def myclip(_province,_kind,_terrain):
clips.Reset()
rules = [rule.strip() for rule in readfile.readfile('rules.txt')]
provinces = [province.strip() for province in readfile.readfile('province.txt')]
for province in provinces:
clips.SendCommand(province.strip())
clips.Assert("(province {0})".format(_province))
clips.Run()
for rule in rules:
clips.SendCommand(rule.strip())
clips.Assert("(kind {0})".format(_kind))
if _terrain is not "":
clips.FactList()[3].Retract()
clips.Assert("(terrain {0})".format(_terrain))
#BEFORE
bf = len(clips.FactList())
#RUN
clips.Run()
#AFTER
af = len(clips.FactList())
for i in range(af - (af - bf)):
clips.FactList()[0].Retract()
#clips.PrintRules()
return [i.PPForm()[8+6:-1] for i in clips.FactList()]
def diffdata(files):
# Error: can only use 2 files to diff
if len(files) != 2:
sys.exit(3)
(t1, w1, a1) = readfile(files.pop(0))
(t2, w2, a2) = readfile(files.pop(0))
# Error: time or wavelength mismatch
if list(t1) != list(t2) or list(w1) != list(w2):
sys.exit(2)
return t1, w1, a1 - a2
def avgdata(files):
nfiles = float(len(files))
(t, w, a) = readfile(files.pop(0))
for file in files:
(t1, w1, a1) = readfile(file)
# Error: time or wavelength mismatch
if list(t1) != list(t) or list(w1) != list(w):
sys.exit(2)
a += a1
a = a / nfiles
return t, w, a
def kmeans():
nr_of_clusters = 5
blognames, words, data = readfile()
kclust = kmeanscluster(data, k=5, max_iter=5)
centroid_0 = []
centroid_1 = []
centroid_2 = []
centroid_3 = []
centroid_4 = []
centroids = []
for r in kclust[0]:
centroid_0.append(blognames[r])
for i in kclust[1]:
centroid_1.append(blognames[i])
for j in kclust[2]:
centroid_2.append(blognames[j])
for k in kclust[3]:
centroid_3.append(blognames[k])
for l in kclust[4]:
centroid_4.append(blognames[l])
centroids = ((centroid_0, len(centroid_0)), (centroid_1, len(centroid_1)),
(centroid_2, len(centroid_2)), (centroid_3, len(centroid_3)),
(centroid_4, len(centroid_4)))
#print centroids
return render_template('kmeans.html', kclust=centroids, noc=nr_of_clusters)
def main():
depots, stations, customers, demand, time_win, tolerate_time_win = readfile.readfile(
"4depot_10station_40customer3.csv",
depot_num=4,
station_num=10,
customer_num=40)
dis_dep_cus = readfile.get_dis_matrix(depots,
customers) #depot到customer的距离矩阵
dis_sta_cus = readfile.get_dis_matrix(stations,
customers) #station到customer的距离矩阵
dis_cus_cus = readfile.get_dis_matrix(customers,
customers) #customer之间的距离矩阵
min_dis_depot = readfile.get_min_dis(
dis_dep_cus) #获取距离每个customer最近的depot及距离
min_dis_station = readfile.get_min_dis(
dis_sta_cus) #获取距离每个customer最近的station及距离
mans = 40
rows = 100
times = 300
# line = [3, 29, 14, 39, 19, 7, 35, 5, 21, 6, 33, 4, 26, 31, 11, 18, 20, 1, 30, 36, 15, 24, 23, 32, 34, 40, 37, 9, 22, 16, 8, 38, 12, 13, 10, 28, 2, 17, 25, 27]
# result = calFitness.fitness(dis_cus_cus, dis_sta_cus, min_dis_depot, min_dis_station, time_win, tolerate_time_win, demand, line, True)
# print result
#
genetic = GeneticAlgorithm(dis_dep_cus, dis_sta_cus, dis_cus_cus,
min_dis_depot, min_dis_station, demand,
time_win, tolerate_time_win, mans, rows, times)
genetic.run()
def polygon_intersect(file):
# Read the input file in the correct format. Use the plot_boundary function
# to plot the polygons
result = readfile.readfile(file)
P1 = result['poly1']
P2 = result['poly2']
plt.cla()
plt.axis('equal')
plt.grid('on')
plt.hold(True)
plot_boundary(P2)
plot_boundary(P1)
# Find the intersections of the polygons and compose the new polygon
result = find_intersections(P1,P2)
P3 = compose_new_polygon(result)
P3 = [[int(float(j)) for j in i] for i in P3]
# Use the plot_boundary and flood_fill functions to plot the new polygon
poly = P3
px = [poly[i][0] for i in range(len(poly))]
py = [poly[i][1] for i in range(len(poly))]
x_sorted = np.sort(px) # replace!!!
y_sorted = np.sort(py) # replace!!!
