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Given a Integer, find the maximum number that can be formed from the digits.
Input : 8754365
output : 8765543
I told solution in $O(n logn)$. He asked me optimize further.
We can use Hash Table to optimize further, $\rightarrow$ O(n)
Algorithm:
1. Take a hash table with size 10 (0 to 9).
2. Store the count of every digit from 0 to 9.
3. Print the index of the Hash table with respect to digit count in the reverse direction (from 9 to 0).
Example:
Hash table after digit count for 8754365 $\rightarrow$ (0 0 0 1 1 2 1 1 1 0)
Print the index of the hash table with respect to their count in reverse order $\rightarrow$ 8765543
Time Complexity : O(n)
Correct me if I am wrong.
The following greedy code does this in O(n) time. Where n is the number of digits in the number.
int num = 8756404;
int[] times = new int[10];
while(true){
if(num==0){
break;
}
int val = num%10;
times[val]++;
num /= 10;
}
for(int i=9; i>=0; i--){
for(int j=0; j<times[i]; j++){
System.out.print(i);
}
}
It works by counting the number of occurences of each of the digits in the input number. Then printing each number the number of times it was in the input number in reverse order, ie. starting from 9 to 0.
RunTime: 00:00:00.01
public int Assignment(int number)
{
// Consider that int.MaxValue equals to 2147483647
var siblingString = String.Join("", number.ToString().ToCharArray().OrderByDescending(n => n));
int sibling = -1;
if (!int.TryParse(siblingString, out sibling) || sibling > 100000000)
{
return -1;
}
return sibling;
}
Performances tested with the following code:
static void Main()
{
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
var result = AssignmentOne(2147483646);
stopWatch.Stop();
TimeSpan ts = stopWatch.Elapsed;
string elapsedTime = String.Format("{0:00}:{1:00}:{2:00}.{3:00}", ts.Hours, ts.Minutes, ts.Seconds, ts.Milliseconds / 10);
Console.WriteLine("RunTime " + elapsedTime);
}
Word Break(with Dynamic Programming: Top->Down) Given a string s and a dictionary of words dict, add spaces in s to construct a sentence
where each word is a valid dictionary word.
Return all such possible sentences.
For example, given s = "catsanddog", dict = ["cat", "cats", "and", "sand", "dog"].
A solution is ["cats and dog", "cat sand dog"].
Question:
Time complexity ?
Space complexity ?
Personally I think,
Time complexity = O(n!), without Dynamic Programming, n is the length of the given string,
Space complexity = O(n).
The puzzled:
Can not figure out the time complexity with Dynamic Programming.
It seems that the space complexity above is not correct.
Code[Java]
public class Solution {
public List<String> wordBreak(String s, Set<String> dict) {
List<String> list = new ArrayList<String>();
// Input checking.
if (s == null || s.length() == 0 ||
dict == null || dict.size() == 0) return list;
int len = s.length();
// memo[i] is recording,
// whether we cut at index "i", can get one of the result.
boolean memo[] = new boolean[len];
for (int i = 0; i < len; i ++) memo[i] = true;
StringBuilder tmpStrBuilder = new StringBuilder();
helper(s, 0, tmpStrBuilder, dict, list, memo);
return list;
}
private void helper(String s, int start, StringBuilder tmpStrBuilder,
Set<String> dict, List<String> list, boolean[] memo) {
// Base case.
if (start >= s.length()) {
list.add(tmpStrBuilder.toString().trim());
return;
}
int listSizeBeforeRecursion = 0;
for (int i = start; i < s.length(); i ++) {
if (memo[i] == false) continue;
String curr = s.substring(start, i + 1);
if (!dict.contains(curr)) continue;
// Have a try.
tmpStrBuilder.append(curr);
tmpStrBuilder.append(" ");
// Do recursion.
listSizeBeforeRecursion = list.size();
helper(s, i + 1, tmpStrBuilder, dict, list, memo);
if (list.size() == listSizeBeforeRecursion) memo[i] = false;
// Roll back.
tmpStrBuilder.setLength(tmpStrBuilder.length() - curr.length() - 1);
}
}
}
With DP:
Time: O(N*M)
N - string size
M - dict size
Memory: O(N)
See my answer here, with code example:
Dynamic Programming - Word Break
It is dynamic problem.
You can maintain two things.
