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)
