type IntSet struct {
words []uint64
}
func (s *IntSet) Has(x int) bool {
word, bit := x/64, uint(x%64)
return word < len(s.words) && s.words[word]&(1<<bit) != 0
}
Lets go through what I think is going on:
A new type is declared called IntSet. Underneath its new type declaration it is unint64 slice.
A method is created called Has(). It can only receive IntSet types, after playing around with ints she returns a bool
Before she can play she needs two ints. She stores these babies on the stack.
Lost for words
This methods purpose is to report whether the set contains the non-negative value x. Here is a the go test:
func TestExample1(t *testing.T) {
//!+main
var x, y IntSet
fmt.Println(x.Has(9), x.Has(123)) // "true false"
//!-main
// Output:
// true false
}
Looking for some guidance understanding what this method is doing inside. And why the programmer did it in such complicated means (I feel like I am missing something).
The return statement:
return word < len(s.words) && s.words[word]&(1<<bit) != 0
Are the order of operations this?
return ( word < len(s.words) && ( s.words[word]&(1<<bit)!= 0 )
And what is the [words] and & doing within:
s.words[word]&(1<<bit)!= 0
edit: Am beginning to see slightly seeing that:
s.words[word]&(1<<bit)!= 0
Is just a slice but don't understand the &
As I read the code, I scribbled some notes:
package main
import "fmt"
// A set of bits
type IntSet struct {
// bits are grouped into 64 bit words
words []uint64
}
// x is the index for a bit
func (s *IntSet) Has(x int) bool {
// The word index for the bit
word := x / 64
// The bit index within a word for the bit
bit := uint(x % 64)
if word < 0 || word >= len(s.words) {
// error: word index out of range
return false
}
// the bit set within the word
mask := uint64(1 << bit)
// true if the bit in the word set
return s.words[word]&mask != 0
}
func main() {
nBits := 2*64 + 42
// round up to whole word
nWords := (nBits + (64 - 1)) / 64
bits := IntSet{words: make([]uint64, nWords)}
// bit 127 = 1 * 64 + 63
bits.words[1] = 1 << 63
fmt.Printf("%b\n", bits.words)
for i := 0; i < nWords*64; i++ {
has := bits.Has(i)
if has {
fmt.Println(i, has)
}
}
has := bits.Has(127)
fmt.Println(has)
}
Playground: https://play.golang.org/p/rxquNZ_23w1
Output:
[0 1000000000000000000000000000000000000000000000000000000000000000 0]
127 true
true
The Go Programming Language Specification
Arithmetic operators
& bitwise AND integers
peterSO's answer is spot on - read it. But I figured this might also help you understand.
Imagine I want to store some random numbers in the range 1 - 8. After I store these numbers I will be asked if the number n (also in the range of 1 - 8) appears in the numbers I recorded earlier. How would we store the numbers?
One, probably obvious, way would be to store them in a slice or maybe a map. Maybe we would choose a map since lookups will be constant time. So we create our map
seen := map[uint8]struct{}{}
Our code might look something like this
type IntSet struct {
seen: map[uint8]struct{}
}
func (i *IntSet) AddValue(v uint8) {
i.seen[v] = struct{}{}
}
func (i *IntSet) Has(v uint8) bool {
_, ok := i.seen[v]
return ok
}
For each number we store we take up (at least) 1 byte (8 bits) of memory. If we were to store all 8 numbers we would be using 64 bits / 8 bytes.
However, as the name implies, this is an int Set. We don't care about duplicates, we only care about membership (which Has provides for us).
But there is another way we could store these numbers, and we could do it all within a single byte. Since a byte provides 8 bits, we can use these 8 bits as markers for values we have seen. The initial value (in binary notation) would be
00000000 == uint8(0)
If we did an AddValue(3) we could change the 3rd bit and end up with
00000100 == uint8(3)
^
|______ 3rd bit
If we then called AddValue(8) we would have
10000100 == uint8(132)
^ ^
| |______ 3rd bit
|___________ 8th bit
So after adding 3 and 8 to our IntSet we have the internally stored integer value of 132. But how do we take 132 and figure out whether a particular bit is set? Easy, we use bitwise operators.
