Trying to solve this problem with recursion and memoization but for input 7168 I'm getting wrong answer.
public int numSquares(int n) {
Map<Integer, Integer> memo = new HashMap();
List<Integer> list = fillSquares(n, memo);
if (list == null)
return 1;
return helper(list.size()-1, list, n, memo);
}
private int helper(int index, List<Integer> list, int left, Map<Integer, Integer> memo) {
if (left == 0)
return 0;
if (left < 0 || index < 0)
return Integer.MAX_VALUE-1;
if (memo.containsKey(left)) {
return memo.get(left);
}
int d1 = 1+helper(index, list, left-list.get(index), memo);
int d2 = 1+helper(index-1, list, left-list.get(index), memo);
int d3 = helper(index-1, list, left, memo);
int d = Math.min(Math.min(d1,d2), d3);
memo.put(left, d);
return d;
}
private List<Integer> fillSquares(int n, Map<Integer, Integer> memo) {
int curr = 1;
List<Integer> list = new ArrayList();
int d = (int)Math.pow(curr, 2);
while (d < n) {
list.add(d);
memo.put(d, 1);
curr++;
d = (int)Math.pow(curr, 2);
}
if (d == n)
return null;
return list;
}
I'm calling like this:
numSquares(7168)
All test cases pass (even complex cases), but this one fails. I suspect something is wrong with my memoization but cannot pinpoint what exactly. Any help will be appreciated.
You have the memoization keyed by the value to be attained, but this does not take into account the value of index, which actually puts restrictions on which powers you can use to attain that value. That means that if (in the extreme case) index is 0, you can only reduce what is left with one square (1²), which rarely is the optimal way to form that number. So in a first instance memo.set() will register a non-optimal number of squares, which later will get updated by other recursive calls which are pending in the recursion tree.
If you add some conditional debugging code, you'll see that map.set is called for the same value of left multiple times, and with differing values. This is not good, because that means the if (memo.has(left)) block will execute for cases where that value is not guaranteed to be optimal (yet).
You could solve this by incorporating the index in your memoization key. This increases the space used for memoization, but it will work. I assume you can work this out.
But according to Lagrange's four square theorem every natural number can be written as the sum of at most four squares, so the returned value should never be 5 or more. You can shortcut the recursion when you get passed that number of terms. This reduces the benefit of using memoization.
Finally, there is a mistake in fillSquares: it should add n itself also when it is a perfect square, otherwise you'll not find solutions that should return 1.
Not sure about your bug, here is a short dynamic programming Solution:
Java
public class Solution {
public static final int numSquares(
final int n
) {
int[] dp = new int[n + 1];
Arrays.fill(dp, Integer.MAX_VALUE);
dp[0] = 0;
for (int i = 1; i <= n; i++) {
int j = 1;
int min = Integer.MAX_VALUE;
while (i - j * j >= 0) {
min = Math.min(min, dp[i - j * j] + 1);
++j;
}
dp[i] = min;
}
return dp[n];
}
}
C++
// Most of headers are already included;
// Can be removed;
#include <iostream>
#include <cstdint>
#include <vector>
#include <algorithm>
// The following block might slightly improve the execution time;
// Can be removed;
static const auto __optimize__ = []() {
std::ios::sync_with_stdio(false);
std::cin.tie(nullptr);
std::cout.tie(nullptr);
return 0;
}();
#define MAX INT_MAX
using ValueType = std::uint_fast32_t;
struct Solution {
static const int numSquares(
const int n
) {
if (n < 1) {
return 0;
}
static std::vector<ValueType> count_perfect_squares{0};
while (std::size(count_perfect_squares) <= n) {
const ValueType len = std::size(count_perfect_squares);
ValueType count_squares = MAX;
for (ValueType index = 1; index * index <= len; ++index) {
count_squares = std::min(count_squares, 1 + count_perfect_squares[len - index * index]);
}
count_perfect_squares.emplace_back(count_squares);
}
return count_perfect_squares[n];
}
};
int main() {
std::cout << std::to_string(Solution().numSquares(12) == 3) << "\n";
return 0;
}
Python
Here we can simply use lru_cache:
class Solution:
dp = [0]
#functools.lru_cache
def numSquares(self, n):
dp = self.dp
while len(dp) <= n:
dp += min(dp[-i * i] for i in range(1, int(len(dp) ** 0.5 + 1))) + 1,
return dp[n]
Here are LeetCode's official solutions with comments:
Java: DP
class Solution {
public int numSquares(int n) {
int dp[] = new int[n + 1];
Arrays.fill(dp, Integer.MAX_VALUE);
// bottom case
dp[0] = 0;
// pre-calculate the square numbers.
