Develop Reference

Rounding integers to one significant digit

I want to round down an int in Java, what i mean is, if I have an int 45678, i want to convert that int into 40000
this is how im calling it
int den = placeValue(startCode,length);
and this is the code
static int placeValue(int N, int num)
{
int total = 1, value = 0, rem = 0;
while (true) {
rem = N % 10;
N = N / 10;
if (rem == num) {
value = total * rem;
break;
}
total = total * 10;
}
return value;
}
so if i have 89765, i would want 80000,
but instead it return the place value of whatever length is.
So,
for 89765, the length would be 5, so the return value is 5 i.e. the value in the ones place.
but if the number was 85760
then it would return 5000.
I hope that makes sense.
Any suggestions would be much appreicated.

In my opinions, if I can avoid 'calculating' I will compute the answer from other concept since I am not confidence on my math (haha).
Here is my answer. (only work in positive numbers)
I think the length of the inputted number is not necessary.
static int placeValue2(int N) {
String tar = N+"";
String rtn = tar.substring(0,1); // take first digital
for (int i=0;i<tar.length()-1;i++) // pad following digitals
rtn+="0";
return Integer.parseInt(rtn);
}

I appreciate you asked the question here.
Here is my solution. I don't know why you are taking two parameters, but I tried it from one param.
class PlaceValue{
int placeValue(int num){
int length = 0; int temp2=1;
boolean result=false;
long temp1=1;
if (num<0){
result=true;
num=num*(-1);
}
if (num==0){
System.out.println("Value 0 not allowed");
return 0;
}
while (temp1 <= num){ //This loop checks for the length, multiplying temp1 with 10
//untill its <= number. length++ counts the length.
length++;
temp1*=10;
}
for (int i=1; i<length; i++){//this loop multiplies temp2 with 10 length number times.
// like if length 2 then 100. if 5 then 10000
temp2=temp2*10;
}
temp2=(num/temp2)*temp2;
/* Let's say number is 2345. This would divide it over 1000, giving us 2;
in the same line multiplying it with the temp2 which is same 1000 resulting 2000.
now 2345 became 2000;
*/
if (result==true){
temp2=temp2*(-1);
}
return temp2;
}
}
Here is the code above. You can try this. If you are dealing with the long numbers, go for long in function type as well as the variable being returned and in the main function. I hope you understand. otherwise, ask me.

Do you want something like this?
public static int roundDown(int number, int magnitude) {
int mag = (int) Math.pow(10, magnitude);
return (number / mag) * mag;
}
roundDown(53278,4) -> 50000
roundDown(46287,3) -> 46000
roundDown(65478,2) -> 65400
roundDown(43298,1) -> 43290
roundDown(43278,0) -> 43278
So the equivalent that will only use the most significant digit is:
public static int roundDown(int number) {
int zeros = (int) Math.log10(number);
int mag = (int) Math.pow(10, zeros);
return (number / mag) * mag;
}

divide (split) the line into segments of arbitrary length each. (short at begining wide at the end)

Assume there is a line (1, 30) (float x = 30)
I need to split it into segments, each next segment should be wider than previous one, and the last segment should be X times wider than first one.
I have idea first split line into equal parts and than increase next and decrease previous until two conditions reached:
- first segment is shorter than last X times
- each segment (except first) is wider than previous for the same multiplier
//input:
int lineDimension = 30;
int numberOfSegments = 5;
int step = 1;
float[] splitLineIntoSegments(float lineDimension, int numberOfSegments, float differenceBetweenFirstAndLastSegmentMultiplicator, float step) {
float[] result = new float[numberOfSegments];
//first split into equal segments
for (int i = 0; i < numberOfSegments; i++) {
result[i] = lineDimension / numberOfSegments;
}
//increase each next value untill difference reached
do {
for (int ii = 0; ii < numberOfSegments; ii++) {
if (result[ii]-step<=0)
return result;
if (ii>numberOfSegments/2){
result[ii] += step;
}
else result[ii] -= step;
}
}
while ((float)result[numberOfSegments] / (float)result[0] > differenceBetweenFirstAndLastSegmentMultiplicator);
return result;
}
float [] res = splitLineIntoSegments(lineDimension,numberOfSegments,2,step);
as the result it should be 4,5,6,7,8
There is a better way to do it ?

