Convert string to palindrome string with minimum insertions - algorithm

In order to find the minimal number of insertions required to convert a given string(s) to palindrome I find the longest common subsequence of the string(lcs_string) and its reverse. Therefore the number of insertions to be made is length(s) - length(lcs_string)
What method should be employed to find the equivalent palindrome string on knowing the number of insertions to be made?
For example :
1) azbzczdzez
Number of insertions required : 5
Palindrome string : azbzcezdzeczbza
Although multiple palindrome strings may exist for the same string but I want to find only one palindrome?

Let S[i, j] represents a sub-string of string S starting from index i and ending at index j (both inclusive) and c[i, j] be the optimal solution for S[i, j].
Obviously, c[i, j] = 0 if i >= j.
In general, we have the recurrence:

To elaborate on VenomFangs answer, there is a simple dynamic programming solution to this one. Note that I'm assuming the only operation allowed here is insertion of characters (no deletion, updates). Let S be a string of n characters. The simple recursion function P for this is:
= P [i+1 .. j-1], if S[i] = S[j]
P[i..j]
= min (P[i..j-1], P[i+1..j]) + 1,
If you'd like more explanation on why this is true, post a comment and i'd be happy to explain (though its pretty easy to see with a little thought). This, by the way, is the exact opposite of the LCS function you use, hence validating that your solution is in fact optimal. Of course its wholly possible I bungled, if so, someone do let me know!
Edit: To account for the palindrome itself, this can be easily done as follows:
As stated above, P[1..n] would give you the number of insertions required to make this string a palindrome. Once the above two-dimensional array is built up, here's how you find the palindrome:
Start with i=1, j=n. Now,
string output = "";
while(i < j)
{
if (P[i][j] == P[i+1][j-1]) //this happens if no insertions were made at this point
{
output = output + S[i];
i++;
j--;
}
else
if (P[i][j] == P[i+1][j]) //
{
output = output + S[i];
i++;
}
else
{
output = S[j] + output;
j--;
}
}
cout<<output<<reverse(output);
//You may have to be careful about odd sized palindromes here,
// I haven't accounted for that, it just needs one simple check
Does that make better reading?

The solution looks to be a dynamic programming solution.
You may be able to find your answer in the following post: How can I compute the number of characters required to turn a string into a palindrome?

PHP Solution of O(n)
function insertNode(&$arr, $idx, $val) {
$arr = array_merge(array_slice($arr, 0, $idx), array($val), array_slice($arr, $idx));
}
function createPalindrome($arr, $s, $e) {
$i = 0;
while(true) {
if($s >= $e) {
break;
} else if($arr[$s] == $arr[$e]) {
$s++; $e--; // shrink the queue from both sides
continue;
} else {
insertNode($arr, $s, $arr[$e]);
$s++;
}
}
echo implode("", $arr);
}
$arr = array('b', 'e', 'a', 'a', 'c', 'd', 'a', 'r', 'e');
echo createPalindrome ( $arr, 0, count ( $arr ) - 1 );

Simple. See below :)
String pattern = "abcdefghgf";
boolean isPalindrome = false;
int i=0,j=pattern.length()-1;
int mismatchCounter = 0;
while(i<=j)
{
//reverse matching
if(pattern.charAt(i)== pattern.charAt(j))
{
i++; j--;
isPalindrome = true;
continue;
}
else if(pattern.charAt(i)!= pattern.charAt(j))
{
i++;
mismatchCounter++;
}
}
System.out.println("The pattern string is :"+pattern);
System.out.println("Minimum number of characters required to make this string a palidnrome : "+mismatchCounter);

