def maketeams(request): """ Actually adds the team that was requested by the addteams page """ #fh = open("log.txt") #fh.write("I got to step 1 \n") if auth(request): parse.getData(request.POST['url']) # Sanitize your inputs #fh.write("I got to step 2 \n") t = Team.objects.get(link=request.POST['url']) #fh.write("I got to step 3 \n") return redirect('/admin/') else: return redirect('/login/')
def main(): decision_lists = {} trainingFile = '/data/cs65/senseval3/train/EnglishLS.train' trainData = getData(trainingFile) testFile = '/data/cs65/senseval3/test/EnglishLS.test' testData = getData(testFile) k = 10 for word in trainData.keys(): decision_lists[word] = build_decision_list(trainData, word, k) total = 0 correct = 0 correct_less = 0 correct_cutoff = 0 for word in testData.keys(): MFS = most_frequent_sense(trainData, word) modified_declist = [x for x in decision_lists[word] if x[2] > 1.0] for instance in testData[word].keys(): total += 1 instanceData = testData[word][instance] classification = classify(instanceData, decision_lists[word], k, MFS) less_rules = classify(instanceData, decision_lists[word][:100], k, MFS) cutoff = classify(instanceData, modified_declist, k, MFS) if classification in instanceData['answers']: correct += 1 if less_rules in instanceData['answers']: correct_less += 1 if cutoff in instanceData['answers']: correct_cutoff += 1 accuracy = float(correct) / float(total) accuracy_less = float(correct_less) / float(total) accuracy_cutoff = float(correct_cutoff) / float(total) print "Accurately classified %f of all words" % (accuracy) print "%d correct of %d total" % (correct, total) print "With 100 rules, accurately classified %f of all words" % (accuracy_less) print "%d correct of %d total" % (correct_less, total) print "With cutoff at 1.0, Accurately classified %f of all words" % (accuracy_cutoff) print "%d correct of %d total" % (correct_cutoff, total) """ 12. Got 0.593 accuracy, slightly better than the 0.571 accuracy of the MFS baseline. 14. The modified classifications perform approximately the same; see output """ # print decision_lists['organization.n'][:100] """
def classify(train, test): trainData, testData = getData(train, test); #print len(trainData[0]), len(testData[0]) #print len(trainData[0][0]), len(trainData[1]), len(testData[0][0]), len(testData[1]) #annClass = learnANN(trainData[0], trainData[1]); #annRes = annClass.predict(testData[0]); #annAcc = 0; bayesAcc = 0; svmAcc = 0; bayesClass = learnBayes(trainData[0], trainData[1]); bayesRes = bayesClass.predict(testData[0]); bayesRes = map(lambda x: 0 if x < 0.5 else 1, bayesRes) svmClass = learnSVM(trainData[0], trainData[1]); svmRes = svmClass.predict(testData[0]); svmRes = map(lambda x: 0 if x < 0.5 else 1, svmRes) for i in xrange(len(testData[1])): #if annRes[i] == testData[1][i]: # annAcc += 1 if bayesRes[i] == testData[1][i]: bayesAcc += 1 if svmRes[i] == testData[1][i]: svmAcc += 1 #print "ANN Accuracy:", annAcc/(len(testData[1])*1.0); print "Bayes Accuracy:", bayesAcc/(len(testData[1])*1.0); print "SVM Accuracy:", svmAcc/(len(testData[1])*1.0);
def updateDB(): data = getData('data.pickle') with open('data.csv', 'w') as csvFile: writer = csv.writer(csvFile) writer.writerow(['Company', 'Keyword', 'Salience']) for company in data: for pair in data[company]: writer.writerow([company, pair[0], pair[1]])
def buildBook(filename): if not os.access(filename, os.R_OK): print filename, 'is not exist' return basename = os.path.basename(filename) WORKDIR = basename try: data = parse.getData(filename) TITLE, AUTHOR, SECTIONS = parse.parse(data) except ParseError, e: print 'parse error', filename, e return
for rule in decList[key]: if rule[1][1] in words: return rule[1][0] if rule[0] < 0: break tsenses = getSenses(testData, key) tsenseFreq = map(lambda x: (x, tsenses.count(x)), set(tsenses)) mfs = freqSense(tsenseFreq)[0] return mfs if __name__=='__main__': trainingFile = '/data/cs65/senseval3/train/EnglishLS.train' data = getData(trainingFile) testingFile = '/data/cs65/senseval3/test/EnglishLS.test' testData = getData(testingFile) k = 10 mfs = {} #values are tuples of mfs, count of mfs decList = {} for key in data.keys(): senses = list(set(getSenses(data, key))) #below is list of frequencies senseFreq = map(lambda x: (x, senses.count(x)), set(senses)) mfs[key] = freqSense(senseFreq)[0] scores = [] counts = {} for sense in senses: senseInst = instanceSense(data, key, sense) counts[sense] = countf(data, key, k, senseInst)
