Solution: Random Pick with Weight
Understand how to implement weighted random selection by transforming weights into running sums and applying a modified binary search. This lesson helps you efficiently pick indices based on weight probabilities, improving selection time from linear to logarithmic complexity.
Statement
You’re given an array of positive integers, weights, where weights[i] is the weight of the index.
Write a function, Pick Index(), which performs weighted random selection to return an index from the weights array. The larger the value of weights[i], the heavier the weight is, and the higher the chances of its index being picked.
Suppose that the array consists of the weights . In this case, the probabilities of picking the indexes will be as follows:
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Index 0:
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Index 1: ...