Related

Complexity of the word break algorithm

My Q is similar to the one asked on stack overflow in the past
http://www.geeksforgeeks.org/dynamic-programming-set-32-word-break-problem/
The solution I wrote, I am not able to understand that since I do not use DP but still how is it that my sol is solving overlapping problems. I think it is not. Can someone clarify?
my dicitonary that i use is {"cat", "catdog", "dog", "mouse"} and test string as "catdogmouse"
Here is the method i wrote
public static boolean recursiveWordBreak2(String s, int start) {
System.out.println("s is:"+s.substring(start));
if (s.isEmpty() || start >= s.length()) {
return true;
}
for (int i = start; i <= s.length(); i++) {
String str = s.substring(start, i);
System.out.println("substr:" + str);
if (dictSet.contains(str)) {
return recursiveWordBreak2(s, i);
}
}
return false;
}
Your solution uses recursion -only-. recognising that that problem is DP allows you to MEMOIZE (remember) previous results so that you can reuse them without doing the recursion again.
in the link you provided if the dictionary is {a,b,c,d,e} and the input is "abcde", you would need to check if "cde" is valid twice with recursive code, where a DP solution would remember "cde" is valid and only have to check once.
edit: dictionary {a,b,c,d,e} should be {a, ab, cde} to illustrate checking 'cde' twice
edit2 (see comment on algo having logic issue):
if (dictSet.contains(str)) {
return recursiveWordBreak2(s, i);
}
should be
if (dictSet.contains(str) && recursiveWordBreak2(s, i)) { return true }
that way if contains = true but recursiveWB = false, the outer loop will continue to check length+1 instead of returning false

Return the number of elements of an array that is the most "expensive"

