92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
import numpy as np
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from keras.datasets import mnist
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import matplotlib.pyplot as plt
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# Load MNIST
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print("Loading MNIST...") # debugging
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(X_train_raw, y_train), (X_test_raw, y_test) = mnist.load_data()
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# print("X_train_raw[0]:")
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# print(X_train_raw[0]) # output: 28x28 vector spelling 5
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# print("y_train[0]:")
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# print(y_train[0]) # output: 5
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# print("X_test_raw[0]:")
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# print(X_test_raw[0]) # output: 28x28 vector spelling 7
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# print("y_test[0]:")
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# print(y_test[0]) # output: 7
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# Flatten images from (samples, 28, 28) to (samples, 784) -> one dimensional
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X_train = X_train_raw.reshape(X_train_raw.shape[0], -1).astype(np.float32)
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X_test = X_test_raw.reshape(X_test_raw.shape[0], -1).astype(np.float32)
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print("Train:", X_train.shape, "Test:", X_test.shape)
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# Select first 1000 prototype vectors
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prototypes = X_train[:1000]
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prototype_labels = y_train[:1000]
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print("Using", len(prototypes), "prototype vectors.") # debugging
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# kNN function with explicit loops for readability
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def knn_predict_batch(X_batch, k=3):
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"""
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Predicts labels for a batch of test vectors using kNN.
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X_batch: shape (batch_size, 784)
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returns: shape (batch_size,)
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"""
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preds = []
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# For each test image
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for test_img in X_batch:
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distances = []
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# Euclidean distance to each prototype
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for prototype in prototypes:
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# distance = sqrt(sum((test_img - prototype)^2))
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diff = test_img - prototype
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distance = np.sqrt(np.sum(diff ** 2))
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distances.append(distance)
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# Find indices of k nearest neighbors (smallest distances)
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distances = np.array(distances)
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nearest_k_indices = np.argsort(distances)[:k] # returns indices of array with sorted distances
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# Get labels of the k nearest neighbors
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nearest_k_labels = prototype_labels[nearest_k_indices]
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# Majority vote
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prediction = np.bincount(nearest_k_labels, minlength=10).argmax()
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preds.append(prediction)
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return np.array(preds)
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# Evaluate on first N_TEST test samples
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N_TEST = 1000
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print(f"Evaluating on {N_TEST} test samples...") # debugging
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X_eval = X_test[:N_TEST]
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y_eval = y_test[:N_TEST]
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preds = knn_predict_batch(X_eval, k=5)
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accuracy = np.mean(preds == y_eval)
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print("Predictions:", preds[:20])
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print("True labels:", y_eval[:20])
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print("Accuracy:", accuracy)
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# Visualize first 20 predictions
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fig, axes = plt.subplots(4, 5, figsize=(12, 10))
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axes = axes.flatten()
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for i in range(0, 20):
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# Reshape flattened image back to 28x28
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img = X_eval[i].reshape(28, 28)
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axes[i].imshow(img, cmap='gray')
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axes[i].set_title(f"Pred: {preds[i]}, True: {y_eval[i]}")
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axes[i].axis('off')
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plt.tight_layout()
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plt.show()
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