Add row and column functions #1189

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1. JohanMabille

   added

   Feature Request

   Easy Fix

   on Oct 18, 2018
2. JohanMabille

   changed the title [-]Add row and column[/-] [+]Add row and column functions[/+] on Oct 18, 2018
3. SylvainCorlay commented on Oct 18, 2018

   

   SylvainCorlay

   on Oct 18, 2018

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   Well, since trailing alls can be ommitted, row should be less necessary.
4. JohanMabille commented on Oct 18, 2018

   

   JohanMabille

   on Oct 18, 2018

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   I agree, that's for completness and also that would be weird to mix view and col in the code instead of using row and col.
5. cha-ku commented on May 17, 2019

   

   cha-ku

   on May 17, 2019

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   Hi @JohanMabille , I'm here from https://twitter.com/JohanMabille/status/1052449785301614592
   Would love to work on this. Any primers on how to get started on this?
6. JohanMabille commented on May 17, 2019

   

   JohanMabille

   on May 17, 2019

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   Hi @cha-ku and welcome!

   Here are the steps I would recommend if you are totally new to xtensor; if not, you can probably skip some of them:

   • read the Getting started section and start to play a bit with xtensor. If you're familiar with Numpy, the cheat sheet can be of great help
   • The quick ref about views will give you more insights about what views are and how they work.

   row and column functions should be free functions in xview.hpp that simply call the view function with the right slices. You should start with row which is pretty straightforward, column would require a little more metaprogramming.

   👍React with 👍1Reacted by Chaitanya Kukde
7. kolibri91 commented on Dec 27, 2019

   

   kolibri91

   on Dec 27, 2019

   Last edited by kolibri91

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   I assume this issue is still open and I like to contribute to it.

   Just to clarify, what should happen if the row or col function is called on a tensor with more than 2 dimensions. I made a test with your starting point:

   Typing row(e, 1) is more convenient and expressive than xt::view(e, 1, xt::all())

   and the behavior is quite weird:

   TEST(xview, row_on_3dim_array)
   {
     xt::xarray<int> arr{ { { 1, 2 }, { 3, 4 } }, { { 5, 6 }, { 7, 8 } } };
     
     const auto row0 = xt::row(arr, 0);
     const auto row1 = xt::row(arr, 1);
   
     std::cout << row0 << std::endl;  // prints: {{1, 2}, {3, 4}}
     std::cout << row1 << std::endl;  // prints: {{5, 6}, {7, 8}}
   
     std::cout << row0(0) << std::endl;  // prints: 1
     std::cout << row0(1) << std::endl;  // prints: 2
     std::cout << row0(2) << std::endl;  // prints: 3
     std::cout << row0(3) << std::endl;  // prints: 4
   
     std::cout << row1(0) << std::endl;  // prints: 3
     std::cout << row1(1) << std::endl;  // prints: 4
     std::cout << row1(2) << std::endl;  // prints: 5
     std::cout << row1(3) << std::endl;  // prints: 6
   }
   

   So the print of row0 and row1 seems to be right. Of course we could discuss what a row on a 3-dimensional tensor should be. But at least they access different values.

   When I access the elements in the rows, something went wrong. I hadn't the time to check it in detail.
   I like to use the TDD approach to get the function row and col and therefore I should know what the expected behavior of such code should be.
8. kolibri91 commented on Dec 27, 2019

   

   kolibri91

   on Dec 27, 2019

   Last edited by kolibri91

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   I checked the code and I see now why I have this behavior.
   Obviously, xt::view(e, 1, xt::all()) will just return a xt::view to a tensor with one dimension less as the tensor has. Therefore, I have to use two indices for accessing the elements in the row of a tensor with 3 dimensions.

   The question remains the same:
   What should be the behavior of calling row or col on a tensor with more than 2 dimensions?

   • Should not be possible?
   • row is always first dimension, col is always second dimension?
   • row is always first dimension, col is always last dimension?
9. JohanMabille commented on Dec 28, 2019

   

   JohanMabille

   on Dec 28, 2019

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   Indeed xt::view appends xt::all slices to its arguments list until it matches the number of dimensions of the underlying expression.

   Regarding the behavior of row and col on a tensor with more than 2 dimensions, This has to be discussed, but I would be in favor of forbidding it for expressions with more than 2 dimensions.

   👍React with 👍1Reacted by Nicolas Koller
10. KappaDistributive commented on Jul 4, 2020

    

    KappaDistributive

    on Jul 4, 2020

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    It seems to me that this issue can be closed. Or am I missing something?
11. JohanMabille commented on Jul 5, 2020

    

    JohanMabille

    on Jul 5, 2020

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    Indeed, good catch!
12. JohanMabille

    closed this as completedon Jul 5, 2020