Implement 1-Nearest Neighbor (1NN) Classifier with NumPy; Rewrite as Neural Network Weights

Implement 1NN with NumPy; Rewrite into a “Neural Network Weights” Form You are given training features X_train with labels y_train, and test features X_test. Part 1: Implement 1NN Prediction Implement a 1-Nearest Neighbor (1NN) classifier using NumPy only (no sklearn-style models): For each sample in X_test, compute distances to all samples in X_train Find the training sample with the minimum distance Predict its label Distance metric: Euclidean (L2). Input X_train: float array of shape (n_train, d) y_train: int array of shape (n_train,) X_test: float array of shape (n_test, d) Output y_pred: int array of shape (n_test,) Constraints n_train, n_test may be large; prefer vectorization (looping over n_test is acceptable, but avoid heavy nested loops). Part 2: Rewrite 1NN as “Neural Network Weights” Without changing the behavior of 1NN, rewrite the computation in terms of “weights/parameters” akin to neural network layers (e.g., matrix multiplications and additions), and clearly state/return those parameters. Note: The original post does not specify the exact required format (what weights to output, fixed depth, allowed non-linearities/argmin, etc.), so this part is necessarily high-level. Example Input X_train=[[0,0],[1,1],[2,2]] y_train=[0,1,1] X_test=[[1.1,1.1],[0.2,0.1]] Output [1,0]