colors = plot_boundary(poly)
plt.plot(int(np.median(px)),int(np.median(py)), marker='.', markersize = 15, markerfacecolor = 'g')
flood_fill(int(np.median(px)),int(np.median(py)), 'w', 'g', x_sorted[0], y_sorted[0], colors)
plt.show(block=False)
def __init__(self,):
tablist=readfile()
self.values=[]
for i in range(3):
value=tuple(tablist[0])
del tablist[0]
self.values.append(value)
self.pfdict=dict(tablist)
def main():
fabdict=dict(f.readfile("fabtest.csv"))
localpath=fabdict['localpath']
remotepath=fabdict['remotepath']
#putfile(localpath,remotepath)
#check_file(localpath,remotepath)
untar(remotepath)
def top(path, f_type, hang, lie):
f_list = readfile.readfile(path, f_type)
for i in f_list:
extract.extract(i, hang, lie)
return
def __init__(self, ):
tablist = readfile()
self.values = []
for i in range(3):
value = tuple(tablist[0])
del tablist[0]
self.values.append(value)
self.pfdict = dict(tablist)
def run(path=resource_manager.Properties.getDefaultDataFold()+"txt"+resource_manager.getSeparator()+"build.txt",sep=' '):
'''
return cluster id
i,j distance
'''
(dist,xxdist,ND,N) = readfile(path, dimensions = 2, sep=sep)
XY, eigs = mds(dist)
(rho,delta,ordrho,dc,nneigh) = rhodelta(dist, xxdist, ND, N, percent = 2.0)
DCplot(dist, XY, ND, rho, delta,ordrho,dc,nneigh,17,0.1)
def db(request):
import sys,readfile
from rice.models import Rice
rules = [rule.strip() for rule in readfile.readfile("kem.csv")]
for i in rules:
i = i.split(',')
r = Rice(name=i[0],kind=i[1],number=i[2],area=i[3],terrain=i[4],character=i[5],seed=i[6],detail=i[7],pic=i[8],ref=i[9])
r.save()
return HttpResponse("GG")
def run(*args, **kwargs):
'''
return cluster id
'''
file = kwargs.get('fi')
sep = kwargs.get('sep',' ')
########
(dist,xxdist,ND,N) = readfile(file, dimensions = 2, sep=sep)
XY, eigs = mds(dist)
(rho,delta,ordrho,dc,nneigh) = rhodelta(dist, xxdist, ND, N, percent = 2.0)
DCplot(dist, XY, ND, rho, delta,ordrho,dc,nneigh,17,0.1,31)
def ___init__(filename):
file=readfile.readfile()
file.getvaluefromconfig()
firstline=file.firstline
lastline=file.lastline
linenum=file.linenum
f.getvaluefromfile()
ffirstline=file.firstline()
flastline=file.lastline()
flinenum=file.linenum()
def done(self):
# Dictionary containing {filename:configuration} pairs
results = {}
i=0
for file in self.filenames:
results.update({self.trim_filename(self.filenames[i]):readfile(file)})
i = i + 1
openwindow(root, results)
openwindow2(root, results)
def draw_table_price():
pl.ioff()
contents = readfile.readfile("000007.csv")
high, low = readfile.get_prices(contents)
ranges = range(len(contents))
prices = [ content[2] for content in contents ]
pl.plot(ranges, prices, color="red")
#pl.plot([(float(low) - float(low)*0.02), (float(high) + float(high)*0.02)])
pl.plot([float(low), float(high)])
pl.title("Hello world")
pl.show()
def main():
fname = sys.argv[1]
X, Y = readfile.readfile(fname)
X, Y = np.array(X), np.array(Y)
times = []
np.random.seed(1208)
for i in range(1126):
w, updates = PLA(X, Y)
times.append(updates)
print(w, updates)
print('average update steps : {}'.format(np.average(times)))
plt.hist(np.array(times), bins=range(min(times), max(times),1))
plt.xlabel('Update counts')
plt.ylabel('Counts')
plt.savefig('hw1_7.png')
def convex_hull_plot(file):
# Format input file and plot data
result = readfile.readfile(file)
points = result['points']
plt.cla()
plt.axis('equal')
plt.grid('on')
plt.hold(True)
# Find convex hull and plot it using plot_boundary
hull = convex_hull(points)
plt.scatter(*zip(*points))
plot_boundary(hull)
plt.show(block=False)
def main():
depots, stations, customers, demand, time_win, tolerate_time_win = readfile.readfile(
"4depot_10station_40customer3.csv",
depot_num=4,
station_num=10,
customer_num=40)
dis_dep_cus = readfile.get_dis_matrix(depots,
customers) #depot到customer的距离矩阵
dis_sta_cus = readfile.get_dis_matrix(stations,
customers) #station到customer的距离矩阵
dis_cus_cus = readfile.get_dis_matrix(customers,
customers) #customer之间的距离矩阵
min_dis_depot = readfile.get_min_dis(
dis_dep_cus) #获取距离每个customer最近的depot及距离
min_dis_station = readfile.get_min_dis(
dis_sta_cus) #获取距离每个customer最近的station及距离
customer_means = []
cus1 = []
cus2 = []
cus3 = []
cus4 = []
for i in range(0, len(min_dis_depot)):
if min_dis_depot[i][0] == 0:
cus1.append(i + 1)
if min_dis_depot[i][0] == 1:
cus2.append(i + 1)
if min_dis_depot[i][0] == 2:
cus3.append(i + 1)
if min_dis_depot[i][0] == 3:
cus4.append(i + 1)
customer_means.append(cus1)
customer_means.append(cus2)
customer_means.append(cus3)
customer_means.append(cus4)
for i in range(len(customer_means)):
customers = customer_means[i]
mans = len(customers)
dep_num = i + 1 + 40
rows = 100
times = 300
genetic = GeneticAlgorithm(dep_num, customers, dis_dep_cus[i],
dis_sta_cus, dis_cus_cus, min_dis_station,
demand, time_win, tolerate_time_win, mans,
rows, times)
genetic.run()
def draw_table_vol():
pl.ioff()
contents = readfile.readfile("000007.csv")
high, low = readfile.get_prices(contents)
ranges = range(len(contents))
prices = [content[2] for content in contents]
table_x = list()
table_y = list()
