def info_w():
branch_json = src.branch_error_check.branch_main.branch_check(
project_assets)
duplicate_json = src.duplicate_key_check.duplicate_main.duplicate_main(
project_assets)
format_json = src.format_char_check.format_main.format_main(
project_assets)
dir_json = src.lang_dir_check.dir_main.dir_main(project_assets)
word_json = src.word_check.word_main.word_check(
project_assets, "./check_words.txt")
time_json = time_gen()
# 重写文件
branch = open("./branch.js", "w", encoding="utf-8")
duplicate = open("./duplicate.js", "w", encoding="utf-8")
format = open("./format.js", "w", encoding="utf-8")
dir = open('./dir.js', "w", encoding="utf-8")
word = open('./word.js', "w", encoding="utf-8")
time = open("./time.js", "w", encoding="utf-8")
# 写 js 数据到文件
branch.write("export let branches=" + branch_json + ";")
duplicate.write("export let duplicates=" + duplicate_json + ";")
format.write("export let formats=" + format_json + ";")
dir.write("export let dirs=" + dir_json + ";")
word.write("export let words=" + word_json + ";")
time.write("export let times=" + time_json + ";")
branch.close()
duplicate.close()
format.close()
dir.close()
word.close()
time.close()
def sortedProofs(matrix, files):
shutil.rmtree("in", ignore_errors=True)
shutil.rmtree("out", ignore_errors=True)
os.makedirs(os.getcwd() + "/in")
os.makedirs(os.getcwd() + "/out")
i = 1
genProofs(files)
time = open("timeSorted", "w")
while i <= files:
n, entrada = auxBegin("/in" + str(files))
startRunningTime = time.time()
sorted(entrada, key=itemgetter(1))
endRunningTime = time.time() - startRunningTime
# se escribe el archivo de tiempos
time.write(str(n) + ":" + "{:1.5f}".format(endRunningTime) + "\n")
i += 1
time.close()
def main():
time.clock()
infile = open(DATA_PATH + 'Brown_tagged_train.txt', 'r')
brown_train = infile.readlines()
infile.close()
brown_words, brown_tags = split_wordtags(brown_train)
unigram_c, bigram_c, trigram_c = calc_ngram_counts(brown_tags)
unigram_p, bigram_p, trigram_p, trigram_d = calc_ngram_probs(
brown_tags, unigram_c, bigram_c, trigram_c)
q_values = trigram_d
q2_output(q_values, OUTPUT_PATH + 'B2.txt')
known_words = calc_known(brown_words)
brown_words_rare = replace_rare(brown_words, known_words)
q3_output(brown_words_rare, OUTPUT_PATH + 'B3.txt')
e_values, taglist = calc_emission(brown_words_rare, brown_tags)
q4_output(e_values, OUTPUT_PATH + 'B4.txt')
del brown_train
del brown_words_rare
infile = open(DATA_PATH + 'Brown_dev.txt', 'r')
brown_dev = infile.readlines()
infile.close()
brown_dev_words = []
for sentence in brown_dev:
brown_dev_words.append(sentence.split(' ')[:-1])
viterbi_tagged = viterbi(brown_dev_words, taglist, known_words, q_values,
e_values)
q5_output(viterbi_tagged, OUTPUT_PATH + 'B5.txt')
nltk_tagged = nltk_tagger(brown_words, brown_tags, brown_dev_words)
q6_output(nltk_tagged, OUTPUT_PATH + 'B6.txt')
print 'Time: ' + str(time.close()) + ' sec'
ser.write(textthongbao)
rcv = ser.read(9999)
sleep(1)
print rcv
ser.write("\x1A")
sleep(1)
#ser.close()
print "-------------\nDa gui xong sms"
time = open('/var/www/html/system/time.txt', 'w+')
now = datetime.datetime.now()
timedagui = now.strftime("%Y-%m-%d %H:%M")
print timedagui
time.write(timedagui)
time.close()
kt = open('/var/www/html/system/check.txt', 'w')
kt.write("0")
kt.close()
