Division with negative dividend, but rounded towards negative infinity? - c++

Consider the following code (in C++11):
int a = -11, b = 3;
int c = a / b;
// now c == -3
C++11 specification says that division with a negative dividend is rounded toward zero.
It is quite useful for there to be a operator or function to do division with rounding toward negative infinity (e.g. for consistency with positive dividends when iterating a range), so is there a function or operator in the standard library that does what I want? Or perhaps a compiler-defined function/intrinsic that does it in modern compilers?
I could write my own, such as the following (works only for positive divisors):
int div_neg(int dividend, int divisor){
if(dividend >= 0) return dividend / divisor;
else return (dividend - divisor + 1) / divisor;
}
But it would not be as descriptive of my intent, and possibly not be as optimized a standard library function or compiler intrinsic (if it exists).

I'm not aware of any intrinsics for it. I would simply apply a correction to standard division retrospectively.
int div_floor(int a, int b)
{
int res = a / b;
int rem = a % b;
// Correct division result downwards if up-rounding happened,
// (for non-zero remainder of sign different than the divisor).
int corr = (rem != 0 && ((rem < 0) != (b < 0)));
return res - corr;
}
Note it also works for pre-C99 and pre-C++11, i.e. without standarization of rounding division towards zero.

Here's another possible variant, valid for positive divisors and arbitrary dividends.
int div_floor(int n, int d) {
return n >= 0 ? n / d : -1 - (-1 - n) / d;
}
Explanation: in the case of negative n, write q for (-1 - n) / d, then -1 - n = qd + r for some r satisfying 0 <= r < d. Rearranging gives n = (-1 - q)d + (d - 1 - r). It's clear that 0 <= d - 1 - r < d, so d - 1 - r is the remainder of the floor division operation, and -1 - q is the quotient.
Note that the arithmetic operations here are all safe from overflow, regardless of the internal representation of signed integers (two's complement, ones' complement, sign-magnitude).
Assuming two's complement representation for signed integers, a good compiler should optimise the two -1-* operations to bitwise negation operations. On my x86-64 machine, the second branch of the conditional gets compiled to the following sequence:
notl %edi
movl %edi, %eax
cltd
idivl %esi
notl %eax

The standard library has only one function that can be used to do what you want: floor. The division you're after can be expressed as floor((double) n / d). However, this assumes that double has enough precision to represent both n and d exactly. If not, then this may introduce rounding errors.
Personally, I'd go with a custom implementation. But you can use the floating point version too, if that's easier to read and you've verified that the results are correct for the ranges you're calling it for.

C++11 has a std::div(a, b) that returns both a % b and a / b in struct with rem and quot members (so both remainder and quotient primitives) and for which modern processors have a single instruction. C++11 does truncated division.
To do floored division for both the remainder and the quotient, you can write:
// http://stackoverflow.com/a/4609795/819272
auto signum(int n) noexcept
{
return static_cast<int>(0 < n) - static_cast<int>(n < 0);
}
auto floored_div(int D, int d) // Throws: Nothing.
{
assert(d != 0);
auto const divT = std::div(D, d);
auto const I = signum(divT.rem) == -signum(d) ? 1 : 0;
auto const qF = divT.quot - I;
auto const rF = divT.rem + I * d;
assert(D == d * qF + rF);
assert(abs(rF) < abs(d));
assert(signum(rF) == signum(d));
return std::div_t{qF, rF};
}
Finally, it's handy to also have Euclidean division (for which the remainder is always non-negative) in your own library:
auto euclidean_div(int D, int d) // Throws: Nothing.
{
assert(d != 0);
auto const divT = std::div(D, d);
auto const I = divT.rem >= 0 ? 0 : (d > 0 ? 1 : -1);
auto const qE = divT.quot - I;
auto const rE = divT.rem + I * d;
assert(D == d * qE + rE);
assert(abs(rE) < abs(d));
assert(signum(rE) != -1);
return std::div_t{qE, rE};
}
There is a Microsoft research paper discussing the pros and cons of the 3 versions.

