def t3(A=None, B=None, C=None):
A = init(A,i,j)
B = init(B,i,j)
C = init(C,j)
task = tf.tensordot(tf.add(A,B),C,1)
with tf.Session(config = config) as sess:
return sess.run(task)
def dot3(A=None, B=None, C=None):
A = init(A, i)
B = init(B, i)
C = init(C, i)
task = tf.tensordot(tf.add(A, B), tf.subtract(A, C), 1)
with tf.Session(config=config) as sess:
return sess.run(task)
def __init__(self):
self.state = 'pre'
# init pygame
pygame.init()
pygame.mixer.init(44100, 16, 2, 1024*4)
pygame.display.set_caption("MR. TRAPEZIUS ")
try:
self.screen = pygame.display.set_mode((640, 480),
HWSURFACE | SRCALPHA, 32)
except:
self.screen = pygame.display.set_mode((640, 480),
SRCALPHA, 32)
try:
pygame.display.set_icon(pygame.image.load(
util.file_path("Inky.png")).convert_alpha())
except:
# some platfom do not allow change icon after shown
pass
# init fonts and music lists
util.init()
# init sub states objects
self.pre = Pre(self.screen)
self.go = MainGame(self.screen)
self.level_info = Level(self.screen)
self.re = Retry(self.screen)
self.next = NextLevel(self.screen)
sound.load()
def t7(A=None, C=None, D=None):
A = init(A,i,j)
C = init(C,j,k)
D = init(D,k)
task = tf.tensordot(A,tf.tensordot(C,D,1),1)
with tf.Session(config = config) as sess:
return sess.run(task)
def dot1(A=None, B=None, C=None):
A = init(A, i)
B = init(B, i)
C = init(C, i)
task = tf.reduce_sum(tf.multiply(tf.multiply(A, B), C))
with tf.Session(config=config) as sess:
return sess.run(task)
def main():
framecount = 0
exit = False
nodes = []
tiles = load_tiles()
tile_size = 16
pygame.init()
pygame.display.set_caption("Monster AI Prototype")
screen = pygame.display.set_mode(
(len(tiles) * tile_size, len(tiles) * tile_size))
clock = pygame.time.Clock()
background = pygame.Surface(screen.get_size())
background = background.convert()
background.fill((255, 255, 255))
util.init(background, tile_size, tiles)
nodes = ai.init(len(tiles))
key_player = entity.Entity(tiles, 1, 1, (255, 0, 0))
mouse_player = entity.Entity(tiles, len(tiles) - 2, 1, (0, 255, 0))
monster = entity.Entity(tiles, int(len(tiles) / 2), int(len(tiles) / 2),
(0, 0, 0))
while not exit:
clock.tick(60)
screen.blit(background, (0, 0))
draw_tiles(background, tiles, tile_size)
key_player.draw()
mouse_player.draw()
monster.draw()
for event in pygame.event.get():
if event.type == QUIT or (event.type == KEYDOWN
and event.key == K_ESCAPE):
return
elif event.type == KEYDOWN:
if event.key == K_DOWN:
key_player.add_pos(0, 1)
elif event.key == K_UP:
key_player.add_pos(0, -1)
elif event.key == K_LEFT:
key_player.add_pos(-1, 0)
elif event.key == K_RIGHT:
key_player.add_pos(1, 0)
mx, my = pygame.mouse.get_pos()
mouse_player.set_pos(mx / tile_size, my / tile_size)
ai_move = ai.ai_step(framecount, nodes, monster, (mouse_player, ))
framecount += 1
pygame.display.flip()
def t2(A=None, B=None, C=None):
A = init(A,i,j)
B = init(B,j)
C = init(C,i)
task = tf.multiply(tf.tensordot(A,B,1),C)
with tf.Session(config = config) as sess:
return sess.run(task)
def main():
print("{0:-^30}".format(copy_right))
print("-----------------------------------------------------")
print("\n")
#初始化配置文件读取和日志
My_logger.init()
util.init()
##开始寻找讲座
My_logger.my_logger.info("开始按照配置文件搜寻讲座信息！")
all_obj = get_seminar.find_seminaer()
