CoCalc -- public

GitHub Repository: greyhatguy007/Machine-Learning-Specialization-Coursera
Path: blob/main/C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A1/public_tests.py
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import numpy as np
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def compute_centroids_test(target):
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    # With 3 centroids
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    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, 1],
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                  [-1, 1.5], [2.5, 1.5], [-1.1, -1.7], [-1.6, 1.2]])
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    idx = np.array([1, 1, 1, 0, 0, 0, 2])
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    K = 3
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    centroids = target(X, idx, K)
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    expected_centroids = np.array([[0.13333333,  0.43333333],
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                                   [-1.33333333, -0.5      ],
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                                   [-1.6,        1.2       ]])
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    assert type(centroids) == np.ndarray, "Wrong type"
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    assert centroids.shape == (K, X.shape[1]), f"Wrong shape. Expected: {(len(X),)} got: {idx.shape}"
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    assert np.allclose(centroids, expected_centroids), f"Wrong values. Expected: {expected_centroids}, got: {centroids}"
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    X = np.array([[2, 2.5], [2.5, 2.5], [-1.5, -1.5],
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                  [2, 2], [-1.5, -1], [-1, -1]])
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    idx = np.array([0, 0, 1, 0, 1, 1])
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    K = 2
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    centroids = target(X, idx, K)
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    expected_centroids = np.array([[[ 2.16666667,  2.33333333],
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                                    [-1.33333333, -1.16666667]]])
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    assert type(centroids) == np.ndarray, "Wrong type"
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    assert centroids.shape == (K, X.shape[1]), f"Wrong shape. Expected: {(len(X),)} got: {idx.shape}"
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    assert np.allclose(centroids, expected_centroids), f"Wrong values. Expected: {expected_centroids}, got: {centroids}"
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    print("\033[92mAll tests passed!")
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def find_closest_centroids_test(target):
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    # With 2 centroids
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    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, -1],
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                  [2, 2],[2.5, 2.5],[2, 2.5]])
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    initial_centroids = np.array([[-1, -1], [2, 2]])
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    idx = target(X, initial_centroids)
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    assert type(idx) == np.ndarray, "Wrong type"
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    assert idx.shape == (len(X),), f"Wrong shape. Expected: {(len(X),)} got: {idx.shape}"
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    assert np.allclose(idx, [0, 0, 0, 1, 1, 1]), "Wrong values"
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    # With 3 centroids
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    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, 1],
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                  [-1, 1.5], [2.5, 1.5], [2, 2]])
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    initial_centroids = np.array([[2.5, 2], [-1, -1], [-1.5, 1.]])
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    idx = target(X, initial_centroids)
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    assert type(idx) == np.ndarray, "Wrong type"
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    assert idx.shape == (len(X),), f"Wrong shape. Expected: {(len(X),)} got: {idx.shape}"
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    assert np.allclose(idx, [1, 1, 2, 2, 0, 0]), f"Wrong values. Expected {[2, 2, 0, 0, 1, 1]}, got: {idx}"
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    # With 3 centroids
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    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, 1],
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                  [-1, 1.5], [2.5, 1.5], [-1.1, -1.7], [-1.6, 1.2]])
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    initial_centroids = np.array([[2.5, 2], [-1, -1], [-1.5, 1.]])
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    idx = target(X, initial_centroids)
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    assert type(idx) == np.ndarray, "Wrong type"
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    assert idx.shape == (len(X),), f"Wrong shape. Expected: {(len(X),)} got: {idx.shape}"
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    assert np.allclose(idx, [1, 1, 2, 2, 0, 1, 2]), f"Wrong values. Expected {[2, 2, 0, 0, 1, 1]}, got: {idx}"
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    print("\033[92mAll tests passed!")
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