def __init__(self, base, field, strategy):
"""Build unit from component prototypes."""
for prototype in (base, field, strategy):
for name, dfield in prototype.__dataclass_fields__.items():
value = getattr(prototype, name)
if not isinstance(value, dfield.type):
tools(log=f"wrong type: {name}, {dfield.type}, {value}")()
setattr(self, name, value)
def __call__(self, name, coords, strategy, is_centurion=False):
try:
base = self._registery[name]
except KeyError:
tools(log=f"Unregistered name: {name}")()
base = UnitBase(0, 0, 0, 0)
field = UnitField(self.side, coords, is_centurion)
strat = Strategy(*strategy)
self.units.append(Unit(base, field, strat))
return self.units[-1]
def __init__(self, ip, mac, mask):
self.myip = ip
self.mymac = mac
self.mymask = mask
self.tools = tools()
self.mem = memory()
self.network_map = network_map(ip, mac)
def simulate(self, max_sampling=200):
avail = list(self.pool)
picked = []
for r in range(self.Round):
if r in self.order:
selected = self.R.pick(r, picked, avail, max_sampling)
else:
selected = self.D.pick(r, picked, avail, max_sampling)
if selected not in avail:
print(selected)
avail.remove(selected)
picked.append(selected)
lineup = self.assign_team(picked)
# print(lineup)
lineup = np.array(lineup).reshape(1, 10)
win_probability = self.predictor.predict(lineup)
# print(win_probability)
if self.show_result:
print()
print('Radiant: ', lineup[0, :5], ' ', 'Dire: ', lineup[0, 5:])
if self.show_name:
t = tools.tools()
t.show_name_lineup(lineup[0])
if win_probability > 0.5:
print('Radiant win')
else:
print('Dire win')
return int(win_probability > 0.5)
def __init__(self,ip,mac,mask):
self.myip=ip
self.mymac=mac
self.mymask=mask
self.tools=tools()
self.mem=memory()
self.network_map=network_map(ip,mac)
def __init__(self,ip,mac):
self.myip=ip
self.mymac=mac
self.tools=tools()
self.mem=memory()
self.network=network()
self.replyTimeout=20
def __init__(self, ip, mac):
self.myip = ip
self.mymac = mac
self.tools = tools()
self.mem = memory()
self.network = network()
self.replyTimeout = 20
def __init__(self):
self.tools = tools.tools()
self.include = []
self.stdIncludes = []
self.forwardDeclLHCb = []
self.forwardDeclGlob = {}
self.forwardIncl = []
self.verbatimLHCb = []
def __init__(self):
self.tools = tools.tools()
self.include = []
self.stdIncludes = []
self.forwardDeclLHCb = []
self.forwardDeclGlob = {}
self.forwardIncl = []
self.verbatimLHCb = []
def __init__(self,ip,mac):
self.myip=ip
self.mymac=mac
self.tools=tools()
self.mem=memory()
self.ruleconstructor=ruleconstructor()
self.recv_target=None
self.sent_target=None
self.network=network()
def __init__(self,godRoot):
genSrcUtils.genSrcUtils.__init__(self)
self.godRoot = godRoot
self.tools = tools.tools()
self.packInclude = []
self.packExcludes = []
self.forwardDeclLHCb = []
self.forwardDeclGlob = {}
self.forwardIncl = []
def __init__(self, ip, mac):
self.myip = ip
self.mymac = mac
self.tools = tools()
self.mem = memory()
self.ruleconstructor = ruleconstructor()
self.recv_target = None
self.sent_target = None
self.network = network()
def __init__(self,godRoot):
genSrcUtils.genSrcUtils.__init__(self)
self.godRoot = godRoot
self.tools = tools.tools()
self.packInclude = []
self.packExcludes = []
self.forwardDeclLHCb = []
self.forwardDeclGlob = {}
self.forwardIncl = []
self.log = logging.getLogger('GODGenNamespaces')
def __init__(self):
print('---------------------')
self.energy_output=energy_adhoc()
self.tcp_cbr_output=tcp_cbr()
self.tool=tools()
self.result=[]
self.result_cols=[]
self.result_DataFrame=DataFrame()
print('---------------------')
print('Welcome Pipline')
print('---------------------')
def __init__(self, sizeOfMemory):
#初始化内存
self.__setMemory(sizeOfMemory)
#初始化寄存器
