Python sklearn- gaussian.mixture 如何获取每个集群中的样本/点

Question

我正在使用 GMM 将我的数据集聚类到 K 个组，我的模型运行良好，但是无法从每个聚类中获取原始数据，你们能给我一些解决这个问题的想法吗？非常感谢。

Answer 1

您可以这样做（查看 d0、d1 和 d2）。

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
from pandas import DataFrame 
from sklearn import datasets 
from sklearn.mixture import GaussianMixture 

# load the iris dataset 
iris = datasets.load_iris() 

# select first two columns  
X = iris.data[:, 0:2] 

# turn it into a dataframe 
d = pd.DataFrame(X) 

# plot the data 
plt.scatter(d[0], d[1]) 

gmm = GaussianMixture(n_components = 3) 

# Fit the GMM model for the dataset  
# which expresses the dataset as a  
# mixture of 3 Gaussian Distribution 
gmm.fit(d) 

# Assign a label to each sample 
labels = gmm.predict(d) 
d['labels']= labels 
d0 = d[d['labels']== 0] 
d1 = d[d['labels']== 1] 
d2 = d[d['labels']== 2] 

# here is a possible solution for you:
d0
d1
d2

# plot three clusters in same plot 
plt.scatter(d0[0], d0[1], c ='r') 
plt.scatter(d1[0], d1[1], c ='yellow') 
plt.scatter(d2[0], d2[1], c ='g') 

# print the converged log-likelihood value 
print(gmm.lower_bound_) 

# print the number of iterations needed 
# for the log-likelihood value to converge 
print(gmm.n_iter_)

# it needed 8 iterations for the log-likelihood to converge.