I recently stumbled upon an interesting problem, an I am wondering if my solution is optimal.
You are given an array of zeros and ones. The goal is to return the
amount zeros and the amount of ones in the most expensive sub-array.
The cost of an array is the amount of 1s divided by amount of 0s. In
case there are no zeros in the sub-array, the cost is zero.
At first I tried brute-forcing, but for an array of 10,000 elements it was far too slow and I ran out of memory.
My second idea was instead of creating those sub-arrays, to remember the start and the end of the sub-array. That way I saved a lot of memory, but the complexity was still O(n2).
My final solution that I came up is I think O(n). It goes like this:
Start at the beginning of the array, for each element, calculate the cost of the sub-arrays starting from 1, ending at the current index. So we would start with a sub-array consisting of the first element, then first and second etc. Since the only thing that we need to calculate the cost, is the amount of 1s and 0s in the sub-array, I could find the optimal end of the sub-array.
The second step was to start from the end of the sub-array from step one, and repeat the same to find the optimal beginning. That way I am sure that there is no better combination in the whole array.
Is this solution correct? If not, is there a counter-example that will show that this solution is incorrect?
Edit
For clarity:
Let's say our input array is 0101.
There are 10 subarrays:
0,1,0,1,01,10,01,010,101 and 0101.
The cost of the most expensive subarray would be 2 since 101 is the most expensive subarray. So the algorithm should return 1,2
Edit 2
There is one more thing that I forgot, if 2 sub-arrays have the same cost, the longer one is "more expensive".
Let me sketch a proof for my assumption:
(a = whole array, *=zero or more, +=one or more, {n}=exactly n)
Cases a=0* and a=1+ : c=0
Cases a=01+ and a=1+0 : conforms to 1*0{1,2}1*, a is optimum
For the normal case, a contains one or more 0s and 1s.
This means there is some optimum sub-array of non-zero cost.
(S) Assume s is an optimum sub-array of a.
It contains one or more zeros. (Otherwise its cost would be zero).
(T) Let t be the longest `1*0{1,2}+1*` sequence within s
(and among the equally long the one with with most 1s).
(Note: There is always one such, e.g. `10` or `01`.)
Let N be the number of 1s in t.
Now, we prove that always t = s.
By showing it is not possible to add adjacent parts of s to t if (S).
(E) Assume t shorter than s.
We cannot add 1s at either side, otherwise not (T).
For each 0 we add from s, we have to add at least N more 1s
later to get at least the same cost as our `1*0+1*`.
This means: We have to add at least one run of N 1s.
If we add some run of N+1, N+2 ... somewhere than not (T).
If we add consecutive zeros, we need to compensate
with longer runs of 1s, thus not (T).
This leaves us with the only option of adding single zeors and runs of N 1s each.
This would give (symmetry) `1{n}*0{1,2}1{m}01{n+m}...`
If m>0 then `1{m}01{n+m}` is longer than `1{n}0{1,2}1{m}`, thus not (T).
If m=0 then we get `1{n}001{n}`, thus not (T).
So assumption (E) must be wrong.
Conclusion: The optimum sub-array must conform to 1*0{1,2}1*.
Here is my O(n) impl in Java according to the assumption in my last comment (1*01* or 1*001*):
public class Q19596345 {
public static void main(String[] args) {
try {
String array = "0101001110111100111111001111110";
System.out.println("array=" + array);
SubArray current = new SubArray();
current.array = array;
SubArray best = (SubArray) current.clone();
for (int i = 0; i < array.length(); i++) {
current.accept(array.charAt(i));