self.__setRegister()
#初始化标志位
self.__setCC()
#初始化程序计数器
#初始化停机状态
self.haltState = 0
self.tools = tools.tools()
def main():
learning_rate = 0.000001
max_iterations = 1
linear_regretion = mlr()
regr = linear_model.LinearRegression()
tools = t.tools()
tools.loadDataset('bike_sharing.csv')
trfeatures, trlabels, ttfeatures, ttlabels = tools.splitDataset()
regr.fit(trfeatures, trlabels)
linear_regretion.fit(trfeatures, trlabels, learning_rate, max_iterations)
print('sklearn = %f' % regr.score(ttfeatures, ttlabels))
print('self = %f' % linear_regretion.score(ttfeatures, ttlabels))
def main():
tools = t.tools()
kn = knn(3)
knn3 = KNeighborsClassifier(algorithm='brute', n_neighbors=3)
tools.loadDataset('haberman.data')
trfeatures, trlabels, ttfeatures, ttlabels = tools.splitDataset()
kn.fit(trfeatures, trlabels)
knn3.fit(trfeatures, trlabels)
selfScore = kn.score(ttfeatures, ttlabels)
spScore = knn3.score(ttfeatures, ttlabels)
print "Score: "
print "implemented knn =", selfScore
print "sklearn knn =", spScore
def getNeighbors(self, trfeatures, trlabels, current, k):
distances = []
neighbors = []
tools = t.tools()
#considering that current doesnt have a label anymore
length = len(current)
# if one wants to pass current with label:
# length = len(current)-1
for x in range(len(trfeatures)):
dist = tools.euclidianDistance(current, trfeatures[x], length)
distances.append((trlabels[x], dist))
# sorting distance's list by dist
distances.sort(key=operator.itemgetter(1))
# getting k nearest neighbors
for x in range(k):
neighbors.append(distances[x][0])
return neighbors
def __init__(self, args):
self.fullCommand = (" ").join(args)
self.tools = tools.tools()
self.version = "v8r0"
self.xmlSources = []
self.xmlDBFile = ""
self.xmlDBFileExtra = []
self.linkDefOutput = os.curdir
self.srcOutput = os.curdir
self.dictOutput = os.curdir
self.argv0 = args[0]
self.godRoot = ""
self.gClasses = 1
self.gLinkDef = 0
self.gClassDicts = 1
self.gNamespaces = 1
self.gAssocDicts = 1
self.allocatorType = "FROMXML"
self.default_namespace = "JM"
self.parseArgs(args[1:])
def pick(self, Round, picked, avail=None, Max_sampling=0):
"""
randomly select hero
avail: list of available heroes
picked: list of picked heroes
max_sampling: no use
"""
if avail == None:
avail = list(self.pool)
for hero in picked:
if hero in avail:
avail.remove(hero)
R_team = picked[::2]
D_team = picked[1::2]
if self.side == 'Radiant':
your_team = R_team
enermy_team = D_team
else:
your_team = D_team
enermy_team = R_team
print()
print('Radiant team: ', R_team, 'Dire team: ', D_team)
if self.show_name:
t = tools.tools()
t.show_name_single(R_team, 'Radiant')
t.show_name_single(D_team, 'Dire')
print(self.side + "'s turn: ")
while True:
try:
hero = int(input('Pick your hero ID: '))
except ValueError:
print("Invalid input")
if hero in avail:
return hero
if hero not in avail:
print('Unavailable hero, pick again')
def __init__(self):
print('Using Tcp_Cbr backend.')
self.tool = tools(False)
for decision in self.root_entity_types:
file.write('Decision' + str(decision_counter) + '\n' + '\n')
for grand_child in decision:
words = grand_child.attrib['href']
(start, stop) = self.get_words_interval_for_speaker(words)
speaker = start[start.find('.') + len('.'):start.rfind('.')]
file.write(speaker + ' : ')
if stop != None:
phrase = self.get_word_interval(start, stop,
word_roots[speaker])
else:
phrase = self.get_Word(start, word_roots[speaker], speaker)
if phrase != None:
file.write(phrase + '\n')
file.write('\n')
decision_counter += 1
file.close()
return
tool = tools.tools('decision/manual')
for file in tool.get_list()[0:1]:
print file
d = decision(file)
d.get_decisions()
def __init__(self, args=None):
"""
Initialize the script.
