Got asked this question in an interview and couldn't find a solution.
Given an array of characters delete all the characters that got repeated k or more times consecutively and add '#' in the end of the array for every deleted character.
Example:
"xavvvarrrt"->"xaat######"
O(1) memory and O(n) time without writing to the same cell twice.
The tricky part for me was that I am not allowed to overwrite a cell more than once, which means I need to know exactly where each character will move after deleting the duplicates.
The best I could come up with is iterating once on the array and saving in a map the occurrences of each character, and when iterating again and checking if the current character is not deleted then move it to the new position according to the offset, if it is deleted then update an offset variable.
The problem with this approach is that it won't work in this scenario:
"aabbaa" because 'a' appears at two different places.
So when I thought about saving an array of occurrences in the map but now it won't use O(1) memory.
Thanks
This seems to work with your examples, although it seems a little complicated to me :) I wonder if we could simplify it. The basic idea is to traverse from left to right, keeping a record of how many places in the current block of duplicates are still available to replace, while the right pointer looks for more blocks to shift over.
JavaScript code:
function f(str){
str = str.split('')
let r = 1
let l = 0
let to_fill = 0
let count = 1
let fill = function(){
while (count > 0 && (to_fill > 0 || l < r)){
str[l] = str[r - count]
l++
count--
to_fill--
}
}
for (; r<str.length; r++){
if (str[r] == str[r-1]){
count++
} else if (count < 3){
if (to_fill)
fill()
count = 1
if (!to_fill)
l = r
} else if (!to_fill){
to_fill = count
count = 1
} else {
count = 1
}
}
if (count < 3)
fill()
while (l < str.length)
str[l++] = '#'
return str.join('')
}
var str = "aayyyycbbbee"
console.log(str)
console.log(f(str)) // "aacee#######"
str = "xavvvarrrt"
console.log(str)
console.log(f(str)) // "xaat######"
str = "xxaavvvaarrrbbsssgggtt"
console.log(str)
console.log(f(str))
Here is a version similar to the other JS answer, but a bit simpler:
function repl(str) {
str = str.split("");
var count = 1, write = 0;
for (var read = 0; read < str.length; read++) {
if (str[read] == str[read+1])
count++;
else {
if (count < 3) {
for (var i = 0; i < count; i++)
str[write++] = str[read];
}
count = 1;
}
}
while (write < str.length)
str[write++] = '#';
return str.join("");
}
function demo(str) {
console.log(str + " ==> " + repl(str));
}
demo("a");
demo("aa");
demo("aaa");
demo("aaaaaaa");
demo("aayyyycbbbee");
demo("xavvvarrrt");
demo("xxxaaaaxxxaaa");
demo("xxaavvvaarrrbbsssgggtt");
/*
Output:
a ==> a
aa ==> aa
aaa ==> ###
aaaaaaa ==> #######
aayyyycbbbee ==> aacee#######
xavvvarrrt ==> xaat######
xxxaaaaxxxaaa ==> #############
xxaavvvaarrrbbsssgggtt ==> xxaaaabbtt############
*/
The idea is to keep the current index for reading the next character and one for writing, as well as the number of consecutive repeated characters. If the following character is equal to the current, we just increase the counter. Otherwise we copy all characters below a count of 3, increasing the write index appropriately.
At the end of reading, anything from the current write index up to the end of the array is the number of repeated characters we have skipped. We just fill that with hashes now.
As we only store 3 values, memory consumption is O(1); we read each array cell twice, so O(n) time (the extra reads on writing could be eliminated by another variable); and each write index is accessed exactly once.
Given a mapping:
A: 1
B: 2
C: 3
...
...
...
Z: 26
Find all possible ways a number can be represented. E.g. For an input: "121", we can represent it as:
ABA [using: 1 2 1]
LA [using: 12 1]
AU [using: 1 21]
I tried thinking about using some sort of a dynamic programming approach, but I am not sure how to proceed. I was asked this question in a technical interview.
Here is a solution I could think of, please let me know if this looks good:
A[i]: Total number of ways to represent the sub-array number[0..i-1] using the integer to alphabet mapping.