My_logger.my_logger.info("搜寻讲座信息完成，共发现:{}场讲座！".format(len(all_obj)))
##讲座分辨
get_seminar.judge_seminar(all_obj)
My_logger.my_logger.info("讲座主题分辨完成！")
##讲座的落库
get_seminar.alltoDB(all_obj)
My_logger.my_logger.info("讲座落库完成！")
# ##讲座持久化到本地
get_seminar.toreal_file(all_obj)
My_logger.my_logger.info("讲座持久化完成！")
##讲座的微信通知
get_seminar.allcontact(all_obj)
My_logger.my_logger.info("讲座信息邮件推送完成！")
def test():
pygame.init()
s = pygame.display.set_mode((640, 480), HWSURFACE | SRCALPHA, 32)
util.init()
m = MainGame(s)
m.run(1)
#test()
def t9(A=None, B=None, C=None, D=None):
A = init(A,i,k)
B = init(B,k,j)
C = init(C,j)
D = init(D,j)
task = tf.tensordot(tf.tensordot(A,B,1),tf.add(C,D),1)
with tf.Session(config = config) as sess:
return sess.run(task)
def t6(A=None, B=None, C=None, D=None):
A = init(A,i,1)
B = init(B,j)
C = init(C,i,1)
D = init(D,j)
task = tf.reduce_sum(tf.multiply(tf.multiply(A,B),tf.multiply(C,D)),1)
with tf.Session(config = config) as sess:
return sess.run(task)
def init():
global initDone
if not initDone:
misc.init(False)
util.init(False)
initDone = True
def init():
global initDone
if not initDone:
misc.init(False)
util.init(False)
initDone = True
def main():
args = cmd_line.parse_args()
util.init(args)
util.pre_problem = 'CV RC circle'
logger = logging.getLogger()
log.configure_logger(logger, "RaceCar")
logger.setLevel(logging.DEBUG)
def main():
init()
save_crop()
logging.info("start at: {}".format(datetime.datetime.now()))
play = get_policy()
while True:
action = play.action()
if action:
get_action_by_name(action).execute()
def start():
util.init()
if _G.AppHwnd == 0:
print("App not found, aborting")
return exit()
util.activate_window(_G.AppHwnd)
while _G.Flags['running']:
main_loop()
def test():
pygame.init()
s = pygame.display.set_mode((640, 480), HWSURFACE | SRCALPHA, 32)
a = Level(s)
util.init()
a.run(1)
#test()
def init(winname,appname,gamedir,icon="sys_icon.png"):
global INIT_DONE
if not INIT_DONE:
util.init(appname,gamedir)
pygame.init()
pygame.display.set_caption(winname,appname)
pygame.display.set_icon(pygame.image.load(util.image_dir(icon)))
# Set the screen size.
if util.config.getboolean( "DEFAULT", "fullscreen" ):
my_state.screen = pygame.display.set_mode( (0,0), pygame.FULLSCREEN )
else:
my_state.screen = pygame.display.set_mode( (800,600), pygame.RESIZABLE )
rpgmenu.init()
global INPUT_CURSOR
INPUT_CURSOR = image.Image( "sys_textcursor.png" , 8 , 16 )
global SMALLFONT
SMALLFONT = pygame.font.Font( util.image_dir( "VeraBd.ttf" ) , 12 )
global TINYFONT
TINYFONT = pygame.font.Font( util.image_dir( "VeraBd.ttf" ) , 9 )
global ANIMFONT
ANIMFONT = pygame.font.Font( util.image_dir( "DejaVuSansCondensed-Bold.ttf" ) , 16 )
global ITALICFONT
ITALICFONT = pygame.font.Font( util.image_dir( "VeraBI.ttf" ) , 12 )
global POSTERS
POSTERS += glob.glob( util.image_dir("poster_*.png") )
global FPS
FPS = util.config.getint( "DEFAULT", "frames_per_second" )
pygame.time.set_timer(TIMEREVENT, 1000 / FPS)
if android:
android.init()
android.map_key(android.KEYCODE_BACK, pygame.K_ESCAPE)
# Set key repeat.
pygame.key.set_repeat( 200 , 75 )
INIT_DONE = True
def main(args):
global NUMBER_FILES_TO_GENERATE
if args and len(args) > 0:
try:
NUMBER_FILES_TO_GENERATE = int(args[0])
except:
raise ValueError("The first argument to main.py must be an integer!")
