# for i in numbers: # letter_to_digit.append(i) data_frame = Task_E.pickDataClass(train_data_file_name, letter_to_digit) train_data_set_without_labels, train_y, test_data_set_without_labels, test_y, train_data_with_labels, test_data_with_labels = Task_E.splitData2TestTrain( data_frame, 39, 9) centroid_data_frame_train = deepcopy(train_data_with_labels) centroid_data_frame_test = deepcopy(test_data_with_labels) # make_file_and_save_data_train = Task_E.store(train_data_set_without_labels.T, train_y, 'jenil_train.csv') # make_file_and_save_data_test = Task_E.store(test_data_set_without_labels.T, test_y, 'jenil_test.csv') k = 5 knn_object = Knn(k) data_with_euclidean_distance = knn_object.calculate_distance( train_data_with_labels.values, test_data_with_labels.values) accuracy = knn_object.get_accuracy([ (k['Test Label'], k['Classification']) for k in data_with_euclidean_distance ]) print('Accuracy of Knn is:', accuracy) # Linear Regression linear_regression_object = LinearRegression.LinearRegression() N_train, L_train, Xtrain = len( train_y), train_y, train_data_set_without_labels.T N_test, Ytest, Xtest = len( test_y), test_y, test_data_set_without_labels.T Ytrain = linear_regression_object.indicator_matrix(L_train) linear_regression_object.accuracy(N_train, N_test, Xtrain, Xtest, Ytrain, Ytest) # SVM