22043104+范纬.zip

1.

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import warnings

warnings.filterwarnings(action = 'ignore')

%matplotlib inline

plt.rcParams['font.sans-serif']=['SimHei']

import scipy.cluster.hierarchy as sch

from itertools import cycle

from matplotlib.patches import Ellipse

from sklearn.mixture import GaussianMixture

N=100

X1, y1 = make_blobs(n_samples=N, centers=4, n_features=2,random_state=0) #2 特征

X2, y2 = make_blobs(n_samples=N, centers=4, n_features=3,random_state=123) #3 特征

print('y1=',y1)

print('y2=',y2)

y1= [0 3 0 0 0 0 2 3 0 3 3 3 3 3 3 1 1 2 2 1 0 3 2 1 0 2 2 0 1 1 1 3 1

0 3 1 2 0 3 0 2 2 2 2 0 2 1 0 1 3 0 1 2 3 0 1 1 2 2 3 2 3 3 3 1 1 0 3

2 2

0 1 2 3 2 3 1 1 0 2 0 2 3 3 0 1 1 1 3 3 2 0 1 2 3 0]

2.

plt.figure(figsize=(18,12))

plt.subplot(121)

plt.scatter(X1[:,0],X1[:,1],s=50)

plt.xlabel("X1-1")

plt.ylabel("X1-2")

plt.title("%d 个样本观测点的分布"%N)

ax.set_ylabel("X2-2")

ax.set_zlabel("X2-3")

ax.set_title("%d 个样本观测点的分布"%N)

3.

labels=np.unique(KM.labels_) #预测

print('labels=',labels)

#预测结果可视化

markers='o*^+'

for i,label in enumerate(labels): #分别绘制每一个小类数据

plt.scatter(X1[KM.labels_==label,0],X1[KM.labels_==label,1],

label="cluster %d"%label,marker=markers[i],s=50)

plt.scatter(KM.cluster_centers_[:,0],KM.cluster_centers_[:,1],marker='X',

s=60,c='r',label="小类中心") #绘制小类中心

plt.legend(loc="best",framealpha=0.5)

plt.xlabel("X1-1")

plt.ylabel("X1-2")

plt.title("%d 个样本观测点的聚类结果"%N)

labels= [0 1 2 3]

Out[9]:

#(获得聚类标签，聚类解存储在 K-均值聚类对象的.labels_属性中)

#预测结果可视化

#(利用 for 循环可视化聚类解，即以不同颜色和形状的符号分别绘制各小类的

散点图)

ax=plt.subplot(111, projection='3d')

markers='o*^+'

for i,label in enumerate(labels): #分别绘制每一个小类

数据

ax.scatter(X2[KM.labels_==label,0],X2[KM.labels_==labe

l,1],X2[KM.labels_==label,2],

ax.legend(loc="best",framealpha=0.5)

ax.set_ylabel("X2-2")

ax.set_zlabel("X2-3")

ax.set_title("%d 个样本观测点的聚类结果"%N)