def test3():
myMat = mat(svdRec.loadExData())
myMat[0, 1] = myMat[0, 0] = myMat[1, 0] = myMat[2, 0] = 4
myMat[3, 3] = 2
print myMat
print "-----"
print svdRec.recommend(myMat, 2)
def test():
data = svdRec.loadExData()
U, Sigma, VT = linalg.svd(data)
print "data"
print data
print " u "
print U
print " sigma "
print Sigma
print " vt "
print VT
#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @Time : 2019/3/2 13:07 # @Author : Arrow and Bullet # @FileName: run.py # @Software: PyCharm # @Blog ：https://blog.csdn.net/qq_41800366 import svdRec from numpy import * Data = svdRec.loadExData() U, Sigma, VT = linalg.svd(Data) # print(Sigma) sig3 = mat([[Sigma[0], 0, 0], [0, Sigma[1], 0], [0, 0, Sigma[2]]]) reconData = U[:, :3] * sig3 * VT[:3, :] # print(reconData) myMat = mat(svdRec.loadExData()) # ecludSim = svdRec.ecludSim(myMat[:, 0], myMat[:, 4]) # print(ecludSim) # ecludSim = svdRec.ecludSim(myMat[:, 0], myMat[:, 0]) # print(ecludSim) # # # cosSim = svdRec.cosSim(myMat[:, 0], myMat[:, 4]) # print(cosSim) # cosSim = svdRec.cosSim(myMat[:, 0], myMat[:, 0]) # print(cosSim) # #
def test2():
myMat = mat(svdRec.loadExData())
print myMat
print svdRec.ecludSim(myMat[:, 0], myMat[:, 4])
print svdRec.cosSim(mat([[4], [2]]), mat([[1], [2]]))
print svdRec.pearsSim(myMat[:, 0], myMat[:, 4])
#coding=utf-8
import svdRec
from numpy import *
if __name__ == '__main__':
datSet = svdRec.loadExData()
U, Signam, VT = linalg.svd(datSet)
print(Signam)
import svdRec
if __name__ == "__main__":
print('This is main of module "hello.py"')
myMat = svdRec.mat(svdRec.loadExData())
svdRec.recommend(myMat, 1, estMethod=svdRec.svdEst)
simTotal = 0.0
ratSimTotal = 0.0
U, Sigma, VT = np.linalg.svd(dataMat)
Sig4 = np.mat(np.eye(4) * Sigma[:4]) # 建立对角矩阵
xformedItems = dataMat.T * U[:, :4] * Sig4.I # 构建转换后的物品,只利用90%的奇异值
for j in range(n):
userRating = dataMat[user, j]
if userRating == 0 or j==item:
continue
# 寻找两个用户都评级的物品
similarity = simMeas(xformedItems[item, :].T, xformedItems[j, :].T)
print 'the %d and %d similarity is: %f' % (item, j, similarity)
simTotal += similarity
ratSimTotal += similarity * userRating
if simTotal == 0:
return 0
else:
return ratSimTotal / simTotal
if __name__ == '__main__':
myMat = np.mat(svd.loadExData())
myMat[0, 1] = myMat[0, 0] = myMat[1, 0] = myMat[2, 0] = 4
myMat[3, 3] = 2
print recommend(myMat, 2, simMeas=svd.ecludSim)
myMat = np.mat(svd.loadExData2())
U, Sigma, VT = np.linalg.svd(myMat)
Sig2 = Sigma ** 2
print sum(Sig2), sum(Sig2) * 0.9
print sum(Sig2[:3]), "3维，大于90%，可以将11维变为3维"
print recommend(myMat, 1, estMethod=svdEst)
# -*- coding: utf-8 -*- ''' Created on 2016��12��27�� @author: Tan Zhuqing ''' from numpy import * ''' U,Sigma,VT=linalg.svd([[1,1],[7,7]]) print(U) print(Sigma) print(VT) ''' import svdRec Data = svdRec.loadExData() U, Sigma, VT = linalg.svd(Data) print(Sigma)
#print U #print #print Sigma #print #print VT #Sig3=mat([[Sigma[0], 0, 0], [0, Sigma[1], 0], [0, 0, Sigma[2]]]) #print U[:,:3]*Sig3*VT[:3,:] ''' myMat = mat(svdRec.loadExData()) myMat[0,1]=myMat[0,0]=myMat[1,0]=myMat[2,0]=4 myMat[3,3]=2 print myMat print print svdRec.recommend(myMat, 2) #print #print svdRec.recommend(myMat, 2, simMeas=svdRec.ecludSim) #print #print svdRec.recommend(myMat, 2, simMeas=svdRec.pearsSim) ''' #myMat = mat(svdRec.loadExData2()) myMat = mat(svdRec.loadExData()) print myMat print svdRec.recommend(myMat, 1, estMethod=svdRec.svdEst)
""" Created on Wed May 17 17:31:05 2017 @author: 凯风 """ import numpy as np from imp import reload import svdRec # SVD的矩阵分解，Data(m*n) = U(m*m) * Σ(m*n) * V^T(n*n) U, Sigma, VT = np.linalg.svd([[1, 1], [7, 7]]) U Sigma # 为啥不是2*2呢？因为Σ除了对角线外都是0，这样子可以节省空间吧 VT data = svdRec.loadExData() U, Sigma, VT = np.linalg.svd(data) # 在一个稍微大点的数据集上看看效果 Sigma # 前三个数值明显大于后两个，数量及上差太多了~ # 所以呢？原数据集可以用Data(m*n) = U(m*3) * Σ(3*3) * V^T(3*n) 来近似？ Sig3 = np.mat([[Sigma[0], 0, 0], [0, Sigma[1], 0], [0, 0, Sigma[2]]]) U[:, :3] * Sig3 * VT[:3, :] # 之后就用这个了？ reload(svdRec) myMat = np.mat(svdRec.loadExData()) # 以下都是查看列向量的相似度 svdRec.eculidSim(myMat[:, 0], myMat[:, 4]) # 欧氏距离 svdRec.eculidSim(myMat[:, 0], myMat[:, 0]) svdRec.cosSim(myMat[:, 0], myMat[:, 4]) # 余弦相似度 svdRec.cosSim(myMat[:, 0], myMat[:, 0]) svdRec.pearsSim(myMat[:, 0], myMat[:, 4]) # 皮尔逊相关系数
import svdRec
#分解与重构
def loadExData():
return [
[1 ,1 ,1 ,0 ,0 ],
[2 ,2 ,2 ,0 ,0 ],
[1 ,1 ,1 ,0 ,0 ],
[5 ,5 ,5 ,0 ,0 ],
[1 ,1 ,0 ,2 ,2 ],
[0 ,0 ,0 ,1 ,1]
]
Data = svdRec.loadExData()
U,Sigma,VT = linalg.svd(Data)
Sig3 = mat( [
[Sigma[0] ,0 ,0 ] ,
[0 ,Sigma[1] ,0 ] ,
[0 ,0 ,Sigma[2] ]
] )
Data_Rec = U[: , :3] *Sig3*VT[:3, :]
print Data_Rec
