def page_fault(algorithm, pages, frames, results): if algorithm not in results: if algorithm == "FIFO": results[algorithm] = fifo(pages, frames) elif algorithm == "LRU": results[algorithm] = lru(pages, frames) elif algorithm == "LFU": results[algorithm] = lfu(pages, frames) elif algorithm == "OPT": results[algorithm] = opt(pages, frames) elif algorithm == "RAND": results[algorithm] = rand(pages, frames) elif algorithm == "MFU": results[algorithm] = mfu(pages, frames) elif algorithm == "MRU": results[algorithm] = mru(pages, frames) else: return -1 return results[algorithm]
def ILP_optA_solution(tasks, cpu, tk, model=Model()): # init A = cpu.A M = cpu.M Sk = tk.s tasks = [t for t in tasks if t != tk] n = len(tasks) I = [t.W(Sk) for t in tasks] a = [t.a for t in tasks] Ak = opt(tasks, cpu, tk) if (n <= 0): return 0 if (Ak == 0): Ak = A # Ak = A # model = Model() model.clear() x = { i: model.add_var(obj=0, var_type="C", name="x[%d]" % i) for i in range(n) } y = { i: model.add_var(obj=0, var_type="C", name="y[%d]" % i) for i in range(n) } model.objective = maximize( xsum((1 / M) * x[i] + (a[i] / Ak) * y[i] for i in range(n))) model.verbose = False for i in range(n): model += x[i] + y[i] <= I[i] for i in range(n): model += x[i] <= xsum((1 / M) * x[j] for j in range(n)) for i in range(n): model += y[i] <= xsum((a[j] / Ak) * y[j] for j in range(n)) model.optimize() return model.objective_value
def main(testfunc, Npop=100, Ngen=100, repeat=1, Extra=None, chromname='chrom'): """ testfunc --> name of MOOP test function Ngenome --> number of total possible genes (# pressure ports, infinite for math) constr --> list of gene numbers that are constrained (not used) repeat --> number of duplcate runs to do Extra --> extra text to add to filename chromname --> name of chromosome, can be specific to optimization """ #MAKE OPTIMIZATION OBJECT optobj = opt(testfunc, dup=repeat) #keys of objective function (traits we are optimizing form) params = optobj.optfunc.params #number of genes in chromosome (number of independent variables) ngenes = optobj.optfunc.ngenes #MAKE GENETIC ALGORITHM OBJECT #Initialize NSGA2 object gaobj = nsga2(optobj, params, Npop=Npop, Ngen=Ngen) #Run evolution for Ngen generations gaobj.Evolution() gaobj.SaveData(chromname) print('\n\nBest Individual:') print(gaobj.best['chrom']) #Print members of final pareto frontier print("\n\nFinal Pareto Frontier Individuals' Chromosomes:") for f in gaobj.mate['fronts'][0]: print(gaobj.mate['indv'][f]['chrom'])
num_epochs = 100 save_every = 50 print_every = 10 valid_every = 20 # test the valid data when batch step is (int) grad_clip = 5. learning_rate = 0.001 # Store every options to opt class data structure opt = opt(data_dir=data_dir, save_dir=save_dir, gen_dir=gen_dir, ratio=ratio, num_layers=num_layers, hidden_size=hidden_size, embedding_size=embedding_size, cuda=cuda, batch_size=batch_size, seq_len=seq_len, num_epochs=num_epochs, save_every=save_every, print_every=print_every, valid_every=valid_every, grad_clip=grad_clip, learning_rate=learning_rate) # load the vocab data with open('vocab/vocab.pkl', 'rb') as f: vocab = pickle.load(f) # load the hidden data if resume is true if args.resume: with open(os.path.join(opt.gen_dir, "hidden.pkl"), 'rb') as f:
def uphkidxoptall(): hkequ.hkequ() idx.idx() opt.opt()
import os import sys import csv import itemSimList import filterCustRate import opt def recommendItem(rateList,rec_itemList): rec_userItem={} rec_items=[] for user,itemList in rateList.items(): if user not in rec_userItem: rec_userItem[user]=[] for items in itemList: rec_items=rec_itemList[items] for rec_item in rec_items: rec_userItem[user].append(rec_item) return rec_userItem if __name__=="__main__": #sys.argv[1] should be opt file name rateList=filterCustRate.filterCustRate("custRating.csv") rec_itemList=itemSimList.itemSimList("itemSim.csv") rec_userItem=recommendItem(rateList,rec_itemList) #print rec_userItem opt.opt(rec_userItem,sys.argv[1])