int max_square_index = (int) Math.sqrt(n) + 1;
int square_nums[] = new int[max_square_index];
for (int i = 1; i < max_square_index; ++i) {
square_nums[i] = i * i;
}
for (int i = 1; i <= n; ++i) {
for (int s = 1; s < max_square_index; ++s) {
if (i < square_nums[s])
break;
dp[i] = Math.min(dp[i], dp[i - square_nums[s]] + 1);
}
}
return dp[n];
}
}
Java: Greedy
class Solution {
Set<Integer> square_nums = new HashSet<Integer>();
protected boolean is_divided_by(int n, int count) {
if (count == 1) {
return square_nums.contains(n);
}
for (Integer square : square_nums) {
if (is_divided_by(n - square, count - 1)) {
return true;
}
}
return false;
}
public int numSquares(int n) {
this.square_nums.clear();
for (int i = 1; i * i <= n; ++i) {
this.square_nums.add(i * i);
}
int count = 1;
for (; count <= n; ++count) {
if (is_divided_by(n, count))
return count;
}
return count;
}
}
Java: Breadth First Search
class Solution {
public int numSquares(int n) {
ArrayList<Integer> square_nums = new ArrayList<Integer>();
for (int i = 1; i * i <= n; ++i) {
square_nums.add(i * i);
}
Set<Integer> queue = new HashSet<Integer>();
queue.add(n);
int level = 0;
while (queue.size() > 0) {
level += 1;
Set<Integer> next_queue = new HashSet<Integer>();
for (Integer remainder : queue) {
for (Integer square : square_nums) {
if (remainder.equals(square)) {
return level;
} else if (remainder < square) {
break;
} else {
next_queue.add(remainder - square);
}
}
}
queue = next_queue;
}
return level;
}
}
Java: Most efficient solution using math
Runtime: O(N ^ 0.5)
Memory: O(1)
class Solution {
protected boolean isSquare(int n) {
int sq = (int) Math.sqrt(n);
return n == sq * sq;
}
public int numSquares(int n) {
// four-square and three-square theorems.
while (n % 4 == 0)
n /= 4;
if (n % 8 == 7)
return 4;
if (this.isSquare(n))
return 1;
// enumeration to check if the number can be decomposed into sum of two squares.
for (int i = 1; i * i <= n; ++i) {
if (this.isSquare(n - i * i))
return 2;
}
// bottom case of three-square theorem.
return 3;
}
}
You have been given a binary string containing only the characters '1' and '0'.
Calculate how many characters of the string need to be changed in order to make the binary string such that each of its substrings of at least a certain length contains at least one "1" character.
I came to think of the following idea but it fails for many testcases:
public static int minimumMoves(String s, int d) {
int n = s.length();
int i=0, answer = 0;
while(i<n)
{
boolean hasOne = false;
int j=i;
while(j<n && j<i+d)
{
if(s.charAt(j) == '1')
{
hasOne = true;
break;
}
j++;
}
if(!hasOne) {
answer++;
i += d;
}
else i++;
}
return answer;
}
Also my algorithm runs on O(|s|2) time. Can anyone suggest ideas on O(|s|) time?
