def get_defense_data(opponents): import parse defense_info=map(lambda team: parse.run(filename,team)[-1],opponents) print defense_info by_category=[ ('Portcullis','Cheval'), ('Moat','Ramparts'), ('Drawbridge','Sally Port'), ('Rock Wall','Rough Terrain') ] print sorted(defense_info[0]) def find_better(opts): m=map( lambda def_type: sum(map(lambda x: x[def_type],defense_info)), opts ) if m[0]<m[1]: return (opts[0],m[1]-m[0]) return (opts[1],m[0]-m[1]) return map(find_better,by_category)
def bayesianClassifier(inputSeq_train, inputSeq_test, labels_train, labels_test): """ An experimental implementation using orthogonalization of the input. Gaussian Naive Bayesian Classifier is used. """ print 'Gaussian Naive Bayesian Classifier.' feats_train = RealFeatures(inputSeq_train) feats_test = RealFeatures(inputSeq_test) labels = MulticlassLabels(labels_train) print 'Initializing kernel..' classifier = GaussianNaiveBayes(feats_train, labels) print 'Training svm..' classifier_train = classifier.train() print 'Running test data..' label_pred = classifier.apply(feats_test) out = label_pred.get_labels() # Evaluating the accuracy. if label_test is not None: labels_test_set = MulticlassLabels(numpy.asarray(label_test)) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test_set) # print 'Accuracy : %.4f' % (acc * 100) # Giving the new input. inp = parse.run("513_distribute/7cata.all") inpMatrix = inputParser.createFeatureMatrix(inp) inp_feats = RealFeatures(numpy.transpose(numpy.asarray(inpMatrix))) # Running svm on the input. label_out = classifier.apply(inp_feats) out_new = label_out.get_labels() return out_new
def bayesianClassifier(inputSeq_train, inputSeq_test, labels_train, labels_test ): """ An experimental implementation using orthogonalization of the input. Gaussian Naive Bayesian Classifier is used. """ print 'Gaussian Naive Bayesian Classifier.' feats_train = RealFeatures(inputSeq_train) feats_test = RealFeatures(inputSeq_test) labels = MulticlassLabels(labels_train) print 'Initializing kernel..' classifier = GaussianNaiveBayes(feats_train, labels) print 'Training svm..' classifier_train = classifier.train() print 'Running test data..' label_pred = classifier.apply(feats_test) out = label_pred.get_labels() # Evaluating the accuracy. if label_test is not None: labels_test_set = MulticlassLabels(numpy.asarray(label_test)) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test_set) # print 'Accuracy : %.4f' % (acc * 100) # Giving the new input. inp = parse.run("513_distribute/7cata.all") inpMatrix = inputParser.createFeatureMatrix(inp) inp_feats = RealFeatures(numpy.transpose(numpy.asarray(inpMatrix))) # Running svm on the input. label_out = classifier.apply(inp_feats) out_new = label_out.get_labels() return out_new
import sys sys.path.append(".") from parse import run if __name__ == '__main__': run()
import parse import token # To take Multiline input from user. print("Enter/Paste your code.Ctrl-Z ( windows ) to save it.") contents = "" while True: try: contents += input(">> ") + "\t" except EOFError: break result = parse.run(contents) print(result)
#!/usr/bin/env python import parse import scraper scraper.run() parse.run()
def get_data(team): p=parse.run(filename,team) print 'parsed for',team,':',p ball_time,defense_time,challenge,climb,defense_info=p return ball_time/2,defense_time,ball_time/2,challenge,climb