GP_binary_classification.py 文件源码

def dataset_generator():
    """
    generate dataset for binary classification
    :return:
    """
    X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                               random_state=1, n_clusters_per_class=1)
    rng = np.random.RandomState(2)
    X += 2 * rng.uniform(size=X.shape)
    linearly_separable = (X, y)

    datasets = [make_moons(noise=0.3, random_state=0),
                make_circles(noise=0.2, factor=0.5, random_state=1),
                linearly_separable
                ]

    X, y = datasets[0]
    y[y == 0] = -1
    X = StandardScaler().fit_transform(X)
    return X, y