C 0xb44460, 0xb44478, 0xb44490
Depth 10 on dim 0: [0, 12) med: 6 11813776 11813824 11813872    [1, 0, 4, 3, 2, 5, 6, 7, 8, 9, 10, 11]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb44478, 0xb44484, 0xb44490
Depth 9 on dim 1: [6, 12) med: 9 11813824 11813848 11813872     [1, 0, 4, 3, 2, 5, 11, 10, 9, 6, 7, 8]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb44460, 0xb4446c, 0xb44478
Depth 9 on dim 1: [0, 6) med: 3 11813776 11813800 11813824      [1, 0, 2, 3, 4, 5, 11, 10, 9, 6, 7, 8]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb44484, 0xb44488, 0xb44490
Depth 8 on dim 0: [3, 6) med: 4 11813800 11813808 11813824      [1, 0, 2, 3, 4, 5, 11, 10, 9, 6, 7, 8]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb4446c, 0xb44470, 0xb44478
Depth 8 on dim 0: [9, 12) med: 10 11813848 11813856 11813872    [1, 0, 2, 3, 4, 5, 11, 10, 9, 8, 6, 7]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb44460, 0xb44464, 0xb4446c
Depth 8 on dim 0: [0, 3) med: 1 11813776 11813784 11813800      [0, 1, 2, 3, 4, 5, 11, 10, 9, 8, 6, 7]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb44488, 0xb4448c, 0xb44490
Depth 7 on dim 1: [10, 12) med: 11 11813856 11813864 11813872   [0, 1, 2, 3, 4, 5, 11, 10, 9, 8, 7, 6]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb44470, 0xb44474, 0xb44478
Depth 7 on dim 1: [4, 6) med: 5 11813808 11813816 11813824      [0, 1, 2, 3, 4, 5, 11, 10, 9, 8, 7, 6]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C C 0xb44478, 0xb4447c, 0xb44484
Depth 8 on dim 0: [6, 9) med: 7 11813824 11813832 11813848      [0, 1, 2, 3, 4, 5, 9, 10, 11, 8, 7, 6]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

0xb44464, 0xb44468, 0xb4446c
Depth 7 on dim 1: [1, 3) med: 2 11813784 11813792 11813800      [0, 1, 2, 3, 4, 5, 9, 10, 11, 8, 7, 6]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

C 0xb4447c, 0xb44480, 0xb44484
Depth 7 on dim 1: [7, 9) med: 8 11813832 11813840 11813848      [0, 1, 2, 3, 4, 5, 9, 10, 11, 8, 7, 6]
             [0.000000, 1.000000, 2.000000, 3.000000, 4.000000, 5.000000, 6.000000, 7.000000, 8.000000, 9.000000, 10.000000, 11.000000]

This branch contains the latchang experimentations
regarding the type system for asynchronous computations,
used for the implementation of the KDTree.

Change logging

Verifying the recursive building on several input data.

Add some types definitions and node partitionning in.

This is taken from:
https://lab.nexedi.com/xavier_thompson/scan-filesystem/tree/ea6ba5dd5672a65bf5e9e13c3aa7f36fff9c4a08/cython

Raw source code taken from:
https://github.com/scikit-learn/scikit-learn/blob/579e7de7f38f9f514ff2b2be049e67b14e723d17/sklearn/cluster/_kmeans.py#L525

Not functional, just for sake of reference