def energy(self): if self._energy: return self._energy e1 = get_energy(self.length * 2 + 1, self.depth * 2 + 1, self, get_H(), self.length, self.cut1, self.cut2) e2 = get_energy(self.length * 2 + 1, self.depth * 2 + 1, self, get_H(), self.length + 1, self.cut1, self.cut2) self._energy = (e1 + e2) / 2 self._energy += loss_sign(abs(self.projector_1) - 1) self._energy += loss_sign(abs(self.projector_2) - 1) for r, omega in self.parameter: self._energy += loss_sign(abs(r) - 2) return self._energy
def main(): from get_energy import get_energy (X_1gtrain, y_train, X_1gtest, y_test) = get_energy() dirct = 'load1' fileList = sorted(os.listdir(dirct)) day_cnt = 0 for file in fileList: nbMachines, nbTasks, nbResources, MC, U, D, E, L, P, idle, up, down, q = data_reading( dirct + "/" + file) price = y_train[(day_cnt * 48):(1 + (day_cnt + 1) * 48)] sch = ICON_scheduling(nbMachines, nbTasks, nbResources, MC, U, D, E, L, P, idle, up, down, q, price, verbose=False) print( optimal_value(nbMachines, nbTasks, nbResources, MC, U, D, E, L, P, idle, up, down, q, price, sch)) day_cnt += 1
import pandas as pd import logging from get_energy import get_energy formatter = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(filename='MSEpredictor_solutions.log', level=logging.INFO, format=formatter) logging.info('Started\n') modelPATH_list = [ '../Results/MSE_pred/MSE-prediction_test_epoch2.npy', '../Results/MSE_pred/MSE-prediction_test_epoch4.npy' ] #, #'../Results/MSE_pred/MSE-prediction_test_epoch20.npy'] (X_1gtrain, y_train, X_1gtest, y_test) = get_energy() X_1gvalidation = X_1gtest[0:2880, :] y_validation = y_test[0:2880] y_test = y_test[2880:] X_1gtest = X_1gtest[2880:, :] X_1gtest = np.repeat(X_1gtest, 6, axis=0) X_1gvalidation = np.repeat(X_1gvalidation, 6, axis=0) X_1gtrain = np.repeat(X_1gtrain, 6, axis=0) y_test = np.repeat(y_test, 6) y_train = np.repeat(y_train, 6) y_validation = np.repeat(y_validation, 6) for i in range(0, 4): file = "../../EnergyCost/Hard_Instances/instance0" + str(
from qptl_model import * from get_energy import get_energy import time,datetime import logging formatter = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(filename='QPTL_Load1.log', level=logging.INFO,format=formatter) logging.info('Started\n') file = "../../EnergyCost/load1/day01.txt" filename= "../Results/Load1_qptl.csv" param_data = data_reading(file) (X_1gtrain, y_train, X_1gtest, y_test) = get_energy("../../prices2013.dat") X_1gvalidation = X_1gtest[0:2880,:] y_validation = y_test[0:2880] y_test= y_test[2880:] X_1gtest = X_1gtest[2880:,:] n_iter= 12 H_combinations=[ {'optimizer':optim.Adam,'lr':1e-2,'betas':(0.95, 0.8)}, {'optimizer':optim.Adam,'lr':1e-2}, {'optimizer':optim.Adam,'lr':1e-3}] for i in range(n_iter): for h in H_combinations: print("hyperparams : %s Time:%s \n" %(str(h),datetime.datetime.now())) start = time.time() clf = qptl_ICON(epochs=12,param= param_data,verbose=True,validation=True,validation_relax=True, **h ) pdf = clf.fit(X_1gtrain,y_train,X_1gvalidation,y_validation ,X_1gtest,y_test)