def doTest(): damage_data = parse.getData() print("#### DAMAGE TEST ####") if damage_data["jigglypuff"]["Jab1"] == 3.0: print("jiggylypuff Jab1 -- PASS") else: print("jiggylypuff Jab1 -- FAIL") if damage_data["robin"]["U-smash"] == 15.0: print("robin U-smash -- PASS") else: print("robin U-smash -- FAIL") if damage_data["pikachu"]["Nair"] == 8.5: print("pikachu Nair -- PASS") else: print("pikachu Nair -- FAIL") if damage_data["marth"]["Final Smash"] == 60.0: print("marth Final Smash -- PASS") else: print("marth Final Smash -- FAIL") if damage_data["metaknight"]["U-throw"] == 10.0: print("metaknight U-throw -- PASS") else: print("metaknight U-throw -- FAIL") if damage_data["falco"]["Reflector (ground)"] == 5.0: print("falco Reflector (ground) -- PASS") else: print("falco Reflector (ground) -- FAIL") if damage_data["villager"]["Timber (axe)"] == 14.0: print("village Timber (axe) -- PASS") else: print("village Timber (axe) -- FAIL") if damage_data["gamewatch"]["F-smash (normal)"] == 18.0: print("gamewatch F-smash (normal) -- PASS") else: print("gamewatch F-smash (normal) -- FAIL") if damage_data["wiifit"]["Header (head spike)"] == 15.0: print("wiifit Header (head spike) -- PASS") else: print("wiifit Header (head spike) -- FAIL") if damage_data["zelda"]["Phantom Strike (fully charged uppercut slash)"] == 12.0: print("zelda Phantom Strike (fully charged uppercut slash) -- PASS") else: print("zelda Phantom Strike (fully charged uppercut slash) -- FAIL") print("#### DAMAGE TEST END ####")
import sys import config import parse import nn if len(sys.argv) < 2: sys.exit('Usage: %s directory-name' % sys.argv[0]) d = sys.argv[1] #try: translate = parse.buildTranslate(d) data = parse.getData(d, "training", True) data['translate'] = translate nn = nn.neuralNetwork( data ) #print nn #data = parse.getData(d, "test") #for row in data['inputs']: # print nn.predict( row ) #except Exception as error: # print error
#! coding: UTF-8 import numpy as np import parse as Parser import kmeans as km list = [ "北海道", "青森県", "岩手県", "宮城県", "秋田県", "山形県", "福島県", "茨城県", "栃木県", "群馬県", "埼玉県", "千葉県", "東京", "神奈川県", "新潟県", "山梨県", "長野県", "富山県", "石川県", "福井県", "岐阜県", "静岡県", "愛知県", "三重県", "滋賀県", "京都府", "大阪府", "兵庫県", "奈良県", "和歌山県", "鳥取県", "島根県", "岡山県", "広島県", "山口県", "徳島県", "香川県", "愛媛県", "高知県", "福岡県", "佐賀県", "長崎県", "熊本県", "大分県", "宮崎県", "鹿児島県", "沖縄" ] print("クラスタ数を入力してください") cluster = int(input()) result = km.kmeans(Parser.getData(), cluster) for cluster_num in range(cluster): cluster_list = [] for list_num in range(len(list)): if result[list_num] == cluster_num: cluster_list.append(list[list_num]) print("クラスタ" + str(cluster_num)) print(cluster_list) print("--------------")
import sys import config import parse import nn if len(sys.argv) < 2: sys.exit('Usage: %s directory-name' % sys.argv[0]) d = sys.argv[1] #try: translate = parse.buildTranslate(d) data = parse.getData(d, "training", True) data['translate'] = translate nn = nn.neuralNetwork(data) #print nn #data = parse.getData(d, "test") #for row in data['inputs']: # print nn.predict( row ) #except Exception as error: # print error
trainSenses = retrieveMostCommonSenses(trainData) lexelts = testData.keys() for lexelt in lexelts: instances = testData[lexelt].keys() for instance in instances: senses = testData[lexelt][instance]["answers"] if trainSenses[lexelt][0] in senses: totalCorrect += 1 totalGuesses += 1 print float(totalCorrect)/totalGuesses * 100 if __name__=='__main__': trainingFile = '/data/cs65/senseval3/train/EnglishLS.train' trainData = getData(trainingFile) testingFile = '/data/cs65/senseval3/test/EnglishLS.test' testData = getData(testingFile) # questions on training data print "\nQuestion 1:\n" question1(trainData) print "\nQuestion 2:\n" question2(trainData) print "\nQuestion 3:\n" question3(trainData) print "\nQuestion 4:\n" question4(trainData) print "\nQuestion 5:\n" question5(trainData) print "\nQuestion 6:\n"