print("Optimisation has run.") else: if fg == True: print("Set first_guess to True") input() opt.opt_fg() print("Optimisation has run.") print("Run experiment, press any button") print("Set first_guess to False") input() for i in range(50): os.system('cnee --replay -sp 15 -f xnee.xns >/dev/null 2>&1') time.sleep(10) opt.opt() print("Replication is finished.") sys.exit() clean = False fi_gu = False while clean == False: print("Do you want to start a new experiment?") print("Old data will be moved and you have to restart.") print("Confirm with yes.") aux = input() aux = aux.lower() if (aux in ['no', 'nein', 'n'] or aux[0] in ['n', 'm']): print("You don't want to restart.") clean = True
import pylab from numpy import * from numpy.linalg import * from opt import opt import file_io path = "reg_test/" restart = "restart.out" niter = 1 # max ifDual iterations # read in initial turbulent viscosity temp = path temp += "nut_no.dat" nno, x_no, y_no, nu0 = file_io.read_field(temp) # create an instance of opt o = opt(x_no, y_no, niter, restart, path) tol = 1e-9 nopt = 200 # number of optimization iterations x_opt, J_opt, res = o.lbfgs(nopt, nu0, tol) nu_opt = exp(x_opt) print J_opt # write out mu_opt temp = path temp += "nu_opt" file_io.write_field(temp, x_no, y_no, nu_opt)
from numpy import * from numpy.linalg import * from opt import opt import file_io path = "c004/" restart = "restart.out" niter = 50000 # max ifDual iterations np = 6 # number of cores to run on ctolpri = 2e-6 ctoladj = 1e-4 # read in initial turbulent viscosity temp = path; temp+="nut_no.dat" nno, x_no, y_no, nu0 = file_io.read_field(temp) # create an instance of opt o = opt(x_no, y_no, niter, restart, path, np, ctolpri, ctoladj) tol = 1e-9 nopt = 500 # number of optimization iterations x_opt, J_opt, res = o.lbfgs(nopt, nu0, tol) nu_opt = exp(x_opt) print J_opt # write out mu_opt temp = path; temp+="nut_opt" file_io.write_field(temp, x_no, y_no, nu_opt)
from numpy.linalg import * from opt import opt import file_io path = "c004/" restart = "restart.out" niter = 50000 # max ifDual iterations np = 6 # number of cores to run on ctolpri = 2e-6 ctoladj = 1e-4 # read in initial turbulent viscosity temp = path temp += "nut_no.dat" nno, x_no, y_no, nu0 = file_io.read_field(temp) # create an instance of opt o = opt(x_no, y_no, niter, restart, path, np, ctolpri, ctoladj) tol = 1e-9 nopt = 500 # number of optimization iterations x_opt, J_opt, res = o.lbfgs(nopt, nu0, tol) nu_opt = exp(x_opt) print J_opt # write out mu_opt temp = path temp += "nut_opt" file_io.write_field(temp, x_no, y_no, nu_opt)
for i in range(len(timestep) - 2)) lstm = [] lstm.append( seg_LSTM(input_size, hidden_size=hidden_size, num_layers=num_layers, bi_directional=bi_directional) for i in range(len(len) - 1)) return lstm def get_parameters(*args): return nn.Parameter() if __name__ == '__main__': opts = opt.opt() scale = [4, 8, 16] work_directory = opts.directory annotation_directory = os.path.join(work_directory, opts.annotation) output_path = os.path.join(work_directory, opts.output) resnext_model = generate_model(opts) conv_block = [] conv_block.append(scale_block(_, 1024, opts.cardinality) for _ in scale) LSTM_part = LSTM(1024, opts.time_steps, opts.num_layers, opts.bi) parameters = get_parameters() optimizer = optim.SGD(parameters, lr=0.1, momentum=0.9, dampening=opts.dampening, nesterov=opts.nesterov)