def load_lisp_core(filename="core.lisp"):
# Load up and evaluate core.lisp
f = open(filename)
reader = Reader(f.read(), filename)
f.close()
for expr in reader.read():
expr.evaluate(core.scope)
def load(self):
_path = os.path.join(self.root_dir, 'database', 'master_scol')
with open(_path, 'rb') as _f:
buf = Reader(_f)
I, S = buf.read_int, buf.read_str
def assert_int(i):
if I() != i:
buf.error("Not valid scenario data")
count = I() # 剧情章节总计
for chapter in range(count):
chapter_id = I()
chapter = self.scenario[chapter_id] = Chapter()
assert_int(0)
chapter.id = chapter_id
chapter.i = I()
chapter.title = S()
chapter.sections = []
for i in range(I()):
section = Section()
section.prefix = I()
section.title = S()
section.battle_num = I()
section.talks = []
for j in range(I()):
section.talks.append(I())
chapter.sections.append(section)
if chapter_id < 400:
assert_int(0xc8)
else:
assert_int(0xa1)
buf.end()
def evaluate(expr):
reader = Reader(expr)
try:
exprs = reader.read()
except Exception, e:
print e
return
def message_affiche_choix(message, rcenter):
texte = Reader(message, pos=(rcenter[0] - 290, rcenter[1] + 210), width=cote_fenetre - 20, fontsize=16, height=80,
bg=(150, 150, 150), fgcolor=(20, 20, 20))
# texte.TEXT = message
continuer_2 = 1
choix = "n" # valeur par défaut
while continuer_2:
for event in pygame.event.get():
texte.show()
if event.type == JOYBUTTONUP:
if event.button == 0:
choix = "o"
continuer_2 = 0
elif event.button == 1:
choix = "n"
continuer_2 = 0
elif event.type == KEYDOWN:
if event.key == K_n:
choix = "n"
continuer_2 = 0
elif event.key == K_o:
choix = "o"
continuer_2 = 0
elif event.type != KEYDOWN and event.type != MOUSEBUTTONDOWN and event.type != JOYBUTTONUP:
continue
elif event.type == QUIT:
sys.exit()
return choix
def main(unused_args):
reader = Reader(split = 0.9)
x_train, y_train, x_test, y_test = reader.get_data(glob('../../WSJ-2-12/*/*.POS'))
print('len(reader.word_to_id)',len(reader.word_to_id),
'len(reader.tag_to_id)', len(reader.tag_to_id))
print('len(x_train)',len(x_train),
'len(x_test)', len(x_test))
best_misclass = 1.0
with tf.Graph().as_default(), tf.Session() as session:
initializer = tf.random_uniform_initializer(-FLAGS.init_scale, FLAGS.init_scale)
with tf.variable_scope("model", reuse=None, initializer=initializer):
m = RNNTagger(True, len(reader.word_to_id), len(reader.tag_to_id))
with tf.variable_scope("model", reuse=True, initializer=initializer):
mtest = RNNTagger(False, len(reader.word_to_id), len(reader.tag_to_id))
tf.initialize_all_variables().run()
saver = tf.train.Saver()
for i in range(FLAGS.max_max_epoch):
lr_decay = FLAGS.lr_decay ** max(i - FLAGS.max_epoch, 0.0)
m.assign_lr(session, FLAGS.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
train_perplexity, _ = run_epoch(session, m, x_train, y_train, m.train_op,
verbose=True)
_, misclass = run_epoch(session, mtest, x_test, y_test, tf.no_op(), verbose=True)
if misclass < best_misclass:
best_misclass = misclass
fname = 'dropout_double_rnn_tagger_' + str(best_misclass)
saver.save(session, fname, global_step=i)
print('saving', fname)
def __init__(self, file_path, tree_name):
"""
Constructor
"""
## Execute the base class constructor
Reader.__init__(self, file_path, tree_name)
def get_nuclide_from_file(self, file_name):
try:
reader = Reader(file_name)
reader.read_z_a(self.z, self.a)
nuclide = Nuclide(name=reader.nuclide_data.name, z=int(reader.nuclide_data.z),
a=int(reader.nuclide_data.a))
return nuclide
except Exception, e:
raise e
def loadData(self):
reader = Reader()
print('loading data')
self.X_train, self.y_train, self.meta_train=self.prepareData(reader.getData(TRAIN))
print('train data has been loaded!')
self.X_valid, self.y_valid, self.meta_valid=self.prepareData(reader.getData(DEV))
print('valid data has been loaded!')
self.X_test, self.y_test, self.meta_test=self.prepareData(reader.getData(TEST))
print('test data has been loaded!')
def __switch(self, xiinArgDict):
"""
Traffic director.
"""
from reader import Reader
reader = Reader()
# Write output
if xiinArgDict.filename is not None:
print('Starting xiin...')
print('')
with open(xiinArgDict.filename, 'w') as xiinArgDict.outputFile:
reader.info(xiinArgDict)
#Displays output.
elif xiinArgDict.display:
print('Starting xiin...')
print('')
reader.info(xiinArgDict)
elif xiinArgDict.grep is not None:
print('Starting xiin...')
print('')
print('Searching files...')
print('')
self.grepXiinInfo(xiinArgDict.grep)
elif xiinArgDict.upload is not None:
# xiin.ftp = {'source': '', 'destination': '', 'uname': '', 'password': ''}
from uploader import Uploader
xiinArgDict.ftpSource = None
xiinArgDict.ftpDestination = None
xiinArgDict.ftpUname = None
xiinArgDict.ftpPwd = None
if len(xiinArgDict.upload ) > 0:
xiinArgDict.ftpSource = xiinArgDict.upload[0]
xiinArgDict.ftpDestination = xiinArgDict.upload[1]
if len(xiinArgDict.upload ) > 2:
# Legacy support
if xiinArgDict.ftpUname is 'anon' or xiinArgDict.ftpUname is 'anonymous':
pass
else:
xiinArgDict.ftpUname = xiinArgDict.upload[2]
xiinArgDict.ftpPwd = xiinArgDict.upload[3]
print('Starting xiin uploader...')
print('')
print('Uploading debugging information...')
