How to use test_precedence method in pyshould

Best Python code snippet using pyshould_python

test.py

Source:test.py Github

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...17 self.assertListEqual(rpn('4-8'), [Decimal('4'), Decimal('8'), '-'])18 self.assertListEqual(rpn('5*3'), [Decimal('5'), Decimal('3'), '*'])19 self.assertListEqual(rpn('6/4'), [Decimal('6'), Decimal('4'), '/'])20 self.assertListEqual(rpn('2^3'), [Decimal('2'), Decimal('3'), '^'])21 def test_precedence(self):22 self.assertListEqual(rpn('2+3*4'), [Decimal('2'), Decimal('3'), Decimal('4'), '*', '+'])23 self.assertListEqual(rpn('5*6+7'), [Decimal('5'), Decimal('6'), '*', Decimal('7'), '+'])24 def test_brackets(self):25 self.assertListEqual(rpn('(2+3)-5'),[Decimal('2'), Decimal('3'), '+', Decimal('5'), '-'])26 self.assertListEqual(rpn('4*(6-7)'), [Decimal('4'), Decimal('6'), Decimal('7'), '-', '*'])27 self.assertListEqual(rpn('(2.78+3)*(7-8)'), [Decimal('2.78'), Decimal('3'), '+',28 Decimal('7'), Decimal('8'), '-',29 '*'])30 def test_constants(self):31 self.assertListEqual(rpn('e'), [Decimal(math.e)])32 self.assertListEqual(rpn('e+pi'), [Decimal(math.e), Decimal(math.pi), '+'])33 self.assertListEqual(rpn('e*(3 + 7 - e)'), [Decimal(math.e), Decimal('3'),34 Decimal('7'), Decimal(math.e),35 '-', '+', '*'])36 def test_functions(self):37 self.assertListEqual(rpn('sin(3)'), [Decimal('3'), 'sin'])38 self.assertListEqual(rpn('arcsin(0.5)'), [Decimal('0.5'), 'arcsin'])39 self.assertListEqual(rpn('cos( 8 ) + tan(7)'), [Decimal('8'), 'cos', Decimal('7'), 'tan', '+'])40 self.assertListEqual(rpn('cos(arcsin(1/e))'), [Decimal('1'), Decimal(math.e),41 '/', 'arcsin', 'cos'])42 def test_precedence(self):43 self.assertListEqual(rpn('3 * 4 + 6'), [Decimal('3'), Decimal('4'), '*', Decimal('6'), '+'])44 self.assertListEqual(rpn('1-2*5'), [Decimal('1'), Decimal('2'), Decimal('5'), '*', '-'])45 self.assertListEqual(rpn('1/2*4'), [Decimal('1'), Decimal('2'), '/', Decimal('4'), '*'])46 def test_unary_minus(self):47 self.assertListEqual(rpn('-6'), [Decimal('6'), '--'])48 self.assertListEqual(rpn('-4+3'), [Decimal('4'), '--', Decimal('3'), '+'])49 self.assertListEqual(rpn('2^-2'), [Decimal('2'), Decimal('2'), '--', '^'])50 self.assertListEqual(rpn('-5^2'), [Decimal('5'), Decimal('2'), '^', '--'])51class Test_computing_rpn(TestCase):52 def test_simple_algebra(self):53 self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), '+']), Decimal('5'))54 self.assertEqual(compute_rpn([Decimal('4'), Decimal('8'), '-']), Decimal('-4'))55 self.assertEqual(compute_rpn([Decimal('5'), Decimal('3'), '*']), Decimal('15'))56 self.assertEqual(compute_rpn([Decimal('6'), Decimal('4'), '/']), Decimal('1.5'))57 self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), '^']), Decimal('8'))58 def test_brackets(self):59 self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), '+', Decimal('5'), '-']), Decimal('0'))60 self.assertEqual(compute_rpn([Decimal('4'), Decimal('6'), Decimal('7'), '-', '*']), Decimal('-4'))61 self.assertEqual(compute_rpn([Decimal('2.78'), Decimal('3'), '+',62 Decimal('7'), Decimal('8'), '-', '*']), Decimal('-5.78'))63 def test_constants(self):64 self.assertEqual(compute_rpn([Decimal(math.e)]), Decimal(math.e))65 self.assertEqual(compute_rpn([Decimal(math.e), Decimal(math.pi), '+']), Decimal(math.e)+Decimal(math.pi))66 self.assertEqual(compute_rpn([Decimal(math.e), Decimal('3'), Decimal('7'), Decimal(math.e),67 '-', '+', '*']),68 Decimal(math.e)*(Decimal('3') + Decimal('7')-Decimal(math.e)))69 def test_functions(self):70 self.assertEqual(compute_rpn([Decimal('3'), 'sin']), Decimal(math.sin(3)))71 self.assertEqual(compute_rpn([Decimal('0.5'), 'arcsin']), Decimal(math.asin(0.5)))72 self.assertEqual(compute_rpn([Decimal('8'), 'cos', Decimal('7'), 'tan', '+']),73 Decimal(math.cos(8)+math.tan(7)))74 self.assertEqual(compute_rpn([Decimal('1'), Decimal(math.e), '/', 'arcsin', 'cos']),75 Decimal(math.cos(math.asin(1/math.e))))76 def test_precedence(self):77 self.assertEqual(compute_rpn([Decimal('2'), Decimal('3'), Decimal('4'), '*', '+']), Decimal('14'))78 self.assertEqual(compute_rpn([Decimal('5'), Decimal('6'), '*', Decimal('7'), '+']), Decimal('37'))79 def test_unary_minus(self):80 self.assertEqual(compute_rpn([Decimal('6'), '--']), Decimal('-6'))81 self.assertEqual(compute_rpn([Decimal('4'), '--', Decimal('3'), '+']), Decimal('-1'))82 self.assertEqual(compute_rpn([Decimal('2'), Decimal('2'), '--', '^']), Decimal('0.25'))83 self.assertEqual(compute_rpn([Decimal('5'), Decimal('2'), '^', '--']), Decimal('-25'))84class TestGraphingCalc(TestCase):85 def test_implementable_function(self):86 pass87if __name__ == '__main__':...