high, low = readfile.get_vol(contents)
for x, content in enumerate(contents):
table_x.append(x)
table_y.append(float(content[1]))
pl.plot(table_x, table_y, "-")
pl.show()
def tests(net,
tims=-1,
aimat=float(0.0),
readf=readfile.readfile(),
logs=None):
aimat = float(aimat)
net.eval()
correct = 0
total = 0
tot = 0
for data in readf.testsetsmallloader:
images, labels = data
outputs = net(Variable(images, volatile=True))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
if total > 100:
break
print('现实 : %d %%' % (100 * correct / total))
logs.refline((100 * correct / total), "现实正确率")
if float(100 * correct / total) > float(aimat):
aimat = float(aimat)
aimat = float(100 * correct / total)
print("正确率" + str(float(aimat)) + "刷新数据")
torch.save(net, "./mod2/handalexnetmax34" + str(tims))
# net.state_dict(),
correct = 0
total = 0
for data in readf.testselfloader:
tot += 1
images, labels = data
outputs = net(Variable(images, volatile=True))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
if tot > 100:
break
print('自身 : %d %%' % (100 * correct / total))
logs.refline((100 * correct / total), "自身正确率")
return float(aimat)
def __init__(self,):
tablist=readfile()
self.values=[]
for i in range(3):
value=tuple(tablist[0])
del tablist[0]
self.values.append(value)
self.pfdict=dict(tablist)
try:
self.conn=pymysql.connect(host=self.pfdict["host"],
port=int(self.pfdict['port']),
user=self.pfdict['user'],
passwd=self.pfdict['passwd'],
db=self.pfdict['db'],
charset=self.pfdict['charset'])
self.cur=self.conn.cursor()
except:
print("连接数据库失败！")
else:
print("连接数据库成功！")
def polygon_color(file):
# Read in input file and arrange points in desired format
result = readfile.readfile(file)
poly = result['poly1']
px = [poly[i][0] for i in range(len(poly))]
py = [poly[i][1] for i in range(len(poly))]
# Sort the x and y coordinates
x_sorted = np.sort(px)
y_sorted = np.sort(py)
# Call functions and plot
plt.cla()
plt.axis('equal')
plt.grid('on')
plt.hold(True)
colors = plot_boundary(poly)
flood_fill(int(np.median(px)), int(np.median(py)), 'w', 'g', x_sorted[0],
y_sorted[0], colors)
plt.show(block=False)
def __init__(self, ):
tablist = readfile()
self.values = []
for i in range(3):
value = tuple(tablist[0])
del tablist[0]
self.values.append(value)
self.pfdict = dict(tablist)
try:
self.conn = pymysql.connect(host=self.pfdict["host"],
port=int(self.pfdict['port']),
user=self.pfdict['user'],
passwd=self.pfdict['passwd'],
db=self.pfdict['db'],
charset=self.pfdict['charset'])
self.cur = self.conn.cursor()
except:
print("连接数据库失败！")
else:
print("连接数据库成功！")
def test_readcsv(capsys):
dirpath = Path("C:/code/cohort4/python-IO")
filename = "Census_by_Community_2019.csv"
hdr = "************************************************************\n"
hdr += "************** Calgary Public Data Summary ***************\n"
hdr += "************************************************************\n"
content1 = "Residential and SOUTH: 230129"
content2 = "Industrial and NORTH: 0"
content3 = "Overall Total: 1283177"
content4 = "Total number of records: 307"
ftr = "************************************************************\n"
ftr += "*************** End of Report Summary ********************\n"
ftr += "************************************************************\n"
result = readcsv(dirpath, filename)
captured = capsys.readouterr()
reportdata = readfile(dirpath, "report.txt")
assert result == {
"keyvaluepair": {
"RES_CNT": 10,
"CLASS": 1,
"SECTOR": 5
},
"linenum": 308
}
assert hdr in captured.out
assert ftr in captured.out
assert content1 in captured.out
assert content2 in captured.out
assert content3 in captured.out
assert content4 in captured.out
assert os.path.isfile(os.path.join(dirpath, "report.txt")) == True
assert content1 in reportdata
assert content2 in reportdata
assert content3 in reportdata
assert content4 in reportdata
'pricing': 0.05,
'blog': 0.5,
'payment': 1.0
},
}
id_to_states = {
0: 'Zero',
1: 'Aware',
2: 'Considering',
3: 'Experiencing',
4: 'Ready',
5: 'Lost',
6: 'Satisfied'
}
obs_seq, obs_seq_id = readfile("hmm_customer_1586733275338.txt")
new_emmi_prop = new_emmi_prop(emit_p, obs_seq)
tran_prob = convert_to_list(trans_p)
emmi_prob = convert_to_list(new_emmi_prop)
init_prob = np.array(list(start_p.values()))
hidden_s, max_prob = viterbi(tran_prob, emmi_prob, init_prob, obs_seq_id)
opt_seq = []
for data in hidden_s:
opt_seq.append(id_to_states[data])
print("Observation sequence : " + str(obs_seq))
print()
print("Transition Probability: " + str(trans_p))
print()
def chat():
# keyword conditions
condnext = False
condweather = False
condtime = False
condlocation = False
condtemp = False
condkey = False
condresponse = False
foundinfo = False
condtrain = False
condcountry = False
condspellcheck = True
# global variables
conversation = []
location = ''
prevlocation = location
time = 'today'
key = ''
keytemplate = []
fulltime = ''
numdays = ''
logstr = ''
printstr = ''
responsedict = {} # Dictionary to hold all inputs without predefined responses. This dictionary will be written into predefined_responses.txt before exiting the program.
