android lock password combinations - passwords

I just came across with this interesting question from my colleague. I'm trying now, but meanwhile I thought I could share it here.
With the password grid shown in the Android home screen, how many valid passwords are possible?
min password length: 4 max: 9 (correct me if I'm wrong)

Summary
The full combinations of 4 to 9 distinctive numbers, minus the combinations which include invalid "jump"s.
The Long Version
The rule for Android 3x3 password grid:
one point for once
cannot "jump" over a point
The author of the original post used Mathematica to generate all 985824 combinations.
Because there is no "jump", several pairs of consecutive points are invalid.
Delete all invalid combinations to reach the result.
The combinations for 4-to-9-point paths are respectively 1624, 7152, 26016, 72912, 140704, 140704.
The Original Post In Chinese
The reference is from guokr, a site alike Stack Exchange Skeptics in the form of blogs.

I know this question is old, but I answered it in another question (before finding this question) with a brute force approach in python, so adding it here for posterity:
pegs = {
1: {3:2, 7:4, 9:5},
2: {8:5},
3: {1:2, 7:5, 9:6},
4: {6:5},
5: {},
6: {4:5},
7: {1:4, 3:5, 9:8},
8: {2:5},
9: {1:5, 3:6, 7:8}
}
def next_steps(path):
return (n for n in range(1,10) if (not path or n not in path and
(n not in pegs[path[-1]]
or pegs[path[-1]][n] in path)))
def patterns(path, steps, verbose=False):
if steps == 0:
if verbose: print(path)
return 1
return sum(patterns(path+[n], steps-1) for n in next_steps(path))
So you can list all the # of patterns for any number of steps:
>>> [(steps, patterns([], steps)) for steps in range(1,10)]
[(1, 9),
(2, 56),
(3, 320),
(4, 1624),
(5, 7152),
(6, 26016),
(7, 72912),
(8, 140704),
(9, 140704)]
>>> sum(patterns([], steps) for steps in range(4,10))
389112
This is not the most efficient way of solving it because you could use reflections and only calculate a 4*corner + 4*mid-edge + 1*middle, e.g.:
>>> patterns([], 6) == 4*patterns([1], 5) + 4*patterns([2], 5) + patterns([5], 5)
True

I brute forced the answer with a recursive search and i found a bigger answer, 487272. The algorithm is simple: trying it all. I quoted it down here. I didn't found any error in my code (but I'm not very skilled with c++). Sorry for the grammatical error I'm not English.
#include <iostream>
#include <stdlib.h>
using namespace std;
int combo; //counter
void research(int Ipoints /*number of points already took*/, bool Icheck[9]/*points matrix*/,int Ilast/*last took point*/,
int Icomboval/*combination representation, only for printing purpose*/, int deep/*number of iteration, only for printing purpose*/)
{
// int numcall = 0; //DEBUG
for( int i=0; i<9; i++) //Controlling every free point in search of a valid way to contimue
if( Icheck[i] == false )
{
//Just for security, coping every variable in a new variable. I don't know how c++ works but I will make it works
int points = Ipoints;
int last = Ilast;
int comboval = Icomboval;
bool check[9];
for( int j=0; j<9; j++)
check[j] = Icheck[j];
int e1,e2;
int middle = -1;
e1=i; e2=last; //Ccontrolling duble jumps
if( e1 == 0 && e2 == 2 ) middle = 1;
if( e1 == 3 && e2 == 5 ) middle = 4;
if( e1 == 6 && e2 == 8 ) middle = 7;
if( e1 == 0 && e2 == 6 ) middle = 3;
if( e1 == 1 && e2 == 7 ) middle = 4;
if( e1 == 2 && e2 == 8 ) middle = 5;
if( e1 == 0 && e2 == 8 ) middle = 4;
if( e1 == 6 && e2 == 2 ) middle = 4;
e2=i; e1=last; // in both way
if( e1 == 0 && e2 == 2 ) middle = 1;
if( e1 == 3 && e2 == 5 ) middle = 4;
if( e1 == 6 && e2 == 8 ) middle = 7;
if( e1 == 0 && e2 == 6 ) middle = 3;
if( e1 == 1 && e2 == 7 ) middle = 4;
if( e1 == 2 && e2 == 8 ) middle = 5;
if( e1 == 0 && e2 == 8 ) middle = 4;
if( e1 == 6 && e2 == 2 ) middle = 4;
if((middle != -1) && !(check[middle])) {
check[middle] = true;
points++; //adding middle points
comboval *= 10;
comboval += middle;
}
check[i] = true;
points++; // get the point
comboval*=10;
comboval += i+1;
if(points > 3)
{
combo++; // every iteration over tree points is a valid combo
// If you want to see they all, beware because printing they all is truly slow:
// cout << "Combination n. " << combo << " found: " << comboval << " , points " << points << " with " << deep << " iterations\n";
}
if(points > 9) //Just for sure, emergency shutdown,
{ exit(1); }
research(points,check,i,comboval,deep+1); /*Recursive, here is the true program!*/
// numcall++; //DEBUG
}
// cout << "Ended " << deep << " , with " << numcall << " subs called\n"; // Only for debug purposes,remove with all the //DEBUG thing
}
int main ()
{
combo = 0; //no initial knows combo
bool checkerboard[9];
for( int i=0; i<9; i++) checkerboard[i]=false; //blank initial pattern
research(0/*no point taken*/,checkerboard,-1/*just a useless value*/,0/*blank combo*/,1/*it's the firs iteration*/); //let's search!
cout << "\n" ;
cout << "And the answer is ... " << combo << "\n"; //out
char ans='\0';
while(ans=='\0')
{ //just waiting
cin >> ans;
}
return 0;
}