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train_overfit.py

Source:train_overfit.py Github

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1import time2import numpy as np3import torch4from torch import optim5from utils import *6from Envs.JobShopMultiGymEnv import *7from Models.actorcritic import *89embeddim = 12810actorLR = 1e-411criticLR = 1e-41213device = torch.device("cuda" if torch.cuda.is_available() else "cpu")1415jobs = 1516ops = 1517macs = ops18maxTime = 10019i = 020size_agnostic = True21resume_run = True22seed = 023BS = 1282425start = time.time()26########################################################################2728print('ta{}'.format(i + 1))29test_precedence, test_timepre_ = read_instances('./data/ta/ta{}'.format(i + 1))30test_timepre = test_timepre_ / maxTime3132# Create batch33test_precedence = np.repeat(test_precedence, BS, axis=0)34test_timepre = np.repeat(test_timepre, BS, axis=0)3536test_venv = JobShopMultiGymEnv(BS, jobs, ops, macs)37test_venv.setGame(test_precedence, test_timepre)38testSamples = [i for i in range(BS)]3940filedir = './Results/%dx%d/' % (jobs, macs)41if size_agnostic:42 filedir = './Results/size_agnostic/'4344model_name = 'A2C_Seed%d_BS%d.tar' % (seed, BS)45if size_agnostic:46 model_name = '6_6_100.tar'4748# Instanitate actor-critic49actor = Actor(embeddim, jobs, ops, macs, device).to(device)50critic = Critic(embeddim, jobs, ops, macs, device).to(device)5152# Environment training5354actor_opt = optim.Adam(actor.parameters(), lr=actorLR)55critic_opt = optim.Adam(critic.parameters(), lr=criticLR)5657if resume_run:58 checkpoint = torch.load(filedir + model_name, map_location=device)59 actor.load_state_dict(checkpoint['actor_state_dict'])60 critic.load_state_dict(checkpoint['critic_state_dict'])61 actor_opt.load_state_dict(checkpoint['actor_opt_state_dict'])62 critic_opt.load_state_dict(checkpoint['critic_opt_state_dict'])6364for epi in range(10000):6566 if epi%10==1 or epi==0:67 epi_st = time.time()6869 test_venv.reset(testSamples)7071 train_rollout = Rollout(test_venv, actor, critic, device)72 ite, total_reward, States, Log_Prob, Prob, Action, Value, tr_reward, tr_entropy = train_rollout.play(BS,testSamples)73 Log_Prob = torch.stack(Log_Prob)7475 tr_reward.reverse()7677 #Compute Advantage: Qvalue - Value78 Qvalue = np.zeros((ite, BS))79 q = 0.080 for i in range(ite):81 q += np.array(tr_reward[i]) # q.shape=[BS]82 Qvalue[ite - i - 1] = q8384 Advantage = torch.tensor(Qvalue, device=device) - torch.stack(Value) # advantage.shape=[ite,BS]8586 # Zero grad87 actor.zero_grad()88 critic.zero_grad()8990 # Compute loss fucntion91 # actor_loss = torch.mean(torch.sum(Log_Prob, dim=1)*torch.tensor(total_reward).to(device))92 actor_loss = torch.mean(-Log_Prob * Advantage.detach())93 critic_loss = 0.5 * torch.mean(Advantage ** 2)9495 # Update weights Actor and critic96 actor_loss.backward()97 critic_loss.backward()98 actor_opt.step()99 critic_opt.step()100101 if epi % 10 == 0:102103 epi_et = time.time()104 105 print('epi: %d, al: %.2e, cl: %.2e, trSpan: %.4f, trEntropy: %.2f, time: %.2f' \106 % (epi, actor_loss.data.item(), critic_loss.data.item(), np.mean(total_reward), tr_entropy, epi_et-epi_st)) ...

