def bi_item(m, tuples, k, func, func_w):
# with open('u2U.txt', 'r') as f1:
# u2U = pickle.loads(f1.read())
# with open('m2Ucluster.txt', 'r') as f4:
# m2Ucluster = pickle.loads(f4.read())
with open('m2M.txt', 'r') as f1:
m2M = pickle.loads(f1.read())
with open('u2Mcluster.txt', 'r') as f2:
u2Mcluster = pickle.loads(f2.read())
res = [] # return the list of predictions given the query tuples
# m = np.asarray(m, order = 'F') # column-major order
u2Mcluster = np.asarray(u2Mcluster, order = 'F')
simPair = model.similarityPair(u2Mcluster , func)
for pair in tuples:
#print pair[0]
c = findCentroid(m2M, pair[0])
sim = simPair[c]
temp = model.knn(sim, k)
prediction = model.predict(u2Mcluster, temp, sim, func_w)
pred = prediction[pair[1]] + 3
if pred > 5:
pred = 5
elif pred < 1:
pred = 1
res.append(pred)# plus 3
#print pred
return res
def bi_user(m, tuples, k, func, func_w):
with open('u2U.txt', 'r') as f1:
u2U = pickle.loads(f1.read())
with open('m2Ucluster.txt', 'r') as f2:
m2Ucluster = pickle.loads(f2.read())
m2Ucluster = m2Ucluster.transpose()
res = [] # return the list of predictions given the query tuples
m2Ucluster = np.asarray(m2Ucluster, order = 'F')
simPair = model.similarityPair(m2Ucluster , func)
for pair in tuples:
#print pair[0]
c = findCentroid(u2U, pair[1])
sim = simPair[c]
temp = model.knn(sim, k)
prediction = model.predict(m2Ucluster, temp, sim, func_w)
pred = prediction[pair[0]] + 3
if pred > 5:
pred = 5
elif pred < 1:
pred = 1
res.append(pred)# plus 3
#print pred
return res
