For every test point, find the indices of its k nearest training points using Euclidean distance:
d(x,z)=j=1∑D(xj−zj)2Here, D is the feature count and j indexes a feature. Sort neighbors from smallest to largest distance. If k exceeds the training-set size, append −1 until every row has length k. One-dimensional inputs represent collections of scalar samples. Return an integer NumPy array of shape (n_test, k).
Input: X_train = [1, 3, 5], X_test = [2], k = 2
Output: [[0, 1]]
Explanation: Training values 1 and 3 are equally close to 2, and their indices remain in ascending order.
Input: X_train = [[0, 0], [1, 1], [2, 2]], X_test = [[0.5, 0.5]], k = 2
Output: [[0, 1]]
Reshape scalar datasets to (-1, 1) before broadcasting.
Use X_test[:, None, :] - X_train[None, :, :] to form all pairwise differences.
Concatenate an integer array filled with -1 when padding is required.
Accepts: array
Accepts: array
Accepts: number
For every test point, find the indices of its k nearest training points using Euclidean distance:
d(x,z)=j=1∑D(xj−zj)2Here, D is the feature count and j indexes a feature. Sort neighbors from smallest to largest distance. If k exceeds the training-set size, append −1 until every row has length k. One-dimensional inputs represent collections of scalar samples. Return an integer NumPy array of shape (n_test, k).
Input: X_train = [1, 3, 5], X_test = [2], k = 2
Output: [[0, 1]]
Explanation: Training values 1 and 3 are equally close to 2, and their indices remain in ascending order.
Input: X_train = [[0, 0], [1, 1], [2, 2]], X_test = [[0.5, 0.5]], k = 2
Output: [[0, 1]]
Reshape scalar datasets to (-1, 1) before broadcasting.
Use X_test[:, None, :] - X_train[None, :, :] to form all pairwise differences.
Concatenate an integer array filled with -1 when padding is required.
Accepts: array
Accepts: array
Accepts: number