Just throwing off an idea:
return s.split("(?<=\\G.{" + String.valueof(d) + "})").stream().filter(str -> str.contains("1")).count()
You just need to break ensure there is no run of d zeros.
public static int minimumMoves(String s, int d) {
int result = 0;
int runLength = 0;
for(char c: s.toCharArray()) {
if (c == '0') {
runLength += 1;
if (runLength == d) { // we need to break this run
result += 1;
runLength = 0;
}
} else {
runLength = 0;
}
}
return result;
}
I used the sliding window technique and Deque to solve this. This is my accepted solution:
public static int minimumMoves(String s, int d) {
int n = s.length();
Deque<Character> dq = new LinkedList<>();
int count = 0, answer = 0;
for(int i=0; i<d; i++)
{
if(s.charAt(i) == '1') count++;
dq.addLast(s.charAt(i));
}
if(count == 0) {
answer++;
count++;
dq.removeLast();
dq.addLast('1');
}
int i=d;
while(i<n)
{
if(dq.getFirst() == '1') count--;
dq.removeFirst();
if(s.charAt(i) == '1') count++;
dq.addLast(s.charAt(i));
if(count == 0)
{
answer++;
dq.removeLast();
dq.addLast('1');
count++;
}
i++;
}
return answer;
}
You just need to use a sliding window and a count of 1s so far at each index. Use a sliding window of d and if you don't see any ones so far, update the last index of that window with 1 and increment the result.
Code below:
public static int minimumMoves(String s, int d) {
int n = s.length();
int[] count = new int[n+1];
int res = 0;
for ( int i = 1; i <= d; i++ ) {
if ( s.charAt(i-1) == '1') count[i] = count[i-1]+1;
else count[i] = count[i-1];
}
if ( count[d] == 0 ) {
res++;
count[d] = 1;
}
for ( int i = d+1; i <= n; i++ ) {
if ( s.charAt(i-1) == '0' ) {
count[i] = count[i-1];
int ones = count[i] - count[i-d];
if ( ones == 0 ) {
count[i] = count[i-1] + 1;
res++;
}
} else {
count[i] = count[i-1] + 1;
}
}
return res;
}
Thought of another implementation you can do for this by working from the maximum possible changes (assumes at start that all values are '0' in String), reduce it when it finds a '1' value, and then jump to the next substring start. This allows it to run in O(n) and Ω(n/m) (n = String length, m = Substring length).
public static int minimumMoves(String s, int d)
{
char[] a = s.toCharArray();
//Total possible changes (not counting tail)
if(a.length < d)
return 0;
int t = (int) a.length / d;
//Total possible changes (counting tail)
//int t = (int) Math.ceil((double) a.length / (double) d);
for(int i = 0; i < a.length; i++)
{
if(a[i] == '1')
{
t--;
//Calculate index for start of next substring
i = (i / d + 1) * d - 1;
}
}
return t;
}
I'm trying to implement recursive Knapsack I used the common algorithm to write it as following:
int pack(int n, int s) {
if (n < 0)
return 0;
if (List[n].s > s)
return pack(n-1, s);
else {
int max = Math.max(pack(n-1,s), pack(n-1, s -List[n].s) + List[n].v);
return max;
}
}
Is there anyway I can know which items were packed?
Update: I want only the items that belong to best choice and I don't want to change the function header.
EDIT Using array to track items, what's wrong with this?
int pack(int n , int s)
{
if(n < 0)
{
counter =0;
return 0;
}
if (itemsList[n].s > s)
{
return pack(n-1, s);
}
else
{
int max1 = pack(n-1,s);
int max2 = pack(n-1, s - itemsList[n].s) + itemsList[n].v ;
if(max2 > max1)
{
flag1[counter] = new item();
flag1[counter] = itemsList[n];
counter ++;
}
return max(max1, max2);
}
}
Something like this ?
int pack(int n, int s) {
if (n < 0)
return 0;
if (List[n].s > s)
return pack(n-1, s);
else {
int without = pack(n-1,s);
int with = pack(n-1, s-List[n].s) + List[n].v;
if (with >= without) {
System.out.println(n);
}
return Math.max(with, without);
}
}
or, you can return the list of results:
int pack(int n, int s) {
return reallyPack(n, s, new ArrayList<Item>());
}
int reallyPack(int n, int s, List<Item> l) {
if (n < 0)
return 0;
if (List[n].s > s)
return reallyPack(n-1, s);
else {
int without = reallyPack(n-1,s);
int with = reallyPack(n-1, s-List[n].s) + List[n].v;
if (with >= without) {
l.add(itemsList[n]);
}
return Math.max(with, without);
}
}
and of course, you still know how many items were selected: this is simply the size of the returned list.
