py-06-numpy-distances

0.000

0/3 tests· math

Proposed solution

```python
import numpy as np

def pairwise_distances(points: np.ndarray) -> np.ndarray:
    # Calculate the squared differences along each dimension
    squared_diffs = np.sum((points[:, np.newaxis, :] - points[np.newaxis, :, :]) ** 2, axis=-1)
    # Take the square root to get the Euclidean distances
    distances = np.sqrt(squared_diffs + np.eye(points.shape[0]) * np.finfo(points.dtype).eps)
    return distances
```

Test output (stdout)

FFF                                                                      [100%]
=================================== FAILURES ===================================
_________________________________ test_simple __________________________________

    def test_simple():
        pts = np.array([[0.0, 0.0], [3.0, 4.0]])
        d = pairwise_distances(pts)
        assert isinstance(d, np.ndarray)
        assert d.shape == (2, 2)
>       np.testing.assert_allclose(d, [[0.0, 5.0], [5.0, 0.0]], atol=1e-9)
E       AssertionError: 
E       Not equal to tolerance rtol=1e-07, atol=1e-09
E       
E       Mismatched elements: 2 / 4 (50%)
E       Mismatch at indices:
E        [0, 0]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)
E        [1, 1]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)
E       Max absolute difference among violations: 1.49011612e-08
E       Max relative difference among violations: inf
E        ACTUAL: array([[1.490116e-08, 5.000000e+00],
E              [5.000000e+00, 1.490116e-08]])
E        DESIRED: array([[0., 5.],
E              [5., 0.]])

test_distances.py:10: AssertionError
_________________________ test_symmetric_zero_diagonal _________________________

    def test_symmetric_zero_diagonal():
        rng = np.random.default_rng(0)
        pts = rng.standard_normal((6, 3))
        d = pairwise_distances(pts)
        assert d.shape == (6, 6)
        np.testing.assert_allclose(d, d.T, atol=1e-9)
>       np.testing.assert_allclose(np.diag(d), np.zeros(6), atol=1e-9)
E       AssertionError: 
E       Not equal to tolerance rtol=1e-07, atol=1e-09
E       
E       Mismatched elements: 6 / 6 (100%)
E       First 5 mismatches are at indices:
E        [0]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)
E        [1]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)
E        [2]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)
E        [3]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)
E        [4]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)
E       Max absolute difference among violations: 1.49011612e-08
E       Max relative difference among violations: inf
E        ACTUAL: array([1.490116e-08, 1.490116e-08, 1.490116e-08, 1.490116e-08,
E              1.490116e-08, 1.490116e-08])
E        DESIRED: array([0., 0., 0., 0., 0., 0.])

test_distances.py:19: AssertionError
___________________________ test_matches_bruteforce ____________________________

    def test_matches_bruteforce():
        rng = np.random.default_rng(42)
        pts = rng.standard_normal((5, 4))
        d = pairwise_distances(pts)
        for i in range(5):
            for j in range(5):
                expected = np.sqrt(((pts[i] - pts[j]) ** 2).sum())
>               assert abs(d[i, j] - expected) < 1e-9
E               assert np.float64(1.4901161193847656e-08) < 1e-09
E                +  where np.float64(1.4901161193847656e-08) = abs((np.float64(1.4901161193847656e-08) - np.float64(0.0)))

test_distances.py:29: AssertionError
=========================== short test summary info ============================
FAILED test_distances.py::test_simple - AssertionError: 
FAILED test_distances.py::test_symmetric_zero_diagonal - AssertionError: 
FAILED test_distances.py::test_matches_bruteforce - assert np.float64(1.49011...
3 failed in 0.08s

