def ubh(alpha, nbits, k):
if k == 5:
unum = 25
vnum = 5
else:
unum = 50
vnum = 10
trainMat, testMat = MovieLens.loadDataSet(unum, vnum)
featMat = itembasedDCF.pca(trainMat, nbits)
B, R = itembasedDCF.ITQ(featMat, nbits)
IDX = nonzero(trainMat.T)
D0, X = itembasedDCF.parDinit(trainMat.T, nbits, IDX, 5, 1, alpha, B, 5)
D = itembasedDCF.disOpt(5, 1, trainMat.T, IDX, nbits, alpha, B, D0, X, 10)
return B, D, testMat
def expe_AL(nbits, para):
trainMat, testMat = MovieLens.loadDataSet()
# dataMat=trainMat
dataMat = trainMat.T
featMat = itembasedDCF.pca(dataMat, nbits)
D, R = itembasedDCF.ITQ(featMat, nbits)
# IDX=np.nonzero(trainMat.T)
IDX = np.nonzero(trainMat)
for i in range(len(para)):
# B0,X=itembasedDCF.parDinit(trainMat.T, nbits, IDX, 5, 1, para[i], D,5)
B0, X = itembasedDCF.parDinit(trainMat, nbits, IDX, 5, 1, para[i], D,
5)
# B=itembasedDCF.disOpt(5,1,trainMat.T, IDX, nbits, para[i], B0, D,X,5)
B = itembasedDCF.disOpt(5, 1, trainMat, IDX, nbits, para[i], B0, D, X,
5)
ndcg = evaluate.NDCG_cal(B, D, testMat, 5)
print ndcg
#itembased,NDCG@5,nbits=256
#alpha[0.0001,0.001,0.01,0.1,1,10,100]
import MovieLens
import itembasedDCF
import evaluate
import numpy as np
para=[8,16,32,64,128,256,512]
NDCG5=[]
for i in range(5):
print "the",i,"time"
trainMat, testMat=MovieLens.loadDataSet(25, 5)
PARA=[]
for j in range(len(para)):
featMat=itembasedDCF.pca(trainMat.T, para[j])
D,R=itembasedDCF.ITQ(featMat,para[j])
IDX=np.nonzero(trainMat)
B0,X=itembasedDCF.parDinit(trainMat,para[j],IDX,5,1,10,D,5)
B=itembasedDCF.disOpt(5,1,trainMat,IDX, para[j],10, B0,D,X,10)
ndcg=evaluate.NDCG_cal(B,D,testMat,5)
PARA.append(ndcg)
print PARA
NDCG5.append(PARA)
result=np.mean(NDCG5,axis=0)
print NDCG5,result
import MovieLens
import itembasedDCF
import evaluate
import numpy as np
para = [8, 16, 32, 64, 128, 256, 512]
NDCG10 = []
for i in range(5):
print 'the', i, 'time'
trainMat, testMat = MovieLens.loadDataSet(50, 5)
PARA = []
for j in range(len(para)):
featMat = itembasedDCF.pca(trainMat, para[j])
B, R = itembasedDCF.ITQ(featMat, para[j])
IDX = np.nonzero(trainMat.T)
D0, X = itembasedDCF.parDinit(trainMat.T, para[j], IDX, 5, 1, 10, B, 5)
D = itembasedDCF.disOpt(5, 1, trainMat.T, IDX, para[j], 10, D0, B, X,
10)
ndcg = evaluate.NDCG_cal(B, D, testMat, 10)
PARA.append(ndcg)
print PARA
NDCG10.append(PARA)
result = np.mean(NDCG10, axis=0)
print NDCG10, result
import MovieLens
import itembasedDCF
import evaluate
import numpy as np
#para=[8,16,32,64,128,256,512]
alpha = [0.0001,0.001,0.01,0.1,1,10,100]
NDCG5=[]
for i in range(5):
print 'the',i,'time'
trainMat,testMat=MovieLens.loadDataSet(25,5)
PARA=[]
for j in range(len(alpha)):
# featMat=itembasedDCF.pca(trainMat,para[j])
featMat = itembasedDCF.pca(trainMat, 256)
B,R=itembasedDCF.ITQ(featMat,256)
IDX=np.nonzero(trainMat.T)
D0,X=itembasedDCF.parDinit(trainMat.T,256,IDX,5,1,10,B,5)
D=itembasedDCF.disOpt(5,1,trainMat.T,IDX,256,alpha[j],D0,B,X,10)
ndcg=evaluate.NDCG_cal(B,D,testMat,5)
PARA.append(ndcg)
print PARA
NDCG5.append(PARA)
result=np.mean(NDCG5,axis=0)
print NDCG5,result