目录一、介绍二、函数的使用
一、介绍
make_blobs() 是 sklearn.datasets中的一个函数。
主要是产生聚类数据集，产生一个数据集和相应的标签。
函数的源代码如下：
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 `.
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 += 1
for i, (n, std) in enumerate(zip(n_samples_per_center, cluster_std)):
X.append(centers + generator.normal(scale = std,
size = (n, n_features)))
y += * 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
二、函数的使用
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)
可以看到它有 7 个参数：
n_samples = 100 ，表示数据样本点个数,默认值100；n_features = 2 ，是每个样本的特征（或属性）数，也表示数据的维度，默认值是2；centers = 3 ，表示类别数（标签的种类数），默认值3；cluster_std = 1.0 ，表示每个类别的方差，例如我们希望生成2类数据，其中一类比另一类具有更大的方差，可以将cluster_std设置为[1.0, 3.0]，浮点数或者浮点数序列，默认值1.0；center_box = (-10.0, 10.0) ，中心确定之后的数据边界，默认值(-10.0, 10.0)；shuffle = True ，将数据进行洗乱，默认值是True；random_state = None ，官网解释是随机生成器的种子，可以固定生成的数据，给定数之后，每次生成的数据集就是固定的。若不给定值，则由于随机性将导致每次运行程序所获得的的结果可能有所不同。在使用数据生成器练习机器学习算法练习或python练习时建议给定数值。
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发表于 2023-5-4 17:19:36