print('')
uploader = Uploader()
uploader.upload(xiinArgDict.ftpSource, xiinArgDict.ftpDestination, xiinArgDict.ftpUname, xiinArgDict.ftpPwd)
else:
print('ERROR: Unknown')
exit(7)
def main(size, path):
checker = Checker(size)
reader = Reader(checker, size)
data = reader.read("data\\" + path)
solver = Solver(checker, size)
try:
return solver.solve(data)
except Exception as e:
return str(e)
def main():
SUCCESS = 0
#arguments declaration
parser = argparse.ArgumentParser()
parser.add_argument('--omitheader','-m', help='Do not show the header about pizza and kittens', action='store_true', default=False)
parser.add_argument('--hublist', help='Shows all available hubs', action='store_true', default=False)
parser.add_argument('--similar','-s', help='Displays similar hubs as a histogram', nargs=1, metavar=("hub_name"))
parser.add_argument('--alsoread','-a', help='Displays what else people read from this hub as a histogram', nargs=1, metavar=("hub_name"))
parser.add_argument('--max', help='Print several hubs that maximize the score function e.g. --similar or --alsoread', nargs=1, metavar=("number_of_hubs"), type=int)
parser.add_argument('--min', help='Print several hubs that minimize the score function e.g. --similar or --alsoread', nargs=1, metavar=("number_of_hubs"), type=int)
parser.add_argument('--company', help='If a name is ambiguous, like yandex: it is a hub and a company, then enforce company interpretation', action="store_true", default=False)
args = vars(parser.parse_args())
#check flags and delegate functions to src/reader.py and src/hubs_wrapper.py
if len(sys.argv)==1:
print_header_hubs()
parser.print_help()
return SUCCESS
if args['omitheader']:
pass
else:
print_header_hubs()
if args['hublist']:
Reader.print_hubs()
return SUCCESS
isCompany = False
if args['company']:
isCompany = True
flag = None
flagopts = None
if args['max']:
flag = "max"
flagopts = args['max'][0]
if args['min']:
flag = "min"
flagopts = args['min'][0]
if args['similar']:
hub_name = args['similar'][0]
display_preferences(hub_name, isCompany, "similarity", flag, flagopts)
return SUCCESS
if args['alsoread']:
hub_name = args['alsoread'][0]
display_preferences(hub_name, isCompany, "inclusion", flag, flagopts)
return SUCCESS
class Wayterm(object):
def __init__(self):
self.app_key = '1746312660'
self.app_secret = 'a113b12f49266b12125f6df1f9808045'
self.callback_url = 'http://wayterm.nerocrux.org/done'
self.template = Template()
self.url = Url()
self.reader = Reader()
self.token = {}
if self._read_access_token():
self.client = Client(self.app_key, self.app_secret, self.callback_url, self.token)
else:
self.client = Client(self.app_key, self.app_secret, self.callback_url)
self.auth_url = self.url.shorten(self.client.authorize_url)
print '[1] Open this url in your browser: ' + self.auth_url
self.auth_code = raw_input('[2] Enter authorization code: ')
self.client.set_code(self.auth_code)
token = {
'access_token':self.client.token['access_token'],
'expires_at':self.client.token['expires_at'],
'uid':self.client.token['uid'],
}
self._write_access_token(token)
print 'Authorization done. Enjoy!'
def _read_access_token(self):
try:
self.token = yaml.load(open(os.path.join(os.getenv('HOME'), '.wayterm.yaml')).read())
except:
return False
return True
def _write_access_token(self, token):
stream = file(os.path.join(os.getenv('HOME'), '.wayterm.yaml'), 'w')
yaml.dump(token, stream)
def _init_print(self):
self.reader.printfile('logo')
def call(self, command):
if command[0].lower() == 'exit':
exit()
if command[0].lower() == 'help':
self.reader.printfile('help')
return
api = Api(self.client)
api.call(command)
def main_test():
reader = Reader()
retainer = Retainer()
params = reader.read()
api_targets = []
for name, param in params.items():
parser = Parser(name, param)
api_targets.append(parser)
for target in api_targets:
creater = Creater(target)
api_parameters = creater.create()
retainer.insert(target.name, api_parameters)
retainer.close()
def __init__(self):
reader = Reader()
print('loading data')
self.X_train=reader.getData(TRAIN)
print('train data has been loaded!')
self.X_valid=reader.getData(DEV)
print('valid data has been loaded!')
self.X_test=reader.getData(TEST)
print('test data has been loaded!')
self.c_title=[]
self.c_body=[]
self.bigram=Phrases.load('./data/bigram.dat')
self.trigram=Phrases.load('./data/trigram.dat')
def load(self):
_path = path.join(self.root_dir, 'database', 'master_card')
with open(_path, 'rb') as _f:
buf = Reader(_f)
count = buf.read_int() # 卡牌数量
offsets = [0] * count
self.cards = cards = [None] * count
for i in range(count):
cards[i] = MACard()
offsets[i] = buf.read_int()
for i, card in enumerate(cards):
buf.seek(offsets[i])
card.load(buf)
def evaluate_file(filename):
try:
f = open(filename)
except IOError:
print "Cannot open file %s" % repr(filename)
return
reader = Reader(f.read())
f.close()
try:
exprs = reader.read()
except Exception, e:
print e
return
class Generator:
def __init__(self, filename):
self.reader = Reader(filename)
def execute(self):
self.reader.execute()
datas=self.reader.getData()
tps=[InterfaceTemplate(datas), ImplementTemplate(datas), \
TestTemplate(datas)]
for tp in tps:
print tp.execute()
class Notifier(wx.App):
"""main notifier app"""
def __init__(self):
wx.App.__init__(self, redirect=0)
# menu handlers
menu = [
("Show again", self.again),
("Settings", self.settings),
("Exit", self.exit),
]
# main objects
self.icon = Icon(menu)
self.popup = Popup()
self.reader = Reader(feeds=["http://digg.com/rss/index.xml"])
# main timer routine
timer = wx.Timer(self, -1)
self.Bind(wx.EVT_TIMER, self.main, timer)
timer.Start(500)
self.MainLoop()
def main(self, event):
if not self.popup.opened():
# show popup for next new item
for item in self.reader.items():
self.popup.show("%(feed)s\n%(title)s%(title)s%(title)s%(title)s%(title)s%(title)s%(title)s%(title)s" % item)
status = "on"
break
else:
status = "off"