# read data files
citylist = readfile.readfile('cities.txt')
keylist = readfile.readfile('keywords.txt')
timelist = readfile.readfile('time.txt')
condlist = readfile.readfile('conditions.txt')
numlist = readfile.readfile('numbers.txt')
countrylist = readfile.readfile('countries.txt')
exitlist = ['exit', 'quit', 'bye', 'ok']
# Greeting message
printstr = 'Hello! You can ask me questions about the weather in any major city in the world. What would you like to know?'
print printstr
logstr += '\n\n' + printstr
# Start main loop
while True :
foundinfo = False
condtrain = False
condcountry = False
# read input from user
input = raw_input('\nMe > ')
logstr += '\nMe > ' + input + '\nBot > '
if input in exitlist:
if input == 'ok':
exitans = raw_input("Do you want to quit? (y/n)")
if exitans in ('y','Y','Yes','YES','yes'):
break
else:
continue
break
if input == 'disable spellcheck':
condspellcheck = False
continue
if input == 'enable spellcheck':
condspellcheck = True
continue
condcorrected = False
if condspellcheck:
corrected_input = ''
for i in input.split():
str = spellcheck.correct(i)
if str != i:
condcorrected = True
corrected_input += str + ' '
if condcorrected:
print 'did you mean: \"' + corrected_input + '\"?'
input = corrected_input
currentstring = input.split()
conversation.append(currentstring)
# Start searching input for each of the keywords
if input == 'train':
condtrain = True
printstr = 'Entering training mode. Enter input and response seperated by a "|": input|response. Type "exit" to quit training mode'
print printstr
logstr += '\n' + printstr + '\n'
while True:
traininput = raw_input('>')
if traininput == 'exit':
break
if traininput.find('|') < 0:
printstr = 'Format error: use input|response'
print printstr
logstr += '\n' + printstr + '\n'
continue
traininput = traininput.split('|')
responsedict[traininput[0]] = traininput[1]
if condtrain:
continue
for i in countrylist:
for j in currentstring:
if lower(i[0]) == lower(j):
printstr = 'Which city in ' + i[0] + '?'
condcountry = True
foundinfo = True
break
if condcountry:
print printstr
logstr += printstr
continue
if 'next' in input:
foundinfo = True
condnext = True
condtime = False
numdays = currentstring[currentstring.index('next') + 1]
for i in numlist:
if numdays == i[0]:
numdays = i[1]
break
if re.match('[0-9]*$',numdays):
numdays = int(numdays)
else:
numdays = ''
if 'weather' in input:
foundinfo = True
condweather = True
condkey = False
condtemp = False
key = ''
keytemplate = []
# get key from input
for i in keylist:
if i[0] in input:
if 'sunday' in lower(input) and i[0] == 'sun':
break
else:
foundinfo = True
condkey = True
condweather = False
condtemp = False
key = i[0]
keytemplate = i
break
# get time from input
for i in timelist:
if lower(i[0]) in input:
foundinfo = True
condtime = True
numdays = ''
if lower(i[0]) != 'today' and lower(i[0]) != 'tomorrow':
time = i[1]
fulltime = i[0]
break
else:
time = i[0]
fulltime = time
break
if fulltime == '':
fulltime = time
if numdays != '':
condtime = True
if numdays > 4:
printstr = 'Forecast is available only for the next 4 days.'
print printstr
logstr += '\n' + printstr + '\n'
else:
time = ''
fulltime = ''
count = numdays
# get location from input
for i in citylist:
if lower(i[0]) in input:
foundinfo = True
condlocation = True
location = i[0]
break
# find if a new location has been mentioned. if not, don't fetch data again
if location != prevlocation:
newlocation = True
condlocation = True
prevlocation = location
else:
newlocation = False
if location is '':
if prevlocation is '':
condlocation = False
else:
location = prevlocation
newlocation = False
location = location.replace(' ','-') #Google requires a '-' in 2-word city names
result = False
# get temperature from input
if 'temperature' in input:
foundinfo = True
condtemp = True
# User gave no infomation about weather. Switching to general predefined response based chat
if not foundinfo:
response = predefined_responses.respond(input, responsedict)
if response == '':
printstr = "I don't know what that means. If I asked you the same question, what would you reply?"
print printstr
logstr += printstr
responseinput = raw_input('Me > ')
logstr += '\nMe > ' + responseinput
if not responseinput in ('exit', 'quit'):
responsedict[input] = responseinput
print 'response learnt'
else:
printstr = response
print printstr
logstr += printstr
continue
if condlocation:
if newlocation: #If location hasn't changed, don't fetch data again. It's already available
printstr = 'Fetching weather information from Google...'
print printstr
logstr += printstr
# Call Google weather to get current weather conditions
google_result = weather.get_weather(location)
if google_result == {}:
print 'Could not get data from google.'
continue
# We have a valid location. Get further information
# User has asked about temperature. Return temperature information and continue
if condtemp:
printstr = temperature.temperature(google_result, time)
print printstr
logstr += printstr
continue
# User has asked about a specific weather condition. Print information. There are 2 possibilities:
# 1. Find the condition in the next n days
# 2. Find the condition in a specified day
if condkey:
# 1. User has asked about a specific condition in the 'next x days'. Return appropriate response
printstr = ''
timecounter = 0
day_of_week = ''
condition = ''
if numdays != '':
for i in google_result['forecasts']:
count -= 1
if count < 0:
break
if key in lower(i['condition']):
result = True
day_of_week = i['day_of_week']
condition = i['condition']
break
for i in timelist:
if i[0] != 'today' and i[0] != 'tomorrow':
if i[1] == day_of_week:
fulltime = i[0]
break
if result:
printstr = keytemplate[3] + keytemplate[0] + ' on ' + fulltime
else:
printstr = keytemplate[4] + keytemplate[0] + ' in the next ' + str(numdays) + ' days.'