i just run a python code to get possible combinations
i got 985824 possibilities
from itertools import permutations
numbers = "123456789"
total_combos = list(permutations(numbers,4))+list(permutations(numbers,5))+list(permutations(numbers,6))+list(permutations(numbers,7))+list(permutations(numbers,8))+list(permutations(numbers,9))
print(len(total_combos))

(No of Points- Valid patterns)
(4 - 746)
(5 - 3268)
(6 - 11132)
(7 - 27176)
(8 - 42432)
(9 - 32256)
Total of 117010 valid Patterns are possible

Related

how do i correctly use >= and <= in code?

I have tried many thing involving this, >=, >==, =>, ==>.i can not find one that works. hey all return either primary expression needed or expected initializer before '>'. I am creating a IR receiver latch switch and thus have to create parameters for the code because the receiver is not constant in all conditions. Full code below. Any suggestions to fix the code please reply and don't DM me. Thank you.
code:
int LEDState = 0;
int LEDPin = 8;
int dt = 100;
int recieverOld ==> 500 and recieverOld ==< 2000;
int recieverNew;
int recieverPin = 12;
void setup() {
// put your setup code here, to run once:
Serial.begin(9600);
pinMode(LEDPin, OUTPUT);
pinMode(recieverPin, INPUT);
}
void loop() {
// put your main code here, to run repeatedly:
recieverNew = digitalRead(recieverPin);
if((recieverOld >== 0 && recieverOld <== 10) && (recieverNew >== 500 && recieverNew <== 2000) {
if(LEDState == 0) {
digitalWrite(LEDPin, HIGH);
LEDState = 1;
}
}
recieverOld = recieverNew;
delay(dt);
}
error:
expected initializer before '==' token
if one = used line 4 and related, return error expected primary-expression before '>' token
if > before = line 4 and related, return error expected initializer before '>=' token
Any solutions or suggestions welcome.
TL;DR
Operators that do no exist, and that you should NOT use:
==>, ==<, >==, <==
Operators that works and you can use them:
>= - MORE THAN OR EQUAL, compare operator, for example X >= 5
<= - LESS THAN OR EQUAL, compare operator, for example X <= 5
> - MORE THAN, compare operator, for example X > 5
< - LESS THAN, compare operator, for example X < 5
== - compare operator, when you want to compare values of the variables if they have the same value, for example X == 5, Y == X, 10 == 7
=== - equality operator, similar to compare operator ==, but aditionally checks the type of a variable. for example X === Y, '10' === 10
= - assign operator, when you want to assign something to the variable, for example X = 5
<> OR != - NOT EQUAL, compare operator, for example X != 5, Y <> 10
!== - similar to != or <>, but also checks the type of a value. For example 10 !== '10', and will return opposite result of the equality operator ===

When can an algorithm have square root(n) time complexity?

