test14.py
#-*- coding：utf-8 import sys sys.path.append("svdRec.py") import svdRec from numpy import * from numpy import linalg as la # U, Sigma, VT = linalg.svd([[1, 1], [7, 7]]) # print(U) # print(Sigma) # print(VT) # 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]]]) # res = U[:, :3]*Sig3*VT[:3, :] # print("res:") # print(res) # # myMat = mat(svdRec.loadExData()) # ecl = svdRec.ecludSim(myMat[:, 0], myMat[:, 4]) # print("ecl:") # print(ecl) # cos = svdRec.cosSim(myMat[:, 0], myMat[:, 4]) # print("cos:") # print(cos) # pears = svdRec.pearsSim(myMat[:, 0], myMat[:, 4]) # print("pears:") # print(pears) # myMat = mat(svdRec.loadExData()) # myMat[0, 1] = myMat[0, 0] = myMat[1, 0] = myMat[2, 0] = 4 # myMat[3, 3] = 2 # print("myMat:") # print(myMat) # # tuiJian = svdRec.recommend(myMat, 2) # print("tuiJian:") # print(tuiJian) # # tuiJian1 = svdRec.recommend(myMat, 2, simMeas = svdRec.ecludSim) # print("tuiJian1:") # print(tuiJian1) # # tuiJian2 = svdRec.recommend(myMat, 2, simMeas = svdRec.pearsSim) # print("tuiJian2:") # print(tuiJian2) # myMat = mat(svdRec.loadExData2()) # U, Sigma, VT = la.svd(mat(svdRec.loadExData2())) # print(Sigma) # # Sig2 = Sigma**2 # total = sum(Sig2) # total9 = total*0.9 # print("total9:") # print(total9) # # total3 = sum(Sig2[:3]) # print("total3:") # print(total3) # svdRes = svdRec.recommend(myMat, 1, estMethod = svdRec.svdEst) # print("svdRes:") # print(svdRes) originalMat = svdRec.imgCompress(2) print(originalMat) print("over!!!")
svdRec.py
''' Created on Mar 8, 2011 @author: Peter ''' from numpy import * from numpy import linalg as la def loadExData(): return[[0, 0, 0, 2, 2], [0, 0, 0, 3, 3], [0, 0, 0, 1, 1], [1, 1, 1, 0, 0], [2, 2, 2, 0, 0], [5, 5, 5, 0, 0], [1, 1, 1, 0, 0]] def loadExData2(): return[[0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 5], [0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 3], [0, 0, 0, 0, 4, 0, 0, 1, 0, 4, 0], [3, 3, 4, 0, 0, 0, 0, 2, 2, 0, 0], [5, 4, 5, 0, 0, 0, 0, 5, 5, 0, 0], [0, 0, 0, 0, 5, 0, 1, 0, 0, 5, 0], [4, 3, 4, 0, 0, 0, 0, 5, 5, 0, 1], [0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 4], [0, 0, 0, 2, 0, 2, 5, 0, 0, 1, 2], [0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0], [1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]] def ecludSim(inA,inB): return 1.0/(1.0 + la.norm(inA - inB)) def pearsSim(inA,inB): if len(inA) < 3 : return 1.0 return 0.5+0.5*corrcoef(inA, inB, rowvar = 0)[0][1] def cosSim(inA,inB): num = float(inA.T*inB) denom = la.norm(inA)*la.norm(inB) return 0.5+0.5*(num/denom) def standEst(dataMat, user, simMeas, item): n = shape(dataMat)[1] simTotal = 0.0; ratSimTotal = 0.0 for j in range(n): userRating = dataMat[user,j] if userRating == 0: continue # test0 = dataMat[:,item].A>0 # test1 = dataMat[:,j].A>0 # test2 = logical_and(dataMat[:,item].A>0, dataMat[:,j].A>0) overLap = nonzero(logical_and(dataMat[:,item].A>0, dataMat[:,j].A>0))[0] if len(overLap) == 0: similarity = 0 else: similarity = simMeas(dataMat[overLap,item], dataMat[overLap,j]) 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 def svdEst(dataMat, user, simMeas, item): n = shape(dataMat)[1] simTotal = 0.0; ratSimTotal = 0.0 U,Sigma,VT = la.svd(dataMat) Sig4 = mat(eye(4)*Sigma[:4]) #arrange Sig4 into a diagonal matrix xformedItems = dataMat.T * U[:,:4] * Sig4.I #create transformed items for j in range(n): userRating = dataMat[user,j] if userRating == 0 or j==item: continue 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 def recommend(dataMat, user, N=3, simMeas=cosSim, estMethod=standEst): unratedTest = nonzero(dataMat[user,:].A==0) unratedItems = nonzero(dataMat[user,:].A==0)[1]#find unrated items if len(unratedItems) == 0: return 'you rated everything' itemScores = [] for item in unratedItems: estimatedScore = estMethod(dataMat, user, simMeas, item) itemScores.append((item, estimatedScore)) # testSort = sorted(itemScores, key=lambda jj: jj[1], reverse=True)[:N] return sorted(itemScores, key=lambda jj: jj[1], reverse=True)[:N] def printMat(inMat, thresh=0.8): for i in range(32): for k in range(32): if float(inMat[i,k]) > thresh: print(1), else: print(0), print('') def imgCompress(numSV=3, thresh=0.8): myl = [] for line in open('0_5.txt').readlines(): newRow = [] for i in range(32): newRow.append(int(line[i])) myl.append(newRow) myMat = mat(myl) print("****original matrix******") printMat(myMat, thresh) U,Sigma,VT = la.svd(myMat) SigRecon = mat(zeros((numSV, numSV))) for k in range(numSV):#construct diagonal matrix from vector SigRecon[k,k] = Sigma[k] reconMat = U[:,:numSV]*SigRecon*VT[:numSV,:] print("****reconstructed matrix using %d singular values******" % numSV) printMat(reconMat, thresh)