If the ratios must be constant, let r, the relative lengths of the segments are 1, r, r², r³, … r^(n-1) for n parts and this sums to (r^n-1) / (r-1). We also have X = r^(n-1), giving r = X^(1/(n-1)).
If the length of the line segment is L, the parts are
L.r^k.(r-1) / (r^n-1)
E.g. for 4 parts and X=27/8, we have r=3/2 and the parts are 8/65, 12/65, 18/65, and 27/65 of L.
If the ratios needn't be constant and the parts are proportional to some given numbers Rk (such that X=R[n-1]/R0), take the sum R and use
L.Rk / R

You can force a simple arithmetic sequence for this.
Start with the problem parameters:
N = quantity of segments
X = scale factor: segment[N-1] / segment[0]
L = length of line
First, find the required mean:
mean = L / N
Now, we need the first and last terms to average to the mean. Let a be the length of the first segment, currently unknown. Solve for a
(a + X*a) / 2 = mean
a = 2*mean / (1+X)
You now have the first (a) and last (X*a) terms, and the quantity of terms. Now it's trivial to find the common difference:
d = (X*a - a) / (N-1)
Your sequence of segments is now
[ a + i*d for 0 <= i < N ] // i being a sequence of integers

Multithreaded Segmented Sieve of Eratosthenes in Java

I am trying to create a fast prime generator in Java. It is (more or less) accepted that the fastest way for this is the segmented sieve of Eratosthenes: https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes. Lots of optimizations can be further implemented to make it faster. As of now, my implementation generates 50847534 primes below 10^9 in about 1.6 seconds, but I am looking to make it faster and at least break the 1 second barrier. To increase the chance of getting good replies, I will include a walkthrough of the algorithm as well as the code.
Still, as a TL;DR, I am looking to include multi-threading into the code
For the purposes of this question, I want to separate between the 'segmented' and the 'traditional' sieves of Eratosthenes. The traditional sieve requires O(n) space and therefore is very limited in range of the input (the limit of it). The segmented sieve however only requires O(n^0.5) space and can operate on much larger limits. (A main speed-up is using a cache-friendly segmentation, taking into account the L1 & L2 cache sizes of the specific computer). Finally, the main difference that concerns my question is that the traditional sieve is sequential, meaning it can only continue once the previous steps are completed. The segmented sieve however, is not. Each segment is independent, and is 'processed' individually against the sieving primes (the primes not larger than n^0.5). This means that theoretically, once I have the sieving primes, I can divide the work between multiple computers, each processing a different segment. The work of eachother is independent of the others. Assuming (wrongly) that each segment requires the same amount of time t to complete, and there are k segments, One computer would require total time of T = k * t, whereas k computers, each working on a different segment would require a total amount of time T = t to complete the entire process. (Practically, this is wrong, but for the sake of simplicity of the example).
This brought me to reading about multithreading - dividing the work to a few threads each processing a smaller amount of work for better usage of CPU. To my understanding, the traditional sieve cannot be multithreaded exactly because it is sequential. Each thread would depend on the previous, rendering the entire idea unfeasible. But a segmented sieve may indeed (I think) be multithreaded.