print printstr
logstr += printstr
continue
# 2. User has asked about a particular condition on a particular day. Return appropriate response
if time != 'today' and time != 'tomorrow':
for i in google_result['forecasts']:
if i['day_of_week'] == time:
if key in lower(i['condition']):
printstr = keytemplate[3] + keytemplate[0] + ' on'
else:
printstr = keytemplate[4] + keytemplate[0] + ' on'
elif time == 'today':
fulltime = time
if key in lower(google_result['current_conditions']['condition']):
printstr = keytemplate[1] + keytemplate[0]
else:
printstr = keytemplate[2] + keytemplate[0]
elif time == 'tomorrow':
fulltime = time
if key in lower(google_result['forecasts'][1]['condition']):
printstr = keytemplate[3] + keytemplate[0]
else:
printstr = keytemplate[4] + keytemplate[0]
printstr = printstr + ' ' + fulltime
print printstr
logstr += printstr
continue
# User is asking about today's weather. Print details
elif time == '' or time == 'today' :
printstr = sentence.sentence(google_result['current_conditions']['condition'], time)
printstr += ' ' + fulltime + '. ' + google_result['current_conditions']['humidity'] + ' '
if google_result['current_conditions'].has_key('wind_condition'):
printstr += google_result['current_conditions']['wind_condition']
print printstr
logstr += printstr
continue
# User is asking about weather of a particular day. Print details
elif time == 'tomorrow':
printstr = sentence.sentence(google_result['forecasts'][1]['condition'], time)
printstr += ' ' + fulltime
print printstr
logstr += printstr
else:
found = False
for i in range(4):
if google_result['forecasts'][i]['day_of_week'] == time:
printstr = sentence.sentence(google_result['forecasts'][i]['condition'], time)
printstr += " on" + ' ' + fulltime
print printstr
logstr += printstr
found = True
if not found:
printstr = "Forecast for " + time + " is not available currently."
print printstr
logstr += printstr
continue
else:
printstr = 'What\'s the location?'
print printstr
logstr += printstr
# End of outermost while loop.
# Print message before exiting program
dictcount = 0
for i in responsedict:
dictcount += 1
if dictcount > 0:
printstr = 'Writing new entries to database...'
print printstr
logstr += printstr
datafile = file('predefined_responses.txt', 'a')
for i in responsedict.keys():
trimmedi = re.sub('[^a-zA-Z0-9 ]+','', i)
string = trimmedi + '|' + responsedict[i] + '\n'
datafile.write(string)
log.log(logstr)
print 'Ending the program...'
print 'Bye!'
# End of function chat()
#!/usr/bin/env python #python import logsend import readfile import configure import judge configure = configure.Configure() first = configure.readoption judge = judge.Judge(filename) filename = 'aa.txt' readfile.readfile(filename)
if __name__ == "__main__":
fileNames = [
"a_example.txt", "b_read_on.txt", "c_incunabula.txt",
"d_tough_choices.txt", "e_so_many_books.txt",
"f_libraries_of_the_world.txt"
]
for fileName in fileNames:
Data.nBooks = 0
Data.nLibraries = 0
Data.nScanningDays = 0
Data.bookScores = []
Data.libraries = []
Running.currentProcessing = None # The current library being processed
Running.daysLeft = 0 # How many days are left for the current library to be processed
Running.librariesLeft = [] # The libraries still to be processed
Running.processed = [] # Librarues that have been processed
Running.numberProcessed = 0
Running.totalScore = 0 # The total score
readfile("data/{}".format(fileName))
sortLibraries()
for lib in Data.libraries:
lib.sortBooks()
print("Read/sort files")
simulate(Data.libraries, Data.nScanningDays,
"out/out_{}.txt".format(fileName[0]))
# from mtranslate import translate
from writefile import writefile
from readfile import readfile
from fileLocation import fileLocation
from languages import LANGUAGES
import urllib
from googleapiclient.discovery import build
# translator = Translator()
# print(LANGUAGES)
intents = readfile('en/intent.txt')
# print(intents)
service = build('translate', 'v2',
developerKey='AIzaSyCN5X2fEWsdO4jRSgwB_PZ3v_0A4HbmXCY')
for key, value in LANGUAGES.items():
print (key, value)
list = []
translated = service.translations().list(
source='en',
target=key,
q=intents
).execute()
for tranlation in translated['translations']:
list.append(tranlation['translatedText'])
# break
writefile(key, list)
# print(key + ' : ' , list)
# break
import sys
from readfile import readfile
fileA = sys.argv[1]
fileB = sys.argv[2]
mA = readfile(fileA)
mb = readfile(fileB)
def guass(a, b):
n = len(a)
for k in range(0, n - 1):
for i in range(k + 1, n):
if a[i][k] != 0.0:
lam = a[i][k] / a[k][k]
print(lam)
#a[i,k+1:n] = a[i, k+1:n] - lam*a[k,k+1:n]
#b[i] = b[i] - lam*b[k]
guass(mA, mb)
#coding=utf-8
'''
Created on 2016年8月16日
@author: admin
'''
from fabric.api import *
from readfile import readfile
condict = dict(readfile())
env.host_string = condict["connext"]
env.password = condict["passwd"]
run("rm -rf /taokey")
run("yum -y remove mysql-libs*")
run("yum -y remove mysql-libs")
run("yum install -y perl-Module-Install.noarch")
run("yum install -y libaio")
run("mkdir -p /taokey/tools/")
with cd("/taokey/tools/"):
run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-server-5.6.21-1.rhel5.x86_64.rpm"
)
run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-devel-5.6.21-1.rhel5.x86_64.rpm"
)
run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-client-5.6.21-1.rhel5.x86_64.rpm"
)
rpmlist = run("ls ./").split()
for rpm in rpmlist:
def __init__(self):
self.tab=dict(readfile())
def bot():
conversation = []
location = ''
time = 'today'
key = ''
keytemplate = []
fulltime = ''
numdays = ''
citylist = readfile.readfile('cities.txt')
keylist = readfile.readfile('keywords.txt')
timelist = readfile.readfile('time.txt')
condlist = readfile.readfile('conditions.txt')
numlist = readfile.readfile('numbers.txt')
exitlist = ['exit', 'quit', 'bye', 'ok']
print 'Hello! You can ask me questions about the weather in any major city in the world. What would you like to know?'
