Sklearn notes: make_ Blobs generate clustering data

from sklearn.datasets import make_blobs

1 Basic usage

data, label = make_blobs(
    n_features=2, 
    n_samples=100, 
    centers=3, 
    random_state=3, 
    cluster_std=[0.8, 2, 5])

2 Parameter description

n_features	How many eigenvalues does each sample have
n_samples	The number of samples
centers	Number of cluster centers It can also be a list , Represents each center Corresponding coordinates （ Dimensions and n_features equally ）
random_state	Random seeds
cluster_std	Variance of each category

3 Use examples

3.1 centers Is the number

from sklearn.datasets import make_blobs
data, label = make_blobs(n_features=2, 
                         n_samples=100, 
                         centers=2, 
                         random_state=2019, 
                         cluster_std=[0.6,0.7] )

label
'''
array([1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,
       0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,
       1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1,
       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0,
       0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1])
'''
# 100 dimension 

data
'''
array([[ 2.42739457,  3.06175875],
       [ 7.05586911, -2.34535549],
       [ 7.49164297, -2.09940869],
       [ 8.95763559, -3.40157028],
       [ 2.17353708,  3.06694536],
......
       [ 2.29981982,  3.8569118 ]])
'''
#100*2  dimension 

import matplotlib.pyplot as plt
colors=['green','blue']
for i,color in enumerate(colors):
    color_tmp=np.where(label==i)[0]
    plt.scatter(data[color_tmp,0],data[color_tmp,1],c=color,label=i)
plt.legend()

3.2 center  It's coordinates

from sklearn.datasets import make_blobs
data, label = make_blobs(n_features=2, 
                         n_samples=100, 
                         centers=[[-100,1],[0,5]], 
                         random_state=2019, 
                         cluster_std=[0.6,0.7] 

import matplotlib.pyplot as plt
colors=['green','blue']
for i,color in enumerate(colors):
    color_tmp=np.where(label==i)[0]
    plt.scatter(data[color_tmp,0],data[color_tmp,1],c=color,label=i)
plt.legend()