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

在看sklearn聚类部分的时候碰到的，可以按照需求生成数据，官方源码地址
读代码顺带把注释和心得写了上去

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

中文注释都是个人见解，如果有写的不到位的地方，欢迎大家评论区拍砖