SubArray candidate = (SubArray) current.clone();
candidate.trim();
if (candidate.cost() > best.cost()) {
best = candidate;
System.out.println("better: " + candidate);
}
}
System.out.println("best: " + best);
} catch (Exception ex) { ex.printStackTrace(System.err); }
}
static class SubArray implements Cloneable {
String array;
int start, leftOnes, zeros, rightOnes;
// optimize 1*0*1* by cutting
void trim() {
if (zeros > 1) {
if (leftOnes < rightOnes) {
start += leftOnes + (zeros - 1);
leftOnes = 0;
zeros = 1;
} else if (leftOnes > rightOnes) {
zeros = 1;
rightOnes = 0;
}
}
}
double cost() {
if (zeros == 0) return 0;
else return (leftOnes + rightOnes) / (double) zeros +
(leftOnes + zeros + rightOnes) * 0.00001;
}
void accept(char c) {
if (c == '1') {
if (zeros == 0) leftOnes++;
else rightOnes++;
} else {
if (rightOnes > 0) {
start += leftOnes + zeros;
leftOnes = rightOnes;
zeros = 0;
rightOnes = 0;
}
zeros++;
}
}
public Object clone() throws CloneNotSupportedException { return super.clone(); }
public String toString() { return String.format("%s at %d with cost %.3f with zeros,ones=%d,%d",
array.substring(start, start + leftOnes + zeros + rightOnes), start, cost(), zeros, leftOnes + rightOnes);
}
}
}
If we can show the max array is always 1+0+1+, 1+0, or 01+ (Regular expression notation then we can calculate the number of runs
So for the array (010011), we have (always starting with a run of 1s)
0,1,1,2,2
so the ratios are (0, 1, 0.3, 1.5, 1), which leads to an array of 10011 as the final result, ignoring the one runs
Cost of the left edge is 0
Cost of the right edge is 2
So in this case, the right edge is the correct answer -- 011
I haven't yet been able to come up with a counterexample, but the proof isn't obvious either. Hopefully we can crowd source one :)
The degenerate cases are simpler
All 1's and 0's are obvious, as they all have the same cost.
A string of just 1+,0+ or vice versa is all the 1's and a single 0.
How about this? As a C# programmer, I am thinking we can use something like Dictionary of <int,int,int>.
The first int would be use as key, second as subarray number and the third would be for the elements of sub-array.
For your example
key|Sub-array number|elements
1|1|0
2|2|1
3|3|0
4|4|1
5|5|0
6|5|1
7|6|1
8|6|0
9|7|0
10|7|1
11|8|0
12|8|1
13|8|0
14|9|1
15|9|0
16|9|1
17|10|0
18|10|1
19|10|0
20|10|1
Then you can run through the dictionary and store the highest in a variable.
var maxcost=0
var arrnumber=1;
var zeros=0;
var ones=0;
var cost=0;
for (var i=1;i++;i<=20+1)
{
if ( dictionary.arraynumber[i]!=dictionary.arraynumber[i-1])
{
zeros=0;
ones=0;
cost=0;
if (cost>maxcost)
{
maxcost=cost;
}
}
else
{
if (dictionary.values[i]==0)
{
zeros++;
}
else
{
ones++;
}
cost=ones/zeros;
}
}
This will be log(n^2), i hope and u just need 3n size of memory of the array?
I think we can modify the maximal subarray problem to fit to this question. Here's my attempt at it:
void FindMaxRatio(int[] array, out maxNumOnes, out maxNumZeros)
{
maxNumOnes = 0;
maxNumZeros = 0;
int numOnes = 0;
int numZeros = 0;
double maxSoFar = 0;
double maxEndingHere = 0;
for(int i = 0; i < array.Size; i++){
if(array[i] == 0) numZeros++;
if(array[i] == 1) numOnes++;
if(numZeros == 0) maxEndingHere = 0;
else maxEndingHere = numOnes/(double)numZeros;
if(maxEndingHere < 1 && maxEndingHere > 0) {
numZeros = 0;
numOnes = 0;
}
if(maxSoFar < maxEndingHere){
maxSoFar = maxEndingHere;
maxNumOnes = numOnes;
maxNumZeros = numZeros;
}
}
}
I think the key is if the ratio is less then 1, we can disregard that subsequence because
there will always be a subsequence 01 or 10 whose ratio is 1. This seemed to work for 010011.