The & operator is a logical AND. It will return the value of the bits common between the numbers on each side of the operator. For example
10001100 01110111 11111111
& 01110100 & 01110000 & 00000001
-------- -------- --------
00000100 01110000 00000001
So to find out if n is in our set we simply do
our_set_value & (1 << (value_we_are_looking_for - 1))
which if we were searching for 4 would yield
10000100
& 00001000
----------
0 <-- so 4 is not present
or if we were searching for 8
10000100
& 10000000
----------
10000000 <-- so 8 is present
You may have noticed I subtracted 1 from our value_we_are_looking for. This is because I am fitting 1-8 into our 8bit number. If we only wanted to store seven numbers then we could just skip using the very first bit and assume our counting starts at bit #2 then we wouldn't have to subtract 1, like the code you posted does.
Assuming you understand all of that, here's where things get interesting. So far we have been storing our values in a uint8 (so we could only have 8 values, or 7 if you omit the first bit). But there are larger numbers that have more bits, like uint64. Instead of 8 values, we can store 64 values! But what happens if the range of values we want to track exceed 1-64? What if we want to store 65? This is where the slice of words comes from in the original code.
Since the code posted skips the first bit, from now on I will do so as well.
We can use the first uint64 to store the numbers 1 - 63. When we want to store the numbers 64-127 we need a new uint64. So our slice would be something like
[ uint64_of_1-63, uint64_of_64-127, uint64_of_128-192, etc]
Now, to answer the question about whether a number is in our set we need to first find the uint64 whose range would contain our number. If we were searching for 110 we would want to use the uint64 located at index 1 (uint64_of_64-128) because 110 would fall in that range.
To find the index of the word we need to look at, we take the whole number value of n / 64. In the case of 110 we would get 1, which is exactly what we want.
Now we need to examine the specific bit of that number. The bit that needs to be checked would be the remainder when dividing 110 by 64, or 46. So if the 46th bit of the word at index 1 is set, then we have seen 110 before.
This is how it might look in code
type IntSet struct {
words []uint64
}
func (s *IntSet) Has(x int) bool {
word, bit := x/64, uint(x%64)
return word < len(s.words) && s.words[word]&(1<<bit) != 0
}
func (s *IntSet) AddValue(x int) {
word := x / 64
bit := x % 64
if word < len(s.words) {
s.words[word] |= (1 << uint64(bit))
}
}
And here is some code to test it
func main() {
rangeUpper := 1000
bits := IntSet{words: make([]uint64, (rangeUpper/64)+1)}
bits.AddValue(127)
bits.AddValue(8)
bits.AddValue(63)
bits.AddValue(64)
bits.AddValue(998)
fmt.Printf("%b\n", bits.words)
for i := 0; i < rangeUpper; i++ {
if ok := bits.Has(i); ok {
fmt.Printf("Found %d\n", i)
}
}
}
OUTPUT
Found 8
Found 63
Found 64
Found 127
Found 998
Playground of above
Note
The |= is another bitwise operator OR. It means combine the two values keeping anywhere there is a 1 in either value
10000000 00000001 00000001
& 01000000 & 10000000 & 00000001
-------- -------- --------
11000000 10000001 00000001 <-- important that we
can set the value
multiple times
Using this method we can reduce the cost of storage for 65535 numbers from 131KB to just 1KB. This type of bit manipulation for set membership is very common in implementations of Bloom Filters
An IntSet represents a Set of integers. The presence in the set of any of a contiguous range of integers can be established by writing a single bit in the IntSet. Likewise, checking whether a specific integer is in the IntSet can be done by checking whether the particular integer corresponding to that bit is set.
So the code is finding the specific uint64 in the Intset corresponding to the integer:
word := x/64
and then the specific bit in that uint64:
bit := uint(x%64)
and then checking first that the integer being tested is in the range supported by the IntSet:
word < len(s.words)
and then whether the specific bit corresponding to the specific integer is set:
&& s.words[word]&(1<<bit) != 0
This part:
s.words[word]
pulls out the specific uint64 of the IntSet that tracks whether the integer in question is in the set.
&
is a bitwise AND.
(1<<bit)
means take a 1, shift it to the bit position representing the specific integer being tested.
Performing the bitwise AND between the integer in question, and the bit-shifted 1 will return a 0 if the bit corresponding to the integer is not set, and a 1 if the bit is set (meaning, the integer in question is a member of the IntSet).
