package com.thealgorithms.dynamicprogramming;
public class KnapsackMemoization {
int knapSack(int capacity, int[] weights, int[] profits, int numOfItems) {
int[][] dpTable = new int[numOfItems + 1][capacity + 1];
for (int i = 0; i < numOfItems + 1; i++) {
for (int j = 0; j < capacity + 1; j++) {
dpTable[i][j] = -1;
}
}
return solveKnapsackRecursive(capacity, weights, profits, numOfItems, dpTable);
}
int solveKnapsackRecursive(int capacity, int[] weights, int[] profits, int numOfItems, int[][] dpTable) {
if (numOfItems == 0 || capacity == 0) {
return 0;
}
if (dpTable[numOfItems][capacity] != -1) {
return dpTable[numOfItems][capacity];
}
if (weights[numOfItems - 1] > capacity) {
dpTable[numOfItems][capacity] = solveKnapsackRecursive(capacity, weights, profits, numOfItems - 1, dpTable);
return dpTable[numOfItems][capacity];
} else {
final int includeCurrentItem = profits[numOfItems - 1] + solveKnapsackRecursive(capacity - weights[numOfItems - 1], weights, profits, numOfItems - 1, dpTable);
final int excludeCurrentItem = solveKnapsackRecursive(capacity, weights, profits, numOfItems - 1, dpTable);
dpTable[numOfItems][capacity] = Math.max(includeCurrentItem, excludeCurrentItem);
return dpTable[numOfItems][capacity];
}
}
}