We Keep Coding

How do you introduce probability in your Java methods – in a flexible and concise fashion?

I've been learning Java for only about a week and a half. At this point, the only way I could think of to include an element of randomness in my code is to use the Random class's nextInt method. Below is a portion of my method:
Random random = new Random();
int randomInt = random.nextInt(10)+1;
[...]
if(randomInt <= 3){
System.out.println("Magnificent!");
} else if (randomInt >= 7){
System.out.println("Marvelous!");
} else {
System.out.println("Delectable!");
}
However, it's wordy and lacks flexibility. I would like to be able to distribute pieces of total probability of 1 to different scenarios in a concise way: thing A happens with a probability of 0.3, thing B happens with a probability of, say, 0.5, thing С happens with a probability of 0.2. How could I achieve that?

In a general case, you have a set of weighted values, which can produce a random value based on a probability which is its weight as fraction of the total weights. The weights can be normalized (meaning their sum is 1), or non-normalized, which is the easier assumption.
When rolling a random value out of that set, it's most efficient to check for the values with the highest probability first. Roll a random number between 0 and the total weight, then go through the values in order of their weight (descending), and check if the random number is lower than the cumulative weight -> if so, return that value.
Code:
public class WeightedRandom<T> {
private final Comparator<WeightedValue<T>> byWeight =
Comparator.comparing(wv -> wv.weight);
private final Set<WeightedValue<T>> weightedValues =
new TreeSet<>(byWeight.reversed());
private double totalWeight;
void put(double weight, T value) {
if (weight <= 0) {
return;
}
totalWeight += weight;
weightedValues.add(new WeightedValue<>(weight, value));
}
public T next() {
if (weightedValues.isEmpty()) {
throw new NoSuchElementException();
}
double rnd = ThreadLocalRandom.current().nextDouble(totalWeight);
double sum = 0;
Iterator<WeightedValue<T>> iterator = weightedValues.iterator();
WeightedValue<T> result;
do {
result = iterator.next();
sum += result.weight;
} while (rnd > sum && iterator.hasNext());
return result.value;
}
private static class WeightedValue<T> {
final double weight;
final T value;
public WeightedValue(double weight, T value) {
this.weight = weight;
this.value = value;
}
}
}
Example:
public static void main(String[] args) {
WeightedRandom<String> random = new WeightedRandom<>();
random.put(3, "AAA");
random.put(2, "BBB");
random.put(5, "CCC");
for (int i = 0; i < 1000; i++) {
String value = random.next();
System.out.println(value);
}
}

Usually, you're going to use Random.nextDouble(), and test e.g. random.nextDouble() < 0.3 to have a probability of 0.3.
To test more than one possibility, you will need to sum some things, e.g
double r = random.nextDouble();
if (r < 0.3) {
...0.3 probability
} else if (r < 0.8) {
...0.5 probability
} else {
...0.2 probability
}

Find maximum product using recursion

There's a question I saw and I'm wondering if it's possible to solve it using recursion. It goes as follow:
Write an algorithm that, when given an array of input, finds the maximum product from those inputs. For example:
Input: [1, 2, 3]
Output: 6 (1*2*3)
Input: [-1, 1, 2, 3]
Output: 6 (1*2*3)
Input: [-2, -1, 1, 2, 3]
Output: 12 (-2*-1*1*2*3)
I'm trying to find a way of using recursion to solve it, but the algorithm I tried doesn't work. My algorithm, written in Java is as follow
Integer[] array;
public int maximumProduct(int[] nums) {
array=new Integer[nums.length];
return multiply(nums, 0);
}
public int multiply(int[] nums, int i){
if (array[i]!=null){
return array[i];
}
if (i==(nums.length-1)){
return nums[i];
}
int returnval=Math.max(nums[i]*multiply(nums, i+1), multiply(nums, i+1));
array[i]=returnval;
return returnval;
}
The problem with this algorithm is that it doesn't work well if there's an even number of negative numbers. For example, if nums[0]=-2, nums[1]=-1 and nums[2]=1, then multiply(nums, 1) will always return 1 instead of -1, and thus it will always see 1 as bigger than 1*-2 at multiply(nums, 0). I'm not sure how to solve this problem, however. Is there any way of solving this using recursion or dynamic programming?

