You are given two strings S and T. An infinitely long string is formed in the following manner:
Take an empty string,
Append S one time,
Append T two times,
Append S three times,
Append T four times,
and so on, appending the strings alternately and increasing the number of repetitions by 1 each time.
You will also be given an integer K.
You need to tell the Kth Character of this infinitely long string.
Sample Input (S, T, K):
a
bc
4
Sample Output:
b
Sample Explanation:
The string formed will be "abcbcaaabcbcbcbcaaaaa...". So the 4th character is "b".
My attempt:
public class FindKthCharacter {
public char find(String S, String T, int K) {
// lengths of S and T
int s = S.length();
int t = T.length();
// Counters for S and T
int sCounter = 1;
int tCounter = 2;
// To store final chunks of string
StringBuilder sb = new StringBuilder();
// Loop until K is greater than zero
while (K > 0) {
if (K > sCounter * s) {
K -= sCounter * s;
sCounter += 2;
if (K > tCounter * t) {
K -= tCounter * t;
tCounter += 2;
} else {
return sb.append(T.repeat(tCounter)).charAt(K - 1);
}
} else {
return sb.append(S.repeat(sCounter)).charAt(K - 1);
}
}
return '\u0000';
}
}
But is there any better way to reduce its time complexity?
I've tried to give a guide here, rather than just give the solution.
If s and t are the lengths of the strings S and T, then you need to find the largest odd n such that
(1+3+5+...+n)s + (2+4+6+...+(n+1))t < K.
You can simplify these expressions to get a quadratic equation in n.
Let N be (1+3+..+n)s + (2+4+6+...+(n+1))t. You know that K will lie either in the next (n+2) copies of S, or the (n+3) copies of T that follow. Compare K to N+(n+2)s, and take the appropriate letter of either S or T using a modulo.
The only difficult step here is solving the large quadratic, but you can do it in O(log K) arithmetic operations easily enough by doubling n until it's too large, and then using a binary search on the remaining range. (If K is not too large so that floating point is viable, you can do it in O(1) time using the well-known quadratic formula).
Here my quick attempt, there probably is a better solution. Runtime is still O(sqrt n), but memory is O(1).
public static char find(String a, String b, int k) {
int lenA = a.length();
int lenB = b.length();
int rep = 0;
boolean isA = false;
while (k >= 0) {
++rep;
isA = !isA;
k -= (isA ? lenA : lenB) * rep;
}
int len = (isA ? lenA : lenB);
int idx = (len * rep + k) % len;
return (isA ? a : b).charAt(idx);
}
Here's a O(1) solution that took me some time to come up with (read I would have failed an interview on time). Hopefully the process is clear and you can implement it in code.
Our Goal is to return the char that maps to the kth index.
But How? Just 4 easy steps, actually.
Step 1: Find out how many iterations of our two patterns it would take to represent at least k characters.
Step 2: Using this above number of iterations i, return how many characters are present in the previous i-1 iterations.
Step 3: Get the number of characters n into iteration i that our kth character is. (k - result of step 2)
Step 4: Mod n by the length of the pattern to get index into pattern for the specific char. If i is odd, look into s, else look into t.
For step 1, we need to find a formula to give us the iteration i that character k is in. To derive this formula, it may be easier to first derive the formula needed for step 2.
Step 2's formula is basically given an iteration i, return how many characters are present in that iteration. We are solving for 'k' in this equation and are given i, while it's the opposite for step 1 where were are solving for i given k. If we can derive the equation of find k given i, then we can surely reverse it to find i given k.
Now, let's try to derive the formula for step 2 and find k given i. Here it's best to start with the most basic example to see the pattern.
s = "a", t = "b"
i=1 a
i=2 abb
i=3 abbaaa
i=4 abbaaabbbb
i=5 abbaaabbbbaaaaa
i=6 abbaaabbbbaaaaabbbbbb
Counting the total number of combined chars for each pattern during its next iteration gives us:
#iterations of pattern: 1 2 3 4 5 6 7 8 9 10
every new s iteration: 1, 4, 9, 16, 25, 36, 49, 64, 81, 100
every new t iteration: 2, 6, 12, 20, 30, 42, 56, 72, 90, 110
You might notice some nice patterns here. For example, s has a really nice formula to find out how many combined characters it has at any given iteration. It's simply (# of s iterations^2)*s.length. t also has a simple formula. It is (# of t iterations * (# of t iterations + 1))*t.length. You may have noticed that these formulas are the formulas for sum of odd and even numbers (if you did you get a kudos). This makes sense because each pattern's sum for an iteration i is the sum of all of its previous iterations.
