Is the testing code for knn in assignment1 right?

I am a student in this course. When I am trying to finish the implement of knn in the assignment1, I always cannot pass the test. So I replace the KNN classifier to the one of sklearn package. However, the sklearn's implement still cannot pass the test.

I change the ./fduml/neighbors/tests/test_knn.py to the following codes

"""
test knn
"""

import numpy as np
from numpy.testing import assert_array_equal
# from fduml.neighbors import KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier

def test_kneighbors_classifier(
        num_train=40,
        n_features=5,
        num_test=10,
        n_neighbors=5,
        random_state=0,
):
    # Test k-neighbors classification
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(num_train, n_features) - 1
    y = ((X**2).sum(axis=1) < 2.).astype(int)
    epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1)

    # knn = KNeighborsClassifier(n_neighbors=n_neighbors,num_loops=2)
    # knn.fit(X, y)
    # y_pred = knn.predict(X[:num_test] + epsilon)
    # assert_array_equal(y_pred, y[:num_test])

    # knn = KNeighborsClassifier(n_neighbors=n_neighbors,num_loops=1)
    # knn.fit(X, y)
    # y_pred = knn.predict(X[:num_test] + epsilon)
    # assert_array_equal(y_pred, y[:num_test])

    # knn = KNeighborsClassifier(n_neighbors=n_neighbors,num_loops=0)
    # knn.fit(X, y)
    # y_pred = knn.predict(X[:num_test] + epsilon)
    # assert_array_equal(y_pred, y[:num_test])

    knn = KNeighborsClassifier(n_neighbors=n_neighbors)
    knn.fit(X, y)
    y_pred = knn.predict(X[:num_test] + epsilon)
    assert_array_equal(y_pred, y[:num_test])

and then I run this cell in the notebook.

from fduml.neighbors.tests.test_knn import test_kneighbors_classifier
test_kneighbors_classifier()

But I get the following errors

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
[<ipython-input-8-68d934afc664>](https://localhost:8080/#) in <cell line: 2>()
      1 from fduml.neighbors.tests.test_knn import test_kneighbors_classifier
----> 2 test_kneighbors_classifier()

2 frames
[/content/fduml/neighbors/tests/test_knn.py](https://localhost:8080/#) in test_kneighbors_classifier(num_train, n_features, num_test, n_neighbors, random_state)
     40     knn.fit(X, y)
     41     y_pred = knn.predict(X[:num_test] + epsilon)
---> 42     assert_array_equal(y_pred, y[:num_test])

    [... skipping hidden 1 frame]

[/usr/lib/python3.10/contextlib.py](https://localhost:8080/#) in inner(*args, **kwds)
     77         def inner(*args, **kwds):
     78             with self._recreate_cm():
---> 79                 return func(*args, **kwds)
     80         return inner
     81 

[/usr/local/lib/python3.10/dist-packages/numpy/testing/_private/utils.py](https://localhost:8080/#) in assert_array_compare(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf, strict)
    795                                 verbose=verbose, header=header,
    796                                 names=('x', 'y'), precision=precision)
--> 797             raise AssertionError(msg)
    798     except ValueError:
    799         import traceback

AssertionError: 
Arrays are not equal

Mismatched elements: 2 / 10 (20%)
Max absolute difference: 1
Max relative difference: 0.
 x: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
 y: array([1, 1, 1, 0, 0, 1, 1, 1, 1, 1])

Start with fduml/neighbors/tests/test_knn.py and reproduce test_kneighbors_classifier() using the shown parameters and sklearn comparison. Inspect the Assignment1 KNN implementation and the failing expected predictions; done means the mismatch is explained and the intended test or implementation behavior is corrected and verified.