Python：将iris数据通过近邻转化为图并展示出来

import numpy as np
from sklearn import datasets
import networkx as nx
from scipy.spatial.distance import pdist, squareform
import matplotlib.pyplot as plt


X, y = datasets.load_iris(return_X_y=True)
N = X.shape[0]
distlist = pdist(X,metric='euclidean')
dist_Matrix = squareform(distlist)
simi_Matrix = np.zeros((N,N))
neiNum = 5

for i in range(N):
    ordidx = np.argsort(dist_Matrix[i,:])
    for j in range(neiNum+1):
        if i != ordidx[j]:
            simi_Matrix[i,ordidx[j]] = dist_Matrix[i, ordidx[j]]

G = nx.Graph()

for i in range(N):
    for j in range(N):
        if simi_Matrix[i,j] > 0:
            G.add_weighted_edges_from([(i,j,simi_Matrix[i,j])])

pos = nx.spring_layout(G)
# pos = nx.random_layout(G)
# pos = nx.circular_layout(G)
# pos = nx.shell_layout(G)

nx.draw(G,pos,node_color=y,with_labels=True,font_size=15,node_size=120)
plt.show()

 

本文地址：https://blog.csdn.net/DeniuHe/article/details/108410681