When the operands are both positive, the / operator does floored division.
When the operands are both negative, the / operator does floored division.
When exactly one of the operands is negative, the / operator does ceiling division.
For the last case, the quotient can be adjusted when exactly one operand is negative and there is no remainder (with no remainder, floored division and ceiling division work the same).
int floored_div(int numer, int denom) {
int div = numer / denom;
int n_negatives = (numer < 0) + (denom < 0);
div -= (n_negatives == 1) && (numer % denom != 0);
return div;
}

Related

Exact value of a floating-point number as a rational

I'm looking for a method to convert the exact value of a floating-point number to a rational quotient of two integers, i.e. a / b, where b is not larger than a specified maximum denominator b_max. If satisfying the condition b <= b_max is impossible, then the result falls back to the best approximation which still satisfies the condition.
Hold on. There are a lot of questions/answers here about the best rational approximation of a truncated real number which is represented as a floating-point number. However I'm interested in the exact value of a floating-point number, which is itself a rational number with a different representation. More specifically, the mathematical set of floating-point numbers is a subset of rational numbers. In case of IEEE 754 binary floating-point standard it is a subset of dyadic rationals. Anyway, any floating-point number can be converted to a rational quotient of two finite precision integers as a / b.
So, for example assuming IEEE 754 single-precision binary floating-point format, the rational equivalent of float f = 1.0f / 3.0f is not 1 / 3, but 11184811 / 33554432. This is the exact value of f, which is a number from the mathematical set of IEEE 754 single-precision binary floating-point numbers.
Based on my experience, traversing (by binary search of) the Stern-Brocot tree is not useful here, since that is more suitable for approximating the value of a floating-point number, when it is interpreted as a truncated real instead of an exact rational.
Possibly, continued fractions are the way to go.
The another problem here is integer overflow. Think about that we want to represent the rational as the quotient of two int32_t, where the maximum denominator b_max = INT32_MAX. We cannot rely on a stopping criterion like b > b_max. So the algorithm must never overflow, or it must detect overflow.
What I found so far is an algorithm from Rosetta Code, which is based on continued fractions, but its source mentions it is "still not quite complete". Some basic tests gave good results, but I cannot confirm its overall correctness and I think it can easily overflow.
// https://rosettacode.org/wiki/Convert_decimal_number_to_rational#C
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <stdint.h>
/* f : number to convert.
* num, denom: returned parts of the rational.
* md: max denominator value. Note that machine floating point number
* has a finite resolution (10e-16 ish for 64 bit double), so specifying
* a "best match with minimal error" is often wrong, because one can
* always just retrieve the significand and return that divided by
* 2**52, which is in a sense accurate, but generally not very useful:
* 1.0/7.0 would be "2573485501354569/18014398509481984", for example.
*/
