grades = func.findGrades(stds, cos)
print("Finish")
# Compute the similarity between every students
print("Getting the similarity between student . . .")
similarity = np.zeros((len(stds), len(stds)))
s_len = len(stds)
for a in range(s_len):
for b in range(s_len):
similarity[a][b] = func.getSimilarity(grades[a], grades[b])
print("Finish")
# Predict the cos score of not pass cos for every student
print("Predicting cos score and generate recommend. . .")
pred = func.predict(similarity, grades)
# Generate the top K high score of not pass cos for every student
suggest = func.generate(cos, pred, 30)
# Fill the empty suggest base on K-Means clustering
func.fillEmpty(suggest, pred)
print("Finish")
# Parse the recommend cos with specify semester and K courses
print("Matching the course name . . .")
result = func.parseCurrentCos(stds, suggest, sem, K)
print("Finish")
print("Transfer to csv file . . .")
result = pd.DataFrame(result)
result.to_csv('RS.csv', index=False)
currentCos = func.findCurrentCos(sem)
AutoEncoder = at.AutoEncoder
autoencoder = torch.load('net2.pkl')
# start encode
print('Encoding')
encoded, _ = autoencoder(score)
encoded = encoded.detach().numpy()
# get similarity
print('Getting Similarity')
s_len = len(encoded)
similarity = np.zeros((s_len, s_len))
for a in range(s_len):
for b in range(s_len):
if a == b:
similarity[a][b] = 0
else:
similarity[a][b] = func.getSimilarity(encoded[a], encoded[b])
# start predict
score = score.numpy()
pred = func.predict(similarity, score)
# Generate the top K high score of not pass cos for every student
suggest = func.generate(allCos, pred, 50)
# Parse the recommend cos with specify semester and K courses
result = func.parseCurrentCos(stds, suggest, sem, K)
print("Transfer to csv file . . .")
result = pd.DataFrame(result)
result.to_csv('RS_auto_2.csv', index=False)
sem = input('Enter the semester: ')
# parameter
INF = 123.0
# prepare data and model
stds = func.findAllStudent()
allCos = func.findAllCos()
score = func.findGrades(stds, allCos)
score = np.float32(np.nan_to_num(score))
score = torch.FloatTensor(score)
currentCos = func.findCurrentCos(sem)
AutoEncoder = at.AutoEncoder
autoencoder = torch.load('net.pkl')
# start predict
_, decoded = autoencoder(score)
decoded = decoded.detach().numpy()
score = score.numpy()
decoded[score != 0] = -INF
res = func.generate(allCos, decoded, 30)
res = func.parseCurrentCos(stds, res, sem, 10)
# write to file
with open('RS_auto.csv', 'w') as out:
for se in res:
temp = se[0] + ',"' + ','.join(se[1:]) + '"'
print(temp)
print(temp, file=out)