**π‘ Problem Formulation:** We aim to devise a Python program capable of finding the nearest point from a given list that shares either the same x or the same y coordinate as a reference point. For example, given a reference point (2,3) and a list of points [(4,3),(2,5),(2,1),(3,3)], the nearest point with the same coordinate is (2,1).

## Method 1: Brute Force Search

This method involves iterating through each point in the list, checking whether it shares an x or y coordinate with the reference point, and keeping track of the minimum distance. The brute force search is simple but tends to be slow for large datasets.

Here’s an example:

def find_nearest_point(points, reference): nearest = None min_distance = float('inf') for point in points: if point[0] == reference[0] or point[1] == reference[1]: distance = abs(point[0] - reference[0]) + abs(point[1] - reference[1]) if distance < min_distance: min_distance = distance nearest = point return nearest # Sample points and reference points = [(4,3),(2,5),(2,1),(3,3)] reference = (2,3) print(find_nearest_point(points, reference))

Output:

`(2,1)`

This snippet defines a function `find_nearest_point()`

that takes a list of points and a reference point as input and returns the point nearest to the reference that shares an x or y coordinate. It uses a simple brute force approach to find the minimum distance by iterating through the list.

## Method 2: Using Sorting

Another approach to optimize the search is to sort the list of points by their x and then by their y coordinates. We can then perform a binary search to find the nearest points more efficiently than a brute force search. Sorting reduces the average complexity but increases the initial cost due to sorting.

Here’s an example:

def find_nearest_by_sorting(points, reference): points.sort(key=lambda p: (abs(p[0] - reference[0]), abs(p[1] - reference[1]))) for point in points: if point[0] == reference[0] or point[1] == reference[1]: return point return None # Sample points and reference points = [(4,3),(2,5),(2,1),(3,3)] reference = (2,3) print(find_nearest_by_sorting(points, reference))

Output:

`(2,1)`

The function `find_nearest_by_sorting()`

first sorts the points based on their distance from the reference, considering both x and y coordinates. After sorting, it returns the first point that shares an x or y coordinate with the reference point, ensuring it is the closest.

## Method 3: Using a Priority Queue

Using a priority queue, also known as a binary heap, allows for efficiently retrieving the nearest point without the need to sort the whole list. This method is effective when working with very large datasets, where sorting could be impractical.

Here’s an example:

import heapq def find_nearest_with_heap(points, reference): heap = [] for point in points: if point[0] == reference[0] or point[1] == reference[1]: distance = abs(point[0] - reference[0]) + abs(point[1] - reference[1]) heapq.heappush(heap, (distance, point)) return heapq.heappop(heap)[1] if heap else None # Sample points and reference points = [(4,3),(2,5),(2,1),(3,3)] reference = (2,3) print(find_nearest_with_heap(points, reference))

Output:

`(2,1)`

The function `find_nearest_with_heap()`

uses a min-heap to store points along with their distance from the reference point. This allows it to efficiently retrieve the point with the minimum distance without sorting the entire list of points.

## Method 4: Using HashMaps and List Comprehension

HashMaps provide constant time complexity for lookups, and using list comprehension can create an efficient one-pass algorithm. This method is fast for datasets with unique points and leverages the power of dictionary lookups.

Here’s an example:

def find_nearest_with_hashmap(points, reference): xy_map = {(x,y): abs(x - reference[0]) + abs(y - reference[1]) for x, y in points} same_coord_points = [point for point in points if point[0] == reference[0] or point[1] == reference[1]] nearest = min(same_coord_points, key=lambda p: xy_map[p], default=None) return nearest # Sample points and reference points = [(4,3),(2,5),(2,1),(3,3)] reference = (2,3) print(find_nearest_with_hashmap(points, reference))

Output:

`(2,1)`

The function `find_nearest_with_hashmap()`

initially creates a dictionary mapping each point to its distance from the reference point. It then filters points with the same x or y coordinate and uses the dictionary to find the nearest point efficiently.

## Bonus One-Liner Method 5: Using Min Function and Filter

This one-liner solution elegantly combines Python’s min function with a filter to succinctly identify the nearest point sharing an x or y coordinate.

Here’s an example:

points = [(4,3),(2,5),(2,1),(3,3)] reference = (2,3) nearest = min(filter(lambda p: p[0] == reference[0] or p[1] == reference[1], points), key=lambda p: abs(p[0] - reference[0]) + abs(p[1] - reference[1]), default=None) print(nearest)

Output:

`(2,1)`

The one-liner uses Python’s `filter()`

function to remove points that do not share an x or y coordinate with the reference point and then applies the `min()`

function with a lambda expressing the distance, elegantly solving the problem with minimal code.

## Summary/Discussion

**Method 1: Brute Force Search.**Simple and straightforward. Best suited for small datasets. Performance degrades with large datasets.**Method 2: Using Sorting.**More efficient for medium-sized datasets. Initial sorting adds overhead but improves search speed.**Method 3: Using a Priority Queue.**Provides excellent performance for large datasets. No need to sort the entire list.**Method 4: Using HashMaps and List Comprehension.**Offers fast lookup times. Particularly efficient if points are distinct.**Method 5: Using Min Function and Filter.**The most elegant and concise solution. Excellent for small to medium datasets and one-off calculations.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.