Delete knn.py

knn.py

-import numpy as np
-from sklearn.metrics import accuracy_score
-import matplotlib.pyplot as plt
-
-def distance_matrix(matrix1, matrix2):
-    # Calculate the squared norms of each row in the input matrices
-    norms1 = np.sum(matrix1**2, axis=1, keepdims=True)
-    norms2 = np.sum(matrix2**2, axis=1, keepdims=True)
-
-    # Compute the dot product between the matrices
-    dot_product = np.dot(matrix1, matrix2.T)
-
-    # Calculate the L2 Euclidean distance using the hint formula
-    dists = np.sqrt(norms1 - 2 * dot_product + norms2.T)
-
-    return dists
-
-def knn_predict(dists, labels_train, k):
-    # Number of test samples
-    num_test_samples = dists.shape[0]
-
-    # Initialize an array to store the predicted labels
-    predicted_labels = np.zeros(num_test_samples, dtype=labels_train.dtype)
-
-    for i in range(num_test_samples):
-        # Get the distances for the current test sample
-        distances = dists[i]
-
-        # Find the indices of the k nearest neighbors
-        k_nearest_indices = np.argsort(distances)[:k]
-
-        # Get the labels of the k nearest neighbors
-        k_nearest_labels = labels_train[k_nearest_indices]
-
-        # Use np.bincount to count the occurrences of each label
-        # and choose the label with the highest count
-        predicted_label = np.argmax(np.bincount(k_nearest_labels))
-
-        # Assign the predicted label to the current test sample
-        predicted_labels[i] = predicted_label
-
-    return predicted_labels
-
-
-
-def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
-    # Use the previously defined knn_predict function to get predictions
-    predicted_labels = knn_predict(distance_matrix(data_test, data_train), labels_train, k)
-
-    # Calculate the accuracy by comparing predicted labels to actual labels
-    accuracy = accuracy_score(labels_test, predicted_labels)
-
-    return accuracy
-
-split_factor = 0.9
-k_values = 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 plot
-plt.figure(figsize=(8, 6))
-plt.plot(k_values, accuracies, marker='o')
-plt.title('KNN Accuracy vs. k')
-plt.xlabel('k')
-plt.ylabel('Accuracy')
-plt.grid(True)
-
-# Save the plot as "knn.png" in the "results" directory
-plt.savefig('results/knn.png')
-
-# Show the plot (optional)
-plt.show()
-