Note that for backward compatibility the list of arguments is the full
content of sys.argv (which is not what optparse expects).
"""
self._parser = None # to make pylint happy
self._setupOptParser()
# FIXME: backward compatibility, sys.args[0] should not be in args
if args:
self._parser.prog = args[0]
args = args[1:]
self.opts, self.args = self._parser.parse_args(args)
if "VERBOSE" in os.environ:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.WARNING)
#self._log = logging.getLogger(self._parser.get_prog_name())
self._log = logging.getLogger('GODII')
# check options
if self.opts.dtd == '':
self._parser.error("invalid value for option -t (--dtd)")
if self.opts.namespace == '':
self._parser.error("invalid value for option -n (--namespace)")
if not self.opts.root:
self._parser.error('root dir not defined and environment '
'variable GAUDIOBJDESCROOT not set')
# fix options
if self.opts.classdb is None:
self.opts.classdb = os.path.join(self.opts.root, 'xml_files',
'GODsClassDB.xml')
####
# Backward compatibility
####
self.fullCommand = ' '.join([self._parser.get_prog_name()] + args)
self.tools = tools.tools()
self.version = self._parser.version
self.xmlSources = self.args
self.xmlDBFileExtra = self.opts.info_file
self.argv0 = self._parser.get_prog_name()
self.godRoot = self.opts.root
# if self.opts.generate is not set (None), all the options are selected
self.gClasses = self.opts.generate in ('src', None)
self.gClassDicts = self.opts.generate in ('dct', None)
self.gNamespaces = self.opts.generate in ('src', None)
self.gAssocDicts = self.opts.generate in ('dct', None)
self.allocatorType = self.opts.allocator
self.default_namespace = self.opts.namespace
self.dtdPath = self.opts.dtd
try:
self.xmlDBFile = envOption(self.opts.classdb, 'GODXMLDB', '-x')
self.srcOutput = envOption(self.opts.src_output, 'GODDOTHOUT',
'-s')
self.dictOutput = envOption(self.opts.dict_output, 'GODDICTOUT',
'-d')
except KeyError, x:
msg = 'Option "{1} env" used without environment variable {0} declared'.format(
*x.args)
self._parser.error(msg)
# a root for abstractive resume
self.root_abstract = tree.getroot()
return
def get_abstractive_resume(self):
#this part has several children: abstract, actions, decisions, problems
self.initializations()
self.meeting = self.get_meeting_name_byFile(self.file_name)
self.get_root()
file = open('./manual_resume_abstractive/' + self.meeting + '.txt',
'w')
for child in self.root_abstract:
file.write('\n' + child.tag + ' : \n')
for sententce in child:
file.write(sententce.text + '\n')
file.close()
return
tool = tools.tools('abstractive')
for file in tool.get_list():
print file
d = abstractive(file)
d.get_abstractive_resume()
def __init__(self, ip, mac):
self.myip = ip
self.mymac = mac
self.tools = tools()
self.mem = memory()
self.ruleconstructor = ruleconstructor()
def __init__(self,ip,mac):
self.myip=ip
self.mymac=mac
self.tools=tools()
self.mem=memory()
def __init__(self):
self.tools = tools.tools()
def train(self):
with tf.Graph().as_default():
input_isp = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, self.crop_size, self.crop_size, 3],
name='input_isp')
input_img = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, self.crop_size, self.crop_size, 1],
name='input_img')
gt_img = tf.placeholder(dtype=tf.float32,
shape=[self.batch_size, 2 * self.crop_size, 2 * self.crop_size, 3],
name='gt_img')
learning_rate = tf.placeholder(dtype=tf.float32, name='learning_rate')
mymodel = model(input_img=input_img, input_isp=input_isp, gt_img=gt_img, reuse=None)
mytool = tools()
loss, psnr, ssim, res = mymodel.build_model()
index_queue = tf.train.range_input_producer(limit=self.training_capacity, shuffle=True)
queue_op = index_queue.dequeue_many(self.batch_size)
step = tf.Variable(0, trainable=False)
training_op = tf.train.AdamOptimizer(learning_rate).minimize(loss=loss, global_step=step)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options), graph=tf.get_default_graph())
saver = tf.train.Saver()
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess=sess, coord=coord)
if self.pretrained:
# sess.run(tf.global_variables_initializer())
saver.restore(sess=sess, save_path=tf.train.latest_checkpoint(self.log_dir))
print('Finish loading pretrained model!')
