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)