Solution [am I missing something?]:
A[0] = 1 // there is only 1 way to represent the subarray consisting of only 1 number
for(i = 1:A.size):
A[i] = A[i-1]
if(input[i-1]*10 + input[i] < 26):
A[i] += 1
end
end
print A[A.size-1]
To just get the count, the dynamic programming approach is pretty straight-forward:
A[0] = 1
for i = 1:n
A[i] = 0
if input[i-1] > 0 // avoid 0
A[i] += A[i-1];
if i > 1 && // avoid index-out-of-bounds on i = 1
10 <= (10*input[i-2] + input[i-1]) <= 26 // check that number is 10-26
A[i] += A[i-2];
If you instead want to list all representations, dynamic programming isn't particularly well-suited for this, you're better off with a simple recursive algorithm.
First off, we need to find an intuitive way to enumerate all the possibilities. My simple construction, is given below.
let us assume a simple way to represent your integer in string format.
a1 a2 a3 a4 ....an, for instance in 121 a1 -> 1 a2 -> 2, a3 -> 1
Now,
We need to find out number of possibilities of placing a + sign in between two characters. + is to mean characters concatenation here.
a1 - a2 - a3 - .... - an, - shows the places where '+' can be placed. So, number of positions is n - 1, where n is the string length.
Assume a position may or may not have a + symbol shall be represented as a bit.
So, this boils down to how many different bit strings are possible with the length of n-1, which is clearly 2^(n-1). Now in order to enumerate the possibilities go through every bit string and place right + signs in respective positions to get every representations,
For your example, 121
Four bit strings are possible 00 01 10 11
1 2 1
1 2 + 1
1 + 2 1
1 + 2 + 1
And if you see a character followed by a +, just add the next char with the current one and do it sequentially to get the representation,
x + y z a + b + c d
would be (x+y) z (a+b+c) d
Hope it helps.
And you will have to take care of edge cases where the size of some integer > 26, of course.
I think, recursive traverse through all possible combinations would do just fine:
mapping = {"1":"A", "2":"B", "3":"C", "4":"D", "5":"E", "6":"F", "7":"G",
"8":"H", "9":"I", "10":"J",
"11":"K", "12":"L", "13":"M", "14":"N", "15":"O", "16":"P",
"17":"Q", "18":"R", "19":"S", "20":"T", "21":"U", "22":"V", "23":"W",
"24":"A", "25":"Y", "26":"Z"}
def represent(A, B):
if A == B == '':
return [""]
ret = []
if A in mapping:
ret += [mapping[A] + r for r in represent(B, '')]
if len(A) > 1:
ret += represent(A[:-1], A[-1]+B)
return ret
print represent("121", "")
Assuming you only need to count the number of combinations.
Assuming 0 followed by an integer in [1,9] is not a valid concatenation, then a brute-force strategy would be:
Count(s,n)
x=0
if (s[n-1] is valid)
x=Count(s,n-1)
y=0
if (s[n-2] concat s[n-1] is valid)
y=Count(s,n-2)
return x+y
A better strategy would be to use divide-and-conquer:
Count(s,start,n)
if (len is even)
{
//split s into equal left and right part, total count is left count multiply right count
x=Count(s,start,n/2) + Count(s,start+n/2,n/2);
y=0;
if (s[start+len/2-1] concat s[start+len/2] is valid)
{
//if middle two charaters concatenation is valid
//count left of the middle two characters
//count right of the middle two characters
//multiply the two counts and add to existing count
y=Count(s,start,len/2-1)*Count(s,start+len/2+1,len/2-1);
}
return x+y;
}
else
{
//there are three cases here:
//case 1: if middle character is valid,
//then count everything to the left of the middle character,
//count everything to the right of the middle character,
//multiply the two, assign to x
x=...
//case 2: if middle character concatenates the one to the left is valid,
//then count everything to the left of these two characters
//count everything to the right of these two characters
//multiply the two, assign to y
y=...
//case 3: if middle character concatenates the one to the right is valid,
//then count everything to the left of these two characters
//count everything to the right of these two characters
//multiply the two, assign to z
z=...
return x+y+z;
}
The brute-force solution has time complexity of T(n)=T(n-1)+T(n-2)+O(1) which is exponential.
The divide-and-conquer solution has time complexity of T(n)=3T(n/2)+O(1) which is O(n**lg3).
Hope this is correct.
Something like this?
Haskell code:
import qualified Data.Map as M
import Data.Maybe (fromJust)
combs str = f str [] where
charMap = M.fromList $ zip (map show [1..]) ['A'..'Z']
f [] result = [reverse result]
f (x:xs) result
| null xs =
case M.lookup [x] charMap of
Nothing -> ["The character " ++ [x] ++ " is not in the map."]