You can keep track of all items that are currently selected (using e.g. boolean[] field). Then you have to remember the max in the calls of pack with n < 0.
int maximum;
int currentMax;
boolean[] packed;
boolean[] maxPacked;
int pack(int n, int s) {
if (n < 0) {
if (maximum < currentMax) {
// found better selection
maximum = currentMax;
// copy array
for (int i = 0; i < packed.length; i++)
maxPacked[i] = packed[i];
}
return 0;
}
packed[n] = false;
int maxWithout = pack(n-1, s);
if (List[n].s > s) {
return maxWithout;
} else {
packed[n] = true;
currentMax += List[n].v;
int maxWith = pack(n-1, s -List[n].s) + List[n].v;
currentMax -= List[n].v;
return Math.max(maxWith, maxWithout);
}
}
void callingFunction() {
int maxCost = //...;
// always possible to choose no items
maximum = 0;
currentMax = 0;
packed = new boolean[List.length];
maxPacked = new boolean[List.length];
pack(List.length-1, maxCost);
// print best selection
System.out.println(Arrays.toString(maxPacked));
}
The code below has stylistic flaws plus a bug or two. List as many flaws as you can.
public int palindromeCount (int start, int finish) {
int k = start;
int returnVal = 0;
while (k<finish) {
int temp = k;
int r = 0;
while (temp > 0) {
r = 10 * r + temp%10;
temp = temp/10;
}
if (r == k) {
returnVal++;
}
k++;
}
return returnVal;
}
Palindrome is basically a number that has the same value if reversed, such as 11. The code here needs to browse through a range and finally end up with the amount of palindromes within that range. I'm doing this to learn loops.
Here is my progress:
public class Counter{
public Counter(){
}
public int palindromeCount (int start, int finish) {
int returnVal = 0;
int temp = start;
while (start < finish) {
int reverse = 0;
while (temp != 0) {
reverse = 10 * reverse + temp % 10;
temp = temp/10;
}
if (temp == start) {
returnVal = returnVal + 1;
}
start = start + 1;
}
return returnVal;
}
}
I think you posted this before, as far as I've tested it, it works well.
public static int palindromeCount(int start, int finish) {
int k = start;
int returnVal = 0;
while (k <= finish) { // Changed to <= to consider "finish" too
int temp = k;
int r = 0;
while (temp > 0) {
r = 10 * r + temp % 10;
temp = temp / 10;
}
if (r == k) {
returnVal++;
}
k++;
}
return returnVal;
}
Can I get some help please? I have tried many methods to get this to work i got the array sorted and to print but after that my binary search function doesnt want to run and give me right results. It always gives me -1. Any help?
public class BinarySearch {
public static final int NOT_FOUND = -1;
public static int binarySearch(double[] a, double key) {
int low = 0;
int high = a.length -1;
int mid;
while (low<=high) {
mid = (low+high) /2;
if (mid > key)
high = mid -1;
else if (mid < key)
low = mid +1;
else
return mid;
}
return NOT_FOUND;
}
public static void main(String[] args) {
double key = 10.5, index;
double a[] ={10,5,4,10.5,30.5};
int i;
int l = a.length;
int j;
System.out.println("The array currently looks like");
for (i=0; i<a.length; i++)
System.out.println(a[i]);
System.out.println("The array after sorting looks like");
for (j=1; j < l; j++) {
for (i=0; i < l-j; i++) {
if (a[i] > a[i+1]) {
double temp = a[i];
a[i] = a[i+1];
a[i+1] = temp;
}
}
}
for (i=0;i < l;i++) {
System.out.println(a[i]);
}
System.out.println("Found " + key + " at " + binarySearch(double a[], key));
}
}
you are not actually comparing with the array values. in
while (low <= high) {
mid = (low + high) / 2;
if (mid > key) {
high = mid - 1;
} else if (mid < key) {
low = mid + 1;
} else {
return mid;
}
}
Instead use this section
while (low <= high) {
mid = (low + high) / 2;
if (a[mid] > key) {
high = mid - 1;
} else if (a[mid] < key) {
low = mid + 1;
} else {
return mid;
}
}
You were correct to find the indexes, but what you were doing is that you were just comparing index number with your key, which is obviously incorrect. When you write a[mid] you will actually compare your key with the number which is at index mid.
