There is a homework I should do and I need help. I should write a program to find the first substring of length k that is repeated in the string at least twice.
For example in the string "banana" there are two repeated substrings of length 2: "an" , "na". In this case, the answer is "an" because it appeared sooner than "na"
Note that the simple O(n^2) algorithm is not useful since there is time limit on execution time of program so I guess it should be in linear time.
There is a hint too: Use Hash table.
I don't want the code. I just want you to give me a clue because I have no idea how to do this using a hash table. Should I use a specific data structure too?
Iterate over the character indexes of the string (0, 1, 2, ...) up to and including the index of the second-from-last character (i.e. up to strlen(str) - 2). For each iteration, do the following...
Extract the 2-char substring starting at the character index.
Check whether your hashtable contains the 2-char substring. If it does, you've got your answer.
Insert each 2-char substring into the hashtable.
This is easily modifiable to cope with substrings of length k.
Combine Will A's algorithm with a rolling hash to get a linear-time algorithm.
You can use linked hash map.
public static String findRepeated(String s , int k){
Map<String,Integer> map = new LinkedHashMap<String,Integer>();
for(int i = 0 ; i < s.length() - k ; i ++){
String temp = s.substring(i,i +k);
if(!map.containsKey(temp)){
map.put(temp, 1);
}
else{
map.put(temp, map.get(temp) + 1);
}
}
for(Map.Entry<String,Integer> entry : map.entrySet()){
if(entry.getValue() > 1){
return entry.getKey();
}
}
return "no such value";
}
Related
This question was asked to me in a recent amazon technical interview. It goes as follows:-
Given a string ex: "where am i" and a dictionary of valid words, you have to list all valid distinct permutations of the string. A valid string comprises of words which exists in the dictionary. For ex: "we are him","whim aree" are valid strings considering the words(whim, aree) are part of the dictionary. Also the condition is that a mere rearrangement of words is not a valid string, i.e "i am where" is not a valid combination.
The task is to find all possible such strings in the optimum way.
As you have said, space doesn't count, so input can be just viewed as a list of chars. The output is the permutation of words, so an obvious way to do it is find all valid words then permutate them.
Now problem becomes to divide a list of chars into subsets which each forms a word, which you can find some answers here and following is my version to solve this sub-problem.
If the dictionary is not large, we can iterate dictionary to
find min_len/max_len of words, to estimate how many words we may have, i.e. how deep we recur
convert word into map to accelerate search;
filter the words which have impossible char (i.e. the char our input doesn't have) out;
if this word is subset of our input, we can find word recursively.
The following is pseudocode:
int maxDepth = input.length / min_len;
void findWord(List<Map<Character, Integer>> filteredDict, Map<Character, Integer> input, List<String> subsets, int level) {
if (level < maxDepth) {
for (Map<Character, Integer> word : filteredDict) {
if (subset(input, word)) {
subsets.add(word);
findWord(filteredDict, removeSubset(input, word), subsets, level + 1);
}
}
}
}
And then you can permutate words in a recursive functions easily.
Technically speaking, this solution can be O(n**d) -- where n is dictionary size and d is max depth. But if the input is not large and complex, we can still solve it in feasible time.
I am completely stumped. The question is: given you have a string like "thisisasentence" and a function isWord() that returns true if it is an English word, I would get stuck on "this is a sent"
How can I recursively return and keep track of where I am each time?
You need backtracking, which is easily achievable using recursion. Key observation is that you do not need to keep track of where you are past the moment when you are ready to return a solution.
You have a valid "split" when one of the following is true:
The string w is empty (base case), or
You can split non-empty w into substrings p and s, such that p+s=w, p is a word, and s can be split into a sentence (recursive call).
An implementation can return a list of words when successful split is found, or null when it cannot be found. Base case will always return an empty list; recursive case will, upon finding a p, s split that results in non-null return for s, construct a list with p prefixed to the list returned from the recursive call.
The recursive case will have a loop in it, trying all possible prefixes of w. To speed things up a bit, the loop could terminate upon reaching the prefix that is equal in length to the longest word in the dictionary. For example, if the longest word has 12 characters, you know that trying prefixes 13 characters or longer will not result in a match, so you could cut enumeration short.
Just adding to the answer above.
According to my experience, many people understand recursion better when they see a «linearized» version of a recursive algorithm, which means «implemented as a loop over a stack». Linearization is applicable to any recursive task.