void rat_approx(double f, int64_t md, int64_t *num, int64_t *denom)
{
/* a: continued fraction coefficients. */
int64_t a, h[3] = { 0, 1, 0 }, k[3] = { 1, 0, 0 };
int64_t x, d, n = 1;
int i, neg = 0;
if (md <= 1) { *denom = 1; *num = (int64_t) f; return; }
if (f < 0) { neg = 1; f = -f; }
while (f != floor(f)) { n <<= 1; f *= 2; }
d = f;
/* continued fraction and check denominator each step */
for (i = 0; i < 64; i++) {
a = n ? d / n : 0;
if (i && !a) break;
x = d; d = n; n = x % n;
x = a;
if (k[1] * a + k[0] >= md) {
x = (md - k[0]) / k[1];
if (x * 2 >= a || k[1] >= md)
i = 65;
else
break;
}
h[2] = x * h[1] + h[0]; h[0] = h[1]; h[1] = h[2];
k[2] = x * k[1] + k[0]; k[0] = k[1]; k[1] = k[2];
}
*denom = k[1];
*num = neg ? -h[1] : h[1];
}
All finite double are rational numbers as OP well stated..
Use frexp() to break the number into its fraction and exponent. The end result still needs to use double to represent whole number values due to range requirements. Some numbers are too small, (x smaller than 1.0/(2.0,DBL_MAX_EXP)) and infinity, not-a-number are issues.
The frexp functions break a floating-point number into a normalized fraction and an integral power of 2. ... interval [1/2, 1) or zero ...
C11 ยง7.12.6.4 2/3
#include <math.h>
#include <float.h>
_Static_assert(FLT_RADIX == 2, "TBD code for non-binary FP");
// Return error flag
int split(double x, double *numerator, double *denominator) {
if (!isfinite(x)) {
*numerator = *denominator = 0.0;
if (x > 0.0) *numerator = 1.0;
if (x < 0.0) *numerator = -1.0;
return 1;
}
int bdigits = DBL_MANT_DIG;
int expo;
*denominator = 1.0;
*numerator = frexp(x, &expo) * pow(2.0, bdigits);
expo -= bdigits;
if (expo > 0) {
*numerator *= pow(2.0, expo);
}
else if (expo < 0) {
expo = -expo;
if (expo >= DBL_MAX_EXP-1) {
*numerator /= pow(2.0, expo - (DBL_MAX_EXP-1));
*denominator *= pow(2.0, DBL_MAX_EXP-1);
return fabs(*numerator) < 1.0;
} else {
*denominator *= pow(2.0, expo);
}
}
while (*numerator && fmod(*numerator,2) == 0 && fmod(*denominator,2) == 0) {
*numerator /= 2.0;
*denominator /= 2.0;
}
return 0;
}
void split_test(double x) {
double numerator, denominator;
int err = split(x, &numerator, &denominator);
printf("e:%d x:%24.17g n:%24.17g d:%24.17g q:%24.17g\n",
err, x, numerator, denominator, numerator/ denominator);
}
int main(void) {
volatile float third = 1.0f/3.0f;
split_test(third);
split_test(0.0);
split_test(0.5);
split_test(1.0);
split_test(2.0);
split_test(1.0/7);
split_test(DBL_TRUE_MIN);
split_test(DBL_MIN);
split_test(DBL_MAX);
return 0;
}
Output
e:0 x: 0.3333333432674408 n: 11184811 d: 33554432 q: 0.3333333432674408
e:0 x: 0 n: 0 d: 9007199254740992 q: 0
e:0 x: 1 n: 1 d: 1 q: 1
e:0 x: 0.5 n: 1 d: 2 q: 0.5
e:0 x: 1 n: 1 d: 1 q: 1
e:0 x: 2 n: 2 d: 1 q: 2
e:0 x: 0.14285714285714285 n: 2573485501354569 d: 18014398509481984 q: 0.14285714285714285
e:1 x: 4.9406564584124654e-324 n: 4.4408920985006262e-16 d: 8.9884656743115795e+307 q: 4.9406564584124654e-324
e:0 x: 2.2250738585072014e-308 n: 2 d: 8.9884656743115795e+307 q: 2.2250738585072014e-308
e:0 x: 1.7976931348623157e+308 n: 1.7976931348623157e+308 d: 1 q: 1.7976931348623157e+308
Leave the b_max consideration for later.
More expedient code is possible with replacing pow(2.0, expo) with ldexp(1, expo) #gammatester or exp2(expo) #Bob__
while (*numerator && fmod(*numerator,2) == 0 && fmod(*denominator,2) == 0) could also use some performance improvements. But first, let us get the functionality as needed.