Just a -> [reverse $ a:result]
| otherwise =
case M.lookup [x,head xs] charMap of
Just a -> f (tail xs) (a:result)
++ (f xs ((fromJust $ M.lookup [x] charMap):result))
Nothing -> case M.lookup [x] charMap of
Nothing -> ["The character " ++ [x]
++ " is not in the map."]
Just a -> f xs (a:result)
Output:
*Main> combs "121"
["LA","AU","ABA"]
Here is the solution based on my discussion here:
private static int decoder2(int[] input) {
int[] A = new int[input.length + 1];
A[0] = 1;
for(int i=1; i<input.length+1; i++) {
A[i] = 0;
if(input[i-1] > 0) {
A[i] += A[i-1];
}
if (i > 1 && (10*input[i-2] + input[i-1]) <= 26) {
A[i] += A[i-2];
}
System.out.println(A[i]);
}
return A[input.length];
}
Just us breadth-first search.
for instance 121
Start from the first integer,
consider 1 integer character first, map 1 to a, leave 21
then 2 integer character map 12 to L leave 1.
This problem can be done in o(fib(n+2)) time with a standard DP algorithm.
We have exactly n sub problems and button up we can solve each problem with size i in o(fib(i)) time.
Summing the series gives fib (n+2).
If you consider the question carefully you see that it is a Fibonacci series.
I took a standard Fibonacci code and just changed it to fit our conditions.
The space is obviously bound to the size of all solutions o(fib(n)).
Consider this pseudo code:
Map<Integer, String> mapping = new HashMap<Integer, String>();
List<String > iterative_fib_sequence(string input) {
int length = input.length;
if (length <= 1)
{
if (length==0)
{
return "";
}
else//input is a-j
{
return mapping.get(input);
}
}
List<String> b = new List<String>();
List<String> a = new List<String>(mapping.get(input.substring(0,0));
List<String> c = new List<String>();
for (int i = 1; i < length; ++i)
{
int dig2Prefix = input.substring(i-1, i); //Get a letter with 2 digit (k-z)
if (mapping.contains(dig2Prefix))
{
String word2Prefix = mapping.get(dig2Prefix);
foreach (String s in b)
{
c.Add(s.append(word2Prefix));
}
}
int dig1Prefix = input.substring(i, i); //Get a letter with 1 digit (a-j)
String word1Prefix = mapping.get(dig1Prefix);
foreach (String s in a)
{
c.Add(s.append(word1Prefix));
}
b = a;
a = c;
c = new List<String>();
}
return a;
}
old question but adding an answer so that one can find help
It took me some time to understand the solution to this problem – I refer accepted answer and #Karthikeyan's answer and the solution from geeksforgeeks and written my own code as below:
To understand my code first understand below examples:
we know, decodings([1, 2]) are "AB" or "L" and so decoding_counts([1, 2]) == 2
And, decodings([1, 2, 1]) are "ABA", "AU", "LA" and so decoding_counts([1, 2, 1]) == 3
using the above two examples let's evaluate decodings([1, 2, 1, 4]):
case:- "taking next digit as single digit"
taking 4 as single digit to decode to letter 'D', we get decodings([1, 2, 1, 4]) == decoding_counts([1, 2, 1]) because [1, 2, 1, 4] will be decode as "ABAD", "AUD", "LAD"
case:- "combining next digit with the previous digit"
combining 4 with previous 1 as 14 as a single to decode to letter N, we get decodings([1, 2, 1, 4]) == decoding_counts([1, 2]) because [1, 2, 1, 4] will be decode as "ABN" or "LN"
Below is my Python code, read comments
def decoding_counts(digits):
# defininig count as, counts[i] -> decoding_counts(digits[: i+1])
counts = [0] * len(digits)
counts[0] = 1
for i in xrange(1, len(digits)):
# case:- "taking next digit as single digit"
if digits[i] != 0: # `0` do not have mapping to any letter
counts[i] = counts[i -1]
# case:- "combining next digit with the previous digit"
combine = 10 * digits[i - 1] + digits[i]
if 10 <= combine <= 26: # two digits mappings
counts[i] += (1 if i < 2 else counts[i-2])
return counts[-1]
for digits in "13", "121", "1214", "1234121":
print digits, "-->", decoding_counts(map(int, digits))
outputs:
13 --> 2
121 --> 3
1214 --> 5
1234121 --> 9
note: I assumed that input digits do not start with 0 and only consists of 0-9 and have a sufficent length
For Swift, this is what I came up with. Basically, I converted the string into an array and goes through it, adding a space into different positions of this array, then appending them to another array for the second part, which should be easy after this is done.
