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