1 DP[i] means when the string is in ith character, there is dp[i] ways to cut it.
2 vector < int> pre[i] means the previous position can reach the current ith position.(It's size must be DP[i])
Time is O(n*m)
Firstly, i is in [0,n):
then find j in [0,i): that substring(j+1,i) is valid.
The validation can be calculated previously. So the time is O(n*m), and you can use vector < int> pre[i] to get all the cutting solution you want.
If the input is 'abba' then the possible palindromes are a, b, b, a, bb, abba.
I understand that determining if string is palindrome is easy. It would be like:
public static boolean isPalindrome(String str) {
int len = str.length();
for(int i=0; i<len/2; i++) {
if(str.charAt(i)!=str.charAt(len-i-1) {
return false;
}
return true;
}
But what is the efficient way of finding palindrome substrings?
This can be done in O(n), using Manacher's algorithm.
The main idea is a combination of dynamic programming and (as others have said already) computing maximum length of palindrome with center in a given letter.
What we really want to calculate is radius of the longest palindrome, not the length.
The radius is simply length/2 or (length - 1)/2 (for odd-length palindromes).
After computing palindrome radius pr at given position i we use already computed radiuses to find palindromes in range [i - pr ; i]. This lets us (because palindromes are, well, palindromes) skip further computation of radiuses for range [i ; i + pr].
While we search in range [i - pr ; i], there are four basic cases for each position i - k (where k is in 1,2,... pr):
no palindrome (radius = 0) at i - k
(this means radius = 0 at i + k, too)
inner palindrome, which means it fits in range
(this means radius at i + k is the same as at i - k)
outer palindrome, which means it doesn't fit in range
(this means radius at i + k is cut down to fit in range, i.e because i + k + radius > i + pr we reduce radius to pr - k)
sticky palindrome, which means i + k + radius = i + pr
(in that case we need to search for potentially bigger radius at i + k)
Full, detailed explanation would be rather long. What about some code samples? :)
I've found C++ implementation of this algorithm by Polish teacher, mgr Jerzy Wałaszek.
I've translated comments to english, added some other comments and simplified it a bit to be easier to catch the main part.
Take a look here.
Note: in case of problems understanding why this is O(n), try to look this way:
after finding radius (let's call it r) at some position, we need to iterate over r elements back, but as a result we can skip computation for r elements forward. Therefore, total number of iterated elements stays the same.
Perhaps you could iterate across potential middle character (odd length palindromes) and middle points between characters (even length palindromes) and extend each until you cannot get any further (next left and right characters don't match).
That would save a lot of computation when there are no many palidromes in the string. In such case the cost would be O(n) for sparse palidrome strings.
For palindrome dense inputs it would be O(n^2) as each position cannot be extended more than the length of the array / 2. Obviously this is even less towards the ends of the array.
public Set<String> palindromes(final String input) {
final Set<String> result = new HashSet<>();
for (int i = 0; i < input.length(); i++) {
// expanding even length palindromes:
expandPalindromes(result,input,i,i+1);
// expanding odd length palindromes:
expandPalindromes(result,input,i,i);
}
return result;
}
public void expandPalindromes(final Set<String> result, final String s, int i, int j) {
while (i >= 0 && j < s.length() && s.charAt(i) == s.charAt(j)) {
result.add(s.substring(i,j+1));
i--; j++;
}
}
So, each distinct letter is already a palindrome - so you already have N + 1 palindromes, where N is the number of distinct letters (plus empty string). You can do that in single run - O(N).
Now, for non-trivial palindromes, you can test each point of your string to be a center of potential palindrome - grow in both directions - something that Valentin Ruano suggested.
This solution will take O(N^2) since each test is O(N) and number of possible "centers" is also O(N) - the center is either a letter or space between two letters, again as in Valentin's solution.
Note, there is also O(N) solution to your problem, based on Manacher's algoritm (article describes "longest palindrome", but algorithm could be used to count all of them)
I just came up with my own logic which helps to solve this problem.
