"Which Algorithm Is This? If you step back, this maps almost perfectly to the Top K Frequent Elements problem.We usually solve it for integers in a list. Here, the "elements" are audience profiles age and body-type combinations. First, define what an audience profile looks like: case class Profile(age: Int, height: Int, weight: Int) What we want is a function like this:"
"A man runs a mid-sized auditorium. Before every show, he faces the same question: How should seating, aisles, and safety staff be arranged? He doesn't know the audience in advance. Planning for every edge case wastes resources.Planning for an "average" audience doesn't really work either. Over time, he starts noticing patterns. Certain age and body-type combinations show up far more often than others. So he begins planning resources around the few audience types that appear most frequently, while keeping a small buffer for the rest."
A mid-sized auditorium faces repeated uncertainty about seating, aisles, and safety staffing because audience composition is unknown before each show. Observed patterns show a small number of age and body-type combinations occurring most frequently; planning around these frequent profiles with a small buffer improves comfort and operations. The problem maps to Top K Frequent Elements by representing profiles as Profile(age: Int, height: Int, weight: Int) and implementing topKFrequentProfiles(profiles: List[Profile], k: Int): List[Profile]. The algorithm counts profile frequencies, maintains a small min-heap of the most frequent k profiles, and ignores less frequent profiles to optimize resource allocation.
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