附上LDA的python代码实现，后期在补充上基于西瓜数据的分类效果
#!/usr/bin/env python #-*- coding: utf-8 -*- ''' LDA算法实现 ''' import os import sys import numpy as np from numpy import * import operator import matplotlib import matplotlib.pyplot as plt def createDataSet(): #group=array([[1.0,1.1], [1.0,1.0], [0,0], [0,0.1], [1.1, 1.2], [0.1, 0.2]]) #labels=['A','A','B','B'] #group1=mat([[x for x in range(1,6)], [x for x in range(1,6)]]) #group2=mat([[x for x in range(10,15)], [x for x in range(15, 20)]]) group1=mat(random.random((2,8))*5+20) group2=mat(random.random((2,8))*5+2) return group1, group2 #end of createDataSet def draw(group): fig=plt.figure() plt.ylim(0, 30) plt.xlim(0, 30) ax=fig.add_subplot(111) ax.scatter(group[0,:], group[1,:]) plt.show() #end of draw #计算样本均值 #参数samples为nxm维矩阵，其中n表示维数，m表示样本个数 def compute_mean(samples): mean_mat=mean(samples, axis=1) return mean_mat #end of compute_mean #计算样本类内离散度 #参数samples表示样本向量矩阵，大小为nxm，其中n表示维数，m表示样本个数 #参数mean表示均值向量，大小为1xd，d表示维数，大小与样本维数相同，即d=m def compute_withinclass_scatter(samples, mean): #获取样本维数，样本个数 dimens,nums=samples.shape[:2] #将所有样本向量减去均值向量 samples_mean=samples-mean #初始化类内离散度矩阵 s_in=0 for i in range(nums): x=samples_mean[:,i] s_in+=dot(x,x.T) #endfor return s_in #end of compute_mean if __name__=='__main__': group1,group2=createDataSet() print "group1 :\n",group1 print "group2 :\n",group2 draw(hstack((group1, group2))) mean1=compute_mean(group1) print "mean1 :\n",mean1 mean2=compute_mean(group2) print "mean2 :\n",mean2 s_in1=compute_withinclass_scatter(group1, mean1) print "s_in1 :\n",s_in1 s_in2=compute_withinclass_scatter(group2, mean2) print "s_in2 :\n",s_in2 #求总类内离散度矩阵 s=s_in1+s_in2 print "s :\n",s #求s的逆矩阵 s_t=s.I print "s_t :\n",s_t #求解权向量 w=dot(s_t, mean1-mean2) print "w :\n",w #判断(2,3)是在哪一类 test1=mat([1,1]) g=dot(w.T, test1.T-0.5*(mean1-mean2)) print "g(x) :",g #判断(4,5)是在哪一类 test2=mat([10,10]) g=dot(w.T, test2.T-0.5*(mean1-mean2)) print "g(x) :",g #endif
