def doHttpGet(self, extPath):
headers = {'User-Agent': self.USER_AGENT, 'Username': self.get_user_name()}
if self.username is not None:
auth = 'Basic ' + string.strip(base64.encodestring(self.username + ':' + self.password))
headers['Authorization'] = auth
try:
httpServ = httplib.HTTPConnection(self.hostname, self.port)
httpServ.request('GET', self.context + extPath, None, headers)
response = httpServ.getresponse()
return response
except httplib.HTTPException:
print "ERROR! Looks like the server is not running on " + self.hostname
exit()
def doHttpGet(self, extPath):
headers = {
'User-Agent': self.USER_AGENT,
'Username': self.get_user_name()
}
if self.username is not None:
auth = 'Basic ' + string.strip(
base64.encodestring(self.username + ':' + self.password))
headers['Authorization'] = auth
try:
httpServ = httplib.HTTPConnection(self.hostname, self.port)
httpServ.request('GET', self.context + extPath, None, headers)
response = httpServ.getresponse()
return response
except httplib.HTTPException:
print "ERROR! Looks like the server is not running on " + self.hostname
exit()
def doHttpPost(self, extPath, postdata='', headers=None):
if postdata and self.message:
postdata += '&YRS_MESSAGE=' + str(self.message)
if not headers:
headers = {}
headers['User-Agent'] = self.USER_AGENT
headers['Username'] = self.get_user_name()
if self.username is not None:
auth = 'Basic ' + string.strip(base64.encodestring(self.username + ':' + self.password))
headers['Authorization'] = auth
try:
httpServ = httplib.HTTPConnection(self.hostname, self.port)
httpServ.connect()
httpServ.request('POST', self.context + extPath, postdata, headers)
response = httpServ.getresponse()
return response
except httplib.HTTPException:
print "ERROR! Looks like the server is not running on " + self.hostname
exit()
def __init__(self, AOT_DIR):
""" Загрузка DLL. """
print 'RML is', os.environ.get('RML')
# os.environ['RML'] = AOT_DIR # нужно бинарникам АОТ для работы. Они тупые.
# интерфейс доступа к библиотечке
self.gra_dll = ctypes.cdll.LoadLibrary(os.path.join(AOT_DIR, "sentencer.dll"))
r = self.gra_dll.init_lib()
if r:
print "init error", r
exit()
print "SENTENCER LOAD COMPLETE"
self.gra_dll.next_sentence.restype = ctypes.c_char_p
self.ready = True
def doHttpPost(self, extPath, postdata='', headers=None):
if postdata and self.message:
postdata += '&YRS_MESSAGE=' + str(self.message)
if not headers:
headers = {}
headers['User-Agent'] = self.USER_AGENT
headers['Username'] = self.get_user_name()
if self.username is not None:
auth = 'Basic ' + string.strip(
base64.encodestring(self.username + ':' + self.password))
headers['Authorization'] = auth
try:
httpServ = httplib.HTTPConnection(self.hostname, self.port)
httpServ.connect()
httpServ.request('POST', self.context + extPath, postdata, headers)
response = httpServ.getresponse()
return response
except httplib.HTTPException:
print "ERROR! Looks like the server is not running on " + self.hostname
exit()
def main():
parser = optparse.OptionParser('usage %prog -H <tgtHost> -u <user> -d <direction>')
parser.add_option('-H', dest='tgtHost', type='string', help='specify target host')
parser.add_option('-u', dest='user', type='string', help='user 用户')
parser.add_option('-d', dest='direction', type='string', help='弱密钥 所在目录')
(options,args) = parser.parse_args()
tgtHost = options.tgtHost
user = options.user
direction = options.direction
if tgtHost == None or user == None or direction == None:
print options.usage
exit(0)
for fileName in os.listdir(direction):
if Stop:
print('[*] Exiting: Key Found.')
exit(0)
if Fails > 5:
print('[!] Exiting: Too Many Connections Closed By Remote Host.')
print('[!] Adjust number of simultaneous threads.')