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test_instances.py

Source:test_instances.py Github

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1from Envs.JobShopMultiGymEnv import *2from utils import *3from Models.actorcritic import *4from torch import optim5import time6embeddim = 1287device = torch.device("cuda" if torch.cuda.is_available() else "cpu")8# Change9instances = 'ta' # Select: ta or dmu10size_agnostic = True11testsize = 112jobs = 1513macs = 1514ops = macs 15maxTime = 100 # maxTime: ta -> 100, for dmu -> 20016seed = 017BS = 12818beam_size = 219model_jobs = 620model_ops = 621start = time.time()22########################################################################23for i in range(0,10):24 25 print('{}{}'.format(instances,i+1))26 if instances=='ta':27 test_precedence,test_timepre_ = read_instances('./data/ta/ta{}'.format(i+1))28 else:29 test_precedence,test_timepre_ = read_instances('./data/dmu/dmu{}.txt'.format(i+1))30 test_timepre = test_timepre_ / maxTime31 test_venv = JobShopMultiGymEnv(testsize,jobs,ops,macs)32 test_venv.setGame(test_precedence,test_timepre)33 testSamples = [i for i in range(testsize)]34 test_venv.reset(testSamples)35 # Instanitate actor-critic36 actor = Actor(embeddim,jobs,ops,macs,device).to(device)37 critic = Critic(embeddim,jobs,ops,macs,device).to(device)38 # Environment training39 actor_opt = optim.Adam(actor.parameters())40 critic_opt = optim.Adam(critic.parameters())41 filedir = './Results/%dx%d/'%(jobs,macs)42 if size_agnostic:43 filedir = './Results/size_agnostic/%dx%d_%d/'%(model_jobs,model_ops,maxTime)44 model_name = 'A2C_Seed%d_BS%d.tar'%(seed,BS)45 checkpoint = torch.load(filedir+model_name, map_location=torch.device('cpu'))46 actor.load_state_dict(checkpoint['actor_state_dict'])47 critic.load_state_dict(checkpoint['critic_state_dict'])48 actor_opt.load_state_dict(checkpoint['actor_opt_state_dict'])49 critic_opt.load_state_dict(checkpoint['critic_opt_state_dict'])50 test_rollout = Rollout(test_venv,actor,critic,device)51 te_ite,te_total_reward,te_States,te_Log_Prob,te_Prob,te_Action,te_Value,te_reward, te_entropy = test_rollout.play(testsize,testSamples,False,beam_size)52 print(te_total_reward)53end = time.time()...

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