Computing (a^x)mod n using addition chaining. Algorithm in C++

unsigned long qe2(unsigned long x, unsigned long y , unsigned long n)
{
unsigned long s,t,u;
s=1; t=x; u=y;
while(u)
{
if(u&1)
s = (s*t)%n;
u>>=1;
t= ( t*t)%n;
}
return s;
}
I was readin up on cryptography and , in the book Applied Cryptography by Bruice Schneier , I found the above algorithm to calculate (x^y)mod n at a low computational cost. It basically uses an algorithm called addition chaining to decrease the amount of multiplications in the calculation. I am able to use the process on pen and paper , but despite reading the above code again and again , I have not been able to understand how it works. So please point me in the right direction ( some kind of link to an article ) where I can analyse the above algorithm, or it will be really helpful if you could give the explanation here.
P.S. : the code is not explained in the book.
The exponent y is written as a sum of powers of two, i.e. in binary.
Consider a practical example: (x**11) % M. Mathematically,
(x**11) % M == ((((((x**1) % M) * x**2) % M) * x**8) % M)
This is useful, because a simple loop is sufficient to calculate the powers of x that are powers of two. For example, if you want to calculate x**(2**i):
for (j = 0; j < i; j++)
x = x * x;
We can examine the logic in detail, if we look at a function that calculates basis**exponent:
unsigned long power(unsigned long basis,
unsigned long exponent)
{
unsigned long result = 1u;
unsigned long factor = basis;
unsigned long i;
for (i = 1u; i < exponent; i = i * 2u) {
/* i is some power of two,
and factor = basis**i.
*/
/* If i is in the sum (of powers of two) for exponent,
we include the corresponding power of basis
in the product. */
if (i & exponent)
result = result * factor;
/* Update factor for the next i. */
factor = factor * factor;
}
return result;
}
Note that the test (i & exponent) is a binary AND operation, which is true if the result is nonzero, false if zero. Because i is a power of two, in binary it has a single 1 with all other binary digits zero, so it essentially tests if the exponent, written in binary, has a 1 in that position.
OP's code is simpler, because instead of using separate variables i and factor, it shifts the exponent right (dropping the rightmost binary digit), and uses the basis itself. That is,
unsigned long power(unsigned long basis,
unsigned long exponent)
{
unsigned long result = 1u;
while (exponent > 0u) {
if (exponent & 1u)
result = result * basis;
basis = basis * basis;
exponent = exponent >> 1;
}
return result;
}
The modulo operation is the final wrinkle, but it too is trivial. If you compute a product modulo some positive integer, you can apply the modulo operator to each term, and to each temporary result, without affecting the result:
(a * b * c * d * ... * z) % m
= ( (a % m) * (b % m) * (c % m) * ... * (z % m) ) % m
= ( ... (((((a % m) * (b % m)) % m) * (c % m)) % m) * ... * (z % m)) % m
This is obviously useful, because you can calculate the product of any number of terms, with all terms smaller than m, and all intermediate terms smaller than m*m.
Time complexity of this kind of exponentiation is O(log N), where N is the exponent.

Difference between ldexp(1, x) and exp2(x)

