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)