What is the best way to recursively generate all binary strings of length n?

I'm looking for a good (easy to implement, intuitive, etc.) recursive method of generating all binary strings of length n, where 1 <= n <= 35.
I would appreciate ideas for a pseudo-code algorithm (no language-specific tricks).
LE: okay, I did go overboard with the upper limit. My intention was to avoid solutions that use the binary representation of a counter from 1 to 1 << n.
Here's an example of recursion in C++.
vector<string> answer;
void getStrings( string s, int digitsLeft )
{
if( digitsLeft == 0 ) // the length of string is n
answer.push_back( s );
else
{
getStrings( s + "0", digitsLeft - 1 );
getStrings( s + "1", digitsLeft - 1 );
}
}
getStrings( "", n ); // initial call
According to the Divide et Impera paradigm, the problem of generating all binary strings of length n can be splitted in two subproblems: the problem of printing all binary strings of lenght n-1 preceeded by a 0 and the one of printing all binary strings of lenght n-1 preceeded by a 1. So the following pseudocode solves the problem:
generateBinary(length, string)
if(length > 0)
generateBinary(length-1, string + "0")
generateBinary(length-1, string + "1")
else
print(string)
def genBins(n):
"""
generate all the binary strings with n-length
"""
max_int = '0b' + '1' * n
for i in range(0, int(max_int, 2)+1, 1):
yield str(format(i, 'b').zfill(n))
if __name__ == '__main__':
print(list(genBins(5)))
The problem you have can be solved with a Backtracking algorithm.
Pseudo-code for such an algorithm is:
fun(input, n)
if( base_case(input, n) )
//print result
else
//choose from pool of choices
//explorer the rest of choices from what's left
//unchoose
Implementation:
Base case: we want to print our result string when its size is equal to n
Recursive case:
our pool of choices consists of 0 and 1
choosing in this case means take 0 or 1 and add it to the input as last character
explore by recursing, where we pass the new input value from the choose step until base case is reached
un-choosing in this case means remove the last character
function binary(n) {
binaryHelper('', n);
}
function binaryHelper(str, n) {
if (str.length === n) {
//base case
console.log(str); //print string
} else {
for (let bit = 0; bit < 2; bit++) {
str = str + bit; // choose
binaryHelper(str, n); // explore
str = str.slice(0, -1); // un-choose
}
}
}
console.log('Size 2 binary strings:');
binary(2);
console.log('Size 3 binary strings:');
binary(3);
You can re-write the code above like this, where you choose & un-choose by stateless transition from one loop iteration to another. This is less intuitive though.
function binary(n) {
binaryHelper('', n);
}
function binaryHelper(str, n) {
if(str.length === n) {
console.log(str);
} else {
for(let bit = 0; bit < 2; bit++) {
binaryHelper(str+bit, n);
}
}
}
console.log('Size 2 binary strings:');
binary(2);
console.log('Size 3 binary strings:');
binary(3);

