def _new_epoch(self): start_time = time.time() np.random.shuffle(self.indexs) self.data = [] self.scores = [] total_correct = 0.0 for i in xrange(self.opt.training_images): curi = self.indexs[i] rgb = cv2.imread( path.join(self.opt.dataset_dir, self.rgb_paths[curi])) pose = getInfo( path.join(self.opt.dataset_dir, self.pose_paths[curi])) sampling, patches = stochasticSubSample(rgb, self.opt.obj_size, self.opt.input_size) estObj = getCoordImg(patches, self.sess, self.ol, self.opt) estObj = estObj.reshape(-1, 3) for h in xrange(self.opt.training_hyps): diffMap, score, correct = createScore(pose, estObj, sampling) self.data.append(diffMap.reshape(40, 40, 1)) self.scores.append(score.reshape(1)) total_correct += correct self.step = 0 if self.opt.time_info: print "Generated {} patches ({:2f}% correct) in {}s".format( len(self.data), total_correct / len(self.data) * 100.0, time.time() - start_time)
def loadInfo(self, csvInput): getInfo = utils.getInfo(csvInput) try: studentID = next(getInfo) questionsID = next(getInfo) correct = next(getInfo) except: # print ('Execption occurs in function--loadInfo') return None # print ('-------------loadInfo begin-----------') n = int(studentID[0]) # print ('studentID[0] is',n) # print ('-------------------------------------') newID = [ ] # to replace questionSet by simplifying each questions serial number # use replacement to shorten one hot list length for i in range(len(questionsID)): if not questionsID[i] in self.questions: self.questions.update({questionsID[i]: self.n_questions}) self.n_questions += 1 newID.append(self.questions[questionsID[i]]) # print ('questionSet is ',questionSet) # print ('-------------------------------------') # print ('newQuestionSet is',newQuestionSet) # print ('-------------------------------------') stu = student(n, newID, correct) return stu
def update(): information = utils.getInfo() print information return json.dumps(information)
# -*- coding: utf-8 -*- """ main """ import time import utils import config while True: try: time.sleep(config.REFRESH) print(50 * "\n") url = config.URL samples = utils.parserHtml(utils.getInfo(url)) for tram in samples: print(utils.clean(tram.text)) print(1 * "\n") except ConnectionError: print(50 * "\n") print("Tram doesn´t exist anymore!") print(1 * "\n")
def main(): """ Driver.py """ upperCaseLettersInfoFile = 'info_1.csv' greekLettersInfoFile = 'info_2.csv' trainingData1File = 'train_1.csv' trainingData2File = 'train_2.csv' val1File = 'val_1.csv' val2File = 'val_2.csv' testWithLabel1File = 'test_with_label_1.csv' testWithLabel2File = 'test_with_label_2.csv' baseDTFile1 = 'Base-DT-DS1.csv' baseDTFile2 = 'Base-DT-DS2.csv' bestDTFile1 = 'Best-DT-DS1.csv' bestDTFile2 = 'Best-DT-DS2.csv' upperCaseLettersDict = utils.getInfo(upperCaseLettersInfoFile) greekLettersDict = utils.getInfo(greekLettersInfoFile) #Get data uses Pandas library. Returns 2d array with column headers in first row trainingData1 = utils.getData(trainingData1File) trainingData2 = utils.getData(trainingData2File) #Generating our ML Models baseDTUpperCase = decisionTree.generateBaseDT(trainingData1) baseDTGreek = decisionTree.generateBaseDT(trainingData2) bestDTUpperCase = decisionTree.generateBestDT(trainingData1) bestDTGreek = decisionTree.generateBestDT(trainingData2) val1Data = utils.getData(val1File) val2Data = utils.getData(val2File) testWithLabel1 = utils.getData(testWithLabel1File) testWithLabel2 = utils.getData(testWithLabel2File) utils.plotInstances(trainingData1, upperCaseLettersDict, 'Uppercase Letters', 'Training', 'trainingUppercase') utils.plotInstances(trainingData2, greekLettersDict, 'Greek Letters', 'Training', 'trainingGreek') utils.plotInstances(val1Data, upperCaseLettersDict, 'Uppercase Letters', 'Validation', 'validationUppercase') utils.plotInstances(val2Data, greekLettersDict, 'Greek Letters', 'Validation', 'validationGreek') utils.plotInstances(testWithLabel1, upperCaseLettersDict, 'Uppercase Letters', 'Test', 'testUppercase') utils.plotInstances(testWithLabel2, greekLettersDict, 'Greek Letters', 'Test', 'testGreek') print('Running