Also the last line of code is giving compile error, it should be
System.out.println("Found " + key + " at " + binarySearch(a, key));
Here
if (mid > key)
high = mid -1;
else if (mid < key)
low = mid +1;
else
return mid;
You're comparing index to a value (key) in array. You should instead compare it to a[mid]
And,
System.out.println("Found " + key + " at " + binarySearch(double a[], key));
Should be
System.out.println("Found " + key + " at " + binarySearch(a, key));
public static double binarySearch(double[] a, double key) {
if (a.length == 0) {
return -1;
}
int low = 0;
int high = a.length-1;
while(low <= high) {
int middle = (low+high) /2;
if (b> a[middle]){
low = middle +1;
} else if (b< a[middle]){
high = middle -1;
} else { // The element has been found
return a[middle];
}
}
return -1;
}
int binarySearch(int list[], int lowIndex, int highIndex, int find)
{
if (highIndex>=lowIndex)
{
int mid = lowIndex + (highIndex - lowIndex)/2;
// If the element is present at the
// middle itself
if (list[mid] == find)
return mid;
// If element is smaller than mid, then
// it can only be present in left subarray
if (list[mid] > find)
return binarySearch(list, lowIndex, mid-1, find);
// Else the element can only be present
// in right subarray
return binarySearch(list, mid+1, highIndex, find);
}
// We reach here when element is not present
// in array
return -1;
}
I somehow find the iterative version not quite easy to read, recursion makes it nice and easy :-)
public class BinarySearch {
private static int binarySearchMain(int key, int[] arr, int start, int end) {
int middle = (end-start+1)/2 + start; //get index of the middle element of a particular array portion
if (arr[middle] == key) {
return middle;
}
if (key < arr[middle] && middle > 0) {
return binarySearchMain(key, arr, start, middle-1); //recurse lower half
}
if (key > arr[middle] && middle < arr.length-1) {
return binarySearchMain(key, arr, middle+1, end); //recurse higher half
}
return Integer.MAX_VALUE;
}
public static int binarySearch(int key, int[] arr) { //entry point here
return binarySearchMain(key, arr, 0, arr.length-1);
}
}
Here is a solution without heap. The same thing can be done in an array.
If we need to find 'k' largest numbers, we take an array of size 'k' populated with first k items from the main data source. Now, keep on reading an item, and place it in the result array, if it has a place.