Longest common prefix for n string

Given n string of max length m. How can we find the longest common prefix shared by at least two strings among them?
Example: ['flower', 'flow', 'hello', 'fleet']
Answer: fl
I was thinking of building a Trie for all the string and then checking the deepest node (satisfies longest) that branches out to two/more substrings (satisfies commonality). This takes O(n*m) time and space. Is there a better way to do this
Why to use trie(which takes O(mn) time and O(mn) space, just use the basic brute force way. first loop, find the shortest string as minStr, which takes o(n) time, second loop, compare one by one with this minStr, and keep an variable which indicates the rightmost index of minStr, this loop takes O(mn) where m is the shortest length of all strings. The code is like below,
public String longestCommonPrefix(String[] strs) {
if(strs.length==0) return "";
String minStr=strs[0];
for(int i=1;i<strs.length;i++){
if(strs[i].length()<minStr.length())
minStr=strs[i];
}
int end=minStr.length();
for(int i=0;i<strs.length;i++){
int j;
for( j=0;j<end;j++){
if(minStr.charAt(j)!=strs[i].charAt(j))
break;
}
if(j<end)
end=j;
}
return minStr.substring(0,end);
}
there is an O(|S|*n) solution to this problem, using a trie. [n is the number of strings, S is the longest string]
(1) put all strings in a trie
(2) do a DFS in the trie, until you find the first vertex with more than 1 "edge".
(3) the path from the root to the node you found at (2) is the longest common prefix.
There is no possible faster solution then it [in terms of big O notation], at the worst case, all your strings are identical - and you need to read all of them to know it.
I would sort them, which you can do in n lg n time. Then any strings with common prefixes will be right next to eachother. In fact you should be able to keep a pointer of which index you're currently looking at and work your way down for a pretty speedy computation.
As a completely different answer from my other answer...
You can, with one pass, bucket every string based on its first letter.
With another pass you can sort each bucket based on its second later. (This is known as radix sort, which is O(n*m), and O(n) with each pass.) This gives you a baseline prefix of 2.
You can safely remove from your dataset any elements that do not have a prefix of 2.
You can continue the radix sort, removing elements without a shared prefix of p, as p approaches m.
This will give you the same O(n*m) time that the trie approach does, but will always be faster than the trie since the trie must look at every character in every string (as it enters the structure), while this approach is only guaranteed to look at 2 characters per string, at which point it culls much of the dataset.
The worst case is still that every string is identical, which is why it shares the same big O notation, but will be faster in all cases as is guaranteed to use less comparisons since on any "non-worst-case" there are characters that never need to be visited.
public String longestCommonPrefix(String[] strs) {
if (strs == null || strs.length == 0)
return "";
char[] c_list = strs[0].toCharArray();
int len = c_list.length;
int j = 0;
for (int i = 1; i < strs.length; i++) {
for (j = 0; j < len && j < strs[i].length(); j++)
if (c_list[j] != strs[i].charAt(j))
break;
len = j;
}
return new String(c_list).substring(0, len);
}
It happens that the bucket sort (radix sort) described by corsiKa can be extended such that all strings are eventually placed alone in a bucket, and at that point, the LCP for such a lonely string is known. Further, the shustring of each string is also known; it is one longer than is the LCP. The bucket sort is defacto the construction of a suffix array but, only partially so. Those comparisons that are not performed (as described by corsiKa) indeed represent those portions of the suffix strings that are not added to the suffix array. Finally, this method allows for determination of not just the LCP and shustrings, but also one may easily find those subsequences that are not present within the string.
Since the world is obviously begging for an answer in Swift, here's mine ;)
func longestCommonPrefix(strings:[String]) -> String {
var commonPrefix = ""
var indices = strings.map { $0.startIndex}
outerLoop:
while true {
var toMatch: Character = "_"
for (whichString, f) in strings.enumerate() {
let cursor = indices[whichString]
if cursor == f.endIndex { break outerLoop }
indices[whichString] = cursor.successor()
if whichString == 0 { toMatch = f[cursor] }
if toMatch != f[cursor] { break outerLoop }
}
commonPrefix.append(toMatch)
}
return commonPrefix
}
Swift 3 Update:
func longestCommonPrefix(strings:[String]) -> String {
var commonPrefix = ""
var indices = strings.map { $0.startIndex}
outerLoop:
while true {
var toMatch: Character = "_"
for (whichString, f) in strings.enumerated() {
let cursor = indices[whichString]
if cursor == f.endIndex { break outerLoop }
indices[whichString] = f.characters.index(after: cursor)
if whichString == 0 { toMatch = f[cursor] }
if toMatch != f[cursor] { break outerLoop }
}
commonPrefix.append(toMatch)
}
return commonPrefix
}
What's interesting to note:
this runs in O^2, or O(n x m) where n is the number of strings and m
is the length of the shortest one.
this uses the String.Index data type and thus deals with Grapheme Clusters which the Character type represents.
And given the function I needed to write in the first place:
/// Takes an array of Strings representing file system objects absolute
/// paths and turn it into a new array with the minimum number of common
/// ancestors, possibly pushing the root of the tree as many level downwards
/// as necessary
///
/// In other words, we compute the longest common prefix and remove it
func reify(fullPaths:[String]) -> [String] {
let lcp = longestCommonPrefix(fullPaths)
return fullPaths.map {
return $0[lcp.endIndex ..< $0.endIndex]
}
}
here is a minimal unit test:
func testReifySimple() {
let samplePaths:[String] = [
"/root/some/file"
, "/root/some/other/file"
, "/root/another/file"
, "/root/direct.file"
]
let expectedPaths:[String] = [
"some/file"
, "some/other/file"
, "another/file"
, "direct.file"
]
let reified = PathUtilities().reify(samplePaths)
for (index, expected) in expectedPaths.enumerate(){
XCTAssert(expected == reified[index], "failed match, \(expected) != \(reified[index])")
}
}
Perhaps a more intuitive solution. Channel the already found prefix out of earlier iteration as input string to the remaining or next string input. [[[w1, w2], w3], w4]... so on], where [] is supposedly the LCP of two strings.
public String findPrefixBetweenTwo(String A, String B){
String ans = "";
for (int i = 0, j = 0; i < A.length() && j < B.length(); i++, j++){
if (A.charAt(i) != B.charAt(j)){
return i > 0 ? A.substring(0, i) : "";
}
}
// Either of the string is prefix of another one OR they are same.
return (A.length() > B.length()) ? B.substring(0, B.length()) : A.substring(0, A.length());
}
public String longestCommonPrefix(ArrayList<String> A) {
if (A.size() == 1) return A.get(0);
String prefix = A.get(0);
for (int i = 1; i < A.size(); i++){
prefix = findPrefixBetweenTwo(prefix, A.get(i)); // chain the earlier prefix
}
return prefix;
}