Can someone give me example of an algorithm that has square root(n) time complexity. What does square root time complexity even mean?
Square root time complexity means that the algorithm requires O(N^(1/2)) evaluations where the size of input is N.
As an example for an algorithm which takes O(sqrt(n)) time, Grover's algorithm is one which takes that much time. Grover's algorithm is a quantum algorithm for searching an unsorted database of n entries in O(sqrt(n)) time.
Let us take an example to understand how can we arrive at O(sqrt(N)) runtime complexity, given a problem. This is going to be elaborate, but is interesting to understand. (The following example, in the context for answering this question, is taken from Coding Contest Byte: The Square Root Trick , very interesting problem and interesting trick to arrive at O(sqrt(n)) complexity)
Given A, containing an n elements array, implement a data structure for point updates and range sum queries.
update(i, x)-> A[i] := x (Point Updates Query)
query(lo, hi)-> returns A[lo] + A[lo+1] + .. + A[hi]. (Range Sum Query)
The naive solution uses an array. It takes O(1) time for an update (array-index access) and O(hi - lo) = O(n) for the range sum (iterating from start index to end index and adding up).
A more efficient solution splits the array into length k slices and stores the slice sums in an array S.
The update takes constant time, because we have to update the value for A and the value for the corresponding S. In update(6, 5) we have to change A[6] to 5 which results in changing the value of S1 to keep S up to date.
The range-sum query is interesting. The elements of the first and last slice (partially contained in the queried range) have to be traversed one by one, but for slices completely contained in our range we can use the values in S directly and get a performance boost.
In query(2, 14) we get,
query(2, 14) = A[2] + A[3]+ (A[4] + A[5] + A[6] + A[7]) + (A[8] + A[9] + A[10] + A[11]) + A[12] + A[13] + A[14] ;
query(2, 14) = A[2] + A[3] + S[1] + S[2] + A[12] + A[13] + A[14] ;
query(2, 14) = 0 + 7 + 11 + 9 + 5 + 2 + 0;
query(2, 14) = 34;
The code for update and query is:
def update(S, A, i, k, x):
S[i/k] = S[i/k] - A[i] + x
A[i] = x
def query(S, A, lo, hi, k):
s = 0
i = lo
//Section 1 (Getting sum from Array A itself, starting part)
while (i + 1) % k != 0 and i <= hi:
s += A[i]
i += 1
//Section 2 (Getting sum from Slices directly, intermediary part)
while i + k <= hi:
s += S[i/k]
i += k
//Section 3 (Getting sum from Array A itself, ending part)
while i <= hi:
s += A[i]
i += 1
return s
Let us now determine the complexity.
Each query takes on average
Section 1 takes k/2 time on average. (you might iterate atmost k/2)
Section 2 takes n/k time on average, basically number of slices
Section 3 takes k/2 time on average. (you might iterate atmost k/2)
So, totally, we get k/2 + n/k + k/2 = k + n/k time.
And, this is minimized for k = sqrt(n). sqrt(n) + n/sqrt(n) = 2*sqrt(n)
So we get a O(sqrt(n)) time complexity query.
Prime numbers
As mentioned in some other answers, some basic things related to prime numbers take O(sqrt(n)) time:
Find number of divisors
Find sum of divisors
Find Euler's totient
Below I mention two advanced algorithms which also bear sqrt(n) term in their complexity.
MO's Algorithm
try this problem: Powerful array
My solution:
#include <bits/stdc++.h>
using namespace std;
const int N = 1E6 + 10, k = 500;
struct node {
int l, r, id;
bool operator<(const node &a) {
if(l / k == a.l / k) return r < a.r;
else return l < a.l;
}
} q[N];
long long a[N], cnt[N], ans[N], cur_count;
void add(int pos) {
cur_count += a[pos] * cnt[a[pos]];
++cnt[a[pos]];
cur_count += a[pos] * cnt[a[pos]];
}
void rm(int pos) {
cur_count -= a[pos] * cnt[a[pos]];
--cnt[a[pos]];
cur_count -= a[pos] * cnt[a[pos]];
}
int main() {
int n, t;
cin >> n >> t;
for(int i = 1; i <= n; i++) {
cin >> a[i];
}
for(int i = 0; i < t; i++) {
cin >> q[i].l >> q[i].r;
q[i].id = i;
}
sort(q, q + t);
memset(cnt, 0, sizeof(cnt));
memset(ans, 0, sizeof(ans));
int curl(0), curr(0), l, r;
for(int i = 0; i < t; i++) {
l = q[i].l;
r = q[i].r;
/* This part takes O(n * sqrt(n)) time */
while(curl < l)
rm(curl++);
while(curl > l)
add(--curl);
while(curr > r)
rm(curr--);
while(curr < r)
add(++curr);
ans[q[i].id] = cur_count;
}
for(int i = 0; i < t; i++) {
cout << ans[i] << '\n';
}
return 0;
}
Query Buffering
try this problem: Queries on a Tree