If there is only one non-zero element in the array, and it happens to be a negative number, then then answer is either 0, if there is a 0 present in the input, or if the array contains only that single negative element, the answer is that element itself.
In all other cases, the final answer is going to be positive.
We first make a linear scan to find the number of negative integers. If this number is even, then the answer is the product of all the non-zero elements. If there are an odd number of negative elements, we need to leave out one negative element from the answer, so that the answer is positive. As we want the maximum possible answer, the number we want to leave out should have as small an absolute value as possible. So among all the negative numbers, find the one with the minimum absolute value, and find the product of the remaining non-zero elements, which should be the answer.
All this requires only two linear scans of the array, and hence runs in O(n) time.

What is the maximum product of integers?
To obtain the maximum sum, you will want to multiply all the positive integers with the product of the largest negative integers, with the number of negative integers included in the product being even to obtain a positive final result.
In an algorithm for a single traversal
I am going to treat the positive integers and the negative integers in the input separately. You will want to keep a running product of positive integers, a running product of negative integers and the largest negative integer (ie. the negative integer with the smallest absolute value) found so far.
Let us ignore the edge cases where the final answer is <= 0. That can be handled easily.
//Initialization
int [] nums // Input
int posProduct = 1;
int negProduct = 1;
int smallestNeg = 1;
//Input Traversal
for (int i : nums) {
if ( i == 0 ) {
// ignore
} else if ( i < 0 ) {
if (smallestNeg == 1) {
smallestNeg = i;
} else if ( i > smallestNeg ) {
negProduct *= smallestNeg; //Integrate the old smallest into the running product
smallestNeg = i; // i is the new smallest
} else {
negProduct *= i;
}
} else {
// i is strictly positive
posProduct *= i;
}
}
//Result Computation
int result = posProduct;
if ( negProduct < 0 ) {
// The running product of negative number numbers is negative
// We use the smallestNeg to turn it back up to a positive product
result *= smallestNeg;
result *= negProduct;
} else {
result *= negProduct
}
edit: In a recursive traversal
I personally find that writing the array traversal in a recursive manner to be clumsy but it can be done.
For the beauty of the exercise and to actually answer the question of the OP, here is how I would do it.
public class RecursiveSolver {
public static int findMaxProduct (int [] nums) {
return recursiveArrayTraversal(1, 1, 1, nums, 0);
}
private static int recursiveArrayTraversal(int posProduct, int negProduct,
int smallestNeg, int [] nums, int index) {
if (index == nums.length) {
// End of the recursion, we traversed the whole array
posProduct *= negProduct;
if (posProduct < 0) {
posProduct *= smallestNeg;
}
return posProduct;
}
// Processing the "index" element of the array
int i = nums[index];
if ( i == 0 ) {
// ignore
} else if ( i < 0 ) {
if (smallestNeg == 1) {
smallestNeg = i;
} else if ( i > smallestNeg ) {
negProduct *= smallestNeg;
smallestNeg = i;
} else {
negProduct *= i;
}
} else {
// i is strictly positive
posProduct *= i;
}
//Recursive call here!
//Notice the index+1 for the index parameter which carries the progress
//in the array traversal
return recursiveArrayTraversal(posProduct, negProduct,
smallestNeg, nums, index+1);
}
}