Using s,t as length of their respective patterns, we now have the following formula to find the total number of chars at a given iteration.
#chars = s*(# of s iterations)^2 + t * (# of t iterations * (# of t iterations + 1))
Now we just need to do some math to get the number of iterations for each pattern given i.
# of s iterations given i = ceil(i/2.0)
# of t iterations given i = floor(i/2) which / operation gives us by default
Plugging these back into our formula we get:
total # of chars = s*(ceil(i/2.0)^2) + t*((i/2)*((i/2)+1))
We have just completed step 2, and we now know at any given iteration how many total chars there are. We could stop here and start picking random iterations and adjusting accordingly until we get near k, but we can do better than that. Let's use the above formula now to complete step 1 which we skipped. We just need to reorganize our equation to solve for i now.
Doing some simplyfying we get:
// 2
// i i i
// s (-) + t - ( - + 1 ) = k
// 2 2 2
// ----------------------------
// 2
// i t i
// s - + - ( - + 1 )i = k
// 4 2 2
// ----------------------------
// 2 2
// si ti ti
// ---- + ---- + ---- - k = 0
// 4 4 2
// ----------------------------
//
// 2 2
// si + ti + 2ti - 4k = 0
// ----------------------------
// 2
// (s + t)i + 2ti - 4k = 0
// ----------------------------
This looks like a polynomial. Wow! You're right! That means we can solve it using the quadratic formula.
A=(s+t), B=2t, C=-4k
quadratic formula = (-2t + sqrt(2t^2 + 16(s+t)k)) / 2(s+t)
This is our formula for step 1, and it will give us the iteration that the kth character is on. We just need to ceil it. I'm actually not smart enough to know why this works. It just does. Here is a desmos graph that graphs our two polynomials from step 2: s(Siterations)^2 and t(Titerations (Titerations + 1)).
The area under both curves is our total number of chars at an iteration (the vertical line). The formula from step 1 is also graphed, and we can see that for any s, t, k that the x intercept (which represents our xth iteration) is always: previous iteration < x <= current iteration, which is why the ceil works.
We have now completed steps 1 and 2. We have a formula to get the ith iteration that the kth character is on and a formula that gives us how many characters are in an ith iteration. Steps 3 and 4 should follow and we get our answer. This is constant time.
Related
I need a fast way to find maximum value when intervals are overlapping, unlike finding the point where got overlap the most, there is "order". I would have int[][] data that 2 values in int[], where the first number is the center, the second number is the radius, the closer to the center, the larger the value at that point is going to be. For example, if I am given data like:
int[][] data = new int[][]{
{1, 1},
{3, 3},
{2, 4}};
Then on a number line, this is how it's going to looks like:
x axis: -2 -1 0 1 2 3 4 5 6 7
1 1: 1 2 1
3 3: 1 2 3 4 3 2 1
2 4: 1 2 3 4 5 4 3 2 1
So for the value of my point to be as large as possible, I need to pick the point x = 2, which gives a total value of 1 + 3 + 5 = 9, the largest possible value. It there a way to do it fast? Like time complexity of O(n) or O(nlogn)
This can be done with a simple O(n log n) algorithm.
Consider the value function v(x), and then consider its discrete derivative dv(x)=v(x)-v(x-1). Suppose you only have one interval, say {3,3}. dv(x) is 0 from -infinity to -1, then 1 from 0 to 3, then -1 from 4 to 6, then 0 from 7 to infinity. That is, the derivative changes by 1 "just after" -1, by -2 just after 3, and by 1 just after 6.
For n intervals, there are 3*n derivative changes (some of which may occur at the same point). So find the list of all derivative changes (x,change), sort them by their x, and then just iterate through the set.
Behold:
intervals = [(1,1), (3,3), (2,4)]
events = []
for mid, width in intervals:
before_start = mid - width - 1
at_end = mid + width
events += [(before_start, 1), (mid, -2), (at_end, 1)]
events.sort()
prev_x = -1000
v = 0
dv = 0
best_v = -1000
best_x = None
for x, change in events:
dx = x - prev_x
v += dv * dx
if v > best_v:
best_v = v
best_x = x
dv += change
prev_x = x
print best_x, best_v
And also the java code:
TreeMap<Integer, Integer> ts = new TreeMap<Integer, Integer>();
for(int i = 0;i<cows.size();i++) {
int index = cows.get(i)[0] - cows.get(i)[1];
if(ts.containsKey(index)) {
ts.replace(index, ts.get(index) + 1);
}else {
ts.put(index, 1);
}
index = cows.get(i)[0] + 1;
if(ts.containsKey(index)) {
ts.replace(index, ts.get(index) - 2);
}else {
ts.put(index, -2);
}
index = cows.get(i)[0] + cows.get(i)[1] + 2;
if(ts.containsKey(index)) {
ts.replace(index, ts.get(index) + 1);
}else {
ts.put(index, 1);
}
}
int value = 0;
int best = 0;
int change = 0;
int indexBefore = -100000000;
while(ts.size() > 1) {
int index = ts.firstKey();
value += (ts.get(index) - indexBefore) * change;
best = Math.max(value, best);
change += ts.get(index);
ts.remove(index);
}
where cows is the data
Hmmm, a general O(n log n) or better would be tricky, probably solvable via linear programming, but that can get rather complex.