//test case
let input = [1,2,2,1]
func combination(_ input: String) {
var arr = Array(input)
var possible = [String]()
//... means inclusive range
for i in 2...arr.count {
var temp = arr
//basically goes through it backwards so
// adding the space doesn't mess up the index
for j in (1..<i).reversed() {
temp.insert(" ", at: j)
possible.append(String(temp))
}
}
print(possible)
}
combination(input)
//prints:
//["1 221", "12 21", "1 2 21", "122 1", "12 2 1", "1 2 2 1"]
def stringCombinations(digits, i=0, s=''):
if i == len(digits):
print(s)
return
alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
total = 0
for j in range(i, min(i + 1, len(digits) - 1) + 1):
total = (total * 10) + digits[j]
if 0 < total <= 26:
stringCombinations(digits, j + 1, s + alphabet[total - 1])
if __name__ == '__main__':
digits = list()
n = input()
n.split()
d = list(n)
for i in d:
i = int(i)
digits.append(i)
print(digits)
stringCombinations(digits)
Following is a very famous question in native string matching. Please can someone explain me the answer.
Suppose that all characters in the pattern P are different. Show how to accelerate NAIVE-STRING MATCHER to run in time O(n) on an n-character text T.
The basic idea:
Iterate through the input and the pattern at the same time, comparing their characters to each other
Whenever you get a non-matching character between the two, you can just reset the pattern position and keep the input position as is
This works because the pattern characters are all different, which means that whenever you have a partial match, there can be no other match overlapping with that, so we can just start looking from the end of the partial match.
Here's some pseudo-code that shouldn't be too difficult to understand:
input[n]
pattern[k]
pPos = 0
iPos = 0
while iPos < n
if pPos == k
FOUND!
if pattern[pPos] == input[iPos]
pPos++
iPos++
else
// if pPos is already 0, we need to increase iPos,
// otherwise we just keep comparing the same characters
if pPos == 0
iPos++
pPos = 0
It's easy to see that iPos increases at least every second loop, thus there can be at most 2n loop runs, making the running time O(n).
When T[i] and P[j] mismatches in NAIVE-STRING-MATCHER, we can skip all characters before T[i] and begin new matching from T[i + 1] with P[1].
NAIVE-STRING-MATCHER(T, P)
1 n length[T]
2 m length[P]
3 for s 0 to n - m
4 do if P[1 . . m] = T[s + 1 . . s + m]
5 then print "Pattern occurs with shift" s
Naive string search algorithm implementations in Python 2.7:
https://gist.github.com/heyhuyen/4341692
In the middle of implementing Boyer-Moore's string search algorithm, I decided to play with my original naive search algorithm. It's implemented as an instance method that takes a string to be searched. The object has an attribute 'pattern' which is the pattern to match.
1) Here is the original version of the search method, using a double for-loop.
Makes calls to range and len
def search(self, string):
for i in range(len(string)):
for j in range(len(self.pattern)):
if string[i+j] != self.pattern[j]:
break
elif j == len(self.pattern) - 1:
return i
return -1
2) Here is the second version, using a double while-loop instead.
Slightly faster, not making calls to range
def search(self, string):
i = 0
while i < len(string):
j = 0
while j < len(self.pattern) and self.pattern[j] == string[i+j]:
j += 1
if j == len(self.pattern):
return i
i += 1
return -1
3) Here is the original, replacing range with xrange.
Faster than both of the previous two.
def search(self, string):
for i in xrange(len(string)):
for j in xrange(len(self.pattern)):
if string[i+j] != self.pattern[j]:
break
elif j == len(self.pattern) - 1:
return i
return -1
4) Storing values in local variables = win! With the double while loop, this is the fastest.
def search(self, string):
len_pat = len(self.pattern)
len_str = len(string)
i = 0
while i < len_str:
j = 0
while j < len_pat and self.pattern[j] == string[i+j]:
j += 1
if j == len_pat:
return i
i += 1
return -1
I have read this problem
Find the most common entry in an array
and the answer from jon skeet is just mind blowing .. :)
Now I am trying to solve this problem find an element which occurs more than n/3 times in an array ..
I am pretty sure that we cannot apply the same method because there can be 2 such elements which will occur more than n/3 times and that gives false alarm of the count ..so is there any way we can tweak around jon skeet's answer to work for this ..?