public static void largestkNumbers() {
int k = 4; // find 4 largest numbers
int[] arr = {4,90,7,10,-5,34,98,1,2};
int[] result = new int[k];
//initial formation of elems
for (int i = 0; i < k; ++i) {
result[i] = arr[i];
}
Arrays.sort(result);
for ( int i = k; i < arr.length; ++i ) {
int index = binarySearch(result, arr[i]);
if (index > 0) {
// insert arr[i] at result[index] and remove result[0]
insertInBetweenArray(result, index, arr[i]);
}
}
}
public static void insertInBetweenArray(int[] arr, int index, int num) {
// insert num at arr[index] and remove arr[0]
for ( int i = 0 ; i < index; ++i ) {
arr[i] = arr[i+1];
}
arr[index-1] = num;
}
public static int binarySearch(int[] arr, int num) {
int lo = 0;
int hi = arr.length - 1;
int mid = -1;
while( lo <= hi ) {
mid = (lo+hi)/2;
if ( arr[mid] > num ) {
hi = mid-1;
} else if ( arr[mid] < num ) {
lo = mid+1;
} else {
return mid;
}
}
return mid;
}
int BinSearch(int[] array, int size, int value)
{
if(size == 0) return -1;
if(array[size-1] == value) return size-1;
if(array[0] == value) return 0;
if(size % 2 == 0) {
if(array[size-1] == value) return size-1;
BinSearch(array,size-1,value);
}
else
{
if(array[size/2] == value) return (size/2);
else if(array[size/2] > value) return BinSearch(array, (size/2)+1, value);
else if(array[size/2] < value) return (size/2)+BinSearch(array+size/2, size/2, value);
}
}
or
Binary Search in Array
/**
* Find whether 67 is a prime no
* Domain consists 25 of prime numbers
* Binary Search
*/
int primes[] = {2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97};
int min = 0,
mid,
max = primes.length,
key = 67,
count= 0;
boolean isFound = false;
while (!isFound) {
if (count < 6) {
mid = (min + max) / 2;
if (primes[mid] == key) {
isFound = true;
System.out.println("Found prime at: " + mid);
} else if (primes[mid] < key) {
min = mid + 1;
isFound = false;
} else if (primes[mid] > key) {
max = mid - 1;
isFound = false;
}
count++;
} else {
System.out.println("No such number");
isFound = true;
}
}
/**
HOPE YOU LIKE IT
A.K.A Binary Search
Take number array of 10 elements, input a number a check whether the number
is present:
**/
package array;
import java.io.InputStreamReader;
import java.io.BufferedReader;
import java.io.IOException;
class BinaryS
{
public static void main(String args[]) throws IOException
{
BufferedReader br=new BufferedReader(new InputStreamReader(System.in));
System.out.print("Enter a number: ");
int n=Integer.parseInt(br.readLine());
int a[]={10,20,30,40,50,60,70,80,90,100};
int upper=a.length-1,lower=0,mid;
boolean found=false;
int pos=0;
while(lower<=upper)
{
mid=(upper+lower)/2;
if(n<a[mid])upper=mid-1;
else if(n>a[mid])lower=mid+1;
else
{
found=true;
pos=mid;
break;
}
}
if(found)System.out.println(n+" found at index "+pos);
else System.out.println(n+" not found in array");
}
}
Well I know I am posting this answer much later.
But according to me its always better to check boundary condition at first.
That will make your algorithm more efficient.
public static int binarySearch(int[] array, int element){
if(array == null || array.length == 0){ // validate array
return -1;
}else if(element<array[0] || element > array[array.length-1]){ // validate value our of range that to be search
return -1;
}else if(element == array[0]){ // if element present at very first element of array
return 0;
}else if(element == array[array.length-1]){ // if element present at very last element of array
return array.length-1;
}
int start = 0;
int end = array.length-1;
while (start<=end){
int midIndex = start + ((end-start)/2); // calculate midIndex
if(element < array[midIndex]){ // focus on left side of midIndex
end = midIndex-1;
}else if(element > array[midIndex]){// focus on right side of midIndex
start = midIndex+1;
}else {
return midIndex; // You are in luck :)
}
}
return -1; // better luck next time :(
}
static int binarySearchAlgorithm() {
// Array should be in sorted order. Mandatory requirement
int[] a = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
int lowIndex = 0;
int valueToFind = 8;
int highIndex = a.length - 1;
while (lowIndex <= highIndex) {
//Finding the midIndex;
int midIndex = (highIndex + lowIndex) / 2;
// Checking if midIndex value of array contains the value to be find.
if (a[midIndex] == valueToFind) {
return midIndex;
}
// Checking the mid Index value is less than the value to be find.
else if (a[midIndex] < valueToFind) {
// If Yes, changing the lowIndex value to midIndex value + 1;
lowIndex = midIndex + 1;
} else if (a[midIndex] > valueToFind) {
// If Yes, changing the highIndex value to midIndex value - 1;
highIndex = midIndex - 1;
} else {
return -1;
}
}
return -1;
}