After a bit of wrangling, I think this can be solved via line intersections and summation of function (represented by line segment intersections). Basically, think of each as a triangle on top of a line. If the inputs are (C,R) The triangle is centered on C and has a radius of R. The points on the line are C-R (value 0), C (value R) and C+R (value 0). Each line segment of the triangle represents a value.
Consider any 2 such "triangles", the max value occurs in one of 2 places:
The peak of one of the triangle
The intersection point of the triangles or the point where the two triangles overall. Multiple triangles just mean more possible intersection points, sadly the number of possible intersections grows quadratically, so O(N log N) or better may be impossible with this method (unless some good optimizations are found), unless the number of intersections is O(N) or less.
To find all the intersection points, we can just use a standard algorithm for that, but we need to modify things in one specific way. We need to add a line that extends from each peak high enough so it would be higher than any line, so basically from (C,C) to (C,Max_R). We then run the algorithm, output sensitive intersection finding algorithms are O(N log N + k) where k is the number of intersections. Sadly this can be as high as O(N^2) (consider the case (1,100), (2,100),(3,100)... and so on to (50,100). Every line would intersect with every other line. Once you have the O(N + K) intersections. At every intersection, you can calculate the the value by summing the of all points within the queue. The running sum can be kept as a cached value so it only changes O(K) times, though that might not be posible, in which case it would O(N*K) instead. Making it it potentially O(N^3) (in the worst case for K) instead :(. Though that seems reasonable. For each intersection you need to sum up to O(N) lines to get the value for that point, though in practice, it would likely be better performance.
There are optimizations that could be done considering that you aim for the max and not just to find intersections. There are likely intersections not worth pursuing, however, I could also see a situation where it is so close you can't cut it down. Reminds me of convex hull. In many cases you can easily reduce 90% of the data, but there are cases where you see the worst case results (every point or almost every point is a hull point). For example, in practice there are certainly causes where you can be sure that the sum is going to be less than the current known max value.
Another optimization might be building an interval tree.
In Gayle Laakman's book "Cracking the Coding Interview", chapter VI (Big O), example 12, the problem states that given the following Java code for computing a string's permutations, it is required to compute the code's complexity
public static void permutation(String str) {
permutation(str, "");
}
public static void permutation(String str, String prefix) {
if (str.length() == 0) {
System.out.println(prefix);
} else {
for (int i = 0; i < str.length(); i++) {
String rem = str.substring(0, i) + str.substring(i + 1);
permutation(rem, prefix + str.charAt(i));
}
}
}
The book assumes that since there will be n! permutations, if we consider each of the permutations to be a leaf in the call tree, where each of the leaves is attached to a path of length n, then there will be no more that n*n! nodes in the tree (i.e.: the number of calls is no more than n*n!).
But shouldn't the number of nodes be:
since the number of calls is equivalent to the number of nodes (take a look at the figure in the video Permutations Of String | Code Tutorial by Quinston Pimenta).
If we follow this method, the number of nodes will be 1 (for the first level/root of the tree) + 3 (for the second level) + 3*2 (for the third level) + 3*2*1 (for the fourth/bottom level)
i.e.: the number of nodes = 3!/3! + 3!/2! + 3!/1! + 3!/0! = 16
However, according to the aforementioned method, the number of nodes will be 3*3! = 18
Shouldn't we count shared nodes in the tree as one node, since they express one function call?
You're right about the number of nodes. That formula gives the exact number, but the method in the book counts some multiple times.
Your sum also seems to be approach e * n! for large n, so can be simplified to O(n!).
It's still technically correct to say the number of calls is no more than n * n!, as this is a valid upper bound. Depending on how this is used, this can be fine, and may be easier prove.
For the time complexity, we need to multiply by the average work done for each node.
First, check the String concatenation. Each iteration creates 2 new Strings to pass to the next node. The length of one String increases by 1, and the length of the other decreases by 1, but the total length is always n, giving a time complexity of O(n) for each iteration.