Instead of jumping straight into my question, I think it is important to introduce my code first, so I am hereby including my current fastest implementation of the segmented sieve. I have worked quite hard on it. It took quite some time, slowly tweaking and adding optimizations to it. The code is not simple. It is rather complex, I would say. I therefore assume the reader is familiar with the concepts I am introducing, such as wheel factorization, prime numbers, segmentation and more. I have included notes to make it easier to follow.
import java.math.BigInteger;
import java.util.ArrayList;
import java.util.Arrays;
public class primeGen {
public static long x = (long)Math.pow(10, 9); //limit
public static int sqrtx;
public static boolean [] sievingPrimes; //the sieving primes, <= sqrtx
public static int [] wheels = new int [] {2,3,5,7,11,13,17,19}; // base wheel primes
public static int [] gaps; //the gaps, according to the wheel. will enable skipping multiples of the wheel primes
public static int nextp; // the first prime > wheel primes
public static int l; // the amount of gaps in the wheel
public static void main(String[] args)
{
long startTime = System.currentTimeMillis();
preCalc(); // creating the sieving primes and calculating the list of gaps
int segSize = Math.max(sqrtx, 32768*8); //size of each segment
long u = nextp; // 'u' is the running index of the program. will continue from one segment to the next
int wh = 0; // the will be the gap index, indicating by how much we increment 'u' each time, skipping the multiples of the wheel primes
long pi = pisqrtx(); // the primes count. initialize with the number of primes <= sqrtx
for (long low = 0 ; low < x ; low += segSize) //the heart of the code. enumerating the primes through segmentation. enumeration will begin at p > sqrtx
{
long high = Math.min(x, low + segSize);
boolean [] segment = new boolean [(int) (high - low + 1)];
int g = -1;
for (int i = nextp ; i <= sqrtx ; i += gaps[g])
{
if (sievingPrimes[(i + 1) / 2])
{
long firstMultiple = (long) (low / i * i);
if (firstMultiple < low)
firstMultiple += i;
if (firstMultiple % 2 == 0) //start with the first odd multiple of the current prime in the segment
firstMultiple += i;
for (long j = firstMultiple ; j < high ; j += i * 2)
segment[(int) (j - low)] = true;
}
g++;
//if (g == l) //due to segment size, the full list of gaps is never used **within just one segment** , and therefore this check is redundant.
//should be used with bigger segment sizes or smaller lists of gaps
//g = 0;
}
while (u <= high)
{
if (!segment[(int) (u - low)])
pi++;
u += gaps[wh];
wh++;
if (wh == l)
wh = 0;
}
}
System.out.println(pi);
long endTime = System.currentTimeMillis();
System.out.println("Solution took "+(endTime - startTime) + " ms");
}
public static boolean [] simpleSieve (int l)
{
long sqrtl = (long)Math.sqrt(l);
boolean [] primes = new boolean [l/2+2];
Arrays.fill(primes, true);
int g = -1;
for (int i = nextp ; i <= sqrtl ; i += gaps[g])
{
if (primes[(i + 1) / 2])
for (int j = i * i ; j <= l ; j += i * 2)
primes[(j + 1) / 2]=false;
g++;
if (g == l)
g=0;
}
return primes;
}
public static long pisqrtx ()
{
int pi = wheels.length;
if (x < wheels[wheels.length-1])
{
if (x < 2)
return 0;
int k = 0;
while (wheels[k] <= x)
k++;
return k;
}
int g = -1;
for (int i = nextp ; i <= sqrtx ; i += gaps[g])
{
if(sievingPrimes[( i + 1 ) / 2])
pi++;
g++;
if (g == l)
g=0;
}
return pi;
}
public static void preCalc ()
{
sqrtx = (int) Math.sqrt(x);
int prod = 1;
for (long p : wheels)
prod *= p; // primorial
nextp = BigInteger.valueOf(wheels[wheels.length-1]).nextProbablePrime().intValue(); //the first prime that comes after the wheel
int lim = prod + nextp; // circumference of the wheel
boolean [] marks = new boolean [lim + 1];
Arrays.fill(marks, true);
for (int j = 2 * 2 ;j <= lim ; j += 2)
marks[j] = false;
for (int i = 1 ; i < wheels.length ; i++)
{
int p = wheels[i];
for (int j = p * p ; j <= lim ; j += 2 * p)
marks[j]=false; // removing all integers that are NOT comprime with the base wheel primes
}
