def predict(otrain):
binary = (otrain > 0)
norm = NormalizePositive(axis=1)
train = norm.fit_transform(otrain)
dists = distance.pdist(binary, 'correlation')
dists = distance.squareform(dists)
neighbors = dists.argsort(axis=1)
filled = train.copy()
for u in range(filled.shape[0]):
# n_u are the neighbors of user
n_u = neighbors[u, 1:]
for m in range(filled.shape[1]):
# This code could be faster using numpy indexing trickery as the
# cost of readibility (this is left as an exercise to the reader):
revs = [train[neigh, m] for neigh in n_u if binary[neigh, m]]
if len(revs):
n = len(revs)
n //= 2
n += 1
revs = revs[:n]
filled[u, m] = np.mean(revs)
return norm.inverse_transform(filled)
def predict(otrain):
binary = (otrain > 0)
norm = NormalizePositive(axis=1)
train = norm.fit_transform(otrain)
dists = distance.pdist(binary, 'correlation')
dists = distance.squareform(dists)
neighbors = dists.argsort(axis=1)
filled = train.copy()
for u in range(filled.shape[0]):
# n_u are the neighbors of user
n_u = neighbors[u, 1:]
for m in range(filled.shape[1]):
# This code could be faster using numpy indexing trickery as the
# cost of readibility (this is left as an exercise to the reader):
revs = [train[neigh, m]
for neigh in n_u
if binary[neigh, m]]
if len(revs):
n = len(revs)
n //= 2
n += 1
revs = revs[:n]
filled[u,m] = np.mean(revs)
return norm.inverse_transform(filled)
def predict(otrain):
binary = (otrain > 0)
norm = NormalizePositive(axis=1)
train = norm.fit_transform(otrain)
dists = distance.pdist(binary, 'correlation')
dists = distance.squareform(dists)
neighbors = dists.argsort(axis=1)
filled = train.copy()
for u in range(filled.shape[0]):
# n_u는 사용자 이웃이다
n_u = neighbors[u, 1:]
for m in range(filled.shape[1]):
# 이 코드는 numpy 인덱싱을 사용하여 빠르게 할 수 있다
# 이해하기는 좀 더 힘들다
revs = [train[neigh, m]
for neigh in n_u
if binary[neigh, m]]
if len(revs):
n = len(revs)
n //= 2
n += 1
revs = revs[:n]
filled[u,m] = np.mean(revs)
return norm.inverse_transform(filled)
def predict(otrain):
#拿到打过分的index
binary = (otrain > 0)
norm = NormalizePositive(axis=1)
#归一化
train = norm.fit_transform(otrain)
#计算用户打分之间的相似度，这里是只关心打分与否，而不关心多少分？
dists = distance.pdist(binary, 'correlation')
dists = distance.squareform(dists)
#对dists进行排序，axis=1代表对行排序，每一行代表从近到远对应列下标，如 2 0 1代表第二列是距离最近，第0列其次..
neighbors = dists.argsort(axis=1)
filled = train.copy()
for u in range(filled.shape[0]):
# n_u 是第u个用户的邻居
n_u = neighbors[u, 1:]
#m是电影
for m in range(filled.shape[1]):
# This code could be faster using numpy indexing trickery as the
# cost of readibility (this is left as an exercise to the reader):
#对于用户u的邻居n_u，如果他对电影m打分了，则进入到revs列表中
revs = [train[neigh, m] for neigh in n_u if binary[neigh, m]]
#将revs列表中的前一半的均值作为 用户u对电影m的预测
if len(revs):
n = len(revs)
n //= 2
n += 1
revs = revs[:n]
filled[u, m] = np.mean(revs)
return norm.inverse_transform(filled)
def predict(train):
binary = (train > 0)
reg = ElasticNetCV(fit_intercept=True,
alphas=[0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])
norm = NormalizePositive()
train = norm.fit_transform(train)
filled = train.copy()
for u in range(filled.shape[0]):
curtrain = np.delete(train, u, axis=0)
bu = binary[u]
if np.sum(bu) > 5:
reg.fit(curtrain[:, bu].T, train[u, bu])
filled[u, ~bu] = reg.predict(curtrain[:, ~bu].T)
return norm.inverse_transform(filled)
def predict(train):
binary = (train > 0)
reg = ElasticNetCV(fit_intercept=True, alphas=[
0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])
norm = NormalizePositive()
train = norm.fit_transform(train)
filled = train.copy()
# iterate over all users
for u in range(train.shape[0]):
# remove the current user for training
curtrain = np.delete(train, u, axis=0)
bu = binary[u]
if np.sum(bu) > 5:
reg.fit(curtrain[:,bu].T, train[u, bu])
# Fill the values that were not there already
filled[u, ~bu] = reg.predict(curtrain[:,~bu].T)
return norm.inverse_transform(filled)
def predict(train):
binary = (train > 0)
reg = ElasticNetCV(fit_intercept=True, alphas=[
0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])
norm = NormalizePositive()
train = norm.fit_transform(train)
filled = train.copy()
# 모든 사용자에 대해 반복
for u in range(train.shape[0]):
# 훈련에서 현재 사용자 제거
curtrain = np.delete(train, u, axis=0)
bu = binary[u]
if np.sum(bu) > 5:
reg.fit(curtrain[:,bu].T, train[u, bu])
# 이전에 없는 값을 넣는다
filled[u, ~bu] = reg.predict(curtrain[:,~bu].T)
return norm.inverse_transform(filled)
def predict(train):
binary = (train > 0)
reg = ElasticNetCV(fit_intercept=True,
alphas=[0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])
norm = NormalizePositive()
train = norm.fit_transform(train)
filled = train.copy()
# 对于用户u
for u in range(train.shape[0]):
# curtrain是去掉了用户u的训练集
curtrain = np.delete(train, u, axis=0)
bu = binary[u]
#对于那些打分总数超过5的用户才进行预测
if np.sum(bu) > 5:
#输入是其余用户对 用户u打过分的这些电影 的打分，标签是用户u实际的打分
reg.fit(curtrain[:, bu].T, train[u, bu])
# 对于用户u没打分的那部分电影进行预测
filled[u, ~bu] = reg.predict(curtrain[:, ~bu].T)
return norm.inverse_transform(filled)