My solution:
#include <bits/stdc++.h>
using namespace std;
const int N = 2e5 + 10, k = 333;
vector<int> t[N], ht;
int tm_, h[N], st[N], nd[N];
inline int hei(int v, int p) {
for(int ch: t[v]) {
if(ch != p) {
h[ch] = h[v] + 1;
hei(ch, v);
}
}
}
inline void tour(int v, int p) {
st[v] = tm_++;
ht.push_back(h[v]);
for(int ch: t[v]) {
if(ch != p) {
tour(ch, v);
}
}
ht.push_back(h[v]);
nd[v] = tm_++;
}
int n, tc[N];
vector<int> loc[N];
long long balance[N];
vector<pair<long long,long long>> buf;
inline long long cbal(int v, int p) {
long long ans = balance[h[v]];
for(int ch: t[v]) {
if(ch != p) {
ans += cbal(ch, v);
}
}
tc[v] += ans;
return ans;
}
inline void bal() {
memset(balance, 0, sizeof(balance));
for(auto arg: buf) {
balance[arg.first] += arg.second;
}
buf.clear();
cbal(1,1);
}
int main() {
int q;
cin >> n >> q;
for(int i = 1; i < n; i++) {
int x, y; cin >> x >> y;
t[x].push_back(y); t[y].push_back(x);
}
hei(1,1);
tour(1,1);
for(int i = 0; i < ht.size(); i++) {
loc[ht[i]].push_back(i);
}
vector<int>::iterator lo, hi;
int x, y, type;
for(int i = 0; i < q; i++) {
cin >> type;
if(type == 1) {
cin >> x >> y;
buf.push_back(make_pair(x,y));
}
else if(type == 2) {
cin >> x;
long long ans(0);
for(auto arg: buf) {
hi = upper_bound(loc[arg.first].begin(), loc[arg.first].end(), nd[x]);
lo = lower_bound(loc[arg.first].begin(), loc[arg.first].end(), st[x]);
ans += arg.second * (hi - lo);
}
cout << tc[x] + ans/2 << '\n';
}
else assert(0);
if(i % k == 0) bal();
}
}
There are many cases.
These are the few problems which can be solved in root(n) complexity [better may be possible also].
Find if a number is prime or not.
Grover's Algorithm: allows search (in quantum context) on unsorted input in time proportional to the square root of the size of the input.link
Factorization of the number.
There are many problems that you will face which will demand use of sqrt(n) complexity algorithm.
As an answer to second part:
sqrt(n) complexity means if the input size to your algorithm is n then there approximately sqrt(n) basic operations ( like **comparison** in case of sorting). Then we can say that the algorithm has sqrt(n) time complexity.
Let's analyze the 3rd problem and it will be clear.
let's n= positive integer. Now there exists 2 positive integer x and y such that
x*y=n;
Now we know that whatever be the value of x and y one of them will be less than sqrt(n). As if both are greater than sqrt(n)
x>sqrt(n) y>sqrt(n) then x*y>sqrt(n)*sqrt(n) => n>n--->contradiction.
So if we check 2 to sqrt(n) then we will have all the factors considered ( 1 and n are trivial factors).
Code snippet:
int n;
cin>>n;
print 1,n;
for(int i=2;i<=sqrt(n);i++) // or for(int i=2;i*i<=n;i++)
if((n%i)==0)
cout<<i<<" ";
Note: You might think that not considering the duplicate we can also achieve the above behaviour by looping from 1 to n. Yes that's possible but who wants to run a program which can run in O(sqrt(n)) in O(n).. We always look for the best one.
Go through the book of Cormen Introduction to Algorithms.
I will also request you to read following stackoverflow question and answers they will clear all the doubts for sure :)
Are there any O(1/n) algorithms?
Plain english explanation Big-O
Which one is better?
How do you calculte big-O complexity?
This link provides a very basic beginner understanding of O() i.e., O(sqrt n) time complexity. It is the last example in the video, but I would suggest that you watch the whole video.
https://www.youtube.com/watch?v=9TlHvipP5yA&list=PLDN4rrl48XKpZkf03iYFl-O29szjTrs_O&index=6
The simplest example of an O() i.e., O(sqrt n) time complexity algorithm in the video is:
p = 0;
for(i = 1; p <= n; i++) {
p = p + i;
}
Mr. Abdul Bari is reknowned for his simple explanations of data structures and algorithms.
Primality test
Solution in JavaScript
const isPrime = n => {
for(let i = 2; i <= Math.sqrt(n); i++) {
if(n % i === 0) return false;
}
return true;
};
Complexity
O(N^1/2) Because, for a given value of n, you only need to find if its divisible by numbers from 2 to its root.
JS Primality Test
O(sqrt(n))
A slightly more performant version, thanks to Samme Bae, for enlightening me with this. 😉
function isPrime(n) {
if (n <= 1)
return false;
if (n <= 3)
return true;
// Skip 4, 6, 8, 9, and 10
if (n % 2 === 0 || n % 3 === 0)
return false;
for (let i = 5; i * i <= n; i += 6) {
if (n % i === 0 || n % (i + 2) === 0)
return false;
}
return true;
}
isPrime(677);