Or is there any solution that will run in linear time ?
Jan Dvorak's answer is probably best:
Start with two empty candidate slots and two counters set to 0.
for each item:
if it is equal to either candidate, increment the corresponding count
else if there is an empty slot (i.e. a slot with count 0), put it in that slot and set the count to 1
else reduce both counters by 1
At the end, make a second pass over the array to check whether the candidates really do have the required count. This isn't allowed by the question you link to but I don't see how to avoid it for this modified version. If there is a value that occurs more than n/3 times then it will be in a slot, but you don't know which one it is.
If this modified version of the question guaranteed that there were two values with more than n/3 elements (in general, k-1 values with more than n/k) then we wouldn't need the second pass. But when the original question has k=2 and 1 guaranteed majority there's no way to know whether we "should" generalize it as guaranteeing 1 such element or guaranteeing k-1. The stronger the guarantee, the easier the problem.
Using Boyer-Moore Majority Vote Algorithm, we get:
vector<int> majorityElement(vector<int>& nums) {
int cnt1=0, cnt2=0;
int a,b;
for(int n: A){
if (n == a) cnt1++;
else if (n == b) cnt2++;
else if (cnt1 == 0){
cnt1++;
a = n;
}
else if (cnt2 == 0){
cnt2++;
b = n;
}
else{
cnt1--;
cnt2--;
}
}
cnt1=cnt2=0;
for(int n: nums){
if (n==a) cnt1++;
else if (n==b) cnt2++;
}
vector<int> result;
if (cnt1 > nums.size()/3) result.push_back(a);
if (cnt2 > nums.size()/3) result.push_back(b);
return result;
}
Updated, correction from #Vipul Jain
You can use Selection algorithm to find the number in the n/3 place and 2n/3.
n1=Selection(array[],n/3);
n2=Selection(array[],n2/3);
coun1=0;
coun2=0;
for(i=0;i<n;i++)
{
if(array[i]==n1)
count1++;
if(array[i]==n2)
count2++;
}
if(count1>n)
print(n1);
else if(count2>n)
print(n2);
else
print("no found!");
At line number five, the if statement should have one more check:
if(n!=b && (cnt1 == 0 || n == a))
I use the following Python solution to discuss the correctness of the algorithm:
class Solution:
"""
#param: nums: a list of integers
#return: The majority number that occurs more than 1/3
"""
def majorityNumber(self, nums):
if nums is None:
return None
if len(nums) == 0:
return None
num1 = None
num2 = None
count1 = 0
count2 = 0
# Loop 1
for i, val in enumerate(nums):
if count1 == 0:
num1 = val
count1 = 1
elif val == num1:
count1 += 1
elif count2 == 0:
num2 = val
count2 = 1
elif val == num2:
count2 += 1
else:
count1 -= 1
count2 -= 1
count1 = 0
count2 = 0
for val in nums:
if val == num1:
count1 += 1
elif val == num2:
count2 += 1
if count1 > count2:
return num1
return num2
First, we need to prove claim A:
Claim A: Consider a list C which contains a majority number m which occurs more floor(n/3) times. After 3 different numbers are removed from C, we have C'. m is the majority number of C'.
Proof: Use R to denote m's occurrence count in C. We have R > floor(n/3). R > floor(n/3) => R - 1 > floor(n/3) - 1 => R - 1 > floor((n-3)/3). Use R' to denote m's occurrence count in C'. And use n' to denote the length of C'. Since 3 different numbers are removed, we have R' >= R - 1. And n'=n-3 is obvious. We can have R' > floor(n'/3) from R - 1 > floor((n-3)/3). So m is the majority number of C'.
Now let's prove the correctness of the loop 1. Define L as count1 * [num1] + count2 * [num2] + nums[i:]. Use m to denote the majority number.
Invariant
The majority number m is in L.
Initialization
At the start of the first itearation, L is nums[0:]. So the invariant is trivially true.
Maintenance
if count1 == 0 branch: Before the iteration, L is count2 * [num2] + nums[i:]. After the iteration, L is 1 * [nums[i]] + count2 * [num2] + nums[i+1:]. In other words, L is not changed. So the invariant is maintained.
if val == num1 branch: Before the iteration, L is count1 * [nums[i]] + count2 * [num2] + nums[i:]. After the iteration, L is (count1+1) * [num[i]] + count2 * [num2] + nums[i+1:]. In other words, L is not changed. So the invariant is maintained.
f count2 == 0 branch: Similar to condition 1.
elif val == num2 branch: Similar to condition 2.
else branch: nums[i], num1 and num2 are different to each other in this case. After the iteration, L is (count1-1) * [num1] + (count2-1) * [num2] + nums[i+1:]. In other words, three different numbers are moved from count1 * [num1] + count2 * [num2] + nums[i:]. From claim A, we know m is the majority number of L.So the invariant is maintained.
