manuel-delverme

[1, 3, 2] achieved fit 26.0
[0, 4, 0] achieved fit 6.0
[-0.39568418  1.01307049 -1.04464656]

==RESULTS==
[0, 4, 0] achieved fit 0.0
[0, 4, 0] achieved fit 0.0
[-0.48153435  1.04255481 -1.08245629]

==RESULTS==

manuel-delverme

import flask
import newspaper
import wtforms

DEBUG = True
app = flask.Flask(__name__)
app.config.from_object(__name__)
app.config['SECRET_KEY'] = 'feeddeadbeeffeeddeadbeeffeedde'

accatiemmeelle = """

manuel-delverme

import newspaper

article = newspaper.Article("http://www.ansa.it/sito/notizie/tecnologia/hitech/2015/04/06/robot-per-amazon-due-italiani-in-gara_e1199b8c-95e0-4700-8283-8a6b0a882a6c.html")
article.download()
article.parse()
article.nlp()

html = f"""
<table border=1>
  <tr>

manuel-delverme

class CachedSomething:
    def __init__(self):
        self.cache = {}

    def __setattr__(self, key, value):
        self.cache[key] = value

    def __getattr__(self, item):
        return self.cache[item]

manuel-delverme

import torch
import tensorboardX
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import torch.utils.data

writer = tensorboardX.SummaryWriter()

manuel-delverme

    def train_network(samples, neural_network, nr_epochs=10, batch_size=64):
        optimizer = optim.Adam(neural_network.parameters())
        neural_network.train()

        for epoch_nr in range(nr_epochs):
            sample_ids = np.random.shuffle(range(len(samples)))

            for start in range(0, len(samples) // batch_size, batch_size):
                mini_batch = samples[sample_ids[start: start + batch_size]]
                boards, pis, vs = zip(*mini_batch)

manuel-delverme

    environment = environments.GoEnvironment(board_size=19)
    player_mcts = mcts.MCTS(
        environment,
        networks.NeuralNetwork(board_size=environment.getStateSize(), action_size=environment.getActionSize()),
    )

    training_samples = collections.deque(maxlen=opt.training_samples_buffer_size)
    
    for iteration_number in range(opt.num_iters):

manuel-delverme

score:	19.46759259259259	options: nr:4	names:7 10 25 27
score:	11.435185185185185	options: nr:5	names:7 10 25 27 28
score:	25.23611111111111	options: nr:6	names:6 7 10 25 27 28
score:	18.35648148148148	options: nr:7	names:6 7 9 10 25 27 28
score:	23.541666666666668	options: nr:8	names:6 7 9 10 24 25 27 28
score:	23.38888888888889	options: nr:9	names:6 7 9 10 13 24 25 27 28
score:	8.62037037037037	options: nr:10	names:6 7 9 10 13 16 24 25 27 28
score:	24.101851851851848	options: nr:11	names:6 7 9 10 13 16 24 25 27 28 31
score:	18.96759259259259	options: nr:12	names:6 7 9 10 13 16 24 25 27 28 31 34
score:	17.833333333333332	options: nr:13	names:6 7 9 10 12 13 16 24 25 27 28 31 34

manuel-delverme

/home/awok/Projects/supervised_reward/env_reward/bin/python /home/awok/Projects/supervised_reward/main.py
(2_w,4mirr1)-aCMA-ES (mu_w=1.5,w_1=80%) in dimension 18 (seed=237288, Fri Jan  5 12:17:27 2018)
score:	7940.826388888889	options: 8	4 5 10 11 15 16 17 23
score:	5839.784722222223	options: 10	21 22 23 26 27 28 29 33 34 35
score:	8771.63888888889	options: 4	4 5 10 11
score:	-1107.7361111111113	options: 14	0 1 2 3 4 6 7 8 9 10 13 14 15 16
[7940.826388888889, 5839.784722222223, 8771.63888888889, -1107.7361111111113]
best [-0.21687281 -0.29423262  0.10809115 -0.3457722  -0.18912326  0.17178892   0.14703262  0.94997003 -0.18883859 -0.82346577  0.50633336 -0.17325047   0.37087813  0.63369408  0.07967291 -0.47341161 -0.68896583 -0.4226999 ] fitness -8771.63888888889
score:	11841.0625	options: 3	29 34 35
score:	11929.47222222222	options: 4	28 29 34 35

manuel-delverme

import timeit
setup = 'import numpy as np; a=np.random.randn(10)'
reshape = timeit.Timer('a.reshape(-1, 10)', setup=setup)
transp = timeit.Timer('a.transpose()', setup=setup)
T = timeit.Timer('a.T', setup=setup)
print("reshape", reshape.timeit(number=int(1e6)))
print("transp", transp.timeit(number=int(1e6)))
print("T", T.timeit(number=int(1e6)))

print("reshape", reshape.timeit(number=int(1e6)))