def predict(train):
tr_train, tr_test = load_ml100k.get_train_test(train, random_state=34)
tr_predicted0 = regression.predict(tr_train)
tr_predicted1 = regression.predict(tr_train.T).T
tr_predicted2 = corrneighbours.predict(tr_train)
tr_predicted3 = corrneighbours.predict(tr_train.T).T
tr_predicted4 = norm.predict(tr_train)
tr_predicted5 = norm.predict(tr_train.T).T
stack_tr = np.array(
[
tr_predicted0[tr_test > 0],
tr_predicted1[tr_test > 0],
tr_predicted2[tr_test > 0],
tr_predicted3[tr_test > 0],
tr_predicted4[tr_test > 0],
tr_predicted5[tr_test > 0],
]
).T
lr = linear_model.LinearRegression()
lr.fit(stack_tr, tr_test[tr_test > 0])
stack_te = np.array(
[
tr_predicted0.ravel(),
tr_predicted1.ravel(),
tr_predicted2.ravel(),
tr_predicted3.ravel(),
tr_predicted4.ravel(),
tr_predicted5.ravel(),
]
).T
return lr.predict(stack_te).reshape(train.shape)
def predict(train):
tr_train, tr_test = load_ml100k.get_train_test(train, random_state=34)
tr_predicted0 = regression.predict(tr_train)
tr_predicted1 = regression.predict(tr_train.T).T
tr_predicted2 = corrneighbours.predict(tr_train)
tr_predicted3 = corrneighbours.predict(tr_train.T).T
tr_predicted4 = norm.predict(tr_train)
tr_predicted5 = norm.predict(tr_train.T).T
stack_tr = np.array([
tr_predicted0[tr_test > 0],
tr_predicted1[tr_test > 0],
tr_predicted2[tr_test > 0],
tr_predicted3[tr_test > 0],
tr_predicted4[tr_test > 0],
tr_predicted5[tr_test > 0],
]).T
lr = linear_model.LinearRegression()
lr.fit(stack_tr, tr_test[tr_test > 0])
stack_te = np.array([
tr_predicted0.ravel(),
tr_predicted1.ravel(),
tr_predicted2.ravel(),
tr_predicted3.ravel(),
tr_predicted4.ravel(),
tr_predicted5.ravel(),
]).T
return lr.predict(stack_te).reshape(train.shape)
def predict(train):
predicted0 = regression.predict(train)
predicted1 = regression.predict(train.T).T
predicted2 = corrneighbours.predict(train)
predicted3 = corrneighbours.predict(train.T).T
predicted4 = norm.predict(train)
predicted5 = norm.predict(train.T).T
stack = np.array([
predicted0,
predicted1,
predicted2,
predicted3,
predicted4,
predicted5,
])
return stack.mean(0)
def predict(train):
predicted0 = regression.predict(train)
predicted1 = regression.predict(train.T).T
predicted2 = corrneighbours.predict(train)
predicted3 = corrneighbours.predict(train.T).T
predicted4 = norm.predict(train)
predicted5 = norm.predict(train.T).T
stack = np.array([
predicted0,
predicted1,
predicted2,
predicted3,
predicted4,
predicted5,
])
return stack.mean(0)
