Comments (10)
That would be awesome! Whenever you can would be great
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Nice! That's awesome!
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@ALMerrill
I'd like to implement "fancy indexing", of course.
OK, I'll try to do it on this weekend, but don't expect me excessively!
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Also, out of curiosity, is there a way to improve performance with the Boolean indexing? With how Iām using it, it looks like things like array > someNumber
is taking the majority of the time. I was just curious if you knew of any way to improve performance on calls like that. Thanks so much!
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it looks like things like array > someNumber is taking the majority of the time.
Yes, your point is correct. The code to improve performance may be here firstly. I think there are much redundant process in this code. So, i should simplify this code.
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Or use boolean as storedType
as you pointed out, but this will change many codes...
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Official code is here; https://github.com/numpy/numpy/blob/cf1306a842d7b1064270bd06951a485121e60816/numpy/core/src/multiarray/mapping.c#L1608
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@ALMerrill
I implemented fancy indexing getter!
You can try it by fetching develop branch.
You can pass Int MfArray
instead of list to MfArray
like this;
let a = MfArray([[1, 2], [3, 4], [5, 6]])
a[MfArray([0, 1, 2]), MfArray([0, 1, 0])]
/*
mfarray =
[ 1, 4, 5], type=Int, shape=[3]
*/
numpy;
>>> x = np.array([[1, 2], [3, 4], [5, 6]])
>>> x[[0, 1, 2], [0, 1, 0]]
array([1, 4, 5])
I don't know if I can implement setter...
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awesome, thank you for the update!
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@ALMerrill
I've just implemented fancy setter!!
I didn't check performance, but anyway you can use fancy indexing for MfArray
Setter;
let a = MfArray([[1, 2], [3, 4], [5, 6]])
a[MfArray([0, 1, 2]), MfArray([0, -1, 0])] = MfArray([999,888,777])
print(a)
/*
mfarray =
[[ 999, 2],
[ 3, 888],
[ 777, 6]], type=Int, shape=[3, 2]
*/
a.T[MfArray([0, 1, -1]), MfArray([0, 1, 0])] = MfArray([-999,-888,-777])
print(a)
/*
mfarray =
[[ -999, -777],
[ 3, -888],
[ 777, 6]], type=Int, shape=[3, 2]
*/
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Related Issues (20)
- Boolean Indexing is slow HOT 10
- Other Cubic spline Interpolation
- not found np.unique() HOT 1
- Wrong Shape after "ufuncReduce add" HOT 5
- Matft.math.pow is not working as expected HOT 1
- Add @inline
- Complex support HOT 9
- Refactoring pointer HOT 3
- Adding demo for image processing HOT 3
- Support for atan2 HOT 3
- Feature Request: Numpy Dot HOT 1
- Image processing HOT 5
- Multidimensional MfArray with different length Arrays in axis 1 HOT 2
- Copy On Write implementation HOT 2
- Subscript bug using `Matft.arange()` HOT 4
- Numpy FFT implementation? HOT 11
- Support MLMultiArray HOT 1
- [Bug] Invalid access on some complex operation
- How to contribute - have some small numpy / scipy vDSP implementations HOT 1
- How to get the content of the MfArray? HOT 3
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