while True :
input = raw_input('Me > ')
if input in exitlist:
break
currentstring = input.split()
conversation.append(currentstring)
if 'next' in currentstring:
numdays = currentstring[currentstring.index('next') + 1]
for i in numlist:
if numdays == i[0]:
numdays = i[1]
break
if re.match('[0-9]*$',numdays):
numdays = int(numdays)
else:
numdays = ''
if 'weather' in currentstring:
key = ''
keytemplate = []
# get key from input
for i in keylist:
if i[0] in input:
key = i[0]
keytemplate = i
break
# get time from input
for i in timelist:
if lower(i[0]) in input:
numdays = ''
if lower(i[0]) != 'today' and lower(i[0]) != 'tomorrow':
time = i[1]
fulltime = i[0]
break
else:
time = i[0]
fulltime = time
break
if fulltime == '':
fulltime = time
if numdays != '':
if numdays > 4:
print 'Forecast is available only for the next 4 days.'
else:
time = ''
fulltime = ''
count = numdays
prevlocation = location
#We store previous location to avoid re-fetching data if the location hasn't been changed
# Below, we check if any token in the input matches a city name, and if so, set location to that city
newlocation = False
# get location from input
foundLocation = False
for i in citylist:
if lower(i[0]) in input:
location = i[0]
foundLocation = True
break
#if not foundLocation:
#if location != '':
#print "I didn't find any city name in your input. I'll get you information about " + location
# find if a new location has been mentioned. if not, don't fetch data again
if location is not prevlocation:
newlocation = True
if location is '':
if prevlocation is '':
print 'City not found'
else:
location = prevlocation
newlocation = False
location = location.replace(' ','-') #Google requires a '-' in 2-word city names
result = False
if location is not '':
if newlocation: #If location hasn't changed, don't fetch data again. It's already available
print 'Fetching weather information from Google...'
# Call Google weather to get current weather conditions
google_result = weather.get_weather(location)
if 'temperature' in currentstring:
print temperature.temperature(google_result, time)
continue
printed = False
if key is not '':
printstring = ''
timecounter = 0
day_of_week = ''
condition = ''
if numdays != '':
for i in google_result['forecasts']:
count -= 1
if count < 0:
break
if key in lower(i['condition']):
result = True
day_of_week = i['day_of_week']
condition = i['condition']
break
for i in timelist:
if i[0] != 'today' and i[0] != 'tomorrow':
if i[1] == day_of_week:
fulltime = i[0]
break
if result:
printstring = keytemplate[3] + keytemplate[0] + ' on ' + fulltime
else:
printstring = keytemplate[4] + keytemplate[0] + ' in the next ' + str(numdays) + ' days.'
print printstring
printed = True
if not printed:
if time != 'today' and time != 'tomorrow':
for i in google_result['forecasts']:
if i['day_of_week'] == time:
if key in lower(i['condition']):
printstring = keytemplate[3] + keytemplate[0] + ' on'
else:
printstring = keytemplate[4] + keytemplate[0] + ' on'
elif time == 'today':
fulltime = time
if key in lower(google_result['current_conditions']['condition']):
printstring = keytemplate[1] + keytemplate[0]
else:
printstring = keytemplate[2] + keytemplate[0]
elif time == 'tomorrow':
fulltime = time
if key in lower(google_result['forecasts'][1]['condition']):
printstring = keytemplate[3] + keytemplate[0]
else:
printstring = keytemplate[4] + keytemplate[0]
print printstring, fulltime
elif time == '' or time == 'today' :
printstring = sentence.sentence(google_result['current_conditions']['condition'], time)
print printstring, fulltime, google_result['current_conditions']['humidity'], google_result['current_conditions']['wind_condition']
else :
if time == 'tomorrow':
printstring = sentence.sentence(google_result['forecasts'][1]['condition'], time)
print printstring, fulltime
else:
found = False
for i in range(4):
if google_result['forecasts'][i]['day_of_week'] == time:
printstring = sentence.sentence(google_result['forecasts'][i]['condition'], time)
print printstring, "on", fulltime
found = True
if not found:
print "Forecast for " + time + " is not available currently."
else:
print 'What\'s the location?'
#end of outermost while loop
print 'ending the program...'
print 'bye!'