First, break the array in subproblems always you find a 0 in the list:
1 -2 4 -1 8 0 4 1 0 -3 -4 0 1 3 -5
|_____________| |____| |____| |_______|
p1 p2 p3 p4
Then, for each problem pi, count how many negative numbers are there.
If pi has an even number of negatives (or no negatives at all), the answer of pi is the product of all its elements.
If pi has only 1 negative number (say n), the answer will be the maximum between the product of all the elements in n's right and the product of all elements in n's left.
If pi has an odd number (bigger than only 1) of negative numbers, call the index of the leftmost negative number l and the index of the rightmost negative number r. Supposing pi has n elements, the answer will be:
max(
pi[ 0 ] * pi[ 1 ] * ... * pi[r - 1],
pi[l + 1] * pi[l + 2] * ... * pi[ n ]
)
Knowing that, it's easy to write a recursion for each step of the solution of this problem: a recursion to divide problems at zeros, another to count negatives and another to find answers, in O(n).

Linear version
List<Integer> vals = new ArrayList<>(List.of(5,1,-2,1,2,3,-4,-1));
int prod = 0;
int min = 1;
for (int v : vals) {
if (v == 0) {
// ignore zero values
continue;
}
if (prod == 0) {
prod = 1;
}
prod *= v;
// compute min to be the largest negative value in the list.
if (v < 0 && min < Math.abs(v)) {
min = v;
}
}
if (prod < 0) {
prod /= min;
}
System.out.println("Maximum product = " + prod);
}
Recursive version
int prod = prod(vals, new int[] {0} , vals.size());
System.out.println("Maximum product = " + prod);
public static int prod(List<Integer> vals, int[]min, int size) {
int prod = 0;
if(vals.size() > 0) {
int t = vals.get(0);
if (t < 0 && min[0] < Math.abs(t)) {
min[0] = t;
}
prod = prod(vals.subList(1,vals.size()), min, vals.size());
}
if (vals.isEmpty() || vals.get(0) == 0) {
return prod;
}
if (prod == 0) {
prod = 1;
}
prod *= t;
if (vals.size() == size && prod < 0) {
prod/=min[0];
}
return prod;
}

This is my solution - leaving it open for optimization and to figure out the runtime. This is a general purpose solution that finds the products of all the combinations of integers in a list. Of course, there is a O(n) solution but I present this solution as well.
import java.util.ArrayList;
import java.util.List;
public class MaxProd {
int[] input = {1, 2, 3};
// int[] input = {-2, -1, 1, 2, 3};
public static void main(String[] args) {
MaxProd m = new MaxProd();
List<Integer> ll = m.max(0);
for (int i : ll) {
System.out.println(i);
}
ll.sort((x,y) -> Integer.compare(x, y));
System.out.println("The max: " + ll.get(ll.size() -1 ));
}
private List<Integer> max(int index) {
if (index < input.length){
List<Integer> l = new ArrayList<>();
List<Integer> retList = max(index + 1);
for (int j : retList){
l.add(input[index] * j);
}
l.add(input[index]);
l.addAll(retList);
return l;
}
else return new ArrayList<>();
}
}
it prints:
6
2
3
1
6
2
3
The max: 6
If the requirements are constrained (as in this case) then one can get by without the need for generating all combinations resulting in a linear solution. Also, I'm sorting at the end. Note: you could easily get the result with a single pass on the returned list to find the maximum product as specified in other answers.