ArrayList <Integer> gs = new ArrayList <Integer>(); //list of the gaps between the integers that are coprime with the base wheel primes
int d = nextp;
for (int p = d + 2 ; p < marks.length ; p += 2)
{
if (marks[p]) //d is prime. if p is also prime, then a gap is identified, and is noted.
{
gs.add(p - d);
d = p;
}
}
gaps = new int [gs.size()];
for (int i = 0 ; i < gs.size() ; i++)
gaps[i] = gs.get(i); // Arrays are faster than lists, so moving the list of gaps to an array
l = gaps.length;
sievingPrimes = simpleSieve(sqrtx); //initializing the sieving primes
}
}
Currently, it produces 50847534 primes below 10^9 in about 1.6 seconds. This is very impressive, at least by my standards, but I am looking to make it faster, possibly break the 1 second barrier. Even then, I believe it can be made much faster still.
The whole program is based on wheel factorization: https://en.wikipedia.org/wiki/Wheel_factorization. I have noticed I am getting the fastest results using a wheel of all primes up to 19.
public static int [] wheels = new int [] {2,3,5,7,11,13,17,19}; // base wheel primes
This means that the multiples of those primes are skipped, resulting in a much smaller searching range. The gaps between numbers which we need to take are then calculated in the preCalc method. If we make those jumps between the the numbers in the searching range we skip the multiples of the base primes.
public static void preCalc ()
{
sqrtx = (int) Math.sqrt(x);
int prod = 1;
for (long p : wheels)
prod *= p; // primorial
nextp = BigInteger.valueOf(wheels[wheels.length-1]).nextProbablePrime().intValue(); //the first prime that comes after the wheel
int lim = prod + nextp; // circumference of the wheel
boolean [] marks = new boolean [lim + 1];
Arrays.fill(marks, true);
for (int j = 2 * 2 ;j <= lim ; j += 2)
marks[j] = false;
for (int i = 1 ; i < wheels.length ; i++)
{
int p = wheels[i];
for (int j = p * p ; j <= lim ; j += 2 * p)
marks[j]=false; // removing all integers that are NOT comprime with the base wheel primes
}
ArrayList <Integer> gs = new ArrayList <Integer>(); //list of the gaps between the integers that are coprime with the base wheel primes
int d = nextp;
for (int p = d + 2 ; p < marks.length ; p += 2)
{
if (marks[p]) //d is prime. if p is also prime, then a gap is identified, and is noted.
{
gs.add(p - d);
d = p;
}
}
gaps = new int [gs.size()];
for (int i = 0 ; i < gs.size() ; i++)
gaps[i] = gs.get(i); // Arrays are faster than lists, so moving the list of gaps to an array
l = gaps.length;
sievingPrimes = simpleSieve(sqrtx); //initializing the sieving primes
}
At the end of the preCalc method, the simpleSieve method is called, efficiently sieving all the sieving primes mentioned before, the primes <= sqrtx. This is a simple Eratosthenes sieve, rather than segmented, but it is still based on wheel factorization, perviously computed.
public static boolean [] simpleSieve (int l)
{
long sqrtl = (long)Math.sqrt(l);
boolean [] primes = new boolean [l/2+2];
Arrays.fill(primes, true);
int g = -1;
for (int i = nextp ; i <= sqrtl ; i += gaps[g])
{
if (primes[(i + 1) / 2])
for (int j = i * i ; j <= l ; j += i * 2)
primes[(j + 1) / 2]=false;
g++;
if (g == l)
g=0;
}
return primes;
}
Finally, we reach the heart of the algorithm. We start by enumerating all primes <= sqrtx, with the following call:
long pi = pisqrtx();`
which used the following method:
public static long pisqrtx ()
{
int pi = wheels.length;
if (x < wheels[wheels.length-1])
{
if (x < 2)
return 0;
int k = 0;
while (wheels[k] <= x)
k++;
return k;
}
int g = -1;
for (int i = nextp ; i <= sqrtx ; i += gaps[g])
{
if(sievingPrimes[( i + 1 ) / 2])
pi++;
g++;
if (g == l)
g=0;
}
return pi;
}
Then, after initializing the pi variable which keeps track of the enumeration of primes, we perform the mentioned segmentation, starting the enumeration from the first prime > sqrtx:
int segSize = Math.max(sqrtx, 32768*8); //size of each segment
long u = nextp; // 'u' is the running index of the program. will continue from one segment to the next
int wh = 0; // the will be the gap index, indicating by how much we increment 'u' each time, skipping the multiples of the wheel primes
long pi = pisqrtx(); // the primes count. initialize with the number of primes <= sqrtx
for (long low = 0 ; low < x ; low += segSize) //the heart of the code. enumerating the primes through segmentation. enumeration will begin at p > sqrtx
{
long high = Math.min(x, low + segSize);
boolean [] segment = new boolean [(int) (high - low + 1)];
int g = -1;
for (int i = nextp ; i <= sqrtx ; i += gaps[g])
{
if (sievingPrimes[(i + 1) / 2])
{
long firstMultiple = (long) (low / i * i);
if (firstMultiple < low)
firstMultiple += i;
if (firstMultiple % 2 == 0) //start with the first odd multiple of the current prime in the segment
firstMultiple += i;
for (long j = firstMultiple ; j < high ; j += i * 2)
segment[(int) (j - low)] = true;
}
g++;
//if (g == l) //due to segment size, the full list of gaps is never used **within just one segment** , and therefore this check is redundant.
//should be used with bigger segment sizes or smaller lists of gaps
//g = 0;
}
while (u <= high)
{
if (!segment[(int) (u - low)])
pi++;
u += gaps[wh];
wh++;
if (wh == l)
wh = 0;
}
}
I have also included it as a note, but will explain as well. Because the segment size is relatively small, we will not go through the entire list of gaps within just one segment, and checking it - is redundant. (Assuming we use a 19-wheel). But in a broader scope overview of the program, we will make use of the entire array of gaps, so the variable u has to follow it and not accidentally surpass it:
while (u <= high)
{
if (!segment[(int) (u - low)])
pi++;
u += gaps[wh];
wh++;
if (wh == l)
wh = 0;
}
Using higher limits will eventually render a bigger segment, which might result in a neccessity of checking we don't surpass the gaps list even within the segment. This, or tweaking the wheel primes base might have this effect on the program. Switching to bit-sieving can largely improve the segment limit though.
As an important side-note, I am aware that efficient segmentation is
one that takes the L1 & L2 cache-sizes into account. I get the
fastest results using a segment size of 32,768 * 8 = 262,144 = 2^18. I am not sure what the cache-size of my computer is, but I do
not think it can be that big, as I see most cache sizes <= 32,768.
Still, this produces the fastest run time on my computer, so this is
why it's the chosen segment size.
As I mentioned, I am still looking to improve this by a lot. I
believe, according to my introduction, that multithreading can result
in a speed-up factor of 4, using 4 threads (corresponding to 4
cores). The idea is that each thread will still use the idea of the
segmented sieve, but work on different portions. Divide the n
into 4 equal portions - threads, each in turn performing the
segmentation on the n/4 elements it is responsible for, using the
above program. My question is how do I do that? Reading about
multithreading and examples, unfortunately, did not bring to me any
insight on how to implement it in the case above efficiently. It
seems to me, as opposed to the logic behind it, that the threads were
running sequentially, rather than simultaneously. This is why I
excluded it from the code to make it more readable. I will really
appreciate a code sample on how to do it in this specific code, but a
good explanation and reference will maybe do the trick too.
Additionally, I would like to hear about more ways of speeding-up
this program even more, any ideas you have, I would love to hear!
Really want to make it very fast and efficient. Thank you!