util.init()
global heads
global pcfg
global objects
global primitives
# Call the main rule-parsing/generation module first to get our pcfg
# and other data ready to go
(heads, pcfg, objects, raw_primitives) = generate_rules.process_all();
# Prepare primitives dictionaries by doing some pre-processing on them
# so that future stuff is accessible much quicker:
primitives = prepare_primitives(raw_primitives)
print "\nStarting generation process. Don't be alarmed if it takes awhile."
numGenerated = 0
while True:
try:
print "Attempting to generate artificial Python file {}/{}".format(numGenerated+1, NUMBER_FILES_TO_GENERATE)
tree = makeNode("Module", 0, [])
outcode = "generated/code{}.py".format(numGenerated+1)
outast = "generated/AST{}.txt".format(numGenerated+1)
with open(outcode, "w") as out:
Unparser.Unparser(tree, out)
with open(outcode, "r") as in_f:
text = in_f.read()
with open(outcode, 'w') as out:
out.write(postprocess.postprocess(text))
with open(outast, "w") as out:
out.write(ast.dump(tree))
print "Successfully created {} and {}".format(outcode, outast)
numGenerated += 1
if numGenerated >= NUMBER_FILES_TO_GENERATE:
break
except Exception as e:
pass # The Fifth Amendment allows me to do this. Shhh...
def __init__(self, path="./config.ini"):
"""
检查配置文件是否存在，不存在则直接return
检查输出文件夹是否存在，不存在则创建
config.ini必须要有的字段
- dump_dir
- base_url
"""
print u'\n in Spider __init__'
if not util.check_file(path):
print '\n\t no file ' + path
return
self.config = util.init(path)
if self.config.has_key('ua'):
self.config['ua'] = util.init(self.config['ua'])
self.config['requests_dir'] = self.config['dump_dir'] + '/requests'
print self.config
util.check_path(self.config['dump_dir'])
def __init__(self, path="./config.ini"):
"""
检查配置文件是否存在，不存在则直接return
检查输出文件夹是否存在，不存在则创建
config.ini必须要有的字段
- dump_dir
- base_url
"""
print u'\n in Spider __init__'
if not util.check_file(path):
print '\n\t no file ' + path
return
self.config = util.init(path)
if self.config.has_key('ua'):
self.config['ua'] = util.init(self.config['ua'])
self.config['requests_dir'] = self.config['dump_dir'] + '/requests'
print self.config
util.check_path(self.config['dump_dir'])
def t5(a=None, A=None, b=None, B=None, c=None, C=None, d=None, D=None):
a = init(a)
A = init(A,i,j)
b = init(b)
B = init(B,i,j)
c = init(c)
C = init(C,j)
d = init(d)
D = init(D,j)
task = tf.tensordot(tf.add(tf.multiply(a,A),tf.multiply(b,B)),tf.add(tf.multiply(c,C),tf.multiply(d,D)),1)
with tf.Session(config = config) as sess:
return sess.run(task)
def dot5(a=None, A=None, b=None, B=None, c=None, C=None, d=None, D=None):
a = init(a)
A = init(A, i)
b = init(b)
B = init(B, i)
c = init(c)
C = init(C, i)
d = init(d)
D = init(D, i)
task = tf.tensordot(tf.add(tf.multiply(A, a), tf.multiply(B, b)),
tf.add(tf.multiply(C, c), tf.multiply(D, d)), 1)
with tf.Session(config=config) as sess:
return sess.run(task)