I am fiddling around with Go at the moment and have stumpled upon a problem where I want to get some feedback and help :)
My problem is that I have a string containing a hexadecimal value as input, such as this:
"60A100"
Now, I want to convert this to the binary representation of the number and be able to look at specific bits within.
My solution to this right now is:
i, err := strconv.ParseUint(rawHex, 16, 32)
if err != nil {
fmt.Printf("%s", err)
}
// Convert int to binary representation
// %024b indicates base 2, padding with 0, with 24 characters.
bin := fmt.Sprintf("%024b", i)
The variable bin now holds exactly what I want, except it is a string which I don't think is optimal. I would rather that I could have an array of the individual bits such that I could just choose index i to get bit number i :)
Because as far as I know right now, if I lookup index 8 like so; bin[8], I will get a decimal that corresponds to the binary number, in the ASCII table.
I have searched quite a bit, but I can't find a solution that fits perfectly, but maybe I am looking in the wrong spot.
I hope you guys can guide me to the correct / optimal solution in this case :)
Thanks in advance!
You could turn it into a slice representing bits
// This could also return []bool
func asBits(val uint64) []uint64 {
bits := []uint64{}
for i := 0; i < 24; i++ {
bits = append([]uint64{val & 0x1}, bits...)
// or
// bits = append(bits, val & 0x1)
// depending on the order you want
val = val >> 1
}
return bits
}
func main() {
rawHex := "60A100"
i, err := strconv.ParseUint(rawHex, 16, 32)
if err != nil {
fmt.Printf("%s", err)
}
fmt.Printf("%024b\n", i)
fmt.Println(asBits(i))
}
OUTPUT
011000001010000100000000
[0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0]
https://play.golang.org/p/KK_AUPgbZu
As #jimb points out, you can also just check an individual bit
fmt.Printf("9th bit is set? %t\n", (i >> 8) & 1 == 1)
which is what #n-carter's answer does.
After parsing the value you can directly access each bit. You can use something like this:
func getNthBit(val, n uint32) int {
n = 32 - n
if 1 << n & val > 0 {
return 1
}
return 0
}
Following #n-carter answer, you can access each bit individually
There are two approaches:
Option 1: Shifting the value:
Shift the bin number to the right n possitions to get the n-th bit the first one. then mask it with 1
func getNthBit(val, n uint32) int {
// 1. reverse the golang endian
nthBit := 32-n
// 2. move the nth bit to the first position
movedVal := val >> nthBit
// 3. mask the value, selecting only this first bit
maskedValue := movedVal & 1
return maskedValue
// can be shortened like so
// return (val >> (32-n)) & 1
}
Explanation:
1. Get the right bit index according to the endian
01100000101000010000000001000101
^
(32-3)=29nth bit
2. Shift the bits to get n-th in the first possition
01100000101000010000000001000101 >> 29
^^^
00000000000000000000000000000011
^^^
3. Mask first bit. This picks(extracts) the value from this bit
00000000000000000000000000000011
& ^
00000000000000000000000000000001
1
Option 2: shifting 1 and masking with it
This can be done the way #n-carter does. Shift a 1 to the left
func getNthBit(val, n uint32) int {
// 1. reverse the golang endian
nthBit := 32-n
// 2. move the mask 1 bit to the nth position
mask := 1 << nthBit
// 3. mask the value, selecting only this nth bit
maskedValue := val & mask
if maskedValue == 0 {
return 0
}
return 1
// can be written shorter like:
//if val & (1 << (32-n)) == 0 {
// return 0
//}
//return 1
}
Explanation:
1. Get the right bit index according to the endian
01100000101000010000000001000101
^
(32-3)=29nth bit
2. Shift the 1 to the n-th position (1 << 29 == 2^(29-1))
00000000000000000000000000000001 << 29
00100000000000000000000000000000
3. Mask n-th bit. This picks(extracts) the value from this bit
01100000101000010000000001000101
&
00100000000000000000000000000000
1
Hope this helps. It takes some time to visualise bit operations in your head.
I was asked this question in a interview, so I don't want the solution, just the guidance regarding how to approach it.
You have been given two numbers low and high. And a random generator which generates 0 and 1. I have to generate a number between low and high using that function.
I can get difference between the two numbers and somehow try to generate a number using bit manipulation. But I am not able to figure out how to do that?
