knn.py

import numpy as np
import matplotlib.pyplot as plt
import os

def distance_matrix(A, B):
    A_square = np.sum(np.square(A), axis=1)
    B_square = np.sum(np.square(B), axis=1)
    
    A_2 = A_square[:, None]
    B_2 = B_square[None, :]

    dists = np.sqrt(A_2 + B_2 - 2 * np.dot(A, B.T))
    
    return dists

def knn_predict(dists, labels_train, k):

    num_test = dists.shape[0]
    predicted_labels = np.zeros(num_test, dtype=int)

    for i in range(num_test):
        # Find the indices of the k-nearest neighbors for the i-th test example
        nearest_neighbor_indices = np.argsort(dists[i])[:k]

        # Get the labels of the k-nearest neighbors
        k_nearest_labels = labels_train[nearest_neighbor_indices]

        # Count the occurrences of each label and select the most common one
        unique_labels, counts = np.unique(k_nearest_labels, return_counts=True)
        most_common_label = unique_labels[np.argmax(counts)]

        predicted_labels[i] = most_common_label


    return predicted_labels


def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
    # Compute the distance matrix
    dists = distance_matrix(data_train, data_test)
    
    # Predict labels for the test data using k-nearest neighbors
    predicted_labels = knn_predict(dists, labels_train, k)

    # Calculate accuracy
    y_pred = knn_predict(dists, labels_train, k)
    accuracy = np.mean(y_pred == labels_test)
    return accuracy


def plot_accuracy_vs_k(data_train, labels_train, data_test, labels_test, split_factor=0.9):
    k_values = list(range(1, 21))
    accuracies = []

    for k in k_values:
        accuracy = evaluate_knn(data_train, labels_train, data_test, labels_test, k)
        accuracies.append(accuracy)

    # Create the "results" directory if it doesn't exist
    os.makedirs("results", exist_ok=True)

    plt.plot(k_values, accuracies)
    plt.xlabel('k')
    plt.ylabel('Accuracy')
    plt.title('Accuracy vs. k for KNN')
    plt.grid(True)
    plt.savefig('results/knn.png')
    plt.show()


if __name__ == "__main__":
    # Load your data and split it into data_train, labels_train, data_test, and labels_test
    data_train = np.random.rand(100, 2)  # Replace with your actual data
    labels_train = np.random.randint(0, 2, 100)  # Replace with your actual labels

    # Generate test data and labels with the same number of samples as data_train
    data_test = np.random.rand(100, 2)  
    labels_test = np.random.randint(0, 2, 100)  

    plot_accuracy_vs_k(data_train, labels_train, data_test, labels_test)