Simulating a card game. degenerate suits

This might be a bit cryptic title but I have a very specific problem. First my current setup
Namely in my card simulator I deal 32 cards to 4 players in sets of 8. So 8 cards per player.
With the 4 standard suits (spades, harts , etc)
My current implementation cycles threw all combinations of 8 out of 32
witch gives me a large number of possibilities.
Namely the first player can have 10518300 different hands be dealt.
The second can then be dealt 735471 different hands.
The third player then 12870 different hands.
and finally the fourth can have only 1
giving me a grand total of 9.9561092e+16 different unique ways to deal a deck of 32 cards to 4 players. if the order of cards doesn’t matter.
On a 4 Ghz processor even with 1 tick per possibility it would take me half a year.
However I would like to simplify this dealing of cards by making the exchange of diamonds, harts and spades. Meaning that dealing of 8 harts to player 1 is equivalent to dealing 8 spades. (note that this doesn’t apply to clubs)
I am looking for a way to generate this. Because this will cut down the possibilities of the first hand by at least a factor of 6. My current implementation is in c++.
But feel free to answer in a different Languages
/** http://stackoverflow.com/a/9331125 */
unsigned cjasMain::nChoosek( unsigned n, unsigned k )
{
//assert(k < n);
if (k > n) return 0;
if (k * 2 > n) k = n-k;
if (k == 0) return 1;
int result = n;
for( int i = 2; i <= k; ++i ) {
result *= (n-i+1);
result /= i;
}
return result;
}
/** [combination c n p x]
* get the [x]th lexicographically ordered set of [r] elements in [n]
* output is in [c], and should be sizeof(int)*[r]
* http://stackoverflow.com/a/794 */
void cjasMain::Combination(int8_t* c,unsigned n,unsigned r, unsigned x){
++x;
assert(x>0);
int i,p,k = 0;
for(i=0;i<r-1;i++){
c[i] = (i != 0) ? c[i-1] : 0;
do {
c[i]++;
p = nChoosek(n-c[i],r-(i+1));
k = k + p;
} while(k < x);
k = k - p;
}
c[r-1] = c[r-2] + x - k;
}
/**http://stackoverflow.com/a/9430993 */
template <unsigned n,std::size_t r>
void cjasMain::Combinations()
{
static_assert(n>=r,"error n needs to be larger then r");
std::vector<bool> v(n);
std::fill(v.begin() + r, v.end(), true);
do
{
for (int i = 0; i < n; ++i)
{
if (!v[i])
{
COUT << (i+1) << " ";
}
}
static int j=0;
COUT <<'\t'<< j++<< "\n";
}
while (std::next_permutation(v.begin(), v.end()));
return;
}
A requirement is that from lexicographical number I can get back the original array.
Even the slightest optimization can help my monto carol simulation I hope.