Self-repair attempts

[
  {
    "total": 3,
    "answer": "```python\nimport numpy as np\n\ndef pairwise_distances(points: np.ndarray) -> np.ndarray:\n    # Calculate the squared differences along each dimension\n    squared_diffs = np.sum((points[:, np.newaxis, :] - points[np.newaxis, :, :]) ** 2, axis=-1)\n    # Take the square root to get the Euclidean distances\n    distances = np.sqrt(squared_diffs + np.eye(points.shape[0]) * np.finfo(points.dtype).eps)\n    return distances\n```",
    "passed": 0,
    "test_error": " (ACTUAL), 0.0 (DESIRED)\nE       Max absolute difference among violations: 1.49011612e-08\nE       Max relative difference among violations: inf\nE        ACTUAL: array([[1.490116e-08, 5.000000e+00],\nE              [5.000000e+00, 1.490116e-08]])\nE        DESIRED: array([[0., 5.],\nE              [5., 0.]])\n\ntest_distances.py:10: AssertionError\n_________________________ test_symmetric_zero_diagonal _________________________\n\n    def test_symmetric_zero_diagonal():\n        rng = np.random.default_rng(0)\n        pts = rng.standard_normal((6, 3))\n        d = pairwise_distances(pts)\n        assert d.shape == (6, 6)\n        np.testing.assert_allclose(d, d.T, atol=1e-9)\n>       np.testing.assert_allclose(np.diag(d), np.zeros(6), atol=1e-9)\nE       AssertionError: \nE       Not equal to tolerance rtol=1e-07, atol=1e-09\nE       \nE       Mismatched elements: 6 / 6 (100%)\nE       First 5 mismatches are at indices:\nE        [0]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)\nE        [1]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)\nE        [2]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)\nE        [3]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)\nE        [4]: 1.4901161193847656e-08 (ACTUAL), 0.0 (DESIRED)\nE       Max absolute difference among violations: 1.49011612e-08\nE       Max relative difference among violations: inf\nE        ACTUAL: array([1.490116e-08, 1.490116e-08, 1.490116e-08, 1.490116e-08,\nE              1.490116e-08, 1.490116e-08])\nE        DESIRED: array([0., 0., 0., 0., 0., 0.])\n\ntest_distances.py:19: AssertionError\n___________________________ test_matches_bruteforce ____________________________\n\n    def test_matches_bruteforce():\n        rng = np.random.default_rng(42)\n        pts = rng.standard_normal((5, 4))\n        d = pairwise_distances(pts)\n        for i in range(5):\n            for j in range(5):\n                expected = np.sqrt(((pts[i] - pts[j]) ** 2).sum())\n>               assert abs(d[i, j] - expected) < 1e-9\nE               assert np.float64(1.4901161193847656e-08) < 1e-09\nE                +  where np.float64(1.4901161193847656e-08) = abs((np.float64(1.4901161193847656e-08) - np.float64(0.0)))\n\ntest_distances.py:29: AssertionError\n=========================== short test summary info ============================\nFAILED test_distances.py::test_simple - AssertionError: \nFAILED test_distances.py::test_symmetric_zero_diagonal - AssertionError: \nFAILED test_distances.py::test_matches_bruteforce - assert np.float64(1.49011...\n3 failed in 0.08s"
  },
  {
    "total": 3,
    "answer": "```python\nimport numpy as np\n\ndef pairwise_distances(points: np.ndarray) -> np.ndarray:\n    # Calculate the squared differences along each dimension\n    squared_diffs = np.sum((points[:, np.newaxis, :] - points[np.newaxis, :, :]) ** 2, axis=-1)\n    # Take the square root to get the Euclidean distances\n    distances = np.sqrt(squared_diffs + np.eye(points.shape[0]) * np.finfo(points.dtype).eps)\n    # Set the diagonal to zero\n    np.fill_diagonal(distances, 0)\n    return distances\n```",
    "passed": 3,
    "test_error": ""
  }
]

System prompt

You are an expert programmer. Solve the task exactly as specified. Output your solution as fenced code blocks using the required file name(s) and the exact function/type signatures requested. Prefer correctness; do not include prose outside code unless asked.