It seems if the floating-point representation has radix 2 (i.e. FLT_RADIX == 2) both std::ldexp(1, x) and std::exp2(x) raise 2 to the given power x.
Does the standard define or mention any expected behavioral and/or performance difference between them? What is the practical experience over different compilers?
exp2(x) and ldexp(x,i) perform two different operations. The former computes 2x, where x is a floating-point number, while the latter computes x*2i, where i is an integer. For integer values of x, exp2(x) and ldexp(1,int(x)) would be equivalent, provided the conversion of x to integer doesn't overflow.
The question about the relative efficiency of these two functions doesn't have a clear-cut answer. It will depend on the capabilities of the hardware platform and the details of the library implementation. While conceptually, ldexpf() looks like simple manipulation of the exponent part of a floating-point operand, it is actually a bit more complicated than that, once one considers overflow and gradual underflow via denormals. The latter case involves the rounding of the significand (mantissa) part of the floating-point number.
As ldexp() is generally an infrequently used function, it is in my experience fairly common that less of an optimization effort is applied to it by math library writers than to other math functions.
On some platforms, ldexp(), or a faster (custom) version of it, will be used as a building block in the software implementation of exp2(). The following code provides an exemplary implementation of this approach for float arguments:
#include <cmath>
/* Compute exponential base 2. Maximum ulp error = 0.86770 */
float my_exp2f (float a)
{
const float cvt = 12582912.0f; // 0x1.8p23
const float large = 1.70141184e38f; // 0x1.0p127
float f, r;
int i;
// exp2(a) = exp2(i + f); i = rint (a)
r = (a + cvt) - cvt;
f = a - r;
i = (int)r;
// approximate exp2(f) on interval [-0.5,+0.5]
r = 1.53720379e-4f; // 0x1.426000p-13f
r = fmaf (r, f, 1.33903872e-3f); // 0x1.5f055ep-10f
r = fmaf (r, f, 9.61817801e-3f); // 0x1.3b2b20p-07f
r = fmaf (r, f, 5.55036031e-2f); // 0x1.c6af7ep-05f
r = fmaf (r, f, 2.40226522e-1f); // 0x1.ebfbe2p-03f
r = fmaf (r, f, 6.93147182e-1f); // 0x1.62e430p-01f
r = fmaf (r, f, 1.00000000e+0f); // 0x1.000000p+00f
// exp2(a) = 2**i * exp2(f);
r = ldexpf (r, i);
if (!(fabsf (a) < 150.0f)) {
r = a + a; // handle NaNs
if (a < 0.0f) r = 0.0f;
if (a > 0.0f) r = large * large; // + INF
}
return r;
}
Most real-life implementations of exp2() do not invoke ldexp(), but a custom version, for example when fast bit-wise transfer between integer and floating-point data is supported, here represented by internal functions __float_as_int() and __int_as_float() that re-interpret an IEEE-754 binary32 as an int32 and vice versa:
/* For a in [0.5, 4), compute a * 2**i, -250 < i < 250 */
float fast_ldexpf (float a, int i)
{
int ia = (i << 23) + __float_as_int (a); // scale by 2**i
a = __int_as_float (ia);
if ((unsigned int)(i + 125) > 250) { // |i| > 125
i = (i ^ (125 << 23)) - i; // ((i < 0) ? -125 : 125) << 23
a = __int_as_float (ia - i); // scale by 2**(+/-125)
a = a * __int_as_float ((127 << 23) + i); // scale by 2**(+/-(i%125))
}
return a;
}
On other platforms, the hardware provides a single-precision version of exp2() as a fast hardware instruction. Internal to the processor these are typically implemented by a table lookup with linear or quadratic interpolation. On such hardware platforms, ldexp(float) may be implemented in terms of exp2(float), for example:
float my_ldexpf (float x, int i)
{
float r, fi, fh, fq, t;
fi = (float)i;
/* NaN, Inf, zero require argument pass-through per ISO standard */
if (!(fabsf (x) <= 3.40282347e+38f) || (x == 0.0f) || (i == 0)) {
r = x;
} else if (abs (i) <= 126) {
r = x * exp2f (fi);
} else if (abs (i) <= 252) {
fh = (float)(i / 2);
r = x * exp2f (fh) * exp2f (fi - fh);
} else {
fq = (float)(i / 4);
t = exp2f (fq);
r = x * t * t * t * exp2f (fi - 3.0f * fq);
}
return r;
}
Lastly, there are platforms that basically provide both exp2() and ldexp() functionality in hardware, such as the x87 instructions F2XM1 and FSCALE on x86 processors.