Determine Position of Most Signifiacntly Set Bit in a Byte

I have a byte I am using to store bit flags. I need to compute the position of the most significant set bit in the byte.
Example Byte: 00101101 => 6 is the position of the most significant set bit
Compact Hex Mapping:
[0x00] => 0x00
[0x01] => 0x01
[0x02,0x03] => 0x02
[0x04,0x07] => 0x03
[0x08,0x0F] => 0x04
[0x10,0x1F] => 0x05
[0x20,0x3F] => 0x06
[0x40,0x7F] => 0x07
[0x80,0xFF] => 0x08
TestCase in C:
#include <stdio.h>
unsigned char check(unsigned char b) {
unsigned char c = 0x08;
unsigned char m = 0x80;
do {
if(m&b) { return c; }
else { c -= 0x01; }
} while(m>>=1);
return 0; //never reached
}
int main() {
unsigned char input[256] = {
0x00,0x01,0x02,0x03,0x04,0x05,0x06,0x07,0x08,0x09,0x0a,0x0b,0x0c,0x0d,0x0e,0x0f,
0x10,0x11,0x12,0x13,0x14,0x15,0x16,0x17,0x18,0x19,0x1a,0x1b,0x1c,0x1d,0x1e,0x1f,
0x20,0x21,0x22,0x23,0x24,0x25,0x26,0x27,0x28,0x29,0x2a,0x2b,0x2c,0x2d,0x2e,0x2f,
0x30,0x31,0x32,0x33,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x3b,0x3c,0x3d,0x3e,0x3f,
0x40,0x41,0x42,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x4b,0x4c,0x4d,0x4e,0x4f,
0x50,0x51,0x52,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x5b,0x5c,0x5d,0x5e,0x5f,
0x60,0x61,0x62,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x6b,0x6c,0x6d,0x6e,0x6f,
0x70,0x71,0x72,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x7b,0x7c,0x7d,0x7e,0x7f,
0x80,0x81,0x82,0x83,0x84,0x85,0x86,0x87,0x88,0x89,0x8a,0x8b,0x8c,0x8d,0x8e,0x8f,
0x90,0x91,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0x9b,0x9c,0x9d,0x9e,0x9f,
0xa0,0xa1,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xab,0xac,0xad,0xae,0xaf,
0xb0,0xb1,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xbb,0xbc,0xbd,0xbe,0xbf,
0xc0,0xc1,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xcb,0xcc,0xcd,0xce,0xcf,
0xd0,0xd1,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xdb,0xdc,0xdd,0xde,0xdf,
0xe0,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xeb,0xec,0xed,0xee,0xef,
0xf0,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa,0xfb,0xfc,0xfd,0xfe,0xff };
unsigned char truth[256] = {
0x00,0x01,0x02,0x02,0x03,0x03,0x03,0x03,0x04,0x04,0x04,0x04,0x04,0x04,0x04,0x04,
0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,0x05,
0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,
0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,0x06,
0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,
0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,
0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,
0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,0x07,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,
0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08,0x08};
int i,r;
int f = 0;
for(i=0; i<256; ++i) {
r=check(input[i]);
if(r !=(truth[i])) {
printf("failed %d : 0x%x : %d\n",i,0x000000FF & ((int)input[i]),r);
f += 1;
}
}
if(!f) { printf("passed all\n"); }
else { printf("failed %d\n",f); }
return 0;
}
I would like to simplify my check() function to not involve looping (or branching preferably). Is there a bit twiddling hack or hashed lookup table solution to compute the position of the most significant set bit in a byte?
Your question is about an efficient way to compute log2 of a value. And because you seem to want a solution that is not limited to the C language I have been slightly lazy and tweaked some C# code I have.
You want to compute log2(x) + 1 and for x = 0 (where log2 is undefined) you define the result as 0 (e.g. you create a special case where log2(0) = -1).
static readonly Byte[] multiplyDeBruijnBitPosition = new Byte[] {
7, 2, 3, 4,
6, 1, 5, 0
};
public static Byte Log2Plus1(Byte value) {
if (value == 0)
return 0;
var roundedValue = value;
roundedValue |= (Byte) (roundedValue >> 1);
roundedValue |= (Byte) (roundedValue >> 2);
roundedValue |= (Byte) (roundedValue >> 4);
var log2 = multiplyDeBruijnBitPosition[((Byte) (roundedValue*0xE3)) >> 5];