# set icon status
self.icon.setStatus(status)
def again(self):
print "again"
def settings(self):
print "settings"
def exit(self):
# close objects and end
self.reader.close()
self.icon.close()
self.Exit()
def main(opts):
if not opts.demo:
from reader import Reader
global q
delay = 0.001
lights = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}
print opts
if not opts.demo:
reader = Reader(lights)
sounder = Sounder()
player = Player(sounder)
songs = os.listdir("./songs/")
songs = filter(lambda x: x.endswith(".js"), songs)
numpy.random.shuffle(songs)
print ("Loaded songs:")
print (songs)
for song in cycle(songs):
# time.sleep(.5)
q = player.song(song)
while True:
debug_str = ""
time.sleep(delay)
if not opts.demo:
reader.fetch()
if not q["play"].empty():
player.chord(q["play"].get_nowait())
for ch in lights:
debug_str += "{}:{} ".format(ch, lights[ch])
if lights[ch] < 650 or opts.demo:
sounder.start(get_str(ch))
else:
sounder.stop(get_str(ch))
# print(debug_str)
time.sleep(delay)
if q["sig"] == "stopped":
print "caught sig... stopping song"
sounder.mute()
break
def test_extractLinks(self):
print ">>> test_extractLinks"
app = LinkCrawler()
reader = Reader(app)
configIsLoaded = app.loadConfigurationSite("unittest")
if configIsLoaded is False:
self.fail("load configuration failed")
else:
response = reader.getResponse('http://www.scandio.de')
responseData = response[3]
links = reader.extractLinks(responseData, 'http://www.scandio.de')
print "<<< test_extractLinks [links: %s]\n" % links
self.failUnless(links)
def message_short(message, rcenter):
# texte.TEXT = message
texte = Reader(message, pos=(rcenter[0] - 290, rcenter[1] + 210), width=cote_fenetre - 20, fontsize=16, height=80,
bg=(150, 150, 150), fgcolor=(20, 20, 20))
continuer_2 = 1
tps_deb = time.time() + 2
while continuer_2:
if time.time() >= tps_deb:
continuer_2 = 0
for event in pygame.event.get():
texte.show()
if event.type == QUIT:
sys.exit()
elif event.type == KEYDOWN and event.key == K_ESCAPE:
sys.exit()
def run_parser(filepath, request):
print >>sys.stderr, 'Parsing started with {}'.format(filepath)
base_file_path = os.getcwd()
try:
padding = 300
file_type = check_file_type(filepath)
if file_type == 'file error':
error = 'This file is not the correct input type, please try an LRG or GB file'
document_list = created_documents.objects.order_by('-created_on')
return render(request, 'web_interface/app_homepage.html', {'form': UploadForm(),
'document_list': document_list,
'error_message': error})
dictionary = {}
if file_type == 'gbk':
gbk_reader = GbkParser(filepath, padding, True)
dictionary = gbk_reader.run()
parser_details = gbk_reader.get_version
elif file_type == 'lrg':
lrg_reader = LrgParser(filepath, padding, True)
dictionary = lrg_reader.run()
parser_details = lrg_reader.get_version
parser_details = '{0} {1} {2}'.format(file_type.upper(), 'Parser:', parser_details)
os.chdir('web_interface')
os.chdir('output')
for transcript in dictionary['transcripts']:
input_reader = Reader()
writer = LatexWriter()
reader_details = 'Reader: ' + input_reader.get_version
writer_details = 'Writer: ' + writer.get_version
xml_gui_details = 'Control: {}'.format(get_version)
list_of_versions = [parser_details, reader_details, writer_details, xml_gui_details]
input_list, nm = input_reader.run(dictionary, transcript, True, list_of_versions, True, file_type, 'web user')
transcript_accession = dictionary['transcripts'][transcript]['NM_number']
file_name = transcript_accession
latex_file, pdf_file = writer.run(input_list, file_name)
call(["pdflatex", "-interaction=batchmode", latex_file])
save_as_model = created_documents(transcript=transcript_accession,
location=pdf_file,
gene=dictionary['genename'],
created_on=datetime.now())
save_as_model.save()
delete_all_but_most_recent()
except:
os.chdir(base_file_path)
os.chdir(base_file_path)
clean_up(os.path.join('web_interface', 'output'))
clean_up(os.path.join('web_interface', 'input'))
class Generator:
def __init__(self, configFile, templetes):
self.reader = Reader(configFile)
self.templetes = templetes
def _templateList(self):
datas = self.reader.getData()
return map(lambda tp : self._template_import__private(tp)(datas), self.templetes)
def _template_import__private(self, model, filename=None):
if not filename:
filename = model.lower()
name = "%sTemplate" % (model)
mod = __import__("lib.template.model.%s" % filename, fromlist=[name])
return getattr(mod, name)
def _write(self, files):
path = "dist"
if not os.path.exists(path):
os.makedirs(path)
for name, body in files.iteritems():
f = open("%s/%s.java" % (path, name), "w")
f.write(body)
f.close()
print "Generated file to %s" % os.path.abspath(path)
def execute(self):
self.reader.execute()
tps = self._templateList()
files = {}
for tp in tps:
body = tp.execute()
m = re.search("public (class|interface) (\S+)", body)
name = m.group(2)
files[name] = body
self._write(files)
class ReadLayer(object):
def __init__(self, rng, h_shape, image_shape, N, name='Default_readlayer'):
print('Building layer: ' + name)
self.lin_transform = HiddenLayer(
rng,
n_in=h_shape[0] * h_shape[1],
n_out=4,
activation=None,
irange=0.001,
name='readlayer: linear transformation')
self.reader = Reader(
rng,
image_shape=image_shape,
N=N,
name='readlayer: reader')
self.params = self.lin_transform.params
def one_step(self, h, image):
linear = self.lin_transform.one_step(h)
read, g_x, g_y, delta, sigma_sq = self.reader.one_step(linear, image)
return read, g_x, g_y, delta, sigma_sq
def __init__(self, config):
## define class attributes
self.logger = logging.getLogger("Viewer")
self.data = None # current data to show
self.header = None # first line of file to use as header
self.title = None # filename
self.useHeader = config['header'] # if using first line as header
self.useRegex = config['regex'] # if searching with regular expression
self.searchText = config['search'] # initial search string (may be empty)