elif kiemtra == '0':
#gui 1 sms
ktsms = open('/var/www/html/sms/check.txt')
kiemtrasms = ktsms.read(1)
ktsms.close()
if kiemtrasms == '1':
smstl = open('/var/www/html/sms/memory/sms.txt')
noidungsms = smstl.read()
smstl.close()
import time #импортирование времени
t = time.time()
from time import time#импортирование времени и только его
import time as f#импортирование как переменная
t = f.time()
sys.path#путс системы
import imp
imp.reload(foo)#перезагрузка модуля
import os#работа с файловой системой
os.getcwd()#где находимся
os.chdir()#смена каталога
os.listdir()#файлы в каталоге
os.path.splittext('filename.ext') == '.ext'#
os.isfile() os.isdir()#что именно
f = open(a)
text = """123"""
f.write(text)
f.close()
g.read() #считывание по символам
g.read(2)
#docs.python.org - документация
def runTrajectories(self,theta, fonction):
'''
Generates all the trajectories of the experimental setup and return the mean cost. This function is used by cmaes to optimize the controller.
Input: -theta: vector of parameters, one dimension normalized numpy array
Ouput: -meanAll: the mean of the cost of all trajectories generated, float
'''
#c = Chrono()
self.initTheta(theta)
#print "theta avant appel :", theta
#compute all the trajectories x times each, x = numberOfRepeat
meanCost, meanTime = fonction(self.numberOfRepeat)
#cma.plot()
#opt = cma.CMAOptions()
#print "CMAES options :", opt
#c.stop()
#print("Indiv #: ", self.call, "\n Cost: ", meanCost)
if (self.call==0):
self.localBestCost = meanCost
self.localWorstCost = meanCost
self.localBestTime = meanTime
self.localWorstTime = meanTime
self.periodMeanCost = 0.0
self.periodMeanTime = 0.0
else:
if meanCost>self.localBestCost:
self.localBestCost = meanCost
elif meanCost<self.localWorstCost:
self.localWorstCost = meanCost
if meanTime>self.localBestTime:
self.localBestTime = meanTime
elif meanTime<self.localWorstTime:
self.localWorstTime = meanTime
if meanCost>self.bestCost:
self.bestCost = meanCost
if meanCost>0:
extension = ".save" + str(meanCost)
filename = findDataFilename(self.foldername+"Theta/", "theta", extension)
np.savetxt(filename, self.theta)
filename2 = self.foldername + "Best.theta"
np.savetxt(filename2, self.theta)
self.periodMeanCost += meanCost
self.periodMeanTime += meanTime
self.call += 1
self.call = self.call%self.period
if (self.call==0):
self.periodMeanCost = self.periodMeanCost/self.period
self.periodMeanTime = self.periodMeanTime/self.period
self.CMAESCostStore.append((self.localWorstCost,self.periodMeanCost,self.localBestCost))
self.CMAESTimeStore.append((self.localWorstTime,self.periodMeanTime,self.localBestTime))
costfoldername = self.foldername+"Cost/"
checkIfFolderExists(costfoldername)
cost = open(costfoldername+"cmaesCost.log","a")
time = open(costfoldername+"cmaesTime.log","a")
cost.write(str(self.localWorstCost)+" "+str(self.periodMeanCost)+" "+str(self.localBestCost)+"\n")
time.write(str(self.localWorstTime)+" "+str(self.periodMeanTime)+" "+str(self.localBestTime)+"\n")
cost.close()
time.close()
#np.savetxt(costfoldername+"cmaesCost.log",self.CMAESCostStore) #Note: inefficient, should rather add to the file
#np.savetxt(costfoldername+"cmaesTime.log",self.CMAESTimeStore) #Note: inefficient, should rather add to the file
return 10.0*(self.rs.rhoCF-meanCost)/self.rs.rhoCF