Termination
When the loop terminates, nums[n:] is empty. L is count1 * [num1] + count2 * [num2].
So when the loop terminates, the majority number is either num1 or num2.
If there are n elements in the array , and suppose in the worst case only 1 element is repeated n/3 times , then the probability of choosing one number that is not the one which is repeated n/3 times will be (2n/3)/n that is 1/3 , so if we randomly choose N elements from the array of size ‘n’, then the probability that we end up choosing the n/3 times repeated number will be atleast 1-(2/3)^N . If we eqaute this to say 99.99 percent probability of getting success, we will get N=23 for any value of “n”.
Therefore just choose 23 numbers randomly from the list and count their occurrences , if we get count greater than n/3 , we will return that number and if we didn’t get any solution after checking for 23 numbers randomly , return -1;
The algorithm is essentially O(n) as the value 23 doesn’t depend on n(size of list) , so we have to just traverse array 23 times at worst case of algo.
Accepted Code on interviewbit(C++):
int n=A.size();
int ans,flag=0;
for(int i=0;i<23;i++)
{
int index=rand()%n;
int elem=A[index];
int count=0;
for(int i=0;i<n;i++)
{
if(A[i]==elem)
count++;
}
if(count>n/3)
{
flag=1;
ans=elem;
}
if(flag==1)
break;
}
if(flag==1)
return ans;
else return -1;
}
I have a number-line between 0 to 1000. I have many line segments on the number line. All line segments' x1 is >= 0 and all x2 are < 1000. All x1 and x2 are integers.
I need to find all of the unions of the line segments.
In this image, the line segments are in blue and the unions are in red:
Is there an existing algorithm for this type of problem?
You can use marzullo's algorithm (see Wikipedia for more details).
Here is a Python implementation I wrote:
def ip_ranges_grouping(range_lst):
## Based on Marzullo's algorithm
## Input: list of IP ranges
## Returns a new merged list of IP ranges
table = []
for rng in range_lst:
start,end = rng.split('-')
table.append((ip2int(start),1))
table.append((ip2int(end),-1))
table.sort(key=lambda x: x[0])
for i in range(len(table) - 1):
if((table[i][0] == table[i+1][0]) and ((table[i][1] == -1) and (table[i+1][1] == 1))):
table[i],table[i+1] = table[i+1],table[i]
merged = []
end = count = 0
while (end < len(table)):
start = end
count += table[end][1]
while(count > 0): # upon last index, count == 0 and loop terminates
end += 1
count += table[end][1]
merged.append(int2ip(table[start][0]) + '-' + int2ip(table[end][0]))
end += 1
return merged
Considering that the coordinates of your segments are bounded ([0, 1000]) integers, you could use an array of size 1000 initialized with zeroes. You then run through your set of segments and set 1 on every cell of the array that the segment covers. You then only have to run through the array to check for contigous sequences of 1.
--- -----
--- ---
1111100111111100
The complexity depends on the number of segments but also on their length.
Here is another method, which also work for floating point segments. Sort the segments. You then only have to travel the sorted segments and compare the boundaries of each adjacent segments. If they cross, they are in the same union.
If the segments are not changed dynamically, it is a simple problem. Just sorting all the segments by the left end, then scanning the sorted elements:
struct Seg {int L,R;};
int cmp(Seg a, Seg b) {return a.L < b.L;}
int union_segs(int n, Seg *segs, Segs *output) {
sort(segs, segs + n, cmp);
int right_most = -1;
int cnt = 0;
for (int i = 0 ; i < n ; i++) {
if (segs[i].L > right_most) {
right_most = segs[i].R;
++cnt;
output[cnt].L = segs[i].L;
output[cnt].R = segs[i].R;
}
if (segs[i].R > right_most) {
right_most = segs[i].R;
output[cnt].R = segs[i].R;
}
}
return cnt+1;
}
The time complexity is O(nlogn) (sorting) + O(n) (scan).
If the segments are inserted and deleted dynamically, and you want to query the union at any time, you will need some more complicated data structures such as range tree.