The number of iterations varies for each level, so we can't just multiply by n. Instead look at the total number of iterations for the whole tree, and get the average for each node. With n = 3:
The 1 node in the first level iterates 3 times: 1 * 3 = 3
The 3 nodes in the second level iterate 2 times: 3 * 2 = 6
The 6 nodes in the third level iterate 1 time: 6 * 1 = 6
The total number of iterations is: 3 + 6 + 6 = 15. This is about the same as number of nodes in the tree. So the average number of iterations for each node is constant.
In total, we have O(n!) iterations that each do O(n) work giving a total time complexity of O(n * n!).
According to your video where we have string with 3 characters (ABC), the number of permutations is 6 = 3!, and 6 happens to be equal to 1 + 2 + 3. However, if we have a string with 4 characters (ABCD), the number of permutations should be 4 * 3! as D could be in any position from 1 to 4. With each position of D you can generate 3! permutations for the rest. If you re-draw the tree and count the number of permutations you will see the difference.
According to your code, we have n! = str.length()! permutations, but in each call of the permutations, you also run a loop from 0 to n-1. Therefore, you have O(n * n!).
Update in response to the edited question
Firstly, in programming, we often have either 0->n-1 or 1->n not 0->n.
Secondly, we don't count the number of nodes in this case as if you take a look at the recursion tree in the clip again, you will see nodes duplicated. The permutations in this case should be the number of leaves which are unique among each other.
For instance, if you have a string with 4 characters, the number of leaves should be 4 * 3! = 24 and it would be the number of permutations. However, in your code snippet, you also have a 0->n-1 = 0->3 loop in each permutation, so you need to count the loops in. Thus, your code complexity in this case is O(n *n!) = O(4 * 4!).
We've got 2 pieces of code:
int a = 3;
while (a <= n) {
a = a * a;
}
And:
public void foo(int n, int m) {
int i = m;
while (i > 100)
i = i / 3;
for (int k = i ; k >= 0; k--) {
for (int j = 1; j < n; j*=2)
System.out.print(k + "\t" + j);
System.out.println();
}
}
What is the time complexity of them?
I think that the first one is: O(logn), because it's progressing to N with power of 2.
So maybe it's O(log2n) ?
And the second one I believe is: O(nlog2n), because it's progressing with jumps of 2, and also running on the outer loop.
Am I right?
I believe, that first code will run in O(Log(LogN)) time. It's simple to understand in this way
Before first iteration you have 3 in power 1
After first iteration you have 3 in power 2
After second iteration you have 3 in power 4
After third iteration you have 3 in power 8
After fourth iteration you have 3 in power 16
and so on.
In the second code first piece of code will work in O(LogM) time, because you divide i by 3 every time. The second piece of code C times (C equals 100 in your case) will perform O(LogN) operations, because you multiply j by 2 every time, so it runs in O(CLogN), and you have complexity O(LogM + CLogN)
For the first one, it is indeed O(log(log(n))). Thanks to #MarounMaroun for the hint, I could find this:
l(k) = l(k-1)^2
l(0) = 3
Solving this system yields:
l(k) = 3^(2^k)
So, we are looking for such a k that satisfies l(k) = n. So simply solve that:
This means we found:
The second code is seems misleading. It looks like O(nlog(n)), but the outer loop limited to 100. So, if m < 100, then it obviously is O(mlog(n)). Otherwise, it kind of depends on where exactly m is. Consider these two:
m: 305 -> 101 -> 33
m: 300 -> 100
In the first case, the outer loop would run 33 times. Whereas the second case would cause 100 iterations. I'm not sure, but I think you can write this as being O(log(n)).
I am trying to write a program that accepts an array of five four digit numbers and sorts the array based off the least significant digit. For example if the numbers were 1234, 5432, 4567, and 8978, the array would be sorted first by the last digit so the nest sort would be 5432, 1224, 4597, 8978. Then after it would be 1224, 5432, 8978, 4597. And so on until it is fully sorted.
I have wrote the code for displaying the array and part of it for sorting. I am not sure how to write the equations I need to compare each digit. This is my code for sorting by each digit so far:
public static void sortByDigit(int[] array, int size)
{
for(int i = 0; i < size; i++)
{
for(int j = 0; j < size; j++)
{
}
for(i = 0; i < size; i++)
{
System.out.println(array[i]);
}
}
}
I am not sure what to put in the nested for loop. I think I need to use the modulus.
I just wrote this to separate the digits but I don't know how to swap the numbers or compare them.
int first = array[i]%10;
int second = (array[i]%100)/10;
int third = (array[i]%1000)/10;
int fourth = (array[i]%10000)/10;
Would this would go in the for loop?
