def iris_data_simulation(k_value): # read iris dataset X_train, y_train, X_test, y_test = import_iris_dataset('new_iris.csv') # run Vanilla k-NN classifier on IRIS dataset k_NN_iris = KNN(k_value) k_NN_iris.training(X_train, y_train) iris_prediction = k_NN_iris.prediction(X_test) k_NN_iris.evaluation_prediction(iris_prediction, y_test) # run kernel k-NN classifier on IRIS dataset kernel_k_NN_iris = KernelKNN(k_value) kernel_k_NN_iris.training(X_train, y_train) predicted_iris = kernel_k_NN_iris.prediction(X_test, 'gaussian') kernel_k_NN_iris.evaluation_prediction(predicted_iris, y_test)
def cifar10_simulation(k_value): # import CIFAR10 dataset X_train, y_train, X_test, y_test = import_dataset() # run Vanilla k-NN classifier on CIFAR10 k_NN_cifar = KNN(k_value) k_NN_cifar.training(X_train, y_train) cifar_prediction = k_NN_cifar.prediction(X_test) k_NN_cifar.evaluation_prediction(cifar_prediction, y_test) # run kernelized k-NN classifier on CIFAR10 kernel_k_NN_cifar = KernelKNN(k_value) kernel_k_NN_cifar.training(X_train, y_train) predicted_cifar = kernel_k_NN_cifar.prediction(X_test, 'gaussian') kernel_k_NN_cifar.evaluation_prediction(predicted_cifar, y_test)
def mnist_simulation(k_value): # import MNIST dataset mnist_train, mnist_train_label = import_mnist_dataset( 'mnist_train.csv', 60000) mnist_test, mnist_test_label = import_mnist_dataset('mnist_test.csv', 1000) # run Vanilla k-NN classifier on MNIST k_NN_mnist = KNN(k_value) k_NN_mnist.training(mnist_train, mnist_train_label) mnist_prediction = k_NN_mnist.prediction(mnist_test) k_NN_mnist.evaluation_prediction(mnist_prediction, mnist_test_label) # run kernel k-NN classifier on MNIST kernel_k_NN_mnist = KernelKNN(k_value) kernel_k_NN_mnist.training(mnist_train, mnist_train_label) predicted_mnist = kernel_k_NN_mnist.prediction(mnist_test, 'gaussian') kernel_k_NN_mnist.evaluation_prediction(predicted_mnist, mnist_test_label)