Issue with recursion because thread suspension

I was playing around with a few practice problems in Java. I wrote a recursive program for program given below. My solution is right except for the suspended (which I believe) gets back to active state and changes the value of the recursive method. I have also added a screenshot of Eclipse in debug mode where the thread stack is shown.
package com.nix.tryout.tests;
/**
* For given two numbers A and B such that 2 <= A <= B,
* Find most number of sqrt operations for a given number such that square root of result is a whole number and it is again square rooted until either the
* number is less than two or has decimals.
* example if A = 6000 and B = 7000, sqrt of 6061 = 81, sqrt of 81 = 9 and sqrt of 9 = 3. Hence, answer is 3
*
* #author nitinramachandran
*
*/
public class TestTwo {
public int solution(int A, int B) {
int count = 0;
for(int i = B; i > A ; --i) {
int tempCount = getSqrtCount(Double.valueOf(i), 0);
if(tempCount > count) {
count = tempCount;
}
}
return count;
}
// Recursively gets count of square roots where the number is whole
private int getSqrtCount(Double value, int count) {
final Double sqrt = Math.sqrt(value);
if((sqrt > 2) && (sqrt % 1 == 0)) {
++count;
getSqrtCount(sqrt, count);
}
return count;
}
public static void main(String[] args) {
TestTwo t2 = new TestTwo();
System.out.println(t2.solution(6550, 6570));
}
}
The above screenshot is from my debugger and I've circled the Thread stack. Can anyone try and run the program and let me know what the problem is and what would be the solution? I could come up with a non recursive solution.

Your recursion is wrong, since the value of count will return in any case 0 or 1 even if it goes deep down into recursive calls. Java is pass by value, meaning that modifying the value of a primitive inside of a method wont be visible outside of that method. In order to correct this, we can write the following recursion:
private int getSqrtCount(Double value) {
final Double sqrt = Math.sqrt(value);
if((sqrt > 2) && (sqrt % 1 == 0)) {
return getSqrtCount(sqrt) + 1;
}
return 0;
}

Your code is wrong, you should have
return getSqrtCount(sqrt, count);
instead of
getSqrtCount(sqrt, count);
Otherwise the recursion is pointless, you're completely ignoring the result of the recursion.

Alpha Beta Pruning with Binary Search Tree

I am working through the Minimax algorithm with Alpha-Beta Pruning example found here. In the example, they use an array to implement the search tree. I followed the example, but also tried implementing it with a binary search tree as well. Here are the values I'm using in the tree: 3, 5, 6, 9, 1, 2, 0, -1.
The optimal value at the end should be 5. With the BST implementation, I keep getting 2.
I think this is the problem, but I don't know how to get around it:
I wrote the code to return out of recursion if it sees a leaf node to stop from getting null pointer exceptions when trying to check the next value. But instead, I think it's stopping the search too early (based off of what I see when stepping through the code with the debugger). If I remove the check though, the code fails on a null pointer.
Can someone point me in the right direction? What am I doing wrong?
Here's the code:
public class AlphaBetaMiniMax {
private static BinarySearchTree myTree = new BinarySearchTree();
static int MAX = 1000;
static int MIN = -1000;
static int opt;
public static void main(String[] args) {
//Start constructing the game
AlphaBetaMiniMax demo = new AlphaBetaMiniMax();
//3, 5, 6, 9, 1, 2, 0, -1
demo.myTree.insert(3);
demo.myTree.insert(5);
demo.myTree.insert(6);
demo.myTree.insert(9);
demo.myTree.insert(1);
demo.myTree.insert(2);
demo.myTree.insert(0);
demo.myTree.insert(-1);
//print the tree
System.out.println("Game Tree: ");
demo.myTree.printTree(demo.myTree.root);
//Print the results of the game
System.out.println("\nGame Results:");
//run the minimax algorithm with the following inputs
int optimalVal = demo.minimax(0, myTree.root, true, MAX, MIN);
System.out.println("Optimal Value: " + optimalVal);
}
/**
* #param alpha = 1000
* #param beta = -1000
* #param nodeIndex - the current node
* #param depth - the depth to search
* #param maximizingPlayer - the current player making a move
* #return - the best move for the current player
*/
public int minimax(int depth, MiniMaxNode nodeIndex, boolean maximizingPlayer, double alpha, double beta) {
//Base Case #1: Reached the bottom of the tree
if (depth == 2) {
return nodeIndex.getValue();
}
//Base Case #2: if reached a leaf node, return the value of the current node
if (nodeIndex.getLeft() == null && maximizingPlayer == false) {
return nodeIndex.getValue();
} else if (nodeIndex.getRight() == null && maximizingPlayer == true) {
return nodeIndex.getValue();
}
//Mini-Max Algorithm
if (maximizingPlayer) {
int best = MIN;
//Recur for left and right children
for (int i = 0; i < 2; i++) {
int val = minimax(depth + 1, nodeIndex.getLeft(), false, alpha, beta);
best = Math.max(best, val);
alpha = Math.max(alpha, best);
//Alpha Beta Pruning
if (beta <= alpha) {
break;
}
}
return best;
} else {
int best = MAX;
//Recur for left and right children
for (int i = 0; i < 2; i++) {
int val = minimax(depth + 1, nodeIndex.getRight(), true, alpha, beta);
best = Math.min(best, val);
beta = Math.min(beta, best);
//Alpha Beta Pruning
if (beta <= alpha) {
break;
}
}
return best;
}
}
}
Output:
Game Tree:
-1 ~ 0 ~ 1 ~ 2 ~ 3 ~ 5 ~ 6 ~ 9 ~
Game Results:
Optimal Value: 2