Validation for Base DT - Upper Case Letters...') utils.testModel(baseDTUpperCase, val1Data) print('Running Validation for Best DT - Upper Case Letters...') utils.testModel(bestDTUpperCase, val1Data) print('\nRunning Tests for Base DT - Upper Case Letters...') baseDTRes1 = utils.testModel(baseDTUpperCase, testWithLabel1) utils.writeMLResults(baseDTRes1, baseDTFile1) print('Running Tests for Best DT - Upper Case Letters...') bestDTRes1 = utils.testModel(bestDTUpperCase, testWithLabel1) utils.writeMLResults(bestDTRes1, bestDTFile1) print('\nRunning Validation for Base DT - Greek Letters...') utils.testModel(baseDTGreek, val2Data) print('Running Validation for Best DT - Greek Letters...') utils.testModel(bestDTGreek, val2Data) print('\nRunning Tests for Base DT - Greek Letters...') baseDTRes2 = utils.testModel(baseDTGreek, testWithLabel2) utils.writeMLResults(baseDTRes2, baseDTFile2) print('Running Tests for Best DT - Greek Letters...') bestDTRes2 = utils.testModel(bestDTGreek, testWithLabel2) utils.writeMLResults(bestDTRes2, bestDTFile2) utils.plotConfusionMatrix(baseDTUpperCase, testWithLabel1, upperCaseLettersDict, 'Base DT Uppercase Letters' ,'Base-DT-DS1-CM') utils.plotConfusionMatrix(baseDTGreek, testWithLabel2, greekLettersDict, 'Base DT Greek Letters' ,'Base-DT-DS2-CM') utils.plotConfusionMatrix(bestDTUpperCase, testWithLabel1, upperCaseLettersDict, 'Best DT Uppercase Letters' ,'Best-DT-DS1-CM') utils.plotConfusionMatrix(bestDTGreek, testWithLabel2, greekLettersDict, 'Best DT Greek Letters' ,'Best-DT-DS2-CM') utils.getClassificationReport(baseDTUpperCase, testWithLabel1, upperCaseLettersDict, 'Base DT Uppercase Letters' ,'Base-DT-DS1-Report') utils.getClassificationReport(baseDTGreek, testWithLabel2, greekLettersDict, 'Base DT Greek Letters' ,'Base-DT-DS2-Report') utils.getClassificationReport(bestDTUpperCase, testWithLabel1, upperCaseLettersDict, 'Base DT Uppercase Letters' ,'Best-DT-DS1-Report') utils.getClassificationReport(bestDTGreek, testWithLabel2, greekLettersDict, 'Base DT Greek Letters' ,'Best-DT-DS2-Report')
raw_input( "1:\tPrivate Key\n2:\tWallet Import Format Private Key\n3:\tPublic Key\n\n[ ] Enter selection: " )) print if q == 1: priv_key = raw_input("[ ] Enter Private Key: ") address = utils.pubKeyToAddr(utils.privateKeyToPublicKey(priv_key)) print print "Address: " + address print "Private Key: " + priv_key print "Wallet Import Format Private Key: " + utils.privateKeyToWif( priv_key) print "Public Key: " + utils.privateKeyToPublicKey(priv_key) info = utils.getInfo(address) if info.balance() > 0: info.display() elif q == 2: wif_priv_key = raw_input("[ ] Enter Wallet Import Format Private Key: ") address = utils.pubKeyToAddr( utils.privateKeyToPublicKey(utils.wifToPrivateKey(wif_priv_key))) print print "Address: " + address print "Private key: " + utils.wifToPrivateKey(wif_priv_key) print "Wallet Import Format Private Key: " + wif_priv_key print "Public Key: " + utils.privateKeyToPublicKey( utils.wifToPrivateKey(wif_priv_key)) info = utils.getInfo(address) if info.balance() > 0:
print q = int(raw_input("1:\tPrivate Key\n2:\tWallet Import Format Private Key\n3:\tPublic Key\n\n[ ] Enter selection: ")) print if q == 1: priv_key = raw_input("[ ] Enter Private Key: ") address = utils.pubKeyToAddr(utils.privateKeyToPublicKey(priv_key)) print print "Address: " + address print "Private Key: " + priv_key print "Wallet Import Format Private Key: " + utils.privateKeyToWif(priv_key) print "Public Key: " + utils.privateKeyToPublicKey(priv_key) info = utils.getInfo(address) if info.balance() > 0: info.display() elif q == 2: wif_priv_key = raw_input("[ ] Enter Wallet Import Format Private Key: ") address = utils.pubKeyToAddr(utils.privateKeyToPublicKey(utils.wifToPrivateKey(wif_priv_key))) print print "Address: " + address print "Private key: " + utils.wifToPrivateKey(wif_priv_key) print "Wallet Import Format Private Key: " + wif_priv_key print "Public Key: " + utils.privateKeyToPublicKey(utils.wifToPrivateKey(wif_priv_key)) info = utils.getInfo(address) if info.balance() > 0: info.display()