matplotlib⼆维矩阵散点图及每个点标记
记录NLP作业中利⽤matplotlib绘制散点图并进⾏标记
原⽂：blog.csdn/wizardforcel/article/details/54782628
M_reduced是要求绘制的散点坐标
words 是单词
word2ind 字典，记录单词与坐标的对应关系
所使⽤的函数是annotate()
annotate(" 标记的⽂本 ", xy, xytext)
第⼀个参数是预标记的⽂本
第⼆个参数是预标注的点坐标
第三个参数是预标记⽂本的坐标
import numpy as np
matplotlib中subplotimport matplotlib.pyplot as plt
M_reduced= np.array([[1,1],[-1,-1],[1,-1],[-1,1],[0,0]])
word2ind ={'test1':0,'test2':1,'test3':2,'test4':3,'test5':4}
words =['test1','test2','test3','test4','test5']
fig = plt.figure()
fig_sub1 = fig.add_subplot(111)
fig_sub1.scatter(M_reduced[:,0],M_reduced[:,1],color='r',marker='x')
for i in range(len(words)):
plt.annotate(words[i], xy =(M_reduced[word2ind[words[i]],0], M_reduced[word2ind[words[i]],1]), xytext =(M_reduced[word2ind[words[i]],0]+0.01, M_ reduced[word2ind[words[i]],1]+0.01))
plt.show()
运⾏结果

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