#0038

Pairwise Distances To a Query Point

MediumNumPy20 min15 XP

Problem

Given a 2D array X and a 1D query point q, return a 1D array of Euclidean distances from each row of X to q.

Why This Matters

KNN, clustering, anomaly detection, and nearest-neighbor search all begin with distance calculations.

Function Signature

def distances_to_query(X, q):

Examples

Example 1

InputX = [[0, 0], [3, 4], [6, 8]], q = [0, 0]

Outputarray([0.0, 5.0, 10.0])

The points are 0, 5, and 10 units away from the origin.

Constraints

X is a 2D numeric array.
q has the same number of features as each row of X.
Return distances in the same row order as X.

CodePython

import numpy as np

def distances_to_query(X, q):
    """
    Input example: X = [[0, 0], [3, 4], [6, 8]], q = [0, 0]
    Return: array([0.0, 5.0, 10.0])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return X

import numpy as np

def distances_to_query(X, q):
    """
    Input example: X = [[0, 0], [3, 4], [6, 8]], q = [0, 0]
    Return: array([0.0, 5.0, 10.0])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return X

Visible browser tests run here when available.

Testcases1 visible / 4 hidden categories

Distances from origin

InputX = [[0, 0], [3, 4], [6, 8]], q = [0, 0]

Expected[0.0, 5.0, 10.0]

This uses the 3-4-5 triangle twice.

Hidden Test Categories

Nonzero query pointThree-dimensional pointsSingle rowNegative coordinates