#!/usr/bin/python3
from readfile import readfile
from emailsetting import sendmail
""" Buscar por nombre y estado"""
search = 'Cassandra'
enable = 'Activo'
""" Lectura de datos """
query = 'SELECT * FROM test'
""" Obteniendo informacion de lectura """
file = readfile(1, query)
""" Realizando busqueda """
file = file[file['name'].str.contains(search)]
""" Enviar corrreo """
for reg in file.itertuples():
name = reg[1]
receiver_mail = reg[2]
mail2 = reg[3]
subject = reg[4]
body = reg[5]
attachment = reg[8]
filemail = reg[9]
sendmail('*****@*****.**', receiver_mail, name, subject, body, attachment, filemail)
from readfile import readfile
import linecache
import string
OCCURENCE_LIST_PATH = "./Occurence_List.dat" ## occurence list save address
if __name__ == '__main__':
keyTrie = readfile()
print("FILE LOAD FINISH.")
print("##################################")
print("INPUT -h FOR HELP")
print("INPUT -q TO QUIT THE SEARCH ENGINE")
print(
"This search engine support multiple search. If you want to search multiple keyword, you should use space to separate the keywords."
)
print("Ex: 'nltk data'")
print("This search engine also support prefix search.")
print(
"Ex: We have 'data', 'database' and 'dat' in trie. We input 'da', algorithm will return the most frequently in data, database and dat."
)
print("##################################\n")
lastSearchKey = ""
lastSearchResult = []
while (True):
inputString = input("\nWhat key word you want to search now: ")
inputString = inputString.strip()
###### INSTRUCTION ######
if inputString == "-h" or inputString == "-H" or inputString == "-help":
print("------- HELP LIST: -------")
print("-h -H -help ======> HELP")
print("-q -Q -quit ======> TO QUIT THE SEARCH ENGINE")
import clips,readfile,sys
rules = [rule.strip() for rule in readfile.readfile(sys.argv[1])]
provinces = [province.strip() for province in readfile.readfile(sys.argv[2])]
for province in provinces:
clips.SendCommand(province)
clips.Assert("(province Bangkok)")
clips.Run()
for rule in rules:
clips.SendCommand(rule)
clips.Assert("(kind round shaped rice)")
print "condition : "
clips.PrintFacts()
#BEFORE
bf = len(clips.FactList())
#RUN
clips.Run()
#AFTER
af = len(clips.FactList())
for i in range(af - (af - bf)):
#print "retract Fact",i
clips.FactList()[0].Retract()
#-------------入口函数，开始执行-----------------------------
"""
输入参数的的意义依次为
self.rows = rows #排列个数
self.times = times #迭代次数
self.mans = mans #客户数量
self.cars = cars #车辆总数
self.tons = tons #车辆载重
self.distance = distance #车辆一次行驶的最大距离
self.PW = PW #当生成一个不可行路线时的惩罚因子
"""
d, q, et, lt, eet, llt = readfile("sss.csv")
ga = GeneticAlgorithm(dist=d,
dema=q,
e_time=et,
l_time=lt,
ee_time=eet,
ll_time=llt,
rows=5,
times=5,
depots=3,
mans=34,
cars=999,
tons=100,
distance=150,
server=10,
PW=10000)
import numpy as np
from gridworld import grid_mat, print_policy ,print_values, all_actions_ingrid, all_rewards_ingrid
from value_iteration_p3 import value_iteration
from policy_iteration_p3 import policy_iteration
from readfile import readfile
import time
if __name__ == '__main__':
# Enter the file name for which you want to excute value and policy iteration.
gamma, noise, gridworld = readfile("Input/i3.txt")
gridworld = np.asarray(gridworld)
all_actions = all_actions_ingrid(gridworld)
all_rewards = all_rewards_ingrid(gridworld)
grid = grid_mat(all_actions, all_rewards)
#Value Iteration
start = time.time()
V,policy = value_iteration(grid,gamma,noise)
end = time.time()
print("Values:")
print_values(V, gridworld.shape)
print("Policy:")
print_policy(policy, gridworld.shape)
print("Runtime of Value iteration", end-start)
#Policy Iteration
start = time.time()
#coding=utf-8
'''
Created on 2016年8月16日
@author: admin
'''
from fabric.api import *
from readfile import readfile
import sys
condict=dict(readfile())
env.host_string=condict["connext"]
env.password=condict["passwd"]
file="linux_mysql_install.log"
path_file=condict["log_path"]+file
f=open(path_file,"w+",1)
sys.stdout=f
def mysql_install():
run("rm -rf /taokey")
run("yum -y remove mysql-libs*")
run("yum -y remove mysql-libs")
run("yum install -y perl-Module-Install.noarch")
run("yum install -y libaio")
run("mkdir -p /taokey/tools/")
with cd("/taokey/tools/"):
run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-server-5.6.21-1.rhel5.x86_64.rpm")
run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-devel-5.6.21-1.rhel5.x86_64.rpm")
run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-client-5.6.21-1.rhel5.x86_64.rpm")
def trains(self, net):
self.logs = logger.logger()
# logs.refline(1,"2334",1)
# logs.refline(4,"2334",5)
# logs.refline(1,"2334",6)
# logs.refline()
readf = readfile.readfile()
aimat = float(0.0)
# test.tests(net, 0, float(aimat), readf, self.logs)
net.train(mode=True)
torch.set_num_threads(8)
criterion = nn.CrossEntropyLoss(
) # use a Classification Cross-Entropy loss