return (Byte) (log2 + 1);
}
This bit twiddling hack is taken from Find the log base 2 of an N-bit integer in O(lg(N)) operations with multiply and lookup where you can see the equivalent C source code for 32 bit values. This code has been adapted to work on 8 bit values.
However, you may be able to use an operation that gives you the result using a very efficient built-in function (on many CPU's a single instruction like the Bit Scan Reverse is used). An answer to the question Bit twiddling: which bit is set? has some information about this. A quote from the answer provides one possible reason why there is low level support for solving this problem:
Things like this are the core of many O(1) algorithms such as kernel schedulers which need to find the first non-empty queue signified by an array of bits.
That was a fun little challenge. I don't know if this one is completely portable since I only have VC++ to test with, and I certainly can't say for sure if it's more efficient than other approaches. This version was coded with a loop but it can be unrolled without too much effort.
static unsigned char check(unsigned char b)
{
unsigned char r = 8;
unsigned char sub = 1;
unsigned char s = 7;
for (char i = 0; i < 8; i++)
{
sub = sub & ((( b & (1 << s)) >> s--) - 1);
r -= sub;
}
return r;
}
I'm sure everyone else has long since moved on to other topics but there was something in the back of my mind suggesting that there had to be a more efficient branch-less solution to this than just unrolling the loop in my other posted solution. A quick trip to my copy of Warren put me on the right track: Binary search.
Here's my solution based on that idea:
Pseudo-code:
// see if there's a bit set in the upper half
if ((b >> 4) != 0)
{
offset = 4;
b >>= 4;
}
else
offset = 0;
// see if there's a bit set in the upper half of what's left
if ((b & 0x0C) != 0)
{
offset += 2;
b >>= 2;
}
// see if there's a bit set in the upper half of what's left
if > ((b & 0x02) != 0)
{
offset++;
b >>= 1;
}
return b + offset;
Branch-less C++ implementation:
static unsigned char check(unsigned char b)
{
unsigned char adj = 4 & ((((unsigned char) - (b >> 4) >> 7) ^ 1) - 1);
unsigned char offset = adj;
b >>= adj;
adj = 2 & (((((unsigned char) - (b & 0x0C)) >> 7) ^ 1) - 1);
offset += adj;
b >>= adj;
adj = 1 & (((((unsigned char) - (b & 0x02)) >> 7) ^ 1) - 1);
return (b >> adj) + offset + adj;
}
Yes, I know that this is all academic :)
It is not possible in plain C. The best I would suggest is the following implementation of check. Despite quite "ugly" I think it runs faster than the ckeck version in the question.
int check(unsigned char b)
{
if(b&128) return 8;
if(b&64) return 7;
if(b&32) return 6;
if(b&16) return 5;
if(b&8) return 4;
if(b&4) return 3;
if(b&2) return 2;
if(b&1) return 1;
return 0;
}
Edit: I found a link to the actual code: http://www.hackersdelight.org/hdcodetxt/nlz.c.txt
The algorithm below is named nlz8 in that file. You can choose your favorite hack.
/*
From last comment of: http://stackoverflow.com/a/671826/315052
> Hacker's Delight explains how to correct for the error in 32-bit floats
> in 5-3 Counting Leading 0's. Here's their code, which uses an anonymous
> union to overlap asFloat and asInt: k = k & ~(k >> 1); asFloat =
> (float)k + 0.5f; n = 158 - (asInt >> 23); (and yes, this relies on
> implementation-defined behavior) - Derrick Coetzee Jan 3 '12 at 8:35
*/
unsigned char check (unsigned char b) {
union {
float asFloat;
int asInt;
} u;
unsigned k = b & ~(b >> 1);
u.asFloat = (float)k + 0.5f;
return 32 - (158 - (u.asInt >> 23));
}
Edit -- not exactly sure what the asker means by language independent, but below is the equivalent code in python.
import ctypes
class Anon(ctypes.Union):
_fields_ = [
("asFloat", ctypes.c_float),
("asInt", ctypes.c_int)
]
def check(b):
k = int(b) & ~(int(b) >> 1)
a = Anon(asFloat=(float(k) + float(0.5)))
return 32 - (158 - (a.asInt >> 23))