exit(0)
connection_lock.acquire()
fullPath = os.path.join(direction,fileName)
print('[-] Testing keyfile ' + str(fullPath))
thread_temp = threading.Thread(target=connect, args=(user, tgtHost, fullPath, True))
thread_temp.start()
def main():
# # Part 1: Load data
#
# DIR_categories=os.listdir('../input_data/training/'); # list and store all categories (classes)
# allFeatures=[] # this list will store all training examples
# imLabels=[] # this list will store all labels
# labelCount=0; # this integer will effectively be the class label in our code (i = 1 to 101)
# labelNames = [] # we will need the label names in order to plot their cardinalities later on
# labelCardinalities = [] # see above
# for cat in DIR_categories: # loop through all categories
# if os.path.isdir('../input_data/training/'+ cat):
# labelNames.append(cat)
# labelCount=labelCount+1; # i = current class label
# count = 0
#
# DIR_image=os.listdir('../input_data/training/'+ cat +'/'); # store all images of category "cat"
# for im in DIR_image: # loop through all images of the current category
# if (not '._image_' in im): # protect ourselves against those pesky Mac OS X - generated files
# F = np.genfromtxt('../input_data/training/'+cat+'/'+im, delimiter=' '); # F is now an 2-D numpy ndarray holding all features of an image
# F = np.reshape(F,21*28); # F is now a 588 - sized 1-D ndarray holding all features of the image
# F = F.tolist(); # listify the vector
# F.append(labelCount) # we'd like to store the label alongside the example
# count = count + 1
# allFeatures.append(F); # store the vector
# imLabels.append(labelCount); # store the label
# labelCardinalities.append(count)
# print "training data loaded!"
# Store some data on disk so we don't have to
# re-read it every time.
# print " We will now count the counts of all classes to see whether something's wrong with them"
# exIndex = 1
# for label in range(1, labelCount + 1):
# examplesOfLabel = [examples for examples in allFeatures if examples[-1] == label]
# print "There are: " + str(len(examplesOfLabel)) + " examples of class " + str(label)
#
# print "We will now exit"
# exit()
# try:
# fp = open("../proc_data/trainingData.pdat",'wb')
# pk.dump(allFeatures, fp)
# fp.close()
# except Exception as e:
# print 'Pickling failed for object allFeatures: Exception: ' + e.message
# exit()
#
# print "Training data stored on disk"
#
# try:
# fp = open("../proc_data/labelCount.pdat",'wb')
# pk.dump(labelCount, fp)
# fp.close()
# except Exception as e:
# print 'Pickling failed for object labelCount: Exception: ' + e.message
# exit()
#
# print "Label count stored on disk"
#
# # Part 2: Initialize OVA structure and classifiers in memory
#
#
# # First of all we need to draw training and tuning
# # data from our original Caltech data.
# # Reminder: testing (development) data has already been made available
# # to us, so we don't need to partition the original data any further.
# allFeatures = util.load("../proc_data/trainingData.pdat")
# labelCount = util.load("../proc_data/labelCount.pdat")
# numTrainExamples = int(np.floor(.8 * len(allFeatures))) # need to convert ndarray scalar to int
# np.random.seed(1)
# np.random.shuffle(allFeatures) # this achieves a degree of randomness
#
# trainingData = allFeatures[:numTrainExamples] # pull training data
# tuningData = allFeatures[numTrainExamples:] # pull tuning data
#
## print "Now that we have the training data in our hands, we will count the cardinalities of class within it: "
## for label in range(1, labelCount + 1):
## examplesOfLabel = [examples for examples in trainingData if examples[-1] == label]
## print "There are: " + str(len(examplesOfLabel)) + " examples of class " + str(label)
#
#
# # Once we're done with data, we need to define the
# # OVA object in memory and add 101 classifiers inside it.
#
# ovaStructure = OVA(trainingData, tuningData, labelCount)
# for _ in range(labelCount):
# ovaStructure.addClassifier(AveragedPerceptron(5)) # training those classifiers for maxiter = 5
#
# print "Created an " + str(ovaStructure)
#
# ovaStructure.dump('../proc_data/stored_classifiers/firstOVA_untuned.pdat')
# print "OVA object dumped in disk."
# Part 3: Tune all classifiers and store the OVA object in memory.
#ovaStructure = util.load("../proc_data/stored_classifiers/firstOVA_untuned.pdat")
#print "Resumed the following OVA object: " + str(ovaStructure) + "."
