Sampling from a discrete probability distribution. Write a class Sample with a constructor that takes an array p[] of double values as argument and supports the following two operations: random()—return an index i with probability p[i]/T (where T is the sum of the numbers in p[])—and change(i, v)—change the value of p[i] to v. Hint: Use a complete binary tree where each node has implied weight p[i]. Store in each node the cumulative weight of all the nodes in its subtree. To generate a random index, pick a random number between 0 and T and use the cumulative weights to determine which branch of the subtree to explore. When updating p[i], change all of the weights of the nodes on the path from the root to i. Avoid explicit pointers, as we do for heaps.
Sampling from a discrete probability distribution. Write a class Sample with a constructor that takes an array p[] of double values as argument and supports the following two operations: random()—return an index i with probability p[i]/T (where T is the sum of the numbers in p[])—and change(i, v)—change the value of p[i] to v. Hint: Use a complete binary tree where each node has implied weight p[i]. Store in each node the cumulative weight of all the nodes in its subtree. To generate a random index, pick a random number between 0 and T and use the cumulative weights to determine which branch of the subtree to explore. When updating p[i], change all of the weights of the nodes on the path from the root to i. Avoid explicit pointers, as we do for heaps.
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