Number of possible combinations

How many possible combinations of the variables a,b,c,d,e are possible if I know that:
a+b+c+d+e = 500
and that they are all integers and >= 0, so I know they are finite.
#Torlack, #Jason Cohen: Recursion is a bad idea here, because there are "overlapping subproblems." I.e., If you choose a as 1 and b as 2, then you have 3 variables left that should add up to 497; you arrive at the same subproblem by choosing a as 2 and b as 1. (The number of such coincidences explodes as the numbers grow.)
The traditional way to attack such a problem is dynamic programming: build a table bottom-up of the solutions to the sub-problems (starting with "how many combinations of 1 variable add up to 0?") then building up through iteration (the solution to "how many combinations of n variables add up to k?" is the sum of the solutions to "how many combinations of n-1 variables add up to j?" with 0 <= j <= k).
public static long getCombos( int n, int sum ) {
// tab[i][j] is how many combinations of (i+1) vars add up to j
long[][] tab = new long[n][sum+1];
// # of combos of 1 var for any sum is 1
for( int j=0; j < tab[0].length; ++j ) {
tab[0][j] = 1;
}
for( int i=1; i < tab.length; ++i ) {
for( int j=0; j < tab[i].length; ++j ) {
// # combos of (i+1) vars adding up to j is the sum of the #
// of combos of i vars adding up to k, for all 0 <= k <= j
// (choosing i vars forces the choice of the (i+1)st).
tab[i][j] = 0;
for( int k=0; k <= j; ++k ) {
tab[i][j] += tab[i-1][k];
}
}
}
return tab[n-1][sum];
}
$ time java Combos
2656615626
real 0m0.151s
user 0m0.120s
sys 0m0.012s
The answer to your question is 2656615626.
Here's the code that generates the answer:
public static long getNumCombinations( int summands, int sum )
{
if ( summands <= 1 )
return 1;
long combos = 0;
for ( int a = 0 ; a <= sum ; a++ )
combos += getNumCombinations( summands-1, sum-a );
return combos;
}
In your case, summands is 5 and sum is 500.
Note that this code is slow. If you need speed, cache the results from summand,sum pairs.
I'm assuming you want numbers >=0. If you want >0, replace the loop initialization with a = 1 and the loop condition with a < sum. I'm also assuming you want permutations (e.g. 1+2+3+4+5 plus 2+1+3+4+5 etc). You could change the for-loop if you wanted a >= b >= c >= d >= e.
I solved this problem for my dad a couple months ago...extend for your use. These tend to be one time problems so I didn't go for the most reusable...
a+b+c+d = sum
i = number of combinations
for (a=0;a<=sum;a++)
{
for (b = 0; b <= (sum - a); b++)
{
for (c = 0; c <= (sum - a - b); c++)
{
//d = sum - a - b - c;
i++
}
}
}
This would actually be a good question to ask on an interview as it is simple enough that you could write up on a white board, but complex enough that it might trip someone up if they don't think carefully enough about it. Also, you can also for two different answers which cause the implementation to be quite different.
Order Matters
If the order matters then any solution needs to allow for zero to appear for any of the variables; thus, the most straight forward solution would be as follows:
public class Combos {
public static void main() {
long counter = 0;
for (int a = 0; a <= 500; a++) {
for (int b = 0; b <= (500 - a); b++) {
for (int c = 0; c <= (500 - a - b); c++) {
for (int d = 0; d <= (500 - a - b - c); d++) {
counter++;
}
}
}
}
System.out.println(counter);
}
}
Which returns 2656615626.
Order Does Not Matter
If the order does not matter then the solution is not that much harder as you just need to make sure that zero isn't possible unless sum has already been found.
public class Combos {
public static void main() {
long counter = 0;
for (int a = 1; a <= 500; a++) {
for (int b = (a != 500) ? 1 : 0; b <= (500 - a); b++) {
for (int c = (a + b != 500) ? 1 : 0; c <= (500 - a - b); c++) {
for (int d = (a + b + c != 500) ? 1 : 0; d <= (500 - a - b - c); d++) {
counter++;
}
}
}
}
System.out.println(counter);
}
}
Which returns 2573155876.
One way of looking at the problem is as follows:
First, a can be any value from 0 to 500. Then if follows that b+c+d+e = 500-a. This reduces the problem by one variable. Recurse until done.
For example, if a is 500, then b+c+d+e=0 which means that for the case of a = 500, there is only one combination of values for b,c,d and e.
If a is 300, then b+c+d+e=200, which is in fact the same problem as the original problem, just reduced by one variable.
Note: As Chris points out, this is a horrible way of actually trying to solve the problem.
link text
If they are a real numbers then infinite ... otherwise it is a bit trickier.
(OK, for any computer representation of a real number there would be a finite count ... but it would be big!)
It has general formulae, if
a + b + c + d = N
Then number of non-negative integral solution will be C(N + number_of_variable - 1, N)
#Chris Conway answer is correct. I have tested with a simple code that is suitable for smaller sums.
long counter = 0;
int sum=25;
for (int a = 0; a <= sum; a++) {
for (int b = 0; b <= sum ; b++) {
for (int c = 0; c <= sum; c++) {
for (int d = 0; d <= sum; d++) {
for (int e = 0; e <= sum; e++) {
if ((a+b+c+d+e)==sum) counter=counter+1L;
}
}
}
}
}
System.out.println("counter e "+counter);
The answer in math is 504!/(500! * 4!).
Formally, for x1+x2+...xk=n, the number of combination of nonnegative number x1,...xk is the binomial coefficient: (k-1)-combination out of a set containing (n+k-1) elements.
The intuition is to choose (k-1) points from (n+k-1) points and use the number of points between two chosen points to represent a number in x1,..xk.
Sorry about the poor math edition for my fist time answering Stack Overflow.
Just a test for code block
Just a test for code block
Just a test for code block
Including negatives? Infinite.
Including only positives? In this case they wouldn't be called "integers", but "naturals", instead. In this case... I can't really solve this, I wish I could, but my math is too rusty. There is probably some crazy integral way to solve this. I can give some pointers for the math skilled around.
being x the end result,
the range of a would be from 0 to x,
the range of b would be from 0 to (x - a),
the range of c would be from 0 to (x - a - b),
and so forth until the e.
The answer is the sum of all those possibilities.
I am trying to find some more direct formula on Google, but I am really low on my Google-Fu today...