Happy coding.. :-)
System.out.println("Finding all palindromes in a given string : ");
subPal("abcacbbbca");
private static void subPal(String str) {
String s1 = "";
int N = str.length(), count = 0;
Set<String> palindromeArray = new HashSet<String>();
System.out.println("Given string : " + str);
System.out.println("******** Ignoring single character as substring palindrome");
for (int i = 2; i <= N; i++) {
for (int j = 0; j <= N; j++) {
int k = i + j - 1;
if (k >= N)
continue;
s1 = str.substring(j, i + j);
if (s1.equals(new StringBuilder(s1).reverse().toString())) {
palindromeArray.add(s1);
}
}
}
System.out.println(palindromeArray);
for (String s : palindromeArray)
System.out.println(s + " - is a palindrome string.");
System.out.println("The no.of substring that are palindrome : "
+ palindromeArray.size());
}
Output:-
Finding all palindromes in a given string :
Given string : abcacbbbca
******** Ignoring single character as substring palindrome ********
[cac, acbbbca, cbbbc, bb, bcacb, bbb]
cac - is a palindrome string.
acbbbca - is a palindrome string.
cbbbc - is a palindrome string.
bb - is a palindrome string.
bcacb - is a palindrome string.
bbb - is a palindrome string.
The no.of substring that are palindrome : 6
I suggest building up from a base case and expanding until you have all of the palindomes.
There are two types of palindromes: even numbered and odd-numbered. I haven't figured out how to handle both in the same way so I'll break it up.
1) Add all single letters
2) With this list you have all of the starting points for your palindromes. Run each both of these for each index in the string (or 1 -> length-1 because you need at least 2 length):
findAllEvenFrom(int index){
int i=0;
while(true) {
//check if index-i and index+i+1 is within string bounds
if(str.charAt(index-i) != str.charAt(index+i+1))
return; // Here we found out that this index isn't a center for palindromes of >=i size, so we can give up
outputList.add(str.substring(index-i, index+i+1));
i++;
}
}
//Odd looks about the same, but with a change in the bounds.
findAllOddFrom(int index){
int i=0;
while(true) {
//check if index-i and index+i+1 is within string bounds
if(str.charAt(index-i-1) != str.charAt(index+i+1))
return;
outputList.add(str.substring(index-i-1, index+i+1));
i++;
}
}
I'm not sure if this helps the Big-O for your runtime, but it should be much more efficient than trying each substring. Worst case would be a string of all the same letter which may be worse than the "find every substring" plan, but with most inputs it will cut out most substrings because you can stop looking at one once you realize it's not the center of a palindrome.
I tried the following code and its working well for the cases
Also it handles individual characters too
Few of the cases which passed:
abaaa --> [aba, aaa, b, a, aa]
geek --> [g, e, ee, k]
abbaca --> [b, c, a, abba, bb, aca]
abaaba -->[aba, b, abaaba, a, baab, aa]
abababa -->[aba, babab, b, a, ababa, abababa, bab]
forgeeksskeegfor --> [f, g, e, ee, s, r, eksske, geeksskeeg,
o, eeksskee, ss, k, kssk]
Code
static Set<String> set = new HashSet<String>();
static String DIV = "|";
public static void main(String[] args) {
String str = "abababa";
String ext = getExtendedString(str);
// will check for even length palindromes
for(int i=2; i<ext.length()-1; i+=2) {
addPalindromes(i, 1, ext);
}
// will check for odd length palindromes including individual characters
for(int i=1; i<=ext.length()-2; i+=2) {
addPalindromes(i, 0, ext);
}
System.out.println(set);
}
/*
* Generates extended string, with dividors applied
* eg: input = abca
* output = |a|b|c|a|
*/
static String getExtendedString(String str) {
StringBuilder builder = new StringBuilder();
builder.append(DIV);
for(int i=0; i< str.length(); i++) {
builder.append(str.charAt(i));
builder.append(DIV);
}
String ext = builder.toString();
return ext;
}
/*
* Recursive matcher
* If match is found for palindrome ie char[mid-offset] = char[mid+ offset]
* Calculate further with offset+=2
*
*
*/
static void addPalindromes(int mid, int offset, String ext) {
// boundary checks
if(mid - offset <0 || mid + offset > ext.length()-1) {
return;
}
if (ext.charAt(mid-offset) == ext.charAt(mid+offset)) {
set.add(ext.substring(mid-offset, mid+offset+1).replace(DIV, ""));
addPalindromes(mid, offset+2, ext);
}
}
Hope its fine
public class PolindromeMyLogic {
static int polindromeCount = 0;
private static HashMap<Character, List<Integer>> findCharAndOccurance(
char[] charArray) {
HashMap<Character, List<Integer>> map = new HashMap<Character, List<Integer>>();
for (int i = 0; i < charArray.length; i++) {
char c = charArray[i];
if (map.containsKey(c)) {
List list = map.get(c);
list.add(i);
} else {
List list = new ArrayList<Integer>();
list.add(i);
map.put(c, list);
}
}
return map;
}
private static void countPolindromeByPositions(char[] charArray,
HashMap<Character, List<Integer>> map) {
map.forEach((character, list) -> {
int n = list.size();
if (n > 1) {
for (int i = 0; i < n - 1; i++) {
for (int j = i + 1; j < n; j++) {
if (list.get(i) + 1 == list.get(j)
|| list.get(i) + 2 == list.get(j)) {
polindromeCount++;
} else {
char[] temp = new char[(list.get(j) - list.get(i))
+ 1];
int jj = 0;
for (int ii = list.get(i); ii <= list
.get(j); ii++) {
temp[jj] = charArray[ii];
jj++;
}
if (isPolindrome(temp))
polindromeCount++;
}
}
}
}
});
}
private static boolean isPolindrome(char[] charArray) {
int n = charArray.length;
char[] temp = new char[n];
int j = 0;
for (int i = (n - 1); i >= 0; i--) {
temp[j] = charArray[i];
j++;
}
if (Arrays.equals(charArray, temp))
return true;
else
return false;
}
public static void main(String[] args) {
String str = "MADAM";
char[] charArray = str.toCharArray();
countPolindromeByPositions(charArray, findCharAndOccurance(charArray));
System.out.println(polindromeCount);
}
}
Try out this. Its my own solution.