An example like this should help you get started.
An outline of a solution:
Define a data structure ("Task") that encompasses a specific segment; you can put all the immutable shared data into it for extra neatness, too. If you're careful enough, you can pass a common mutable array to all tasks, along with the segment limits, and only update the part of the array within these limits. This is more error-prone, but can simplify the step of joining the results (AFAICT; YMMV).
Define a data structure ("Result") that stores the result of a Task computation. Even if you just update a shared resulting structure, you may need to signal which part of that structure has been updated so far.
Create a Runnable that accepts a Task, runs a computation, and puts the results into a given result queue.
Create a blocking input queue for Tasks, and a queue for Results.
Create a ThreadPoolExecutor with the number of threads close to the number of machine cores.
Submit all your Tasks to the thread pool executor. They will be scheduled to run on the threads from the pool, and will put their results into the output queue, not necessarily in order.
Wait for all the tasks in the thread pool to finish.
Drain the output queue and join the partial results into the final result.
Extra speedup may (or may not) be achieved by joining the results in a separate task that reads the output queue, or even by updating a mutable shared output structure under synchronized, depending on how much work the joining step involves.
Hope this helps.

Are you familiar with the work of Tomas Oliveira e Silva? He has a very fast implementation of the Sieve of Eratosthenes.

How interested in speed are you? Would you consider using c++?
$ time ../c_code/segmented_bit_sieve 1000000000
50847534 primes found.
real 0m0.875s
user 0m0.813s
sys 0m0.016s
$ time ../c_code/segmented_bit_isprime 1000000000
50847534 primes found.
real 0m0.816s
user 0m0.797s
sys 0m0.000s
(on my newish laptop with an i5)
The first is from #Kim Walisch using a bit array of odd prime candidates.
https://github.com/kimwalisch/primesieve/wiki/Segmented-sieve-of-Eratosthenes
The second is my tweak to Kim's with IsPrime[] also implemented as bit array, which is slightly less clear to read, although a little faster for big N due to the reduced memory footprint.
I will read your post carefully as I am interested in primes and performance no matter what language is used. I hope this isn't too far off topic or premature. But I noticed I was already beyond your performance goal.

How can I make this Java snippet more efficient?

In a computer contest, I was given a problem where I had to manipulate input data. The input has been split() into an array where data[0] is the number of repetitions. There can be up to 10^18 repetitions. My program returned Exception in thread "main" java.lang.OutOfMemoryError: Java heap space and I failed the contest.
Here's a piece of my code that's eating up memory and CPU:
long product[][]=new long[data[0]][2];
product[0][0]=data[1];
product[0][1]=data[2];
for(int a=1;a<data[0];a++){
product[a][0]=((data[5]*product[a-1][0] + data[6]) % data[3]) + 1; // Pi = ((A*Pi-1 + B) mod M) + 1 (for all i = 2..N)
product[a][1]=((data[7]*product[a-1][1] + data[8]) % data[4]) + 1; // Wi = ((C*Wi-1 + D) mod K) + 1 (for all i = 2..N)
}
Here's some of the input data:
980046644627629799 9 123456 18 10000000 831918484 451864686 840000324 650000765
972766173386786486 123 1 10000000 10000000 590000001 680000000 610000001 970000002
299896237124947938 681206 164538 2280874 981991 416793690 904023823 813682336 774801135
My program can only work up to about 7 or 8 digits, then it takes minutes to run. With 18 digits, it crashed almost as soon as I clicked "Run" in Eclipse.
I'm curious as to how is it possible to manipulate that much data on a normal computer. Please let me know if my question is unclear or you need more information. Thanks!

You can't have, and don't need, an array of such a huge length. You just need to track the most recent 2values. E.g., just have product1 and product2.
Also, consider testing if either number is a NaN after each iteration. If so, throw an Exception and give the iteration number.
Because once you get a NaN they will all be NaN. Except you are using long, so scratch that. "Nevermind". :-)

long product[][]=new long[data[0]][2];
This is the only line in the code you pasted that allocates memory. You allocate an array whose length will be data[0] in length! As data grows, so does the array. What is the formula you're trying to apply here?
The first input data you provide :
980046644627629799
is already too large to even declare an array for. Try creating a single dimension array with that as its length and see what happens....
Are you sure you don't just want a 1 x 2 matrix that you accumulate over? Explain your intended algorithm clearly and we can help you with a more optimal solution.

