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Bhavesh9908/Building a KD-Tree from Points #11547

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  • ### Describe your change: This function constructs a KD-Tree from a list of points in a multi-dimensional space. The KD-Tree is a binary tree used for partitioning space in a way that allows efficient querying, such as nearest neighbor searches.
  • Add an algorithm?
  • Fix a bug or typo in an existing algorithm?
  • Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
  • Documentation change?

Checklist:

  • I have read CONTRIBUTING.md.
  • This pull request is all my own work -- I have not plagiarized.
  • I know that pull requests will not be merged if they fail the automated tests.
  • This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  • All new Python files are placed inside an existing directory.
  • All filenames are in all lowercase characters with no spaces or dashes.
  • All functions and variable names follow Python naming conventions.
  • All function parameters and return values are annotated with Python type hints.
  • All functions have doctests that pass the automated testing.
  • All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
  • If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

@algorithms-keeper algorithms-keeper bot added documentation This PR modified documentation files awaiting reviews This PR is ready to be reviewed labels Sep 3, 2024
@algorithms-keeper algorithms-keeper bot added tests are failing Do not merge until tests pass and removed tests are failing Do not merge until tests pass labels Sep 3, 2024
@Ramy-Badr-Ahmed

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Ramy-Badr-Ahmed
Ramy-Badr-Ahmed reviewed Sep 26, 2024
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data_structures/kd_tree/tests/test_kdtree.py
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@Ramy-Badr-Ahmed Ramy-Badr-Ahmed Sep 26, 2024 •
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Hi,
I have already implemented this in #11532
Is there something wrong with the test?

Ramy-Badr-Ahmed
Ramy-Badr-Ahmed suggested changes Sep 26, 2024
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data_structures/kd_tree/tests/test_kdtree.py

if expected_result is None:
# Empty points list case
assert kdtree is None, f"Expected None for empty points list, got {kdtree}"
else:
# Check if root node is not None
# Check if KD-Tree is built correctly
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The original comment is: Check if root node is not None. It matches the test being made.

data_structures/kd_tree/tests/test_kdtree.py
assert isinstance(
kdtree, KDNode
), f"Expected KDNode instance, got {type(kdtree)}"
assert (
len(kdtree.point) == num_dimensions
), f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
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@Ramy-Badr-Ahmed Ramy-Badr-Ahmed Sep 26, 2024 •
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not sure, is this just reordering tests?

Ramy-Badr-Ahmed
Ramy-Badr-Ahmed suggested changes Sep 26, 2024
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data_structures/kd_tree/tests/test_kdtree.py
"""
points = (
hypercube_points(num_points, cube_size, num_dimensions).tolist()
if num_points > 0
else []
)

kdtree = build_kdtree(points, depth=depth)
kdtree = build_kdtree(points)
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you are always calling build_kdtree() with the default case depth = 0. This is not going to test it correctly.

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@Ramy-Badr-Ahmed Ramy-Badr-Ahmed Ramy-Badr-Ahmed requested changes

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