# ovaStructure.printInfo() # a debugging method that prints some stuff
#ovaStructure.tune()
#ovaStructure.dump("../proc_data/stored_classifiers/firstOVA_tuned.pdat")
#print "We tuned all classifiers of the OVA object and stored them in memory."
#Part 4: Test the trained classifiers on the Caltech 101 development data.
# The first thing we need to do is read the development data in memory.
# We will use the same logic we used to scan the training data.
# validationClasses=os.listdir('../input_data/validation/'); # list and store all categories (classes)
# validationData=[] # this list will store all training examples
# label = 0
# for cat in validationClasses: # loop through all categories
# if os.path.isdir('../input_data/validation/'+ cat):
# DIR_image=os.listdir('../input_data/validation/'+ cat +'/'); # store all images of category "cat"
# label = label + 1
# for im in DIR_image: # loop through all images of the current category
# if (not '._image_' in im): # protect ourselves against those pesky Mac OS X - generated files
# F = np.genfromtxt('../input_data/validation/'+cat+'/'+im, delimiter=' '); # F is now an 2-D numpy ndarray holding all features of an image
# F = np.reshape(F,21*28); # F is now a 588 - sized 1-D ndarray holding all features of the image
# F = F.tolist(); # listify the vector
# F.append(label) # we'd like to store the label alongside the example
# validationData.append(F); # store the vector
# print "Validation data loaded!"
#
# # We would like to have this representation of data stored in our hard disk
# # so that we don't have to read it each and every time
#
# fp = open("../proc_data/validationData.pdat",'wb')
# fp2 = open("../proc_data/labelCount.pdat",'wb')
# pk.dump(validationData, fp)
# pk.dump(label, fp2)
# fp2.close()
# fp.close()
# fp2.close()
#
# print "Validation data stored on disk."
# In order to adhere to the project's specifications, we need to
# train the OVA scheme in 5, 10, 20, 30, 40, 50, 60 random images per category
# and then test it against the validation data.
# To do this, we simply need to train 7 different OVA objects, which means 7 * 101 Averaged Perceptrons,
# and test the accuracy of each against the validation data. We will use the getRandomLabeledExamples()
# method to retrieve the random examples required, and then we will train our OVA
try:
# labelCount = util.load("../proc_data/labelCount.pdat")
# trainingData = util.load("../proc_data/trainingData.pdat")
# validationData = util.load("../proc_data/validationData.pdat")
# accuracy = []
# for exampleNums in [5, 10, 20, 30, 40, 50, 60]:
# reducedTrainingData = getReducedDataset(range(1, labelCount + 1), exampleNums, trainingData)
# ovaClassifier = OVA(reducedTrainingData, None, labelCount) # No tuning data provided because we don't need to (2nd argument is None)
#
# for _label_ in range(labelCount):
# ovaClassifier.addClassifier(AveragedPerceptron()) # default AveragedPerceptron class MaxIter hyper-parameter for training without having tuned first: 15
# print "Training " + str(ovaClassifier)
# ovaClassifier.train()
# print "Testing " + str(ovaClassifier) + " on validation data."
# accuracy.append(1.0 - ovaClassifier.test(validationData)) # OVA.test returns error rate, so we subtract that from 1 to retrieve accuracy
