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