def launch(search_str, auth=None, final_songs=None):
# initialization of global variables
util.init()
# Authentication only occurs when user adds playlist
if (search_str == "True"):
sp_user = auth
user_id = sp_user.me()['id']
seamless.gen_playlist(final_songs, final_songs[0]['title'],
final_songs[0]['artist'], sp_user, user_id)
return True
# creation of song object
song_obj = seamless.make_song_from_id(search_str)
util.year = song_obj.year
util.album_tracks.append(song_obj)
valid_tracks(song_obj, song_obj)
# Songs that went through LastFM API
util.already_chosen_fm.append(song_obj)
logger.info(f'Number of tracks: {len(util.album_tracks)}')
# Filter through reccomended songs first
seamless.recommended_tracks(song_obj)
# finds similar songs of tracks that have already satisfied the requirements
while len(util.album_tracks) < util.limit:
# choosing songs from the LastFM list
song_data = random.sample(util.album_tracks, 1)[0]
seamless.main()
valid_tracks(song_data, song_obj)
seamless.recommended_tracks(song_data)
util.already_chosen_fm.append(song_data)
songsChosen = util.album_tracks
return songsChosen
def main(args):
# general
if torch.cuda.is_available():
device = torch.device("cuda")
util.logging.info("Using CUDA")
else:
device = torch.device("cpu")
util.logging.info("Using CPU")
util.set_seed(args.seed)
# init
checkpoint_path = util.get_checkpoint_path(args)
if not Path(checkpoint_path).exists():
util.logging.info("Training from scratch")
model, optimizer, stats = util.init(args, device)
else:
model, optimizer, stats, args = util.load_checkpoint(checkpoint_path, device)
# train
train.train(model, optimizer, stats, run_args=args)
#!/usr/bin/env python3
import datetime
import os
import shutil
import subprocess
import util
util.init()
name = 'a'
email = '*****@*****.**'
os.environ['GIT_AUTHOR_EMAIL'] = email
os.environ['GIT_AUTHOR_NAME'] = name
os.environ['GIT_COMMITTER_EMAIL'] = email
os.environ['GIT_COMMITTER_NAME'] = name
date0 = datetime.date(2000, 1, 1)
datef = datetime.date(2100, 1, 1)
date = date0
while date < datef:
s = date.strftime('%Y-%m-%dT01:00:00')
print(s)
os.environ['GIT_AUTHOR_DATE'] = s
os.environ['GIT_COMMITTER_DATE'] = s
cmd = ['git', 'commit', '-q', '--allow-empty', '--allow-empty-message', '-m', '']
subprocess.check_output(cmd)
date += datetime.timedelta(days=1)
#!/usr/bin/python2.7
import sys
import util
import numpy as np
required_fields = util.required_fields
attr_n = util.attr_n
if len(sys.argv) != 3:
util.print_usage()
exit()
data = util.read_data(sys.argv[1])
util.init(data)
c_cnt = util.c_cnt
o_cnt = util.o_cnt
simple_attr = util.simple_attr
ranged_attr = util.ranged_attr
# Learning
data = np.array(data)
np.random.shuffle(data)
data = data[:100]
for record in data:
t = int(record[13]) # 0 => Healthy
# 1-4 => Has heart disease
if t > 4:
print "Error"
git --git-dir=tmp/repo.tmp/.git push -f "$remote" "$i:master"