#
#
# # We will use drawError() to draw the accuracy.
# print "Drawing accuracy results"
# util.drawError([5, 10, 20, 30, 40, 50, 60], accuracy, "Learning curve for multi-class Averaged Perceptron.")
#
# # We will store the accuracy for future reference and plotting
#
# print "Dumping accuracy results to disk."
# acFP = open("../proc_data/learningCurve.pdat","wb")
# pk.dump(accuracy, acFP)
# acFP.close()
#
# print "We stored the accuracy on disk."
# print "Exiting..."
# Q 7 : Learn the Perceptron with a varying number of iterations
#
#
labelCount = util.load("../proc_data/labelCount.pdat")
trainingData = util.load("../proc_data/trainingData.pdat")
validationData = util.load("../proc_data/validationData.pdat")
accuracy = []
for maxIterVal in [1,10,50,100, 500]: # not going over 100, took too much time
reducedTrainingData = getReducedDataset(range(1, labelCount+1), 50, trainingData) # get 50 examples per class
ovaClassifier = OVA(reducedTrainingData, None, labelCount)
for _label_ in range(labelCount):
ovaClassifier.addClassifier(AveragedPerceptron())
ovaClassifier.setAllHyperparams(maxIterVal) # brute-force the perceptrons in this case
print "Training " + str(ovaClassifier)
ovaClassifier.train()
print "Testing " + str(ovaClassifier) + " on validation data."
accuracy.append(1.0 - ovaClassifier.test(validationData)) # OVA.test returns error rate, so we subtract that from 1 to retrieve accuracy
# We will use drawError() to draw the accuracy.
print "Drawing accuracy results"
util.drawSimplePlot([1,10,50,100, 500], accuracy, "Accuracy per maxIter for the Averaged Perceptron", "maxIter value", "Accuracy")
# We will store the accuracy for future reference and plotting
print "Dumping accuracy results to disk."
acFP = open("../proc_data/accPerMaxIter.pdat","wb")
pk.dump(accuracy, acFP)
acFP.close()
print "We stored the accuracy on disk."
print "Exiting..."
except DatasetError as d:
print "A dataset-related error occured: " + str(d) + "."
exit()
except LogicalError as l:
print "A logical error occured: " + str(l) + "."
exit()
except Exception as exc:
print "An exception occurred: " + str(exc) + "."
exit()
except:
print "An unknown error occurred."
exit()
logging.basicConfig(level=logging.INFO)
url = 'http://webservice.webxml.com.cn/WebServices/MobileCodeWS.asmx?wsdl'
# url = 'http://webservice.webxml.com.cn/webservices/DomesticAirline.asmx?wsdl'
# url = 'http://webservice.webxml.com.cn/webservices/ChinaTVprogramWebService.asmx?wsdl'
url = 'http://webservice.webxml.com.cn/WebServices/IpAddressSearchWebService.asmx?wsdl'
client = suds.client.Client(url)
# print client
#result = client.service.getDatabaseInfo()
a=1
while a:
ip_name = raw_input("请输入你的ip地址（退出输入exit）:")
if ip_name=='exit':
print '正常退出'
exit()
result = client.service.getCountryCityByIp(ip_name)
print result
print result
print type(result)
# a=0
# for k, v in enumerate(result):
# print v[1][1][0]
# print result
# result = client.service.getMobileCodeInfo('170789456')
# logging.info(result)
logging.basicConfig(level=logging.INFO)
url = 'http://webservice.webxml.com.cn/WebServices/MobileCodeWS.asmx?wsdl'
# url = 'http://webservice.webxml.com.cn/webservices/DomesticAirline.asmx?wsdl'
# url = 'http://webservice.webxml.com.cn/webservices/ChinaTVprogramWebService.asmx?wsdl'
url = 'http://webservice.webxml.com.cn/WebServices/IpAddressSearchWebService.asmx?wsdl'
client = suds.client.Client(url)
# print client
#result = client.service.getDatabaseInfo()
a=1
while a:
ip_name = raw_input("请输入你的ip地址（退出输入exit）:")
if ip_name=='exit':
print '正常退出'
exit()
result = client.service.getCountryCityByIp(ip_name)
print result
print result
print type(result)
# a=0
# for k, v in enumerate(result):
# print v[1][1][0]
# print result
# result = client.service.getMobileCodeInfo('170789456')
# logging.info(result)