Assuming that isWord() has two parameters (1st: string to test; 2nd: its length) and returns a boolean-compatible value, a C implementation of backtracking is as follows:
void doSmth(char *phrase, int *words, int total) {
int i;
for (i = 0; i < total; ++i)
printf("%.*s ", words[i + 1] - words[i], phrase + words[i]);
printf("\n");
}
void parse(char *phrase) {
int current, length, *words;
if (phrase) {
words = (int*)calloc((length = strlen(phrase)) + 2, sizeof(int));
current = 1;
while (current) {
for (++words[current]; words[current] <= length; ++words[current])
if (isWord(phrase + words[current - 1],
words[current] - words[current - 1])) {
words[current + 1] = words[current];
current++;
}
if (words[--current] == length)
doSmth(phrase, words, current); /** parse successful! **/
}
free(words);
}
}
As can be seen, for each word, a pair of stack values are used, the first of which being an offset to the current word`s first character, whereas the second is a potential offset of a character exactly after the current word`s last one (thus being the next word`s first character). The second value of the current word (the one whose pair is at the top of our «stack») is iterated through all characters left in the phrase.
When a word is accepted, a new second value (equalling the current, to only look at positions after it) is pushed to the stack, making the former second the first in a new pair. If the current word (the one just found) completes the phrase, something useful is performed; see doSmth().
If there are no more acceptable words in the remaining part of our phrase, the current word is considered unsuitable, and its second value is discarded from the stack, effectively repeating a search for words at a previous starting location, while the ending location is now farther than the word previously accepted there.
I recently interviewed with a company for software engineering position. I was asked the question of longest unique sub-string in a string. My algorithms was as follows -
Start from the left-most character, and keep storing the character in a hash table with the key as the character and the value as the index_where_it_last_occurred. Add the character to the answer string as long as its not present in the hash table. If we encounter a stored character again, I stop and note down the length. I empty the hash table and then start again from the right index of the repeated character. The right index is retrieved from the (index_where_it_last_occurred) flag. If I ever reach the end of the string, I stop and return the longest length.
For example, say the string was, abcdecfg.
I start with a, store in hash table. I store b and so on till e. Their indexes are stored as well. When I encounter c again, I stop since it's already hashed and note down the length which is 5. I empty the hash table, and start again from the right index of the repeated character. The repeated character being c, I start again from the position 3 ie., the character d. I keep doing this while I don't reach the end of string.
I am interested in knowing what the time complexity of this algorithm will be. IMO, it'll be O(n^2).
This is the code.
import java.util.*;
public class longest
{
static int longest_length = -1;
public static void main(String[] args)
{
Scanner in = new Scanner(System.in);
String str = in.nextLine();
calc(str,0);
System.out.println(longest_length);
}
public static void calc(String str,int index)
{
if(index >= str.length()) return;
int temp_length = 0;
LinkedHashMap<Character,Integer> map = new LinkedHashMap<Character,Integer>();
for (int i = index; i<str.length();i++)
{
if(!map.containsKey(str.charAt(i)))
{
map.put(str.charAt(i),i);
++temp_length;
}
else if(map.containsKey(str.charAt(i)))
{
if(longest_length < temp_length)
{
longest_length = temp_length;
}
int last_index = map.get(str.charAt(i));
// System.out.println(last_index);
calc(str,last_index+1);
break;
}
}
if(longest_length < temp_length)
longest_length = temp_length;
}
}
If the alphabet is of size K, then when you restart counting you jump back at most K-1 places, so you read each character of the string at most K times. So the algorithm is O(nK).
The input string which contains n/K copies of the alphabet exhibits this worst-case behavior. For example if the alphabet is {a, b, c}, strings of the form "abcabcabc...abc" have the property that nearly every character is read 3 times by your algorithm.
You can solve the original problem in O(K+n) time, using O(K) storage space by using dynamic programming.
Let the string be s, and we'll keep a number M which will be the the length of maximum unique_char string ending at i, P, which stores where each character was previously seen, and best, the longest unique-char string found so far.
Start:
Set P[c] = -1 for each c in the alphabet.
M = 0
best = 0
Then, for each i:
M = min(M+1, i-P[s[i]])
best = max(best, M)
P[s[i]] = i
This is trivially O(K) in storage, and O(K+n) in running time.
I have an assignment to create an algorithm to find duplicates in an array which includes number values. but it has not said which kind of numbers, integers or floats. I have written the following pseudocode:
FindingDuplicateAlgorithm(A) // A is the array
mergeSort(A);
for int i <- 0 to i<A.length
if A[i] == A[i+1]
i++
return A[i]
else
i++
have I created an efficient algorithm?
I think there is a problem in my algorithm, it returns duplicate numbers several time. for example if array include 2 in two for two indexes i will have ...2, 2,... in the output. how can i change it to return each duplicat only one time?
I think it is a good algorithm for integers, but does it work good for float numbers too?
To handle duplicates, you can do the following:
if A[i] == A[i+1]:
result.append(A[i]) # collect found duplicates in a list
while A[i] == A[i+1]: # skip the entire range of duplicates
i++ # until a new value is found
Do you want to find Duplicates in Java?
You may use a HashSet.
