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)