// Maintain an Set of palindromes so that we get distinct elements at the end
// Add each char to set. Also treat that char as middle point and traverse through string to check equality of left and right char
static int palindrome(String str) {
Set<String> distinctPln = new HashSet<String>();
for (int i=0; i<str.length();i++) {
distinctPln.add(String.valueOf(str.charAt(i)));
for (int j=i-1, k=i+1; j>=0 && k<str.length(); j--, k++) {
// String of lenght 2 as palindrome
if ( (new Character(str.charAt(i))).equals(new Character(str.charAt(j)))) {
distinctPln.add(str.substring(j,i+1));
}
// String of lenght 2 as palindrome
if ( (new Character(str.charAt(i))).equals(new Character(str.charAt(k)))) {
distinctPln.add(str.substring(i,k+1));
}
if ( (new Character(str.charAt(j))).equals(new Character(str.charAt(k)))) {
distinctPln.add(str.substring(j,k+1));
} else {
continue;
}
}
}
Iterator<String> distinctPlnItr = distinctPln.iterator();
while ( distinctPlnItr.hasNext()) {
System.out.print(distinctPlnItr.next()+ ",");
}
return distinctPln.size();
}
Code is to find all distinct substrings which are palindrome.
Here is the code I tried. It is working fine.
import java.util.HashSet;
import java.util.Set;
public class SubstringPalindrome {
public static void main(String[] args) {
String s = "abba";
checkPalindrome(s);
}
public static int checkPalindrome(String s) {
int L = s.length();
int counter =0;
long startTime = System.currentTimeMillis();
Set<String> hs = new HashSet<String>();
// add elements to the hash set
System.out.println("Possible substrings: ");
for (int i = 0; i < L; ++i) {
for (int j = 0; j < (L - i); ++j) {
String subs = s.substring(j, i + j + 1);
counter++;
System.out.println(subs);
if(isPalindrome(subs))
hs.add(subs);
}
}
System.out.println("Total possible substrings are "+counter);
System.out.println("Total palindromic substrings are "+hs.size());
System.out.println("Possible palindromic substrings: "+hs.toString());
long endTime = System.currentTimeMillis();
System.out.println("It took " + (endTime - startTime) + " milliseconds");
return hs.size();
}
public static boolean isPalindrome(String s) {
if(s.length() == 0 || s.length() ==1)
return true;
if(s.charAt(0) == s.charAt(s.length()-1))
return isPalindrome(s.substring(1, s.length()-1));
return false;
}
}
OUTPUT:
Possible substrings:
a
b
b
a
ab
bb
ba
abb
bba
abba
Total possible substrings are 10
Total palindromic substrings are 4
Possible palindromic substrings: [bb, a, b, abba]
It took 1 milliseconds
I want to develop a recursive word wrap algorithm that takes a specified string and wrap length (the maximum number of characters on one line) to return a wrapped output at the input length. I don't want it to break apart words. So for example, This is the first paragraph that you need to input with length 20 returns as:
This is the first
paragraph that you
need to input
I already have a dynamic programming (bottom-up) solution implemented, but I was wondering if it's possible to write an algorithm to do this using just recursion (top-down) instead? I'd also like to memoize it if I can. Please don't give me any runnable code... I"m just wondering about ideas/pseudocode.
