chore: import upstream snapshot with attribution
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/**
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* @file
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* @brief Implementation of the Unbounded 0/1 Knapsack Problem
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*
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* @details
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* The Unbounded 0/1 Knapsack problem allows taking unlimited quantities of each
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* item. The goal is to maximize the total value without exceeding the given
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* knapsack capacity. Unlike the 0/1 knapsack, where each item can be taken only
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* once, in this variation, any item can be picked any number of times as long
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* as the total weight stays within the knapsack's capacity.
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*
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* Given a set of N items, each with a weight and a value, represented by the
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* arrays `wt` and `val` respectively, and a knapsack with a weight limit W, the
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* task is to fill the knapsack to maximize the total value.
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*
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* @note weight and value of items is greater than zero
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*
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* ### Algorithm
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* The approach uses dynamic programming to build a solution iteratively.
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* A 2D array is used for memoization to store intermediate results, allowing
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* the function to avoid redundant calculations.
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*
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* @author [Sanskruti Yeole](https://github.com/yeolesanskruti)
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* @see dynamic_programming/0_1_knapsack.cpp
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*/
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#include <cassert> // For using assert function to validate test cases
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#include <cstdint> // For fixed-width integer types like std::uint16_t
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#include <iostream> // Standard input-output stream
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#include <vector> // Standard library for using dynamic arrays (vectors)
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/**
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* @namespace dynamic_programming
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* @brief Namespace for dynamic programming algorithms
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*/
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namespace dynamic_programming {
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/**
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* @namespace Knapsack
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* @brief Implementation of unbounded 0-1 knapsack problem
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*/
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namespace unbounded_knapsack {
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/**
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* @brief Recursive function to calculate the maximum value obtainable using
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* an unbounded knapsack approach.
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*
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* @param i Current index in the value and weight vectors.
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* @param W Remaining capacity of the knapsack.
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* @param val Vector of values corresponding to the items.
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* @note "val" data type can be changed according to the size of the input.
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* @param wt Vector of weights corresponding to the items.
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* @note "wt" data type can be changed according to the size of the input.
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* @param dp 2D vector for memoization to avoid redundant calculations.
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* @return The maximum value that can be obtained for the given index and
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* capacity.
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*/
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std::uint16_t KnapSackFilling(std::uint16_t i, std::uint16_t W,
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const std::vector<std::uint16_t>& val,
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const std::vector<std::uint16_t>& wt,
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std::vector<std::vector<int>>& dp) {
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if (i == 0) {
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if (wt[0] <= W) {
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return (W / wt[0]) *
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val[0]; // Take as many of the first item as possible
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} else {
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return 0; // Can't take the first item
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}
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}
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if (dp[i][W] != -1)
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return dp[i][W]; // Return result if available
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int nottake =
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KnapSackFilling(i - 1, W, val, wt, dp); // Value without taking item i
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int take = 0;
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if (W >= wt[i]) {
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take = val[i] + KnapSackFilling(i, W - wt[i], val, wt,
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dp); // Value taking item i
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}
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return dp[i][W] =
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std::max(take, nottake); // Store and return the maximum value
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}
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/**
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* @brief Wrapper function to initiate the unbounded knapsack calculation.
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*
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* @param N Number of items.
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* @param W Maximum weight capacity of the knapsack.
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* @param val Vector of values corresponding to the items.
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* @param wt Vector of weights corresponding to the items.
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* @return The maximum value that can be obtained for the given capacity.
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*/
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std::uint16_t unboundedKnapsack(std::uint16_t N, std::uint16_t W,
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const std::vector<std::uint16_t>& val,
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const std::vector<std::uint16_t>& wt) {
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if (N == 0)
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return 0; // Expect 0 since no items
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std::vector<std::vector<int>> dp(
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N, std::vector<int>(W + 1, -1)); // Initialize memoization table
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return KnapSackFilling(N - 1, W, val, wt, dp); // Start the calculation
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}
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} // namespace unbounded_knapsack
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} // namespace dynamic_programming
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/**
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* @brief self test implementation
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* @return void
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*/
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static void tests() {
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// Test Case 1
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std::uint16_t N1 = 4; // Number of items
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std::vector<std::uint16_t> wt1 = {1, 3, 4, 5}; // Weights of the items
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std::vector<std::uint16_t> val1 = {6, 1, 7, 7}; // Values of the items
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std::uint16_t W1 = 8; // Maximum capacity of the knapsack
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// Test the function and assert the expected output
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assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N1, W1, val1, wt1) == 48);
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std::cout << "Maximum Knapsack value "
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<< dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N1, W1, val1, wt1)
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<< std::endl;
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// Test Case 2
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std::uint16_t N2 = 3; // Number of items
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std::vector<std::uint16_t> wt2 = {10, 20, 30}; // Weights of the items
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std::vector<std::uint16_t> val2 = {60, 100, 120}; // Values of the items
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std::uint16_t W2 = 5; // Maximum capacity of the knapsack
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// Test the function and assert the expected output
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assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N2, W2, val2, wt2) == 0);
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std::cout << "Maximum Knapsack value "
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<< dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N2, W2, val2, wt2)
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<< std::endl;
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// Test Case 3
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std::uint16_t N3 = 3; // Number of items
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std::vector<std::uint16_t> wt3 = {2, 4, 6}; // Weights of the items
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std::vector<std::uint16_t> val3 = {5, 11, 13}; // Values of the items
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std::uint16_t W3 = 27; // Maximum capacity of the knapsack
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// Test the function and assert the expected output
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assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N3, W3, val3, wt3) == 27);
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std::cout << "Maximum Knapsack value "
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<< dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N3, W3, val3, wt3)
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<< std::endl;
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// Test Case 4
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std::uint16_t N4 = 0; // Number of items
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std::vector<std::uint16_t> wt4 = {}; // Weights of the items
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std::vector<std::uint16_t> val4 = {}; // Values of the items
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std::uint16_t W4 = 10; // Maximum capacity of the knapsack
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assert(dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N4, W4, val4, wt4) == 0);
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std::cout << "Maximum Knapsack value for empty arrays: "
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<< dynamic_programming::unbounded_knapsack::unboundedKnapsack(
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N4, W4, val4, wt4)
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<< std::endl;
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std::cout << "All test cases passed!" << std::endl;
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}
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/**
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* @brief main function
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* @return 0 on successful exit
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*/
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int main() {
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tests(); // Run self test implementation
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return 0;
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}
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