HashSet h = new HashSet();
for(Object a:A){
boolean b = h.add(a);
boolean duplicate = !b;
if(duplicate)
// do something with a;
}
The return-Value of add() is defined as:
true if the set did not already
contain the specified element.
EDIT:
I know HashSet is optimized for inserts and contains operations. But I'm not sure if its fast enough for your concerns.
EDIT2:
I've seen you recently added the homework-tag. I would not prefer my answer if itf homework, because it may be to "high-level" for an allgorithm-lesson
http://download.oracle.com/javase/1.4.2/docs/api/java/util/HashSet.html#add%28java.lang.Object%29
Your answer seems pretty good. First sorting and them simply checking neighboring values gives you O(n log(n)) complexity which is quite efficient.
Merge sort is O(n log(n)) while checking neighboring values is simply O(n).
One thing though (as mentioned in one of the comments) you are going to get a stack overflow (lol) with your pseudocode. The inner loop should be (in Java):
for (int i = 0; i < array.length - 1; i++) {
...
}
Then also, if you actually want to display which numbers (and or indexes) are the duplicates, you will need to store them in a separate list.
I'm not sure what language you need to write the algorithm in, but there are some really good C++ solutions in response to my question here. Should be of use to you.
O(n) algorithm: traverse the array and try to input each element in a hashtable/set with number as the hash key. if you cannot enter, than that's a duplicate.
Your algorithm contains a buffer overrun. i starts with 0, so I assume the indexes into array A are zero-based, i.e. the first element is A[0], the last is A[A.length-1]. Now i counts up to A.length-1, and in the loop body accesses A[i+1], which is out of the array for the last iteration. Or, simply put: If you're comparing each element with the next element, you can only do length-1 comparisons.
If you only want to report duplicates once, I'd use a bool variable firstDuplicate, that's set to false when you find a duplicate and true when the number is different from the next. Then you'd only report the first duplicate by only reporting the duplicate numbers if firstDuplicate is true.
public void printDuplicates(int[] inputArray) {
if (inputArray == null) {
throw new IllegalArgumentException("Input array can not be null");
}
int length = inputArray.length;
if (length == 1) {
System.out.print(inputArray[0] + " ");
return;
}
for (int i = 0; i < length; i++) {
if (inputArray[Math.abs(inputArray[i])] >= 0) {
inputArray[Math.abs(inputArray[i])] = -inputArray[Math.abs(inputArray[i])];
} else {
System.out.print(Math.abs(inputArray[i]) + " ");
}
}
}
I have a string s and I want to search for the substring of length X that occurs most often in s. Overlapping substrings are allowed.
For example, if s="aoaoa" and X=3, the algorithm should find "aoa" (which appears 2 times in s).
Does an algorithm exist that does this in O(n) time?
You can do this using a rolling hash in O(n) time (assuming good hash distribution). A simple rolling hash would be the xor of the characters in the string, you can compute it incrementally from the previous substring hash using just 2 xors. (See the Wikipedia entry for better rolling hashes than xor.) Compute the hash of your n-x+1 substrings using the rolling hash in O(n) time. If there were no collisions, the answer is clear - if collisions happen, you'll need to do more work. My brain hurts trying to figure out if that can all be resolved in O(n) time.
Update:
Here's a randomized O(n) algorithm. You can find the top hash in O(n) time by scanning the hashtable (keeping it simple, assume no ties). Find one X-length string with that hash (keep a record in the hashtable, or just redo the rolling hash). Then use an O(n) string searching algorithm to find all occurrences of that string in s. If you find the same number of occurrences as you recorded in the hashtable, you're done.
If not, that means you have a hash collision. Pick a new random hash function and try again. If your hash function has log(n)+1 bits and is pairwise independent [Prob(h(s) == h(t)) < 1/2^{n+1} if s != t], then the probability that the most frequent x-length substring in s hash a collision with the <=n other length x substrings of s is at most 1/2. So if there is a collision, pick a new random hash function and retry, you will need only a constant number of tries before you succeed.
Now we only need a randomized pairwise independent rolling hash algorithm.
Update2:
Actually, you need 2log(n) bits of hash to avoid all (n choose 2) collisions because any collision may hide the right answer. Still doable, and it looks like hashing by general polynomial division should do the trick.
I don't see an easy way to do this in strictly O(n) time, unless X is fixed and can be considered a constant. If X is a parameter to the algorithm, then most simple ways of doing this will actually be O(n*X), as you will need to do comparison operations, string copies, hashes, etc., on a substring of length X at every iteration.
(I'm imagining, for a minute, that s is a multi-gigabyte string, and that X is some number over a million, and not seeing any simple ways of doing string comparison, or hashing substrings of length X, that are O(1), and not dependent on the size of X)
It might be possible to avoid string copies during scanning, by leaving everything in place, and to avoid re-hashing the entire substring -- perhaps by using an incremental hash algorithm where you can add a byte at a time, and remove the oldest byte -- but I don't know of any such algorithms that wouldn't result in huge numbers of collisions that would need to be filtered out with an expensive post-processing step.