Something like the pseudocode below should work. (I'm sure we'll get comments if I made a mistake!)
function Wrap(the_text,line_len)
if length(the_text) > line_len then
text_bit = the first few words of the_text, keeping their length shorter than line_len
remove text_bit from the beginning of the_text
return text_bit + linefeed + Wrap(the_text, line_len)
else
return the_text
end if
end function
import java.lang.Math;
public int RCS(int[] l , int n , int m , int index) {
// first base condition - if index gets beyond the array 'l' , then return 0;
if (index > n - 1) return 0;
// second base condition - if index is the last word i.e there is only one word left in the
// array to be inserted in the line then return the cost if added in that line.
if (index == n - 1) return (m - l[n - 1]) * (m - l[n - 1]) * (m - l[n - 1]);
// make a global cost variable to be returned
int cost = Integer.MAX_VALUE;
// Here , we try to select words from the array and apply RCS on the rest of the array.
// From index to last element , we iteratvely select first , or first two and so on.
for (int i = index ; i < n ; i++) {
int current_space_sum = 0 ;
// we add the length of the selected word. We have selected words in array from index to i.
for (int k = index ; k <= i ; k++) {
current_space_sum = current_space_sum + l[k] ;
}
// Adding the space between the words choses. If 2 words are chosen , there is one space and so on
current_space_sum = current_space_sum + i - index;
// If the length of the chosen words is greater than the line can accept , no need of looking beyond.
if (current_space_sum > m) break;
// Iteratively find the minimum cost
cost = Math.min(cost , (m - current_space_sum) * (m - current_space_sum) * (m - current_space_sum) + RCS(l , n , m , i + 1));
}
return cost;
}
public static void main(String[] args) {
WordWrap w = new WordWrap();
int[] l = {3, 2 , 2 , 5};
int n = l.length;
int m = 6;
int result = w.RCS(l , n , m , 0);
System.out.println(result);
}
The below code will help you to get the optimal cost for that problem.
#include<bits/stdc++.h>
using namespace std;
// method to get the optimal cost
int findOptimalCost(int *arr, int s, int e,int lineLength,map<pair<int,int>,int>dp) {
if(s>=e) // return 0 for the last line because we are not calculating the last line space
return 0;
if(dp.find({s,e}) != dp.end()) { // return cost if we already calculate
return dp[{s,e}];
}
int minCost = INT_MAX;
for(int i=s;i<=e;i++) {
int sum = 0,space=i-s;
for(int j =s; j<=i; j++)
sum += arr[j]; // add the word length
sum += space; // add the space for words (if 2 word then we will count 1 space )
int cost;
if(sum<=lineLength)
cost = (lineLength-sum)*(lineLength-sum) + findOptimalCost(arr,s+1+space,e,lineLength,dp); // calculate the cost for perticular line and call for rest line
if(minCost > cost) {
minCost = cost; // update the minCost variable if the latest cost is less then the previous calculated cost
}
}
return dp[{s,e}] = minCost; // store the minimum cost for particular line and return
}
int main()
{
//code
int len = 4; // total word in the list
int arr[] = {3,2,2,5}; // let us assume the length of word
int lineLength = 6; // size of max line length
map<pair<int,int>,int> dp;
cout<<findOptimalCost(arr,0,len-1,lineLength,dp)<<endl;
return 0;
}
I need to find a dynamic programming algorithm to solve this problem. I tried but couldn't figure it out. Here is the problem:
You are given a string of n characters s[1...n], which you believe to be a corrupted text document in which all punctuation has vanished (so that it looks something like "itwasthebestoftimes..."). You wish to reconstruct the document using a dictionary, which is available in the form of a Boolean function dict(*) such that, for any string w, dict(w) has value 1 if w is a valid word, and has value 0 otherwise.
Give a dynamic programming algorithm that determines whether the string s[*] can be reconstituted as a sequence of valid words. The running time should be at most O(n^2), assuming that each call to dict takes unit time.
In the event that the string is valid, make your algorithm output the corresponding sequence of words.
Let the length of your compacted document be N.
Let b(n) be a boolean: true if the document can be split into words starting from position n in the document.
b(N) is true (since the empty string can be split into 0 words).