self.separator = str(config['separator']) # column separator (e.g. comma, tab, space)
# handle highlighting of alternate lines
self.highlight = config['highlightLines']
#self.highlight_color = wx.Colour(242, 242, 242) # light gray for light themes
self.highlight_color = wx.Colour(32, 32, 32) # dark gray for dark themes
self.text = None # search box object
self.regex_text = None # regex checkbox object
self.header_box = None # header checkbox object
self.reader = Reader(config)
chunk = self.reader.next()
self.ReadData(chunk)
self.header = self.data[0]
self.title = config['filename'].split('/')[-1]
self.InitViewer()
def feeds():
reader = Reader()
for feed in config["feeds"]:
reader.add(feed)
reader.run()
stories = reader.stories[0:100]
return make_response(
json.dumps({"count": len(stories), "stories": stories, "template": file_get_contents("templates/stories.hbs")}),
200,
{"Content-type": "application/json"},
)
def __init__(self, path):
self.path = path
self.queue = Queue.PriorityQueue()
self.watcher = Watcher(path, self.queue)
self.walker = Walker(path, self.queue, Settings.is_rescan_forced())
self.reader = Reader(self.queue)
self.validator = Validator(self.queue)
def __init__(self, log=None): """Initialize a reusable instance of the class.""" super(Parser, self).__init__() self._reader = Reader() self.log = log if log: self._init_logging()
def compute(hubname, isCompany, fun_name):
hub_readers = Reader.check_and_download(hubname, isCompany)
hubs_data_dir = "data/hubs/"
tocut = len(hubs_data_dir)
hubs = glob.glob(hubs_data_dir + "*")
similarity_dict = dict()
for hub_file in hubs:
readers = Reader.read_list_of_users(hub_file)
hub = hub_file[tocut:]
# skip itself
if hub == hubname:
continue
if fun_name == "similarity":
similarity_dict[hub] = jaccard_index(hub_readers, readers)
if fun_name == "inclusion":
similarity_dict[hub] = inclusion(hub_readers, readers)
return similarity_dict
def test_completeRelativePath(self):
print ">>> test_completeRelativePath\n"
app = LinkCrawler()
reader = Reader(app)
array = [['/en/de/index.html', '/aa/bb/index.htm']
, ['/en/de/index.html', 'cc/dd/index.htm']
, ['/en/de/index.html', '../cc/dd/index.htm']
, ['/en/', 'search_iframe_en.htm']
, ['/en/', '/search_iframe_en.htm']
, ['/en/', '../search_iframe_en.htm']]
for i in range(0, len(array)):
relPath = reader.completeRelativePath(array[i][0], array[i][1])
print "test_completeRelativePath [path: %s, parent: %s, relPath: %s]\n" % (array[i][0],array[i][1],relPath)
self.failUnless(relPath)
print "<<< test_completeRelativePath \n"
def test_from_file(filepath):
reader = Reader()
model = DynamicLSTM(None, is_training=False, reuse=False)
model_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="model")
model_saver = tf.train.Saver(model_variables)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)) as sess:
ckpt_path = tf.train.latest_checkpoint(config.model_dir)
if ckpt_path:
model_saver.restore(sess,
tf.train.latest_checkpoint(config.model_dir))
print("Read model parameters from %s" %
tf.train.latest_checkpoint(config.model_dir))
else:
print("model doesn't exists")
model_path = os.path.join(config.model_dir, config.model_name)
model_saver.save(sess, model_path, global_step=0)
data_gen = reader.get_custom_line_from_file(filepath)
for inputs, inputs_len in data_gen:
feed_dict = {model.x: inputs, model.x_len: inputs_len}
prob = sess.run([model.output_prob], feed_dict=feed_dict)
def decode_user_command(record):
rdr = Reader(BytesIO(record.value))
k_rdr = Reader(BytesIO(record.key))
cmd = {}
cmd['type'] = rdr.read_int8()
cmd['str_type'] = decode_user_cmd_type(cmd['type'])
if cmd['type'] == 5 or cmd['type'] == 7:
cmd['user'] = k_rdr.read_string()
cmd['cred'] = {}
cmd['cred']['version'] = rdr.read_int8()
cmd['cred']['salt'] = rdr.read_iobuf().hex()
cmd['cred']['server_key'] = rdr.read_iobuf().hex()
cmd['cred']['stored_key'] = rdr.read_iobuf().hex()
# obfuscate secrets
cmd['cred']['salt'] = obfuscate_secret(cmd['cred']['salt'])
cmd['cred']['server_key'] = obfuscate_secret(cmd['cred']['server_key'])
cmd['cred']['stored_key'] = obfuscate_secret(cmd['cred']['stored_key'])
elif cmd['type'] == 6:
cmd['user'] = k_rdr.read_string()
return cmd
def model(self):
X_reader = Reader(self.X_train_file,
name='X',
image_size=self.image_size,
batch_size=self.batch_size)
Y_reader = Reader(self.Y_train_file,
name='Y',
image_size=self.image_size,
batch_size=self.batch_size)
x = X_reader.feed()
y = Y_reader.feed()
cycle_loss = self.cycle_consistency_loss(self.G, self.F, x, y)
# X -> Y
fake_y = self.G(x)
G_gan_loss = self.generator_loss(self.D_Y,
fake_y,
use_lsgan=self.use_lsgan)
G_loss = G_gan_loss + cycle_loss
D_Y_loss = self.discriminator_loss(self.D_Y,
y,
self.fake_y,
use_lsgan=self.use_lsgan)
# Y -> X
fake_x = self.F(y)
F_gan_loss = self.generator_loss(self.D_X,
fake_x,
use_lsgan=self.use_lsgan)
F_loss = F_gan_loss + cycle_loss
D_X_loss = self.discriminator_loss(self.D_X,
x,
self.fake_x,
use_lsgan=self.use_lsgan)
# Summary
tf.summary.histogram('D_Y/true', self.D_Y(y))
tf.summary.histogram('D_Y/fake', self.D_Y(self.G(x)))
tf.summary.histogram('D_X/true', self.D_X(x))
tf.summary.histogram('D_X/fake', self.D_X(self.F(y)))
tf.summary.scalar('loss/G', G_gan_loss)
tf.summary.scalar('loss/D_Y', D_Y_loss)
tf.summary.scalar('loss/F', F_gan_loss)
tf.summary.scalar('loss/D_X', D_X_loss)
tf.summary.scalar('loss/cycle', cycle_loss)
tf.summary.image('X/generated', utils.batch_convert2int(self.G(x)))
tf.summary.image('X/reconstruction',
utils.batch_convert2int(self.F(self.G(x))))
tf.summary.image('Y/generated', utils.batch_convert2int(self.F(y)))
tf.summary.image('Y/reconstruction',
utils.batch_convert2int(self.G(self.F(y))))
return G_loss, D_Y_loss, F_loss, D_X_loss, fake_y, fake_x
class Explorer:
def __init__(self, url):
self._reader = Reader(url)
if (self._reader is None):
return None
if (self._reader._soup is None):
self._reader = None
return None
