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sklearn 中的 make_blobs()函数详解

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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://.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  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

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