optimizer = optim.SGD([{
'params': net.features.parameters(),
'lr': 0.00001
}, {
'params': net.lin.parameters(),
'lr': 0.001
}, {
'params': net.classifier.parameters(),
'lr': 0.0005
}],
momentum=0.9)
# scheduler = MultiStepLR(optimizer, milestones=[10, 80], gamma=0.1)
tot = 0
for epoch in range(2000): # loop over the dataset multiple times
# scheduler.step()
running_loss = 0.0
net.train(mode=True)
for i, data in enumerate(readf.dataloader, 0):
tot += 1
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data[0]
if i % 25 == 24: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 25))
self.logs.defx = tot
# print(self.logs.defx)
self.logs.refline(running_loss / 25 * 100, "loss")
running_loss = 0.0
aimat = float(aimat)
aimat = test.tests(net, epoch, float(aimat), readf,
self.logs)
net.train(mode=True)
torch.save(net, "./mod2/handalexnet34")
print("saved")
print('Finished Training')
import numpy as np
import preprocess_data as p
import readfile as r
import cal_probability as cd
import naivebayes as nb
data_train = r.readfile("Text_data_for_Project1_train_data.txt")
data_test = r.readfile("Text_Data_for_Project1_test_data.txt")
alabel_train, clabel_train, adata_train, cdata_train = p.p_traindata(
data_train)
alabel_test, adata_test = p.p_testdata(data_test)
atraintestmix = np.vstack((adata_train, adata_test))
att_num = np.shape(adata_train)[1]
dat_num = np.shape(adata_train)[0]
adata_traintest_dummy, a_label = p.indexing(atraintestmix,
att_num,
dat_num,
key="test")
adata_train_dummy = adata_traintest_dummy[:-1, :]
adata_test_dummy = np.array([adata_traintest_dummy[-1, :]])
cdata_train_dummy, c_label = p.indexing(cdata_train, 1, dat_num, key="test")
m, p = 0, 0
prior_probability = cd.cal_prior(adata_train_dummy, cdata_train_dummy, a_label,
c_label, dat_num, m, p)
from Block import Block
from Cube import Cube
from Drone import Drone
from readfile import readfile
import math
import sys
filename = sys.argv[1]
cube = readfile(filename)
size = cube.size
sizec = size**3
mode = 1
stop = 0
storHop = int(math.sqrt(sizec) / 2)
drone = Drone(storHop, cube, 0, 0, size)
print(cube)
drone.hop()
x_limit = size - 1
y_limit = size - 1
def mover(mode, x_limit, y_limit, stop):
if (mode == 1):
drone.MoveRight()
if (drone.x == x_limit):
mode = 3
elif (mode == 2):
drone.MoveLeft()
if (drone.x == size - (x_limit + 1)):
mode = -1
elif (mode == 3):
for v in range(voca_size):
fout.write(str(beta[i][v]) + ' ');
fout.write('\n');
def train(max_iter):
global alpha,beta,Gamma,Phi,doc,doc_cnt;
for i in range(max_iter):
now = mle();
print(now);
print('Estep');
for d in range(doc_size):
Estep(d, 20); # the e step of em algorithm
if (d % 100 == 0):
print('*');
now = mle();
print(now);
print('Mstep');
Mstep(20); # the m step of em algorith
print(alpha);
savemodel(i);
if __name__ == '__main__':
fin = readfile('ap.dat');
doc, voca, doc_cnt = fin.read();
voca_size = len(voca);
doc_size = len(doc);
init();
train(10);
from KEM import settings
from django.core.management import setup_environ
setup_environ(settings)
from rice.models import Rice
import sys,readfile
rules = [rule.strip() for rule in readfile.readfile("kem.csv")]
for i in rules:
i = i.split(',')
r = Rice(name=i[0],kind=i[1],number=i[2],area=i[3],terrain=i[4],character=i[5],seed=i[6],detail=i[7],pic=i[8],ref=i[9])
r.save()
print Rice.objects.all()
ls_nsub = []
ls_subset = []
ls_rel = []
ls_times = []
lbound = []
nelements = []
nsubsets = []
means = ['Mean']
# The first two values printed on the list correspond to the total cost and
# the numbers of subsets chosen respectively
for name in files:
# Read file
path = os.path.join(datasets_path, name)
df, costs = readfile(path)
nelements.append(df.shape[0])
nsubsets.append(df.shape[1])
# Lower bound
lb = lowerbound(df, costs)
lbound.append(lb)
# VND
print('GA')
start_ga = time.perf_counter()
ga_cost, ga_subsets = GA(df, costs, npop, maxtime, nchilds, pmut)
time_ga = time.perf_counter() - start_ga
print('C:\t', [ga_cost, len(ga_subsets)] + ga_subsets, '\t', time_ga)
ga_scores.append(ga_cost)
ga_rel.append(np.float32(np.round(ga_cost / lb, 3)))
import sys,readfile
class rule(object):
def __init__(self,name,kind,terrain,area,count):
self.name = name
self.terrain = terrain
self.area = area
self.kind = kind
self.count = count
def getrule(self):
return "(defrule {0}.{4} \"{0}\" (kind {1}) (terrain {2}) (area {3}) => (assert (rice {0})))\n".format(self.name,self.kind,self.terrain,self.area,self.count)
data = [i.strip() for i in readfile.readfile(sys.argv[2])]
lst = []
for i in data:
x = i.split(",")
lst.append([x[0],x[1],x[3],x[4]])
print lst
for i in lst:
with open(sys.argv[1],"a") as data:
name = i[0]
kind = i[1]
area = i[2]
terrain = i[3]
count = 0
for k in area.split("/"):
for j in terrain.split("/"):
tmp_rule = rule(name,kind,j.lower(),k.title(),count)
count+=1
print tmp_rule.getrule()
data.write(tmp_rule.getrule())