Let's put the numbers into perspective.
Memory: One long takes 8 bytes. 1018 longs take 16,000,000 terabytes. Way too much.
Time: 10,000,000 operations ≈ 1 second. 1018 steps ≈ 30 centuries. Also way too much.
You can solve the memory problem by realising that you only need the most recent values at any time, and that the entire array is redundant:
long currentP = data[1];
long currentW = data[2];
for (int a = 1; a < data[0]; a++)
{
currentP = ((data[5] * currentP + data[6]) % data[3]) + 1;
currentW = ((data[7] * currentW + data[8]) % data[4]) + 1;
}
The time problem is a bit trickier to solve. Since modulus is used, you can observe that the numbers must enter a cycle at some point. Once you find the cycle, you can predict what the value will be after n iterations without having to do each iteration manually.
The simplest method for finding cycles is to keep track of whether or not you visited each element, and then go through until you encounter an element you've seen before. In this situation, the amount of memory required is proportional to M and K (data[3] and data[4]). If they are too large, a more space-efficient cycle detection algorithm must be used.
Here is an example which finds the value for P:
public static void main(String[] args)
{
// value = (A * prevValue + B) % M + 1
final long NOT_SEEN = -1; // the code used for values not visited before
long[] data = { 980046644627629799L, 9, 123456, 18, 10000000, 831918484, 451864686, 840000324, 650000765 };
long N = data[0]; // the number of iterations
long S = data[1]; // the initial value of the sequence
long M = data[3]; // the modulus divisor
long A = data[5]; // muliply by this
long B = data[6]; // add this
int max = (int) Math.max(M, S); // all the numbers (except first) must be less than or equal to M
long[] seenTime = new long[max + 1]; // whether or not a value was seen and how many iterations it took
// initialize the values of 'seenTime' to 'not seen'
for (int i = 0; i < seenTime.length; i++)
{
seenTime[i] = NOT_SEEN;
}
// find the cycle
long count = 0;
long cycleValue = S; // the current value in the series
while (seenTime[(int)cycleValue] == NOT_SEEN)
{
seenTime[(int)cycleValue] = count;
cycleValue = (A * cycleValue + B) % M + 1;
count++;
}
long cycleLength = count - seenTime[(int)cycleValue];
long cycleOffset = seenTime[(int)cycleValue];
long result;
if (N < cycleOffset)
{
// Special case: requested iteration occurs before the cycle starts
// Straightforward simulation
long value = S;
for (long i = 0; i < N; i++)
{
value = (A * value + B) % M + 1;
}
result = value;
}
else
{
// Normal case: requested iteration occurs inside the cycle
// Simulate just the relevant part of one cycle
long positionInCycle = (N - cycleOffset) % cycleLength;
long value = cycleValue;
for (long i = 0; i < positionInCycle; i++)
{
value = (A * value + B) % M + 1;
}
result = value;
}
System.out.println(result);
}
I am only giving you the solution because it looks like the contest is over. The important lesson to learn from this is that you should always check the bounds to see whether your solution is practical before you start coding it up.

