def main(): loadData() data = getData() NBresults = list() SVMresults = list() for x in xrange(0, 10): (train_set, test_set) = splitData(data) classifier = getClassifier(train_set, 'NaiveBayes') NBresults.append(classify(classifier, test_set)) classifier = getClassifier(train_set, 'SVM') SVMresults.append(classify(classifier, test_set)) print '\nNaive Bayes Classifier' printTable(NBresults) print '\nSVM' printTable(SVMresults)
def main(): loadData(); data = getData(); NBresults = list() SVMresults = list() for x in xrange(0,10): (train_set,test_set) = splitData(data) classifier = getClassifier(train_set,'NaiveBayes') NBresults.append(classify(classifier,test_set)) classifier = getClassifier(train_set,'SVM') SVMresults.append(classify(classifier,test_set)) print '\nNaive Bayes Classifier' printTable(NBresults) print '\nSVM' printTable(SVMresults)
newX[ row, 5 ] = 3 else: newX[ row, 5 ] = 4 return X ''' Fetching Data ''' my_list = classifier.readFile( 'car_evals.csv' ) X, Y = classifier.getLabel( my_list, 'unacc' ) X = convertFeatures( X ) X = classifier.One_Hot_Encoding( X ) print "Car Evaluation Dataset" ''' Splitting data ''' print "\nTraining Size = 80%\n" Xtrain, Xtest, Ytrain, Ytest = classifier.splitData( X, Y, 0.4 ) ''' Decision Tree Classifier ''' val_err, train_err, max_depth = classifier.K_Fold_crossValidation_Decision_Tree( Xtrain, Ytrain ) test_err = classifier.decision_tree( Xtrain, Ytrain, Xtest, Ytest, depth = max_depth ) print "Decision Tree Classifier\n" print "Validation Error = ", val_err print "Training Error = ", train_err print "Testing Error = ", test_err print "Optimal Depth = ", max_depth print "\n" ''' K Nearest Neighbor ''' val_err, train_err, opt_K = classifier.K_Fold_crossValidation_KNN( Xtrain, Ytrain ) test_err = classifier.KNN( Xtrain, Ytrain, Xtest, Ytest, K = opt_K ) print "K Nearest Neighbor Classifier\n"