else:
sess.run(tf.global_variables_initializer())
for num_epoch in range(self.epoch_num, self.max_epoch + 1):
train_loss = []
train_psnr = []
train_ssim = []
for batch_num in range(0, int(self.batch_per_epoch)):
batch_index = sess.run(queue_op)
train_data, ground_data, isp_data = mytool.load_train_data(batch_index)
l_r = max(self.learning_rate * pow(self.decay_coef, num_epoch), self.switch_learning_rate) if num_epoch < self.switch_epoch else self.switch_learning_rate
print(l_r)
feed_dict = {input_img: train_data, input_isp: isp_data, gt_img: ground_data,
learning_rate: l_r}
_, l, p, s, ss, r = sess.run([training_op, loss, psnr, ssim, step, res],
feed_dict=feed_dict)
print('epoch: %d, batch: %d, l1 loss: %.6f, psnr: %.6f, ssim: %.6f' % (
num_epoch, batch_num, l, p, s))
train_loss.append(l)
train_psnr.append(p)
train_ssim.append(s)
saver.save(sess=sess, save_path=self.log_dir + '/model.ckpt', global_step=step)
if num_epoch % self.test_ratio == 0 and self.testing and not self.real:
print('Come to test')
test_loss = []
test_psnr = []
test_ssim = []
test_input_img = tf.placeholder(dtype=tf.float32, shape=[1, self.crop_size, self.crop_size, 1],
name='test_input_img')
test_input_isp = tf.placeholder(dtype=tf.float32, shape=[1, self.crop_size, self.crop_size, 3],
name='test_input_isp')
test_model = model(input_img=test_input_img, input_isp=test_input_isp, gt_img=None, reuse=True)
t_loss, t_psnr, t_ssim, t_res = test_model.build_model()
for i in range(self.testing_capacity):
print(i)
test_train_data, test_isp_data, h, w, test_name, gt_img_, mask = \
mytool.load_test_data(i)
res_list = []
for j in range(test_train_data.shape[0]):
feed_dict = {test_input_img: np.expand_dims(test_train_data[j, :, :, :], axis=0),
test_input_isp: np.expand_dims(test_isp_data[j, :, :, :], axis=0)}
rt = sess.run([t_res], feed_dict=feed_dict)
rt = np.array(rt)
h_, w_, c = rt.shape[-3:]
rt = rt.reshape([h_, w_, c])
res_list.append(rt)
res_img = mytool.merge(np.stack(res_list), h, w, mask)
gtr_img = gt_img_*255.
