test_ransac.py 文件源码

def test_ransac_multi_dimensional_targets():

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, random_state=0)

    # 3-D target values
    yyy = np.column_stack([y, y, y])

    # Estimate parameters of corrupted data
    ransac_estimator.fit(X, yyy)

    # Ground truth / reference inlier mask
    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False

    assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)


# XXX: Remove in 0.20