from numpy import * from numpy import linalg as la def ecludSim(inA, inB): return 1.0/(1.0+ la.norm(inA - inB)) 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 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]) simTotal += similarity ratSimTotal += similarity * userRating if simTotal == 0:return 0 else: return ratSimTotal/simTotal def recommend(dataMat, user, N=4, simMeas = cosSim, estMethod = standEst): unratedIte