test_ransac.py 文件源码

def test_ransac_dynamic_max_trials():
    # Numbers hand-calculated and confirmed on page 119 (Table 4.3) in
    #   Hartley, R.~I. and Zisserman, A., 2004,
    #   Multiple View Geometry in Computer Vision, Second Edition,
    #   Cambridge University Press, ISBN: 0521540518

    # e = 0%, min_samples = X
    assert_equal(_dynamic_max_trials(100, 100, 2, 0.99), 1)

    # e = 5%, min_samples = 2
    assert_equal(_dynamic_max_trials(95, 100, 2, 0.99), 2)
    # e = 10%, min_samples = 2
    assert_equal(_dynamic_max_trials(90, 100, 2, 0.99), 3)
    # e = 30%, min_samples = 2
    assert_equal(_dynamic_max_trials(70, 100, 2, 0.99), 7)
    # e = 50%, min_samples = 2
    assert_equal(_dynamic_max_trials(50, 100, 2, 0.99), 17)

    # e = 5%, min_samples = 8
    assert_equal(_dynamic_max_trials(95, 100, 8, 0.99), 5)
    # e = 10%, min_samples = 8
    assert_equal(_dynamic_max_trials(90, 100, 8, 0.99), 9)
    # e = 30%, min_samples = 8
    assert_equal(_dynamic_max_trials(70, 100, 8, 0.99), 78)
    # e = 50%, min_samples = 8
    assert_equal(_dynamic_max_trials(50, 100, 8, 0.99), 1177)

    # e = 0%, min_samples = 10
    assert_equal(_dynamic_max_trials(1, 100, 10, 0), 0)
    assert_equal(_dynamic_max_trials(1, 100, 10, 1), float('inf'))

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       stop_probability=-0.1)
    assert_raises(ValueError, ransac_estimator.fit, X, y)
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       stop_probability=1.1)
    assert_raises(ValueError, ransac_estimator.fit, X, y)