Your problem is your iterations are depending on a loop control of 2, and not a node == null finding for nodeIndex.getRight()(for max) getLeft(for min.)
Remember a tree has
1 head(first level)
2nd level = 2
3rd level = 4
4th 8
and so on. So your algorithm for looping will not even go down 3 levels.
for (int i = 0; i < 2; i++) {
int val = minimax(depth + 1, nodeIndex.getLeft(), false, alpha, beta);
best = Math.max(best, val);
alpha = Math.max(alpha, best);
//Alpha Beta Pruning
if (beta <= alpha) {
break;
}
Change your loops to control iteration correctly and you should find the highest value easily.

Least Coin Used Algorithm Java

I am stuck on the coin denomination problem.
I am trying to find the lowest number of coins used to make up $5.70 (or 570 cents). For example, if the coin array is {100,5,2,5,1} (100 x 10c coins, 5 x 20c, 2 x 50c, 5 x $1, and 1 x $2 coin), then the result should be {0,1,1,3,1}
At the moment the coin array will consist of the same denominations ( $2, $1, 50c, 20c, 10c)
public static int[] makeChange(int change, int[] coins) {
// while you have coins of that denomination left and the total
// remaining amount exceeds that denomination, take a coin of that
// denomination (i.e add it to your result array, subtract it from the
// number of available coins, and update the total remainder). –
for(int i= 0; i< coins.length; i++){
while (coins[i] > 0) {
if (coins[i] > 0 & change - 200 >= 0) {
coins[4] = coins[4]--;
change = change - 200;
} else
if (coins[i] > 0 & change - 100 >= 0) {
coins[3] = coins[3]--;
change = change - 100;
} else
if (coins[i] > 0 & change - 50 >= 0) {
coins[2] = coins[2]--;
change = change - 50;
} else
if (coins[i] > 0 & change - 20 >= 0) {
coins[1] = coins[1]--;
change = change - 20;
} else
if (coins[i] > 0 & change - 10 >= 0) {
coins[0] = coins[0]--;
change = change - 10;
}
}
}
return coins;
}
I am stuck on how to deduct the values from coins array and return it.
EDIT: New code

The brute force solution is to try up to the available number of coins of the highest denomination (stopping when you run out or the amount would become negative) and for each of these recurse on solving the remaining amount with a shorter list that excludes that denomination, and pick the minimum of these. If the base case is 1c the problem can always be solved, and the base case is return n otherwise it is n/d0 (d0 representing the lowest denomination), but care must be taken to return a large value when not evenly divisible so the optimization can pick a different branch. Memoization is possible, and parameterized by the remaining amount and the next denomination to try. So the memo table size would be is O(n*d), where n is the starting amount and d is the number of denominations.
So the problem can be solved in pseudo-polynomial time.