def read_pritty(self):
print(self._reader._soup.get_text())
def write_to_file(self, file_name):
if (self._reader is None):
return None
text = self._reader._soup.get_text()
write_text_to_file(file_name, text)
def explore_links_from_articles(self, file_name):
if (self._reader is None):
return None
list = self._reader.links_to_articles_reader()
f = open(file_name, "a")
for element in list:
f.write(str(element))
f.write('\n')
f.close()
def explore_authors(self, file_name):
if (self._reader is None):
return None
list = self._reader.read_comments_authors()
if (len(list) > 10):
for i in range(10):
write_text_to_file(str(i) + file_name, list[i])
def __most_expensive_node_car(car_number, solution, data: Reader):
"""
take node with highest travel and delivery cost in a car
Returns: index of node in solution
"""
vehicleDict = data.getVehiclesDict()
vertexDict = data.getVertexDict()
callsDict = data.getCallsDict()
nodeDict = data.getNodes()
home, _, _ = vehicleDict[car_number]
cur_node = home
car_index = car_number
started_calls = []
record_cost = 0
record_index = 0
start, stop = __get_car_index(car_number, solution, data.num_cars)
for index, call in enumerate(solution[start:stop + 1]):
assert call is not 0
(origin, dest, _, failCost, _, _, _, _) = callsDict[call]
_, origin_cost, _, dest_cost = nodeDict[(car_index, call)]
if call not in started_calls:
started_calls.append(call)
cur_cost = origin_cost
next_node = origin
else:
started_calls.remove(call)
cur_cost = dest_cost
next_node = dest
_, travel_cost = vertexDict[(car_index, cur_node, next_node)]
cur_cost = cur_cost + travel_cost
cur_node = next_node
if cur_cost > record_cost:
record_cost = cur_cost
record_index = index
assert solution[record_index + start] == solution[start:stop +
1][record_index]
return record_index + start
def evaluate(source, env):
if isinstance(source, str):
return env.find(source)[source]
elif not isinstance(source, list):
return source
elif source[0] == 'define':
key, value = source[1], evaluate(source[2], env)
env[key] = value
return "'{}' => {}".format(key, value)
elif source[0] == 'quote':
return source[1]
elif source[0] == 'cons':
new = evaluate(source[1], env)
lst = [evaluate(expr, env) for expr in source[2:]][0]
# lst = evaluate(source[2:], env)
lst.insert(0, new)
return lst
elif source[0] == 'car':
return evaluate(evaluate(source[1], env)[0], env)
elif source[0] == 'cdr':
return evaluate(source[1], env)[1:]
elif source[0] == 'cond':
for statement in source[1:]:
cond, expr = statement[0], statement[1]
if cond == "else" or evaluate(cond, env):
return evaluate(expr, env)
raise SyntaxError( "Invalid syntax of 'cond'")
elif source[0] == 'lambda':
def gen_body_of_lamda(exps, vargs, args):
inner_env = Env(vargs, args, env)
for exp in exps:
result = evaluate(exp, inner_env)
return result
vargs, expr = source[1], source[2:]
return lambda args: gen_body_of_lamda(expr, vargs, args)
elif source[0] == 'load':
match = re.search(re.compile(r'(?<=\").*(?=\")'), source[1])
file_path = match.group(0)
with open(file_path) as f:
module_contents = Reader.parse(f.read())
for target in module_contents:
print(evaluate(target, env))
elif source[0] == 'eq?':
return evaluate(source[1], env) == evaluate(source[2], env)
else:
operator = env.find(source[0])[source[0]]
args = [evaluate(expr, env) for expr in source[1:]]
return operator(args) if callable(operator) else evaluate(operator, env)
def __init__(self, n, file):
self.path_width = 4 # Width of the paths drawn on screen (px)
self.n = n # Number of circles
self.upr = 100 # Updates per rotation, for upr=50, it takes 50 updates to reach a full rotation
self.t = 0 # 'time' passed since start.
self.width, self.height = 1920, 1080 # Width and height of screen shown on screen/video
self.fullscreen = True
self.video_mode = False # Video mode creates a video of the drawing
self.fps = 40 # Frames per second in video
self.video_length = 200 # Number of frames in video
self.finished = False
self.framecount = 0
if self.video_mode:
fourcc = VideoWriter_fourcc(*'MP42')
self.video = VideoWriter('C:\\Koding\\Scripts\\Python\\Fourier Series\\Videoer\\' +file+ '_' +str(self.n)+ '.avi', fourcc, float(self.fps), (self.width, self.height))
self.root = Tk() # Main window
if self.fullscreen:
self.root.attributes("-fullscreen", True) # set fullscreen
else:
self.root.geometry(str(self.width) + "x" + str(self.height)) # Setting width and height of video
self.root.bind("<Escape>", self.close) # Pressing escape closes the window
self.canvas = Canvas(self.root, width=self.width, height=self.height) # Canvas to draw on
self.canvas.pack()
self.canvas.create_text(100,100,text="n="+str(n),font="Times 50 italic bold")
self.reader = Reader(self.n, self.upr, self.width, self.height, self.path_width)
self.paths = self.reader.read_svg(file + ".svg") # Read SVG file and calculate vectors
for path in self.paths: # Draw startposition of all vectors on the canvas
for vector in path.vectors:
vector.create(self.canvas)
self.root.after(1, self.frame) # Run first frame after 1ms
self.root.mainloop() # Start window
def create_all_csvs(r: Reader, a: Analyzer):
diffs = defaultdict(list)
diffs_sq = defaultdict(list)
for filename in get_all_log_files():
r.load_log_file(filename)
out_folder = "csvs/{}".format(filename[:-4])
if not os.path.exists(out_folder):
os.mkdir(out_folder)
all_csvs = a.get_all_csv_list()
for key, csv_list in all_csvs.items():
vals = csv_list[1].split(",")[-2:]
avg_diff = int(float(vals[0]))
avg_diff_sq = int(float(vals[1]))
diffs[key].append(avg_diff)
diffs_sq[key].append(avg_diff_sq)
with open("{}/{}.csv".format(out_folder, key.lower()),
"w") as file:
for line in csv_list:
file.write(line)
avgs = {}
avgs_sq = {}
for key, l in diffs.items():
avgs[key] = int(sum(l) / len(l))
for key, l in diffs_sq.items():
avgs_sq[key] = int(sum(l) / len(l))
print("AVG:", avgs)
print("AVG SQS:", avgs_sq)
def test_7_get_timestamps_for_epoch(self):