Update
Keith Randall points out that this kind of hash is known as a rolling hash. It still remains, though, that you would have to store the starting string position for each match in your hash table, and then verify after scanning the string that all of your matches were true. You would need to sort the hashtable, which could contain n-X entries, based on the number of matches found for each hash key, and verify each result -- probably not doable in O(n).
It should be O(n*m) where m is the average length of a string in the list. For very small values of m then the algorithm will approach O(n)
Build a hashtable of counts for each string length
Iterate over your collection of strings, updating the hashtable accordingly, storing the current most prevelant number as an integer variable separate from the hashtable
done.
Naive solution in Python
from collections import defaultdict
from operator import itemgetter
def naive(s, X):
freq = defaultdict(int)
for i in range(len(s) - X + 1):
freq[s[i:i+X]] += 1
return max(freq.iteritems(), key=itemgetter(1))
print naive("aoaoa", 3)
# -> ('aoa', 2)
In plain English
Create mapping: substring of length X -> how many times it occurs in the s string
for i in range(len(s) - X + 1):
freq[s[i:i+X]] += 1
Find a pair in the mapping with the largest second item (frequency)
max(freq.iteritems(), key=itemgetter(1))
Here is a version I did in C. Hope that it helps.
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
int main(void)
{
char *string = NULL, *maxstring = NULL, *tmpstr = NULL, *tmpstr2 = NULL;
unsigned int n = 0, i = 0, j = 0, matchcount = 0, maxcount = 0;
string = "aoaoa";
n = 3;
for (i = 0; i <= (strlen(string) - n); i++) {
tmpstr = (char *)malloc(n + 1);
strncpy(tmpstr, string + i, n);
*(tmpstr + (n + 1)) = '\0';
for (j = 0; j <= (strlen(string) - n); j++) {
tmpstr2 = (char *)malloc(n + 1);
strncpy(tmpstr2, string + j, n);
*(tmpstr2 + (n + 1)) = '\0';
if (!strcmp(tmpstr, tmpstr2))
matchcount++;
}
if (matchcount > maxcount) {
maxstring = tmpstr;
maxcount = matchcount;
}
matchcount = 0;
}
printf("max string: \"%s\", count: %d\n", maxstring, maxcount);
free(tmpstr);
free(tmpstr2);
return 0;
}
You can build a tree of sub-strings. The idea is to organise your sub-strings like a telephone book. You then look up the sub-string and increase its count by one.
In your example above, the tree will have sections (nodes) starting with the letters: 'a' and 'o'. 'a' appears three times and 'o' appears twice. So those nodes will have a count of 3 and 2 respectively.
Next, under the 'a' node a sub-node of 'o' will appear corresponding to the sub-string 'ao'. This appears twice. Under the 'o' node 'a' also appears twice.
We carry on in this fashion until we reach the end of the string.
A representation of the tree for 'abac' might be (nodes on the same level are separated by a comma, sub-nodes are in brackets, counts appear after the colon).
a:2(b:1(a:1(c:1())),c:1()),b:1(a:1(c:1())),c:1()
If the tree is drawn out it will be a lot more obvious! What this all says for example is that the string 'aba' appears once, or the string 'a' appears twice etc. But, storage is greatly reduced and more importantly retrieval is greatly speeded up (compare this to keeping a list of sub-strings).
To find out which sub-string is most repeated, do a depth first search of the tree, every time a leaf node is reached, note the count, and keep a track of the highest one.
The running time is probably something like O(log(n)) not sure, but certainly better than O(n^2).
Python-3 Solution:
from collections import Counter
list = []
list.append([string[i: j] for i in range(len(string)) for j in range(i + 1, len(string) + 1) if len(string[i:j]) == K]) # Where K is length
# now find the most common value in this list
# you can do this natively, but I prefer using collections
most_frequent = Counter(list).most_common(1)[0][0]
print(most_freqent)
Here is the native way to get the most common (for those that are interested):
most_occurences = 0
current_most = ""
for i in list:
frequency = list.count(i)
if frequency > most_occurences:
most_occurences = frequency
current_most = list[i]
print(f"{current_most}, Occurences: {most_occurences}")
[Extract K length substrings (geeks for geeks)][1]
[1]: https://www.geeksforgeeks.org/python-extract-k-length-substrings/
LZW algorithm does this
This is exactly what Lempel-Ziv-Welch (LZW used in GIF image format) compression algorithm does. It finds prevalent repeated bytes and changes them for something short.
LZW on Wikipedia
There's no way to do this in O(n).
Feel free to downvote me if you can prove me wrong on this one, but I've got nothing.