l1, p, s = train.help_eval(self, gtr_img, res_img, sess)
test_loss.append(l1)
test_psnr.append(p)
test_ssim.append(s)
if num_epoch % self.save_freq == 0:
rt_img = res_img
if not os.path.isdir(os.path.join(self.result_path, str(num_epoch))):
os.mkdir(os.path.join(self.result_path, str(num_epoch)))
os.mkdir(os.path.join(self.result_path, str(num_epoch), self.test_image_folder))
mytool.imgsave(rt_img, num_epoch, test_name)
mytool.write_test(epoch=num_epoch, name=test_name, testing_sad_loss=l1,
testing_psnr_loss=p)
mytool.write(epoch=num_epoch, training_sad_loss=np.mean(train_loss),
training_psnr_loss=np.mean(train_psnr), training_ssim_loss=np.mean(train_ssim),
testing_sad_loss=np.mean(test_loss),
testing_psnr_loss=np.mean(test_psnr), testing_ssim_loss=np.mean(test_ssim))
elif not self.real:
mytool.write(epoch=num_epoch, training_sad_loss=np.mean(train_loss),
training_ssim_loss=np.mean(train_ssim),
training_psnr_loss=np.mean(train_psnr))
if self.real:
test_input_img = tf.placeholder(dtype=tf.float32, shape=[1, self.crop_size, self.crop_size, 1],
name='test_input_img')
test_input_isp = tf.placeholder(dtype=tf.float32, shape=[1, self.crop_size, self.crop_size, 3],
name='test_input_isp')
test_model = model(input_img=test_input_img, input_isp=test_input_isp, gt_img=None, reuse=True)
t_loss, t_psnr, t_ssim, t_res = test_model.build_model()
for i in range(self.testing_capacity):
test_train_data, test_isp_data, h, w, test_name, mask = \
mytool.load_test_real(i)
res_list = []
for j in range(test_train_data.shape[0]):
feed_dict = {test_input_img: np.expand_dims(test_train_data[j, :, :, :], axis=0),
test_input_isp: np.expand_dims(test_isp_data[j, :, :, :], axis=0)}
rt = sess.run([t_res], feed_dict=feed_dict)
rt = np.array(rt)
h_, w_, c = rt.shape[-3:]
rt = rt.reshape([h_, w_, c])
res_list.append(rt)
res_img = mytool.merge(np.stack(res_list), h, w, mask)
rt_img = res_img
mytool.imgsave(rt_img, num_epoch, test_name)
# CSS Selectors
username_login_input = "#username"
password_login_input = "#password"
login_button = "#login-submit"
plus_sign_button = "button[class='sc-ghsgMZ jJfqRs']:nth-last-of-type(1)"
summary_input = "#summary"
due_date_input = "#duedate"
assign_to_me_link = "#assign-to-me-trigger"
issue_input = "#issuelinks-issues-textarea"
original_estimate_input = "#timetracking_originalestimate"
sprint_input = "#customfield_10103-field"
create_another_check_box = "#qf-create-another"
create_button = "#create-issue-submit"
#TODO yolo
tool = tools.tools()
tool.goto_page(atlassian_login_url)
time.sleep(3)
tool.clear_and_enter_text(username_login_input, USERNAME)
tool.click_on(login_button)
tool.clear_and_enter_text(password_login_input, PASSWORD)
tool.click_on(login_button)
time.sleep(2)
tool.goto_page(jira_login_url)
time.sleep(3)
# click_on(plus_sign_button)
tool.press_key('c')
tool.clear_and_enter_text(summary_input, "Show off selenium skills")
tool.clear_and_enter_text(due_date_input, "4/Jun/18")
tool.click_on(assign_to_me_link)
def __init__(self,ip,mac):
self.myip=ip
self.mymac=mac
self.tools=tools()
self.mem=memory()
self.ruleconstructor=ruleconstructor()
from reconstr_actions import reconstr_actions
from forensic_protocol_prober import forensic_protocol_prober
from forensic_switchport_prober import forensic_switchport_prober
from network_map import network_map
from ruleconstructor import ruleconstructor
from map_net import map_net
from tools import tools
from memory import memory
from network import network
import socket
import fcntl
import struct
import os
import sys
tools=tools()
mem=memory()
network=network()
#get public IP address of host
def get_ip_address(ifname):
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