done
# TODO for some reason I needed this afterwards.
git --git-dir=tmp/repo.tmp/.git push "$remote" 'master'
"""
import datetime
import subprocess
import time
import util
email = b'*****@*****.**'
name = b''
util.init()
tree = util.create_tree_with_one_file()
commit = None
n = 1000000
for i in range(n):
now = int(time.time())
commit, _, _ = util.save_commit_object(
tree,
(commit, ),
author_date_s=0,
author_email=email,
author_name=name,
committer_date_s=0,
committer_email=email,
committer_name=name,
import time
import botocore
import boto3
import db
import util
import treehash
print("AWS Photo Library")
if util.data_dir(err=False) is None:
data_dir = os.path.join(os.getcwd(),util.dir_name)
os.makedirs(data_dir)
db_conn = util.init()
glacier_conn = boto3.client('glacier')
accountId = '-'
vaultName = 'test'
parameters = {"Type":"inventory-retrieval"}
requestNewInventory = False
uploadNewArchive = True
showInventory = True
uploadD3JS = False
uploadD3JSTAR = True
uploadABC = True
retrieveArchive = True
showJobs = False
res = glacier_conn.describe_vault(accountId=accountId, vaultName=vaultName)
logging.info('make submission')
sub_acc = pd.concat([agg_acc, each_acc])
sub_unc = pd.concat([agg_unc, each_unc])
sub_acc = modify_acc(sub_acc)
sample_sub_acc = pd.read_csv(
'../input/m5-forecasting-accuracy/sample_submission.csv')
sample_sub_unc = pd.read_csv(
'../input/m5-forecasting-uncertainty/sample_submission.csv')
sub_acc = sample_sub_acc[['id']].merge(sub_acc)
sub_unc = sample_sub_unc[['id']].merge(sub_unc)
assert len(sample_sub_acc) == len(sub_acc)
assert len(sample_sub_unc) == len(sub_unc)
now = pd.Timestamp.now()
sub_acc.to_csv(f'../submissions/acc-{now:%Y%m%d}-{now:%H%M%S}.csv',
index=False,
float_format='%.3g')
sub_unc.to_csv(f'../submissions/unc-{now:%Y%m%d}-{now:%H%M%S}.csv',
index=False,
float_format='%.3g')
if __name__ == "__main__":
run_name = init(__file__)
try:
main(run_name)
except:
logging.exception('exception')
finally:
logging.info('end')
#!/usr/bin/env python
# add words to ../dict_en.dat in the correct place
import sys
if len(sys.argv) < 2:
raise "add_word.py word1 word2..."
sys.path.insert(0, "..")
import util
util.init(False)
s = util.loadFile("../dict_en.dat", None)
if s == None:
raise "error"
words = {}
lines = s.splitlines()
for it in lines:
words[util.lower(it)] = None
for arg in sys.argv[1:]:
words[util.lower(arg)] = None
words = words.keys()
words.sort()
f = open("../dict_en.dat", "wb")
for w in words:
def run():
config = util.init('unittest')
suites = find_tests()
run_tests(suites, config)
def init(winname,appname,gamedir,icon="sys_icon.png",poster_pattern="poster_*.png"):
global INIT_DONE
if not INIT_DONE:
util.init(appname,gamedir)
# Init image.py
image.init_image(util.image_dir(""))
pygame.init()
pygame.mixer.init()
pygame.display.set_caption(winname,appname)
pygame.display.set_icon(pygame.image.load(util.image_dir(icon)))
# Set the screen size.
if util.config.getboolean( "GENERAL", "fullscreen" ):
my_state.screen = pygame.display.set_mode( (0,0), pygame.FULLSCREEN )
else:
my_state.screen = pygame.display.set_mode( (800,600), pygame.RESIZABLE )
global INPUT_CURSOR
INPUT_CURSOR = image.Image( "sys_textcursor.png" , 8 , 16 )
global SMALLFONT
SMALLFONT = pygame.font.Font( util.image_dir( "DejaVuSansCondensed-Bold.ttf" ) , 12 )
my_state.small_font = SMALLFONT
global TINYFONT
TINYFONT = pygame.font.Font( util.image_dir( "DejaVuSansCondensed-Bold.ttf" ) , 9 )
my_state.tiny_font = TINYFONT
global ANIMFONT
ANIMFONT = pygame.font.Font( util.image_dir( "DejaVuSansCondensed-Bold.ttf" ) , 16 )
my_state.anim_font = ANIMFONT
global MEDIUMFONT
MEDIUMFONT = pygame.font.Font( util.image_dir( "DejaVuSansCondensed-Bold.ttf" ) , 14 )
my_state.medium_font = MEDIUMFONT
global ITALICFONT
ITALICFONT = pygame.font.Font( util.image_dir( "DejaVuSansCondensed-BoldOblique.ttf" ) , 12 )
global BIGFONT
BIGFONT = pygame.font.Font( util.image_dir( "Anita semi square.ttf" ) , 16 )
my_state.big_font = BIGFONT
my_state.huge_font = pygame.font.Font( util.image_dir( "Anita semi square.ttf" ) , 24 )
global POSTERS
POSTERS += glob.glob( util.image_dir(poster_pattern) )
global FPS
FPS = util.config.getint( "GENERAL", "frames_per_second" )
pygame.time.set_timer(TIMEREVENT, 1000 / FPS)
if android:
android.init()
android.map_key(android.KEYCODE_BACK, pygame.K_ESCAPE)