You can do:
range = high - low
find n such that 2^n-1 < range <= 2^n
run the random generator n times to generate an int thanks to its binary representation. Something like 010011010 (= 154 in decimal)
add the obtained number to low to get your final number!
Here's a basic bit-by-bit comparison algorithm that gives a random number between low and high, using a random-bit function:
Decrease high by 1 and increase low by 1 (in case the random bits introduced later all equal those in high or low).
Create booleans high_dec and low_inc to store whether at least one 1 in high has been changed into 0, and at least one 0 in low has been changed into 1, and set both of them to false (these will help avoid the result going out of range).
Compare high and low bit-by-bit from MSB to LSB with these cases:
If you find high:1 and low:1 then store a 1 if low_inc=false or store a random bit otherwise (and update high_dec as necessary).
If you find high:1 and low:0 then store a random bit (and update high_dec or low_inc as necessary).
If you find high:0 and low:1 then store a 0 if high_dec=false or store a 1 if low_inc=false or store a random bit otherwise.
If you find high:0 and low:0 then store a 0 if high_dec=false or store a random bit otherwise (and update low_inc as necessary).
Note that the distribution of the random numbers is only uniform if the lowest possible result is a power of 2, and the range is a power of 2. In all cases the whole range is used, but there may be an emphasis on values near the beginning or end of the range.
function between(a, b) {
var lo = (a + 1).toString(2).split(''), // conversion to bit array because
hi = (b - 1).toString(2).split(''), // there is no bit manipulation in JS
lc = false, // low changed
hc = false, // high changed
result = [];
while (lo.length < hi.length) lo.unshift(0); // add leading zeros to low
for (var i = 0; i < hi.length; i++) { // iterate over bits, msb to lsb
var bit = Math.round(Math.random()); // random bit generator
if (hi[i] == 1) {
if (lo[i] == 1) { // case hi:1 lo:1
if (lc == false) bit = 1
else if (bit == 0) hc = true;
} else { // case hi:1 lo:0
if (bit == 0) hc = true
else lc = true;
}
} else {
if (lo[i] == 1) { // case hi:0 lo:1
if (hc == false) bit = 0
else if (lc == false) bit = 1;
} else { // case hi:0 lo:0
if (hc == false) bit = 0
else if (bit == 1) lc = true;
}
}
result.push(bit);
}
return parseInt(result.join(''), 2); // convert bit array to integer
}
document.write(between(999999, 1000100) + "<BR>");
I have a very large set (billions or more, it's expected to grow exponentially to some level), and I want to generate seemingly random elements from it without repeating. I know I can pick a random number and repeat and record the elements I have generated, but that takes more and more memory as numbers are generated, and wouldn't be practical after couple millions elements out.
I mean, I could say 1, 2, 3 up to billions and each would be constant time without remembering all the previous, or I can say 1,3,5,7,9 and on then 2,4,6,8,10, but is there a more sophisticated way to do that and eventually get a seemingly random permutation of that set?
Update
1, The set does not change size in the generation process. I meant when the user's input increases linearly, the size of the set increases exponentially.
2, In short, the set is like the set of every integer from 1 to 10 billions or more.
3, In long, it goes up to 10 billion because each element carries the information of many independent choices, for example. Imagine an RPG character that have 10 attributes, each can go from 1 to 100 (for my problem different choices can have different ranges), thus there's 10^20 possible characters, number "10873456879326587345" would correspond to a character that have "11, 88, 35...", and I would like an algorithm to generate them one by one without repeating, but makes it looks random.
Thanks for the interesting question. You can create a "pseudorandom"* (cyclic) permutation with a few bytes using modular exponentiation. Say we have n elements. Search for a prime p that's bigger than n+1. Then find a primitive root g modulo p. Basically by definition of primitive root, the action x --> (g * x) % p is a cyclic permutation of {1, ..., p-1}. And so x --> ((g * (x+1))%p) - 1 is a cyclic permutation of {0, ..., p-2}. We can get a cyclic permutation of {0, ..., n-1} by repeating the previous permutation if it gives a value bigger (or equal) n.
I implemented this idea as a Go package. https://github.com/bwesterb/powercycle
package main
import (
"fmt"
"github.com/bwesterb/powercycle"
)
func main() {
var x uint64
cycle := powercycle.New(10)
for i := 0; i < 10; i++ {
fmt.Println(x)
x = cycle.Apply(x)
}
}
This outputs something like
0
6
4
1
2
9
3
5
8
7
but that might vary off course depending on the generator chosen.