return socket.inet_ntoa(fcntl.ioctl(
s.fileno(),
0x8915, # SIOCGIFADDR
struct.pack('256s', ifname[:15])
)[20:24])
#get MAC address of host
def getHwAddr(ifname):
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
read_path_routing=read_path_routing,
save_path=save_path,
is_initial_data=False,
verbosity=False,
is_path=is_path,
fitter_data=fitter_data,
routing_data=routing_data,
packet_id=packet_id,
node_id=node_id,
is_iter=is_iter,
chunk_size=chunk_size,
)
return pipline.result, pipline.result_cols
tool = tools()
data = pd.read_csv('.//data//test_3.csv ')
data2 = pd.read_csv('.//data//test_routing_packet.csv')
fit_data = tool.split_data_step(data, step=step, by_col=by_col, is_by_col=True)
pack_data = tool.split_data_step(data2,
step=step,
by_col=by_col,
is_by_col=True)
all_result = []
cols_ = 0
count = 0
start_time = time.time()
for fitter_data, routing_data in zip(fit_data, pack_data):
pygame.init()
screen = pygame.display.set_mode((1000, 550))
pygame.display.set_caption('bob')
font = pygame.font.Font('freesansbold.ttf', 40)
font_2 = pygame.font.Font('freesansbold.ttf', 20)
run = True
input_active = True
text = ""
#mixer.music.load('joke.ogg')
parle.repond(tools.tools('presentation'))
aw = ""
while run:
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
elif event.type == pygame.KEYDOWN and input_active:
if event.key == pygame.K_BACKSPACE:
text = text[:-1]
elif event.key == pygame.K_RETURN:
respons = interperter.main(text)
if "blague" in text:
import subprocess
from config import config
from tools import tools
from dbr import dbr
config = config()
tools = tools()
dbr = dbr()
def list_installed():
subprocess.call(config.bash().location_installed_apps_script())
class app_security(object):
def abnormal_installed(self):
'''returns progams that are installed on this system, but not in a clean 12.04'''
file = open("./resources/12.04-clean-installed", "r")
clean = [line.split("\n")[0] for line in file.readlines()]
list_installed()
file = open("./bash/output/list-installed.output", "r")
installed = [line.split("\t")[0] for line in file.readlines()]
file.close()
uhoh = [line for line in installed if line not in clean]
for application in uhoh:
dbr.ok("abnormal_installed_apps")
application_entry = tools.make_organized_dict([[application]], ["apps"])[0]
dbr.fill("abnormal_installed_apps", application_entry)
return uhoh
if __name__ == "__main__":
da_types_file = '../../AMI/ami_public_manual/ontologies/da-types.xml'
tree = ET.parse(da_types_file)
root = tree.getroot()
tempo = param.find('#')
da_type = param[tempo+4:-1]
for d_type in root.iter('da-type'):
if d_type.attrib['{http://nite.sourceforge.net/}id'] == da_type:
return d_type.attrib['gloss']
return
tool = tools.tools('extractive')
meeting_list = []
# for file in tool.get_list():
# print file
# d = extractive(file)
# d.get_extractive_resume()
#lists = list(set(meeting_list))
#print lists
file = 'TS3012a.extsumm.xml'
print(file)
d = extractive(file)
d.get_extractive_resume()
import numpy as np
import matplotlib.pyplot as plt
import tools
tls = tools.tools()
h0 = 69. / (3.08 * 10**19)
om_m = 0.308
om_l = 0.692
om_r = 10**-5
a1 = om_r / om_m
a2 = (om_m / om_l)**(1. / 3)
t1 = a1**2 / (2. * h0 * np.sqrt(om_r))
t2 = 2. / 3 * (a2**(3. / 2) - a1**(3. / 2)) / (h0 * np.sqrt(om_m)) + t1
sec_yr = 365 * 24 * 3600
ax = np.linspace(0, 3 * 10**10 * sec_yr, 100)
ar = lambda t: np.sqrt(2 * h0 * np.sqrt(om_r) * t)
am = lambda t: (3. / 2 * h0 * np.sqrt(om_m) *
(t - t1) + a1**(3. / 2))**(2. / 3)
al = lambda t: a2 * np.exp(h0 * np.sqrt(om_l) * (t - t2))
age = 1 / (np.sqrt(om_l) * h0) * np.log(1 / a2) + t2
print(age)
c = 3 * 10**8
ac = lambda t: c * t / (c * age)