sklearn 中的 make_blobs()函数

make_blobs() 是 sklearn.datasets中的一个函数

主要是产生聚类数据集，需要熟悉每个参数，继而更好的利用

官方链接：https://scikit-learn.org/dev/modules/generated/sklearn.datasets.make_blobs.html

函数的源码：

def make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0,
               center_box=(-10.0, 10.0), shuffle=True, random_state=None):
    """Generate isotropic Gaussian blobs for clustering.

    Read more in the :ref:`User Guide <sample_generators>`.

    Parameters
    ----------
    n_samples : int, optional (default=100)
        The total number of points equally divided among clusters.

    n_features : int, optional (default=2)
        The number of features for each sample.

    centers : int or array of shape [n_centers, n_features], optional
        (default=3)
        The number of centers to generate, or the fixed center locations.

    cluster_std: float or sequence of floats, optional (default=1.0)
        The standard deviation of the clusters.

    center_box: pair of floats (min, max), optional (default=(-10.0, 10.0))
        The bounding box for each cluster center when centers are
        generated at random.

    shuffle : boolean, optional (default=True)
        Shuffle the samples.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    X : array of shape [n_samples, n_features]
        The generated samples.

    y : array of shape [n_samples]
        The integer labels for cluster membership of each sample.

    Examples
    --------
    >>> from sklearn.datasets.samples_generator import make_blobs
    >>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
    ...                   random_state=0)
    >>> print(X.shape)
    (10, 2)
    >>> y
    array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])

    See also
    --------
    make_classification: a more intricate variant
    """
    generator = check_random_state(random_state)

    if isinstance(centers, numbers.Integral):
        centers = generator.uniform(center_box[0], center_box[1],
                                    size=(centers, n_features))
    else:
        centers = check_array(centers)
        n_features = centers.shape[1]

    if isinstance(cluster_std, numbers.Real):
        cluster_std = np.ones(len(centers)) * cluster_std

    X = []
    y = []

    n_centers = centers.shape[0]
    n_samples_per_center = [int(n_samples // n_centers)] * n_centers

    for i in range(n_samples % n_centers):
        n_samples_per_center[i] += 1

    for i, (n, std) in enumerate(zip(n_samples_per_center, cluster_std)):
        X.append(centers[i] + generator.normal(scale=std,
                                               size=(n, n_features)))
        y += [i] * n

    X = np.concatenate(X)
    y = np.array(y)

    if shuffle:
        indices = np.arange(n_samples)
        generator.shuffle(indices)
        X = X[indices]
        y = y[indices]

    return X, y

可以看到它有 7 个参数

• n_samples : int, optional (default=100)
  The total number of points equally divided among clusters.

  样本数据量，默认为 100

• n_features : int, optional (default=2)
  The number of features for each sample.

  样本维度，默认为 2 维数据，测试选取 2 维数据也方便进行可视化展示

• centers : int or array of shape [n_centers, n_features], optional (default=3)
  The number of centers to generate, or the fixed center locations.

  产生数据的中心端，默认为 3

• cluster_std: float or sequence of floats, optional (default=1.0)
  The standard deviation of the clusters.

  数据集的标准差，浮点数或者浮点数序列，默认为1.0

• center_box: pair of floats (min, max), optional (default=(-10.0, 10.0))
  The bounding box for each cluster center when centers are
  generated at random.

  中心确定之后，需要设定的数据边界，默认为(-10.0, 10.0)

• shuffle : boolean, optional (default=True)
  Shuffle the samples.

  洗牌操作，默认是True

• random_state : int, RandomState instance or None, optional (default=None)
  If int, random_state is the seed used by the random number generator;
  If RandomState instance, random_state is the random number generator;
  If None, the random number generator is the RandomState instance used
  by np.random.

  随机数种子，不同的种子产出不同的样本集合