os.chdir(rootwd)
for key in self.file_nostim:
reader_io_nostim = Reader(self.file_nostim[key])
os.chdir(pwd)
orderedepochs_soma = reader_io_nostim.drawout_orderedepochs(
'soma') #drawout orderedepoch
orderedepochs_axon = reader_io_nostim.drawout_orderedepochs('axon')
epoch_id = 0
times_soma = reader_io_nostim.get_timestamps_for_epoch(
epoch_id, orderedepochs_soma)
times_axon = reader_io_nostim.get_timestamps_for_epoch(
epoch_id, orderedepochs_axon)
#
self.assertEqual([len(times_soma), times_soma],
[len(times_axon), times_axon])
reader_io_nostim.closefile()
def _init_test_graph(self):
# Initialize TFRecoder reader
testReader = Reader(tfrecordsFile=self.dataPath[0],
decodeImgShape=self.decodeImgShape,
imgShape=self.inputShape,
batchSize=1,
minQueueExamples=1,
name='test')
# Batch for test data
imgTest, _, self.img_name_test, self.user_id_test = testReader.batch(multi_test=self.multi_test,
use_advanced=self.advanced_multi_test)
# Convert the shape [?, self.num_try, H, W, 1] to [self.num_try, H, W, 1] for multi-test
if self.multi_test:
if self.advanced_multi_test:
shape = [6*2*self.num_try, *self.outputShape]
else:
shape = [2*self.num_try, *self.outputShape]
else:
shape = [1, *self.outputShape]
self.imgTests = tf.reshape(imgTest, shape=shape)
self.predTest = self.forward_network(inputImg=self.normalize(self.inputImgPh), reuse=True)
def test_1_init(self):
# loads nwbfile, extracts modelname, modelscale & instantiate model
os.chdir(rootwd)
for key in self.file_nostim:
reader_io_nostim = Reader(self.file_nostim[key])
for key in self.file_stim:
reader_io_stim = Reader(self.file_stim[key])
os.chdir(pwd)
#
reader_io_nostim.chosenmodel = self.chosenmodel
reader_io_stim.chosenmodel = self.chosenmodel
#print self.chosenmodel.name
# this tests extract_modelname_modelscale & load_model
compare1 = [
reader_io_nostim.modelname, # output of
reader_io_nostim.modelscale
] # extract_modelname_modelscale()
compare2 = [
reader_io_stim.chosenmodel.modelname, # output of
reader_io_stim.chosenmodel.modelscale
] # load_model()
self.assertEqual(compare1, compare2)
reader_io_nostim.closefile()
reader_io_stim.closefile()
def get_model(num_hid_layers, cells_per_layer, dropout_rate): length = Reader.getInputShape() model = Sequential() model.add(Dense(cells_per_layer, input_shape=(length,), activation='relu')) model.add(Dropout(dropout_rate)) for i in range(num_hid_layers): model.add(Dense(cells_per_layer, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(5, activation='softmax'))#softmax se multiclass, sigmoid se 2class model_name = models_directory + 'MLP.hidlay' + str(num_hid_layers) + '.cells' + str(cells_per_layer) + '.drop' + str(dropout_rate) plot_model(model, to_file = model_name + '.2class' + '.png', show_shapes=True) fp_model = open(model_name + '.2class' + '.json', 'w+') fp_model.write(model.to_json()) fp_model.close() return model
def model(self):
X_reader = Reader(X_TRAIN_FILE, name='X')
Y_reader = Reader(Y_TRAIN_FILE, name='Y')
x = X_reader.feed()
y = Y_reader.feed()
cycle_loss = self.cycle_consistency_loss(self.G, self.F, x, y)
# X -> Y
G_gan_loss = self.generator_loss(self.G,
self.D_Y,
x,
use_lsgan=self.use_lsgan)
G_loss = G_gan_loss + cycle_loss
D_Y_loss = self.discriminator_loss(self.G,
self.D_Y,
x,
y,
use_lsgan=self.use_lsgan)
# Y -> X
F_gan_loss = self.generator_loss(self.F,
self.D_X,
y,
use_lsgan=self.use_lsgan)
F_loss = F_gan_loss + cycle_loss
D_X_loss = self.discriminator_loss(self.F,
self.D_X,
y,
x,
use_lsgan=self.use_lsgan)
# summary
tf.summary.histogram('D_Y/true', self.D_Y(y))
tf.summary.histogram('D_Y/fake', self.D_Y(self.G(x)))
tf.summary.histogram('D_X/true', self.D_X(x))
tf.summary.histogram('D_X/fake', self.D_X(self.F(y)))
tf.summary.scalar('loss/G', G_gan_loss)
tf.summary.scalar('loss/D_Y', D_Y_loss)
tf.summary.scalar('loss/F', F_gan_loss)
tf.summary.scalar('loss/D_X', D_X_loss)
tf.summary.scalar('loss/cycle', cycle_loss)
tf.summary.image('X/generated', utils.batch_convert2int(self.G(x)))
tf.summary.image('X/reconstruction',
utils.batch_convert2int(self.F(self.G(x))))
tf.summary.image('Y/generated', utils.batch_convert2int(self.F(y)))
tf.summary.image('Y/reconstruction',
utils.batch_convert2int(self.G(self.F(y))))
self.summary = tf.summary.merge_all()
self.saver = tf.train.Saver()
return G_loss, D_Y_loss, F_loss, D_X_loss
def infer(img_path, model_path, image_shape, label_dict_path):
# 获取标签字典
char_dict = load_dict(label_dict_path)
# 获取反转的标签字典
reversed_char_dict = load_reverse_dict(label_dict_path)
# 获取字典大小
dict_size = len(char_dict)
# 获取reader
my_reader = Reader(char_dict=char_dict, image_shape=image_shape)
# 初始化PaddlePaddle
paddle.init(use_gpu=True, trainer_count=1)
# 获取网络模型
model = Model(dict_size, image_shape, is_infer=True)
# 加载训练好的参数
parameters = paddle.parameters.Parameters.from_tar(gzip.open(model_path))
# 获取预测器
inferer = paddle.inference.Inference(output_layer=model.log_probs, parameters=parameters)
# 裁剪车牌
# cutPlateNumber = CutPlateNumber()
# cutPlateNumber.strat_crop(img_path, True)
# 加载裁剪后的车牌
test_batch = [[my_reader.load_image(img_path)]]
# 开始预测
return start_infer(inferer, test_batch, reversed_char_dict)
class StanzaShuffler():
def __init__(self, split_path):
self.path = split_path
self.reader = Reader()
self.data = [[d[0].split(' </br> '), d[1]] for d in self.reader.read_from_split(split_path)]
def shuffle_data(self):
result = []
for stanza in self.data:
tmp = stanza[0]
shuffle(tmp)
lines = ''
for line in tmp:
lines = lines + ' </br> ' + line
lines = lines[7:]
result.append([lines, stanza[1]])
return result + self.reader.read_from_split(self.path)
def save_data(self, save_path):
data = self.shuffle_data()
shuffle(data)
with open(save_path, 'w', encoding='utf-8', newline='') as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
tsv_writer.writerows(data)
def testExampleA(self):
self.maxDiff = None
Reader(self.mock_file)
expect_output = 'outdir/file_0'
with open(self.mock_expected) as f:
mock_expected = f.read()
mock_expected = mock_expected.split('\n')
with open(expect_output) as f:
actual_output = f.read()
actual_output = actual_output.split('\n')
self.assertItemsEqual(mock_expected, actual_output)
def runKNN_DTW(self):
#Carrega o reader com os valores do conjunto de treino e a linha a ser testada
reader = Reader(self.nomeCjTreino, self.vetordeteste)
matrizDTW = reader.RunDTW()
#Ordena a matriz de ditancias entre o teste e o conjunto de treino em orgem crescente
matrizDTW.sort(key=lambda tup: tup[1])
ocorrencias = []
resultado = []
#Itera no vetor de valores de k (assim o programa não precisa recalcular pra cada valor de k, ja faz tudo de uma vez)
for i in range(self.k.__len__()):
resultadotemp = []
#Pega os K vizinhos mais proximos e vê qual ocorre mais vezes
for x in range(self.k[i]):
ocorrencias.append(matrizDTW[x][0])
c = Counter(ocorrencias).most_common()
resultadotemp.append(self.k[i])
resultadotemp.append(c[0][0])
resultado.append(resultadotemp)
# Retorna a ocorrencia mais comum
return resultado
def model(self):
if self.X_train_file != None and self.Y_train_file != None:
X_reader = Reader(self.X_train_file,
name='X',
image_size=self.image_size,
batch_size=self.batch_size)
Y_reader = Reader(self.Y_train_file,
name='Y',
image_size=self.image_size,
batch_size=self.batch_size)
x = X_reader.feed()
y = Y_reader.feed()
else:
x = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, 3])
y = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, 3])
""" Loss Function """
cycle_loss = self.cycle_consistency_loss(self.G, self.F, x, y)
print(cycle_loss)
# X -> Y
fake_y = self.G(x) # __call__(input)
print(fake_y)
G_gan_loss = self.generator_loss(self.D_Y, fake_y, use_lsgan=True)
print(G_gan_loss)
G_loss = G_gan_loss + cycle_loss
D_Y_loss = self.discriminator_loss(self.D_Y,
y,
self.fake_y,
use_lsgan=True)
# Y -> X
fake_x = self.F(y)
print(fake_x)
F_gan_loss = self.generator_loss(self.D_X, fake_x, use_lsgan=True)
F_loss = F_gan_loss + cycle_loss
D_X_loss = self.discriminator_loss(self.D_X,
x,
self.fake_x,
use_lsgan=True)
# summary
tf.summary.histogram('D_Y/true', self.D_Y(y))
tf.summary.histogram('D_Y/fake', self.D_Y(self.G(x)))
tf.summary.histogram('D_X/true', self.D_X(x))
tf.summary.histogram('D_X/fake', self.D_X(self.F(y)))
tf.summary.scalar('loss/G', G_gan_loss)
tf.summary.scalar('loss/D_Y', D_Y_loss)
tf.summary.scalar('loss/F', F_gan_loss)
tf.summary.scalar('loss/D_X', D_X_loss)
tf.summary.scalar('loss/cycle', cycle_loss)
return G_loss, D_Y_loss, F_loss, D_X_loss, fake_y, fake_x
def get_reader(username=None, password=None, use_cookie_file=False):
reader = Reader(Opener())
if 'cookies' in session:
print "Loading cookies"
reader.opener.load_cookies(session['cookies'])
elif use_cookie_file:
print "Loading cookies from file"
with open(tmp_dir + "cookies.txt", "r") as text_file:
cookies = text_file.read()
print cookies
reader.opener.load_cookies(cookies)
elif username is None:
print "Cannot login, no username provided"
return (None, "No username provided")
else:
print "Logging in as ", username
reader.init()
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
with open(tmp_dir + "cookies.txt", "wb") as text_file:
text_file.write(reader.opener.get_cookies())
result = reader.login(username, password)
if "The userID or password could not be validated" in result:
print "Bad User ID or password"
return (None, "Bad User ID or password")
if "Concurrent Login Error" in result:
print "User already logged in"
return (None, "User already logged in")
print "Logged in"
return (reader, "")
def get_group(self, group_key_name):
group = Group.get_by_key_name(group_key_name)
if group is None:
self.response.write('No group by that name exists.')
else:
# Check for new posts
for group_feed in group.group_feeds:
reader = Reader.create(group_feed.feed)
reader.refresh()
template_values = {
'group_key_name': group_key_name,
'group': group,
'user': users.get_current_user()
}
template = JINJA_ENVIRONMENT.get_template('templates/group.html')
self.response.write(template.render(template_values))
def __init__(self, p=0.01175, n=1): # Init with optimal prune value and no grams as default
self.number_of_reviews = None # Total number of analyzed reviews
self.total_pos_words = 0 # Total positive words or n-grams
self.total_neg_words = 0 # Total negative words or n-grams
self.directory = '' # Directory (alle or subset)
self.pos_freq = defaultdict(int) # Frequency of words or n-grams
self.neg_freq = defaultdict(int) # Frequency of words or n-grams
self.most_common_pos = [] # 25 most common words or n-grams
self.most_common_neg = [] # 25 most common words or n-grams
self.pos_popularity = {} # Popularity values
self.neg_popularity = {} # Popularity values
self.pos_highest_pop = []
self.neg_highest_pop = []
self.pos_information_value = {} # Informational values
self.neg_information_value = {} # Informational values
self.pos_highest_inform_val = [] # 25 highest informational value
self.neg_highest_inform_val = [] # 25 highest informational value
self.pos_all_words_raw = [] # Made in find_frequency (all docs as lists)
self.neg_all_words_raw = [] # Made in find_frequency (all docs as lists)
self.pos_doc_count = defaultdict(int) # Made in prune (in how many docs does the word appear).
self.neg_doc_count = defaultdict(int) # Made in prune (in how many docs does the word appear).
self.set_directory() # Prompt
r = Reader()
if n > 1:
self.pos_all_n_grams_raw = []
self.neg_all_n_grams_raw = []
self.make_n_grams_and_find_frequency(r, n)
self.prune(p, self.pos_all_n_grams_raw, self.neg_all_n_grams_raw)
else:
self.find_frequency(r)
self.prune(p, self.pos_all_words_raw, self.neg_all_words_raw)
self.find_most_common()
self.find_popularity()
self.find_highest_pop_value()
self.find_information_value()
self.find_highest_informational_value()
def main():
"""Shows basic usage of the Sheets API.
Prints values from a sample spreadsheet.
