSharp/issues/359#issuecomment-541301942
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cleaned up some math unit tests
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moved some tests into correct subfolder
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add Maximum_Slice unit test.
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Commits on Oct 11, 2019
Added np.maximum, np.minimum
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np.clip: Added @out argument
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