# Set key repeat.
pygame.key.set_repeat( 200 , 75 )
INIT_DONE = True
from flask import Flask, jsonify, request, session
from flask_cors import CORS
from util import load_autocomplete_data,\
filter_request, add_data_in_session, insert_on_logout, init
import json
import os
from datetime import datetime
app = Flask(__name__)
CORS(app)
session = {}
init(app)
@app.route("/root", methods=['POST'])
def _auto_complete():
user_query = request.form.get('query')
show_more = request.form.get('show_more')
session_id = request.form.get('session_id')
if session_id is not None and session_id:
add_data_in_session(session, str(datetime.now()), session_id, "login_time", False)
if user_query is not None:
data = load_autocomplete_data(user_query, show_more)
return jsonify(data)
@app.route("/filter", methods=['POST'])
def _filter():
user_query = request.form.get('query')
username = request.form.get('username')
session_id = request.form.get('session_id')
# parameters for the code
k = int(sys.argv[1])
numEps = int(sys.argv[2])
dimEmbedding = int(sys.argv[3])
windowSize = int(sys.argv[4])
learningRate = float(sys.argv[5])
numOfEpochs = int(sys.argv[6])
batchSize = int(sys.argv[7])
bestValModel = None
bestValReward = 0
fileBestRLModel = open("bestRlModel.txt", 'w')
# numSteps should be less than k
# window size must be much smaller than k to generate enough samples
historyOfTuples = []
util.init(learningRate, numOfEpochs, batchSize, dimEmbedding)
for episode in range(0, numEps):
#episode=episode+2
print("episode ", episode)
# generate a new graph for each episode
graph = util.Graph(dimEmbedding, episode, k)
print("graph ", graph)
graphEnv.graphEnvironment.append(graph)
# print(graph.graphX.degree())
# if episode==0:
# util.initialze_weights(graph)
# (numsteps == k) => Terminal Condition
previous_spread = 0
for step in range(0, k):
def init(self):
util.init()
sound.load()
import util
util.init('test.bib')
f = open('./People.html', 'w')
f.write(util.getPeople())
f.close()
f = open('./Software.html', 'w')
f.write(util.getSoftware())
f.close()
f = open('./Publications.html', 'w')
f.write(util.getPublications())
f.close()
def baseline_sum(A_host=None):
A = init(A_host, i)
task = tf.reduce_sum(A)
with tf.Session(config=config) as sess:
return sess.run(task)
def init(self):
util.init()
sound.load()
def baseline_inc(A=None):
A = init(A, i)
task = tf.add(A, 1)
with tf.Session(config=config) as sess:
return sess.run(task)
app = Flask(__name__)
openmrs_host = 'http://*****:*****@app.route('/fetch')
def hello_world():
response = requests.get(openmrs_host + idgen_path)
return response.text, response.status_code
@app.route('/register',methods=['POST'])
def register_patient():
print(request.get_json(force=True))
response = requests.post(
openmrs_host + registeration_path,
auth=(os.environ['MOBILE_USERNAME'], os.environ['MOBILE_PASSWORD']),
json=request.get_json(force=True)
)
if response.status_code == 201:
email_notify(request.host)
return response.text, response.status_code
if __name__ == '__main__':
init()
app.run(host='0.0.0.0', port=3000)
def dot(A=None, B=None):
A = init(A, i)
B = init(B, i)
task = tf.tensordot(A, B, 1)
with tf.Session(config=config) as sess:
return sess.run(task)
def baseline_prod(A=None, c=None):
A = init(A, i)
c = init(c)
task = tf.multiply(A, c)
with tf.Session(config=config) as sess:
return sess.run(task)