"""
env = read_env()
print('start main')
lang_list = get_range(env['lang'])
sheet = sheets_api(credential(env['credential_file_path']))
result = sheet.values().get(spreadsheetId=SAMPLE_SPREADSHEET_ID,
range=SAMPLE_RANGE_NAME).execute()
print(result)
reader = Reader(result)
# print(reader.get_column_info())
# print(reader.get_lang_by_column('ko'))
'''
def test():
input_tensor = Input((40, 40, 1))
predicted = LeNetModel(input_tensor)
model = Model(inputs=input_tensor, outputs=predicted)
model.load_weights(
'/home/ld/remote_project/CV_HW1/feature/LeNet/checkpoint-10-2.15.hdf5')
reader = Reader('/home/ld/dataset/affNIST/training_and_validation_batches',
'/home/ld/dataset/affNIST/test.mat')
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
score = model.evaluate(np.expand_dims(reader.test_images, axis=3),
keras.utils.to_categorical(reader.test_labels, 10),
verbose=0)
print('Total Test Accuracy is ', score[1])
predict_res = model.predict(np.expand_dims(reader.test_images, axis=3))
calculate_acc_error(np.argmax(predict_res, axis=1), reader.test_labels)
def onkeypress(event):
print totitle(self.n), event.key
self.data[self.n] = event.key
if (self.default_filename):
self.save(self.default_filename)
self.n = self.g.next()
print "Loading: " + totitle(self.n)
movie = Reader(self.n, adjuststipple=True)
print "Showing"
clf()
imshow(movie[self.iframe], interpolation='nearest')
self.drawrefs()
grid(True)
axis('image')
title(totitle(self.n))
draw()
def load_reader(self, chat):
cid = str(chat.id)
reader = self.get_reader(cid)
if reader is not None:
return reader
reader = self.get_reader_file(cid)
if not reader:
reader = Reader.FromChat(chat, self.min_period, self.max_period,
self.logger)
old_reader = self.memory.add(reader)
if old_reader is not None:
old_reader.commit_memory()
self.store(old_reader)
return reader
def main():
input_csv_file_name = sys.argv[1]
output_csv_file_name = sys.argv[2]
# input values are in the form of [feature_1, feature_2, label]
input_values = Reader.csv(input_csv_file_name)
# Track previous weights and allow to compare against latest weight to check convergence
previous_weights = [0, 0, 0]
weights = None
Reporter.write_output(file_name=output_csv_file_name,
content="",
should_overwrite_file=True)
training_inputs = [[x[0], x[1]] for x in input_values]
results = [x[2] for x in input_values]
iterations = 0
while (previous_weights != weights):
# Past the initial condition, we want to track the previous_weight
if (weights != None):
# update previous weight so we can remember for comparison
previous_weights = weights
# import ipdb; ipdb.set_trace()
# weights will be list in the form of [b or w_0, w_1, w_2]
weights = PerceptronLearning.run(training_inputs=training_inputs,
results=results,
initial_weights=previous_weights,
iterations=1)
# write lines to output file
Reporter.write_output(
file_name=output_csv_file_name,
content=','.join(map(str, [weights[1], weights[2], weights[0]])) +
"\n",
)
# create png images of the figures
Visualizer.draw_chart(input_values=input_values,
weights=weights,
file_name="figures/figure_" + str(iterations))
iterations += 1
def __init__(self, models_dir, fold_name, writer=None, hyper=None):
self._graph = tf.Graph()
with self._graph.as_default():
reader = Reader(fold_name)
self.fold_size = reader.fold_size
with tf.device('/gpu:1'):
self._input = reader.inputs(Tester.BATCH_SIZE, is_train=False)
self._network = Network(self._input['images'], is_train=False, hyper=hyper)
self._probs = self._network.probs()
self._cross_entropy_losses = self._network.cross_entropy_losses(self._input['labels'])
self._all_summaries = tf.merge_all_summaries()
self.models_dir = models_dir
print('Tester model folder: %s' %self.models_dir)
assert os.path.exists(self.models_dir)
self.writer = writer
def train():
batch_size = 32
train_total = 1920000
input_tensor = Input((224, 224, 1), dtype=tf.float32)
predicted = VGG16Model(input_tensor)
model = Model(inputs=input_tensor, outputs=predicted)
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
checkpoint = ModelCheckpoint(filepath='/home/ld/remote_project/CV_HW1/feature/VGG16/checkpoint-{epoch:02d}.hdf5')
reader = Reader(
'/home/ld/dataset/affNIST/training_and_validation_batches',
'/home/ld/dataset/affNIST/test.mat',
batch_size=batch_size,
resize=[224, 224]
)
callbacks = [CustomModelCheckpoint(model, '/home/ld/remote_project/CV_HW1/feature/VGG16/checkpoint-{epoch:02d}.hdf5')]
model.fit_generator(reader.train_generator, epochs=5, steps_per_epoch=int(train_total / batch_size + 1),
callbacks=callbacks)
def test_3_pull_epochindices_chosenregion(self):
os.chdir(rootwd)
for key in self.file_stim:
reader_io_stim = Reader(self.file_stim[key])
os.chdir(pwd)
epoch_indices_soma = reader_io_stim.pull_epochindices_chosenregion(
'soma')
epoch_indices_axon = reader_io_stim.pull_epochindices_chosenregion(
'axon')
#print epoch_indices_soma
#print epoch_indices_axon
self.assertNotEqual(epoch_indices_soma, epoch_indices_axon)
reader_io_stim.closefile()
def assign_unused_call(init_solution, data: Reader, feasible):
"""
assign most expensive dummy call to random vehicle
"""
# find most expensive
solution = init_solution.copy()
call_dict = data.getCallsDict()
record_cost = 0
record_call = None
unused_calls = __get_dummy_calls(solution)
for call in unused_calls:
origin, dest, s, fail_cost, lp, up, ld, ud = call_dict[call]
if fail_cost > record_cost:
record_call = call
record_cost = fail_cost
call = record_call
if call is None:
return solution
# remove call
assert call != 0
assert call in solution
solution.remove(call)
solution.remove(call)
car_number = math.ceil(random.random() * data.num_cars)
start, stop = __get_car_index(car_number, solution, data.num_cars)
if stop - start < brute_force_limit:
new_sol = __insert_call_brute_force(solution, call, car_number, data,
feasible)
if new_sol is not None:
return new_sol
return init_solution
else:
# add randomly to new car
if start == stop + 1:
# empty car
solution.insert(start, call)
solution.insert(start, call)
else:
t1 = random.randint(start, stop)
t2 = random.randint(start, stop)
solution.insert(t1, call)
solution.insert(t2, call)
assert len(solution) == len(init_solution)
return solution