It's fast, but not super-fast: on my five year old i7 it takes less than 210ns to compute one application of a cycle on 1000000000000000 elements. More details:
BenchmarkNew10-8 1000000 1328 ns/op
BenchmarkNew1000-8 500000 2566 ns/op
BenchmarkNew1000000-8 50000 25893 ns/op
BenchmarkNew1000000000-8 200000 7589 ns/op
BenchmarkNew1000000000000-8 2000 648785 ns/op
BenchmarkApply10-8 10000000 170 ns/op
BenchmarkApply1000-8 10000000 173 ns/op
BenchmarkApply1000000-8 10000000 172 ns/op
BenchmarkApply1000000000-8 10000000 169 ns/op
BenchmarkApply1000000000000-8 10000000 201 ns/op
BenchmarkApply1000000000000000-8 10000000 204 ns/op
Why did I say "pseudorandom"? Well, we are always creating a very specific kind of cycle: namely one that uses modular exponentiation. It looks pretty pseudorandom though.
I would use a random number and swap it with an element at the beginning of the set.
Here's some pseudo code
set = [1, 2, 3, 4, 5, 6]
picked = 0
Function PickNext(set, picked)
If picked > Len(set) - 1 Then
Return Nothing
End If
// random number between picked (inclusive) and length (exclusive)
r = RandomInt(picked, Len(set))
// swap the picked element to the beginning of the set
result = set[r]
set[r] = set[picked]
set[picked] = result
// update picked
picked++
// return your next random element
Return temp
End Function
Every time you pick an element there is one swap and the only extra memory being used is the picked variable. The swap can happen if the elements are in a database or in memory.
EDIT Here's a jsfiddle of a working implementation http://jsfiddle.net/sun8rw4d/
JavaScript
var set = [];
set.picked = 0;
function pickNext(set) {
if(set.picked > set.length - 1) { return null; }
var r = set.picked + Math.floor(Math.random() * (set.length - set.picked));
var result = set[r];
set[r] = set[set.picked];
set[set.picked] = result;
set.picked++;
return result;
}
// testing
for(var i=0; i<100; i++) {
set.push(i);
}
while(pickNext(set) !== null) { }
document.body.innerHTML += set.toString();
EDIT 2 Finally, a random binary walk of the set. This can be accomplished with O(Log2(N)) stack space (memory) which for 10billion is only 33. There's no shuffling or swapping involved. Using trinary instead of binary might yield even better pseudo random results.
// on the fly set generator
var count = 0;
var maxValue = 64;
function nextElement() {
// restart the generation
if(count == maxValue) {
count = 0;
}
return count++;
}
// code to pseudo randomly select elements
var current = 0;
var stack = [0, maxValue - 1];
function randomBinaryWalk() {
if(stack.length == 0) { return null; }
var high = stack.pop();
var low = stack.pop();
var mid = ((high + low) / 2) | 0;
// pseudo randomly choose the next path
if(Math.random() > 0.5) {
if(low <= mid - 1) {
stack.push(low);
stack.push(mid - 1);
}
if(mid + 1 <= high) {
stack.push(mid + 1);
stack.push(high);
}
} else {
if(mid + 1 <= high) {
stack.push(mid + 1);
stack.push(high);
}
if(low <= mid - 1) {
stack.push(low);
stack.push(mid - 1);
}
}
// how many elements to skip
var toMid = (current < mid ? mid - current : (maxValue - current) + mid);
// skip elements
for(var i = 0; i < toMid - 1; i++) {
nextElement();
}
current = mid;
// get result
return nextElement();
}
// test
var result;
var list = [];
do {
result = randomBinaryWalk();
list.push(result);
} while(result !== null);
document.body.innerHTML += '<br/>' + list.toString();
Here's the results from a couple of runs with a small set of 64 elements. JSFiddle http://jsfiddle.net/yooLjtgu/
30,46,38,34,36,35,37,32,33,31,42,40,41,39,44,45,43,54,50,52,53,51,48,47,49,58,60,59,61,62,56,57,55,14,22,18,20,19,21,16,15,17,26,28,29,27,24,25,23,6,2,4,5,3,0,1,63,10,8,7,9,12,11,13
30,14,22,18,16,15,17,20,19,21,26,28,29,27,24,23,25,6,10,8,7,9,12,13,11,2,0,63,1,4,5,3,46,38,42,44,45,43,40,41,39,34,36,35,37,32,31,33,54,58,56,55,57,60,59,61,62,50,48,49,47,52,51,53
As I mentioned in my comment, unless you have an efficient way to skip to a specific point in your "on the fly" generation of the set this will not be very efficient.