The wikipedia link is sparse on details on how to decide if a greedy algorithm such as yours will work. A better reference is linked in this CS StackExchange question. Essentially, if the coin system is canonical, a greedy algorithm will provide an optimal solution. So, is [1, 2, 5, 10, 20] canonical? (using 10s of cents for units, so that the sequence starts in 1)
According to this article, a 5-coin system is non-canonical if and only if it satisfies exactly one of the following conditions:
[1, c2, c3] is non-canonical (false for [1, 2, 5])
it cannot be written as [1, 2, c3, c3+1, 2*c3] (true for [1, 2, 5, 10, 20])
the greedyAnswerSize((k+1) * c4) > k+1 with k*c4 < c5 < (k+1) * c4; in this case, this would require a k*10 < 20 < (k+1)*10; there is no integer k in that range, so this is false for [1, 2, 5, 10, 20].
Therefore, since the greedy algorithm will not provide optimal answers (and even if it did, I doubt that it would work with limited coins), you should try dynamic programming or some enlightened backtracking:
import java.util.HashSet;
import java.util.PriorityQueue;
public class Main {
public static class Answer implements Comparable<Answer> {
public static final int coins[] = {1, 2, 5, 10, 20};
private int availableCoins[] = new int[coins.length];
private int totalAvailable;
private int totalRemaining;
private int coinsUsed;
public Answer(int availableCoins[], int totalRemaining) {
for (int i=0; i<coins.length; i++) {
this.availableCoins[i] = availableCoins[i];
totalAvailable += coins[i] * availableCoins[i];
}
this.totalRemaining = totalRemaining;
}
public boolean hasCoin(int coinIndex) {
return availableCoins[coinIndex] > 0;
}
public boolean isPossibleBest(Answer oldBest) {
boolean r = totalRemaining >= 0
&& totalAvailable >= totalRemaining
&& (oldBest == null || oldBest.coinsUsed > coinsUsed);
return r;
}
public boolean isAnswer() {
return totalRemaining == 0;
}
public Answer useCoin(int coinIndex) {
Answer a = new Answer(availableCoins, totalRemaining - coins[coinIndex]);
a.availableCoins[coinIndex]--;
a.totalAvailable = totalAvailable - coins[coinIndex];
a.coinsUsed = coinsUsed+1;
return a;
}
public int getCoinsUsed() {
return coinsUsed;
}
#Override
public String toString() {
StringBuilder sb = new StringBuilder("{");
for (int c : availableCoins) sb.append(c + ",");
sb.setCharAt(sb.length()-1, '}');
return sb.toString();
}
// try to be greedy first
#Override
public int compareTo(Answer a) {
int r = totalRemaining - a.totalRemaining;
return (r==0) ? coinsUsed - a.coinsUsed : r;
}
}
// returns an minimal set of coins to solve
public static int makeChange(int change, int[] availableCoins) {
PriorityQueue<Answer> queue = new PriorityQueue<Answer>();
queue.add(new Answer(availableCoins, change));
HashSet<String> known = new HashSet<String>();
Answer best = null;
int expansions = 0;
while ( ! queue.isEmpty()) {
Answer current = queue.remove();
expansions ++;
String s = current.toString();
if (current.isPossibleBest(best) && ! known.contains(s)) {
known.add(s);
if (current.isAnswer()) {
best = current;
} else {
for (int i=0; i<Answer.coins.length; i++) {
if (current.hasCoin(i)) {
queue.add(current.useCoin(i));
}
}
}
}
}
// debug
System.out.println("After " + expansions + " expansions");
return (best != null) ? best.getCoinsUsed() : -1;
}
public static void main(String[] args) {
for (int i=0; i<100; i++) {
System.out.println("Solving for " + i + ":"
+ makeChange(i, new int[]{100,5,2,5,1}));
}
}
}

You are in wrong direction. This program will not give you an optimal solution. To get optimal solution go with dynamic algorithms implemented and discussed here. Please visit these few links:
link 1
link 2
link 3