It seems like your problem is mainly just getting the value of a digit at a certain index. Once you can do that, you should be able to formulate a solution.
Your hunch that you need modulus is absolutely correct. The modulo operator (%) returns the remainder on a given division operation. This means that saying 10 % 2 would equal 0, as there is no remainder. 10 % 3, however, would yield 1, as the remainder is one.
Given that quick background on modulus, we just need to figure out how to make a method that can grab a digit. Let's start with a general signature:
public int getValueAtIdx(int value, int idx){
}
So, if we call getValueAtIdx(145, 2), it should return 1 (assuming that the index starts at the least significant digit). If we call getValueAtIdx(562354, 3), it should return 2. You get the idea.
Alright, so let's start by using figuring out how to do this on a simple case. Let's say we call getValueAtIdx(27, 0). Using modulus, we should be able to grab that 7. Our equation is 27 % x = 7, and we just need to determine x. So 27 divided by what will give us a remainder of 7? 10, of course! That makes our equation 27 % 10 = 7.
Now that's all find and dandy, but how does 10 relate to 0? Well, let's try and grab the value at index 1 this time (2), and see if we can't figure it out. With what we did last time, we should have something like 27 % x = 27 (WARNING: There is a rabbit-hole here where you could think x should be 5, but upon further examination it can be found that only works in this case). What if we take the 10 we used earlier, but square it (index+1)? That would give us 27 % 100 = 27. Then all we have to do is divide by 10 and we're good.
So what would that look like in the function we are making?
public int getValueAtIdx(int value, int idx){
int modDivisor = (int) Math.pow(10, (idx+1));
int remainder = value % modDivisor;
int digit = remainder / (modDivisor / 10);
return digit;
}
Ok, so let's to back to the more complicated example: getValueAtIdx(562354, 3).
In the first step, modDivisor becomes 10^4, which equals 10000.
In the second step, remainder is set to 562354 % 10000, which equals 2354.
In the third and final step, digit is set to remainder / (10000 / 10). Breaking that down, we get remainder / 1000, which (using integer division) is equal to 2.
Our final step is return the digit we have acquired.
EDIT: As for the sort logic itself, you may want to look here for a good idea.
The general process is to compare the two digits, and if they are equal move on to their next digit. If they are not equal, put them in the bucket and move on.
I am writing a program which I found on a coding competition website, I have sort of figured out how to solve the problem but, I am stuck on a math part of it, I am completely diluting the problem and showing what I need.
first I need to check if a number is part of a sequence, my sequence is 2*a+1 where a is the previous element in the sequence or 2^n-1 to get nth item in the sequence. so it is 1,3,7,15,31,63...
I don't really want to create the whole sequence and check if a number is present, but I am not sure what a quicker method to do this would be.
Second if I am given a number lets say 25, I want to figure out the next highest number in my sequence to this number. So for 25 it would be 31 and for 47 it would be 63, for 8 it would be 13.
How can i do these things without creating the whole sequence.
I have seen similar questions here with different sequences but I am still not sure how to solve this
Start by finding the explicit formula for any term in your sequence. I'm too lazy to write out a proof, so just add 1 to each term in your sequence:
1 + 1 = 2
3 + 1 = 4
7 + 1 = 8
15 + 1 = 16
31 + 1 = 32
63 + 1 = 64
...
You can clearly see that a_n = 2^n - 1.
To check if a particular number is in your sequence, assume that it is:
x = 2^n - 1
x + 1 = 2^n
From Wikipedia:
The binary representation of integers makes it possible to apply a
very fast test to determine whether a given positive integer x is a
power of two:
positive x is a power of two ⇔ (x & (x − 1)) equals to zero.
So to check, just do:
bool in_sequence(int n) {
return ((n + 1) & n) == 0;
}
As #Blender already pointed out your sequence is essentially 2^n - 1, you can use this trick if you use integer format to store it:
boolean inSequence(int value) {
for (int i = 0x7FFF; i != 0; i >>>= 1) {
if (value == i) {
return true;
}
}
return false;
}
Note that for every elements in your sequence, its binary representation will be lots of 0s and then lots of 1s.
For example, 7 in binary is 0000000000000000000000000000111 and 63 in binary is 0000000000000000000000000111111.
This solution starts from 01111111111111111111111111111111 and use an unsigned bitshift, then compare if it is equal to your value.
Nice and simple.
How to find the next higher number :
For example, we get 19 ( 10011 ) , should return 31 (11111)
int findNext(int n){
if(n == 0) return 1;
int ret = 2; // start from 10
while( (n>>1) > 0){ // end with 100000
ret<<1;
}
return ret-1;
}