【Python学习】 - sklearn - 用于生成数据的make

函数原型：

sklearn.datasets.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)

参数含义：

n_samples: int, optional (default=100)
The total number of points equally divided among clusters.
待生成的样本的总数。
n_features: int, optional (default=2)
The number of features for each sample.
每个样本的特征数。
centers: int or array of shape [n_centers, n_features], optional (default=3)
The number of centers to generate, or the fixed center locations.
要生成的样本中心（类别）数，或者是确定的中心点。
cluster_std: float or sequence of floats, optional (default=1.0)
The standard deviation of the clusters.
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]。
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.

返回值

X : array of shape [n_samples, n_features]
The generated samples.
生成的样本数据集。
y : array of shape [n_samples]
The integer labels for cluster membership of each sample.
样本数据集的标签。

实战代码1：

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.datasets.samples_generator import make_blobs
# X为样本特征，Y为样本簇类别， 共1000个样本，每个样本3个特征，共4个簇
X, y = make_blobs(n_samples=10000, n_features=3, centers=[[3,3, 3], [0,0,0], [1,1,1], [2,2,2]], cluster_std=[0.2, 0.1, 0.2, 0.2], random_state =9)
fig = plt.figure()
ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=30, azim=20)
plt.scatter(X[:, 0], X[:, 1], X[:, 2],marker='o')
plt.show()

输出：

实战代码2：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobsX, y = make_blobs(n_samples=100, n_features=2, centers=4)plt.scatter(X[:, 0], X[:, 1], c='b')
plt.show()

输出：