How can I compute the number of characters required to turn a string into a palindrome?

I recently found a contest problem that asks you to compute the minimum number of characters that must be inserted (anywhere) in a string to turn it into a palindrome.
For example, given the string: "abcbd" we can turn it into a palindrome by inserting just two characters: one after "a" and another after "d": "adbcbda".
This seems to be a generalization of a similar problem that asks for the same thing, except characters can only be added at the end - this has a pretty simple solution in O(N) using hash tables.
I have been trying to modify the Levenshtein distance algorithm to solve this problem, but haven't been successful. Any help on how to solve this (it doesn't necessarily have to be efficient, I'm just interested in any DP solution) would be appreciated.
Note: This is just a curiosity. Dav proposed an algorithm which can be modified to DP algorithm to run in O(n^2) time and O(n^2) space easily (and perhaps O(n) with better bookkeeping).
Of course, this 'naive' algorithm might actually come in handy if you decide to change the allowed operations.
Here is a 'naive'ish algorithm, which can probably be made faster with clever bookkeeping.
Given a string, we guess the middle of the resulting palindrome and then try to compute the number of inserts required to make the string a palindrome around that middle.
If the string is of length n, there are 2n+1 possible middles (Each character, between two characters, just before and just after the string).
Suppose we consider a middle which gives us two strings L and R (one to left and one to right).
If we are using inserts, I believe the Longest Common Subsequence algorithm (which is a DP algorithm) can now be used the create a 'super' string which contains both L and reverse of R, see Shortest common supersequence.
Pick the middle which gives you the smallest number inserts.
This is O(n^3) I believe. (Note: I haven't tried proving that it is true).
My C# solution looks for repeated characters in a string and uses them to reduce the number of insertions. In a word like program, I use the 'r' characters as a boundary. Inside of the 'r's, I make that a palindrome (recursively). Outside of the 'r's, I mirror the characters on the left and the right.
Some inputs have more than one shortest output: output can be toutptuot or outuputuo. My solution only selects one of the possibilities.
Some example runs:
radar -> radar, 0 insertions
esystem -> metsystem, 2 insertions
message -> megassagem, 3 insertions
stackexchange -> stegnahckexekchangets, 8 insertions
First I need to check if an input is already a palindrome:
public static bool IsPalindrome(string str)
{
for (int left = 0, right = str.Length - 1; left < right; left++, right--)
{
if (str[left] != str[right])
return false;
}
return true;
}
Then I need to find any repeated characters in the input. There may be more than one. The word message has two most-repeated characters ('e' and 's'):
private static bool TryFindMostRepeatedChar(string str, out List<char> chs)
{
chs = new List<char>();
int maxCount = 1;
var dict = new Dictionary<char, int>();
foreach (var item in str)
{
int temp;
if (dict.TryGetValue(item, out temp))
{
dict[item] = temp + 1;
maxCount = temp + 1;
}
else
dict.Add(item, 1);
}
foreach (var item in dict)
{
if (item.Value == maxCount)
chs.Add(item.Key);
}
return maxCount > 1;
}
My algorithm is here:
public static string MakePalindrome(string str)
{
List<char> repeatedList;
if (string.IsNullOrWhiteSpace(str) || IsPalindrome(str))
{
return str;
}
//If an input has repeated characters,
// use them to reduce the number of insertions
else if (TryFindMostRepeatedChar(str, out repeatedList))
{
string shortestResult = null;
foreach (var ch in repeatedList) //"program" -> { 'r' }
{
//find boundaries
int iLeft = str.IndexOf(ch); // "program" -> 1
int iRight = str.LastIndexOf(ch); // "program" -> 4
//make a palindrome of the inside chars
string inside = str.Substring(iLeft + 1, iRight - iLeft - 1); // "program" -> "og"
string insidePal = MakePalindrome(inside); // "og" -> "ogo"
string right = str.Substring(iRight + 1); // "program" -> "am"
string rightRev = Reverse(right); // "program" -> "ma"
string left = str.Substring(0, iLeft); // "program" -> "p"
string leftRev = Reverse(left); // "p" -> "p"
//Shave off extra chars in rightRev and leftRev
// When input = "message", this loop converts "meegassageem" to "megassagem",
// ("ee" to "e"), as long as the extra 'e' is an inserted char
while (left.Length > 0 && rightRev.Length > 0 &&
left[left.Length - 1] == rightRev[0])
{
rightRev = rightRev.Substring(1);
leftRev = leftRev.Substring(1);
}
//piece together the result
string result = left + rightRev + ch + insidePal + ch + right + leftRev;
//find the shortest result for inputs that have multiple repeated characters
if (shortestResult == null || result.Length < shortestResult.Length)
shortestResult = result;
}
return shortestResult;
}
else
{
//For inputs that have no repeated characters,
// just mirror the characters using the last character as the pivot.
for (int i = str.Length - 2; i >= 0; i--)
{
str += str[i];
}
return str;
}
}
Note that you need a Reverse function:
public static string Reverse(string str)
{
string result = "";
for (int i = str.Length - 1; i >= 0; i--)
{
result += str[i];
}
return result;
}
C# Recursive solution adding to the end of the string:
There are 2 base cases. When length is 1 or 2. Recursive case: If the extremes are equal, then
make palindrome the inner string without the extremes and return that with the extremes.
If the extremes are not equal, then add the first character to the end and make palindrome the
inner string including the previous last character. return that.
public static string ConvertToPalindrome(string str) // By only adding characters at the end
{
if (str.Length == 1) return str; // base case 1
if (str.Length == 2 && str[0] == str[1]) return str; // base case 2
else
{
if (str[0] == str[str.Length - 1]) // keep the extremes and call
return str[0] + ConvertToPalindrome(str.Substring(1, str.Length - 2)) + str[str.Length - 1];
else //Add the first character at the end and call
return str[0] + ConvertToPalindrome(str.Substring(1, str.Length - 1)) + str[0];
}
}

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