if it is enumerable then use a pseudo-random integer generator adjusted to the period 0 .. 2^n - 1 where the upper bound is just greater than the size of your set and generate pseudo-random integers discarding those more than the size of your set. Use those integers to index items from your set.
Pre- compute yourself a series of indices (e.g. in a file), which has the properties you need and then randomly choose a start index for your enumeration and use the series in a round-robin manner.
The length of your pre-computed series should be > the maximum size of the set.
If you combine this (depending on your programming language etc.) with file mappings, your final nextIndex(INOUT state) function is (nearly) as simple as return mappedIndices[state++ % PERIOD];, if you have a fixed size of each entry (e.g. 8 bytes -> uint64_t).
Of course, the returned value could be > your current set size. Simply draw indices until you get one which is <= your sets current size.
Update (In response to question-update):
There is another option to achieve your goal if it is about creating 10Billion unique characters in your RPG: Generate a GUID and write yourself a function which computes your number from the GUID. man uuid if you are are on a unix system. Else google it. Some parts of the uuid are not random but contain meta-info, some parts are either systematic (such as your network cards MAC address) or random, depending on generator algorithm. But they are very very most likely unique. So, whenever you need a new unique number, generate a uuid and transform it to your number by means of some algorithm which basically maps the uuid bytes to your number in a non-trivial way (e.g. use hash functions).
I'm solving Project Euler problem 16, I've ended up with a code that can logically solve it, but is unable to process as I believe its overflowing or something? I tried int64 in place of int but it just prints 0,0. If i change the power to anything below 30 it works, but above 30 it does not work, Can anyone point out my mistake? I believe its not able to calculate 2^1000.
// PE_16 project main.go
package main
import (
"fmt"
)
func power(x, y int) int {
var pow int
var final int
final = 1
for pow = 1; pow <= y; pow++ {
final = final * x
}
return final
}
func main() {
var stp int
var sumfdigits int
var u, t, h, th, tth, l int
stp = power(2,1000)
fmt.Println(stp)
u = stp / 1 % 10
t = stp / 10 % 10
h = stp / 100 % 10
th = stp / 1000 % 10
tth = stp / 10000 % 10
l = stp / 100000 % 10
sumfdigits = u + t + h + th + tth + l
fmt.Println(sumfdigits)
}
Your approach to this problem requires exact integer math up to 1000 bits in size. But you're using int which is 32 or 64 bits. math/big.Int can handle such task. I intentionally do not provide a ready made solution using big.Int as I assume your goal is to learn by doing it by yourself, which I believe is the intent of Project Euler.
As noted by #jnml, ints aren't large enough; if you wish to calculate 2^1000 in Go, big.Ints are a good choice here. Note that math/big provides the Exp() method which will be easier to use than converting your power function to big.Ints.
I worked through some Project Euler problems about a year ago, doing them in Go to get to know the language. I didn't like the ones that required big.Ints, which aren't so easy to work with in Go. For this one, I "cheated" and did it in one line of Ruby:
Removed because I remembered it was considered bad form to show a working solution, even in a different language.
Anyway, my Ruby example shows another thing I learned with Go's big.Ints: sometimes it's easier to convert them to a string and work with that string than to work with the big.Int itself. This problem strikes me as one of those cases.
Converting my Ruby algorithm to Go, I only work with big.Ints on one line, then it's easy to work with the string and get the answer in just a few lines of code.
You don't need to use math/big. Below is a school boy maths way of doubling a decimal number as a hint!
xs holds the decimal digits in least significant first order. Pass in a pointer to the digits (pxs) as the slice might need to get bigger.
func double(pxs *[]int) {
xs := *pxs
carry := 0
for i, x := range xs {
n := x*2 + carry
if n >= 10 {
carry = 1
n -= 10
} else {
carry = 0
}
xs[i] = n
}
if carry != 0 {
*pxs = append(xs, carry)
}
}