Given b(N), b(N - 1), ... b(N - k), you can construct b(N - k - 1) by considering all words that start at character N - k - 1. If there's any such word, w, with b(N - k - 1 + len(w)) set, then set b(N - k - 1) to true. If there's no such word, then set b(N - k - 1) to false.
Eventually, you compute b(0) which tells you if the entire document can be split into words.
In pseudo-code:
def try_to_split(doc):
N = len(doc)
b = [False] * (N + 1)
b[N] = True
for i in range(N - 1, -1, -1):
for word starting at position i:
if b[i + len(word)]:
b[i] = True
break
return b
There's some tricks you can do to get 'word starting at position i' efficient, but you're asked for an O(N^2) algorithm, so you can just look up every string starting at i in the dictionary.
To generate the words, you can either modify the above algorithm to store the good words, or just generate it like this:
def generate_words(doc, b, idx=0):
length = 1
while true:
assert b(idx)
if idx == len(doc): return
word = doc[idx: idx + length]
if word in dictionary and b(idx + length):
output(word)
idx += length
length = 1
Here b is the boolean array generated from the first part of the algorithm.
To formalize what #MinhPham suggested.
This is a dynammic programming solution.
Given a string str, let
b[i] = true if the substring str[0...i] (inclusive) can be split into valid words.
Prepend some starting character to str, say !, to represent the empty word.
str = "!" + str
The base case is the empty string, so
b[0] = true.
For the iterative case:
b[j] = true if b[i] == true and str[i..j] is a word for all i < j
The O(N^2) Dp is clear but if you know the words of the dictionary, i think you can use some precomputations to get it even faster in O(N).
Aho-Corasick
A dp solution in c++:
int main()
{
set<string> dict;
dict.insert("12");
dict.insert("123");
dict.insert("234");
dict.insert("12345");
dict.insert("456");
dict.insert("1234");
dict.insert("567");
dict.insert("123342");
dict.insert("42");
dict.insert("245436564");
dict.insert("12334");
string str = "123456712334245436564";
int size = str.size();
vector<int> dp(size+1, -1);
dp[0] = 0;
vector<string > res(size+1);
for(int i = 0; i < size; ++i)
{
if(dp[i] != -1)
{
for(int j = i+1; j <= size; ++j)
{
const int len = j-i;
string substr = str.substr(i, len);
if(dict.find(substr) != dict.end())
{
string space = i?" ":"";
res[i+len] = res[i] + space + substr;
dp[i+len] = dp[i]+1;
}
}
}
}
cout << *dp.rbegin() << endl;
cout << *res.rbegin() << endl;
return 0;
}
The string s[] can potentially be split into more than one ways. The method below finds the maximum number of words in which we can split s[]. Below is the sketch/pseudocode of the algorithm
bestScore[i] -> Stores the maximum number of words in which the first i characters can be split (it would be MINUS_INFINITY otherwise)
for (i = 1 to n){
bestScore[i] = MINUS_INFINITY
for (k = 1 to i-1){
bestScore[i] = Max(bestSCore[i], bestScore[i-k]+ f(i,k))
}
}
Where f(i,k) is defined as:
f(i,k) = 1 : if s[i-k+1 to i] is in dictionary
= MINUS_INFINITY : otherwise
bestScore[n] would store the maximum number of words in which s[] can be split (if the value is MINUS_INFINIY, s[] cannot be split)
Clearly the running time is O(n^2)
As this looks like a textbook exercise, I will not write the code to reconstruct the actual split positions.
Below is an O(n^2) solution for this problem.
void findstringvalid() {
string s = "itwasthebestoftimes";
set<string> dict;
dict.insert("it");
dict.insert("was");
dict.insert("the");
dict.insert("best");
dict.insert("of");
dict.insert("times");
vector<bool> b(s.size() + 1, false);
vector<int> spacepos(s.size(), -1);
//Initialization phase
b[0] = true; //String of size 0 is always a valid string
for (int i = 1; i <= s.size(); i++) {
for (int j = 0; j <i; j++) {
//string of size s[ j... i]
if (!b[i]) {
if (b[j]) {
//check if string "j to i" is in dictionary
string temp = s.substr(j, i - j);
set<string>::iterator it = dict.find(temp);
if (it != dict.end()) {
b[i] = true;
spacepos[i-1] = j;
}
}
}
}
}
if(b[s.size()])
for (int i = 1; i < spacepos.size(); i++) {
if (spacepos[i] != -1) {
string temp = s.substr(spacepos[i], i - spacepos[i] + 1);
cout << temp << " ";
}
}
}