## Problem "Mado" (window in Japanese) average: given an integer array and an integer `k`, compute the moving average using a window of size `k`. Also implement a streaming version that processes one element at a time. ```python def mado_average(nums: list[float], k: int) -> list[float]: """Return list of averages. First k-1 entries use all available elements.""" pass class StreamingMadoAverage: def __init__(self, k: int): ... def next(self, val: float) -> float: """Return current window average after adding val.""" ... ``` ``` Input: nums = [1, 3, 5, 7, 9], k = 3 Output: [1.0, 2.0, 3.0, 5.0, 7.0] # Window [1]->1, [1,3]->2, [1,3,5]->3, [3,5,7]->5, [5,7,9]->7 Streaming: k=3, next(1)->1.0, next(3)->2.0, next(5)->3.0, next(7)->5.0 ``` ## Follow-ups 1. How do you maintain O(1) per element in the streaming version using a deque and running sum? 2. What happens to floating-point precision after many additions? How would you mitigate drift? 3. Extend to a weighted moving average where recent elements have higher weight. 4. How would you compute the moving median instead of the moving mean efficiently?

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Mado Average: Sliding Window Average with Variable Window Size

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Problem "Mado" (window in Japanese) average: given an integer array and an integer k, compute the moving average using a window of size k. Also implement a streaming version that processes one element at a time. Follow-ups How do you maintain O(1) per element in the streaming version using a deque and running sum? What happens to floating-point precision after many additions? How would you mitigate drift? Extend to a weighted moving average where recent elements have higher weight. How would you…

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This is a reported interview question from a decagon interview during the phone round.

It covers the following topics: Sliding Window, Phone, Coding, Queue, Arrays, Onsite .

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Sliding Window Phone Coding Queue Arrays Onsite

Mado Average: Sliding Window Average with Variable Window Size

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