Translating equivalent formulas in to code isn't giving correct results

I'm trying to calculate the Mean Difference average of a set of data. I have two (supposedly equivalent) formulas which calculate this, with one being more efficient (O^n) than the other (O^n2).
The problem is that while the inefficient formula gives correct output, the efficient one does not. Just by looking at both formulas I had a hunch that they weren't equivalent, but wrote it off because the derivation was made by a statician in a scientific journal. So i'm assuming the problem is my translation. Can anyone help me translate the efficient function properly?
Inefficient formula:
Inefficient formula translation (Java):
public static double calculateMeanDifference(ArrayList<Integer> valuesArrayList)
{
int valuesArrayListSize = valuesArrayList.size();
int sum = 0;
for(int i = 0; i < valuesArrayListSize; i++)
{
for(int j = 0; j < valuesArrayListSize; j++)
sum += (i != j ? Math.abs(valuesArrayList.get(i) - valuesArrayList.get(j)) : 0);
}
return new Double( (sum * 1.0)/ (valuesArrayListSize * (valuesArrayListSize - 1)));
}
Efficient derived formula:
where (sorry, don't know how to use MathML on here):
x(subscript i) = the ith order statistic of the data set
x(bar) = the mean of the data set
Efficient derived formula translation (Java):
public static double calculateMean(ArrayList<Integer> valuesArrayList)
{
double sum = 0;
int valuesArrayListSize = valuesArrayList.size();
for(int i = 0; i < valuesArrayListSize; i++)
sum += valuesArrayList.get(i);
return sum / (valuesArrayListSize * 1.0);
}
public static double calculateMeanDifference(ArrayList<Integer> valuesArrayList)
{
double sum = 0;
double mean = calculateMean(valuesArrayList);
int size = valuesArrayList.size();
double rightHandTerm = mean * size * (size + 1);
double denominator = (size * (size - 1)) / 2.0;
Collections.sort(valuesArrayList);
for(int i = 0; i < size; i++)
sum += (i * valuesArrayList.get(i) - rightHandTerm);
double meanDifference = (2 * sum) / denominator;
return meanDifference;
}
My data set consists of a collection of integers each having a value bounded by the set [0,5].
Randomly generating such sets and using the two functions on them gives different results. The inefficient one seems to be the one producing results in line with what is being measured: the absolute average difference between any two values in the set.
Can anyone tell me what's wrong with my translation?
EDIT: I created a simpler implementation that is O(N) provided the all your data has values limited to a relatively small set.The formula sticks to the methodology of the first method and thus, gives identical results to it (unlike the derived formula). If it fits your use case, I suggest people use this instead of the derived efficient formula, especially since the latter seems to give negative values when N is small).
Efficient, non-derived translation (Java):
public static double calculateMeanDifference3(ArrayList<Integer> valuesArrayList)
{
HashMap<Integer, Double> valueCountsHashMap = new HashMap<Integer, Double>();
double size = valuesArrayList.size();
for(int i = 0; i < size; i++)
{
int currentValue = valuesArrayList.get(i);
if(!valueCountsHashMap.containsKey(currentValue))
valueCountsHashMap.put(currentValue, new Double(1));
else
valueCountsHashMap.put(currentValue, valueCountsHashMap.get(currentValue)+ 1);
}
double sum = 0;
for(Map.Entry<Integer, Double> valueCountKeyValuePair : valueCountsHashMap.entrySet())
{
int currentValue = valueCountKeyValuePair.getKey();
Double currentCount = valueCountKeyValuePair.getValue();
for(Map.Entry<Integer, Double> valueCountKeyValuePair1 : valueCountsHashMap.entrySet())
{
int loopValue = valueCountKeyValuePair1.getKey();
Double loopCount = valueCountKeyValuePair1.getValue();
sum += (currentValue != loopValue ? Math.abs(currentValue - loopValue) * loopCount * currentCount : 0);
}
}
return new Double( sum/ (size * (size - 1)));
}

Your interpretation of sum += (i * valuesArrayList.get(i) - rightHandTerm); is wrong, it should be sum += i * valuesArrayList.get(i);, then after your for, double meanDifference = ((2 * sum) - rightHandTerm) / denominator;
Both equations yields about the same value, but they are not equal. Still, this should help you a little.

You subtract rightHandTerm on each iteration, so it gets [over]multiplied to N.
The big Sigma in the nominator touches only (i x_i), not the right hand term.
One more note: mean * size == sum. You don't have to divide sum by N and then remultiply it back.