scikit-learn源码学习之datasets.samples_generator.make_blobs

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
"""

#根据random_state生成随机数生成器
generator = check_random_state(random_state)

#判断centers对象的类型
#如果是int就根据center_box的范围来随机生成中心点
if isinstance(centers, numbers.Integral):
#uniform表示均匀分布采样
#范围是(center_box[0],center_box[1])
#形状是centers*n_features的
centers = generator.uniform(center_box[0], center_box[1],
size=(centers, n_features))
#把centers转化np.array类型 并得到n_features
else:
centers = check_array(centers)
n_features = centers.shape[1]

#如果cluster_std是一个实数，表示每个中心的标准差都是cluster_std
if isinstance(cluster_std, numbers.Real):
cluster_std = np.ones(len(centers)) * cluster_std

#存放样本的返回值
X = []
y = []

n_centers = centers.shape[0]
#//运算符表示整数除法 平均每个中心的样本数
n_samples_per_center = [int(n_samples // n_centers)] * n_centers

#把余数依次摊在前几个中心里
for i in range(n_samples % n_centers):
n_samples_per_center[i] += 1

#enumrate的返回值为index,value
#zip可以把长度一样的多个序列打包在一起，遍历时下标一样的在一起
for i, (n, std) in enumerate(zip(n_samples_per_center, cluster_std)):
#normal表示正态分布
#根据scale和size生成随机数 然后加在中心点上，让其波动~~
#array类型相加的时候有一个性质如下
#>>> np.array([1,2])+np.array([[3,4],[5,6]])
# array([[4, 6],[6, 8]])
X.append(centers[i] + generator.normal(scale=std,
size=(n, n_features)))
#标签连续
y += [i] * n

#concatenate这个函数就是把原来的不同组的数列合在一起 理解起来有些绕
#>>> np.concatenate([[[1,2],[3,4]],
# ... [[5,6],[7,8]],
# ... [[9,10],[10,11]]])
# array([[ 1,  2],
#      [ 3,  4],
#      [ 5,  6],
#      [ 7,  8],
#      [ 9, 10],
#      [10, 11]])
#其实如果把上面的X.append换成X.extend就能省略这步比较难懂的操作了
X = np.concatenate(X)
y = np.array(y)

#打乱次序
if shuffle:
#获取下标
indices = np.arange(n_samples)
#打乱下标
generator.shuffle(indices)
X = X[indices]
y = y[indices]

return X, y