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GitHub Repository: jxareas/Machine-Learning-Notebooks
Path: blob/master/1_Supervised_Machine_Learning/Week 2. Regression with multiple input variables/public_tests.py
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import numpy as np
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def compute_cost_test(target):
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    # print("Using X with shape (4, 1)")
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    # Case 1
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    x = np.array([2, 4, 6, 8]).T
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    y = np.array([7, 11, 15, 19]).T
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    initial_w = 2
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    initial_b = 3.0
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    cost = target(x, y, initial_w, initial_b)
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    assert cost == 0, f"Case 1: Cost must be 0 for a perfect prediction but got {cost}"
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    # Case 2
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    x = np.array([2, 4, 6, 8]).T
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    y = np.array([7, 11, 15, 19]).T
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    initial_w = 2.0
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    initial_b = 1.0
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    cost = target(x, y, initial_w, initial_b)
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    assert cost == 2, f"Case 2: Cost must be 2 but got {cost}"
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    # print("Using X with shape (5, 1)")
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    # Case 3
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    x = np.array([1.5, 2.5, 3.5, 4.5, 1.5]).T
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    y = np.array([4, 7, 10, 13, 5]).T
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    initial_w = 1
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    initial_b = 0.0
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    cost = target(x, y, initial_w, initial_b)
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    assert np.isclose(cost, 15.325), f"Case 3: Cost must be 15.325 for a perfect prediction but got {cost}"
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    # Case 4
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    initial_b = 1.0
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    cost = target(x, y, initial_w, initial_b)
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    assert np.isclose(cost, 10.725), f"Case 4: Cost must be 10.725 but got {cost}"
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    # Case 5
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    y = y - 2
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    initial_b = 1.0
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    cost = target(x, y, initial_w, initial_b)
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    assert np.isclose(cost, 4.525), f"Case 5: Cost must be 4.525 but got {cost}"
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    print("\033[92mAll tests passed!")
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def compute_gradient_test(target):
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    print("Using X with shape (4, 1)")
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    # Case 1
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    x = np.array([2, 4, 6, 8]).T
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    y = np.array([4.5, 8.5, 12.5, 16.5]).T
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    initial_w = 2.
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    initial_b = 0.5
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    dj_dw, dj_db = target(x, y, initial_w, initial_b)
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    # assert dj_dw.shape == initial_w.shape, f"Wrong shape for dj_dw. {dj_dw} != {initial_w.shape}"
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    assert dj_db == 0.0, f"Case 1: dj_db is wrong: {dj_db} != 0.0"
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    assert np.allclose(dj_dw, 0), f"Case 1: dj_dw is wrong: {dj_dw} != [[0.0]]"
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    # Case 2
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    x = np.array([2, 4, 6, 8]).T
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    y = np.array([4, 7, 10, 13]).T + 2
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    initial_w = 1.5
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    initial_b = 1
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    dj_dw, dj_db = target(x, y, initial_w, initial_b)
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    # assert dj_dw.shape == initial_w.shape, f"Wrong shape for dj_dw. {dj_dw} != {initial_w.shape}"
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    assert dj_db == -2, f"Case 2: dj_db is wrong: {dj_db} != -2"
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    assert np.allclose(dj_dw, -10.0), f"Case 1: dj_dw is wrong: {dj_dw} != -10.0"
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    print("\033[92mAll tests passed!")
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