Fast ceiling of an integer division in C / C++

Given integer values x and y, C and C++ both return as the quotient q = x/y the floor of the floating point equivalent. I'm interested in a method of returning the ceiling instead. For example, ceil(10/5)=2 and ceil(11/5)=3.
The obvious approach involves something like:
q = x / y;
if (q * y < x) ++q;
This requires an extra comparison and multiplication; and other methods I've seen (used in fact) involve casting as a float or double. Is there a more direct method that avoids the additional multiplication (or a second division) and branch, and that also avoids casting as a floating point number?
For positive numbers where you want to find the ceiling (q) of x when divided by y.
unsigned int x, y, q;
To round up ...
q = (x + y - 1) / y;
or (avoiding overflow in x+y)
q = 1 + ((x - 1) / y); // if x != 0
For positive numbers:
q = x/y + (x % y != 0);
Sparky's answer is one standard way to solve this problem, but as I also wrote in my comment, you run the risk of overflows. This can be solved by using a wider type, but what if you want to divide long longs?
Nathan Ernst's answer provides one solution, but it involves a function call, a variable declaration and a conditional, which makes it no shorter than the OPs code and probably even slower, because it is harder to optimize.
My solution is this:
q = (x % y) ? x / y + 1 : x / y;
It will be slightly faster than the OPs code, because the modulo and the division is performed using the same instruction on the processor, because the compiler can see that they are equivalent. At least gcc 4.4.1 performs this optimization with -O2 flag on x86.
In theory the compiler might inline the function call in Nathan Ernst's code and emit the same thing, but gcc didn't do that when I tested it. This might be because it would tie the compiled code to a single version of the standard library.
As a final note, none of this matters on a modern machine, except if you are in an extremely tight loop and all your data is in registers or the L1-cache. Otherwise all of these solutions will be equally fast, except for possibly Nathan Ernst's, which might be significantly slower if the function has to be fetched from main memory.
You could use the div function in cstdlib to get the quotient & remainder in a single call and then handle the ceiling separately, like in the below
#include <cstdlib>
#include <iostream>
int div_ceil(int numerator, int denominator)
{
std::div_t res = std::div(numerator, denominator);
return res.rem ? (res.quot + 1) : res.quot;
}
int main(int, const char**)
{
std::cout << "10 / 5 = " << div_ceil(10, 5) << std::endl;
std::cout << "11 / 5 = " << div_ceil(11, 5) << std::endl;
return 0;
}
There's a solution for both positive and negative x but only for positive y with just 1 division and without branches:
int div_ceil(int x, int y) {
return x / y + (x % y > 0);
}
Note, if x is positive then division is towards zero, and we should add 1 if reminder is not zero.
If x is negative then division is towards zero, that's what we need, and we will not add anything because x % y is not positive
How about this? (requires y non-negative, so don't use this in the rare case where y is a variable with no non-negativity guarantee)
q = (x > 0)? 1 + (x - 1)/y: (x / y);
I reduced y/y to one, eliminating the term x + y - 1 and with it any chance of overflow.
I avoid x - 1 wrapping around when x is an unsigned type and contains zero.
For signed x, negative and zero still combine into a single case.
Probably not a huge benefit on a modern general-purpose CPU, but this would be far faster in an embedded system than any of the other correct answers.
I would have rather commented but I don't have a high enough rep.
As far as I am aware, for positive arguments and a divisor which is a power of 2, this is the fastest way (tested in CUDA):
//example y=8
q = (x >> 3) + !!(x & 7);
For generic positive arguments only, I tend to do it like so:
q = x/y + !!(x % y);
This works for positive or negative numbers:
q = x / y + ((x % y != 0) ? !((x > 0) ^ (y > 0)) : 0);
If there is a remainder, checks to see if x and y are of the same sign and adds 1 accordingly.
simplified generic form,
int div_up(int n, int d) {
return n / d + (((n < 0) ^ (d > 0)) && (n % d));
} //i.e. +1 iff (not exact int && positive result)
For a more generic answer, C++ functions for integer division with well defined rounding strategy
For signed or unsigned integers.
q = x / y + !(((x < 0) != (y < 0)) || !(x % y));
For signed dividends and unsigned divisors.
q = x / y + !((x < 0) || !(x % y));
For unsigned dividends and signed divisors.
q = x / y + !((y < 0) || !(x % y));
For unsigned integers.
q = x / y + !!(x % y);
Zero divisor fails (as with a native operation). Cannot cause overflow.
Corresponding floored and modulo constexpr implementations here, along with templates to select the necessary overloads (as full optimization and to prevent mismatched sign comparison warnings):
https://github.com/libbitcoin/libbitcoin-system/wiki/Integer-Division-Unraveled
Compile with O3, The compiler performs optimization well.
q = x / y;
if (x % y) ++q;

Clean, efficient algorithm for wrapping integers in C++

/**
* Returns a number between kLowerBound and kUpperBound
* e.g.: Wrap(-1, 0, 4); // Returns 4
* e.g.: Wrap(5, 0, 4); // Returns 0
*/
int Wrap(int const kX, int const kLowerBound, int const kUpperBound)
{
// Suggest an implementation?
}
The sign of a % b is only defined if a and b are both non-negative.
int Wrap(int kX, int const kLowerBound, int const kUpperBound)
{
int range_size = kUpperBound - kLowerBound + 1;
if (kX < kLowerBound)
kX += range_size * ((kLowerBound - kX) / range_size + 1);
return kLowerBound + (kX - kLowerBound) % range_size;
}
The following should work independently of the implementation of the mod operator:
int range = kUpperBound - kLowerBound + 1;
kx = ((kx-kLowerBound) % range);
if (kx<0)
return kUpperBound + 1 + kx;
else
return kLowerBound + kx;
An advantage over other solutions is, that it uses only a single % (i.e. division), which makes it pretty efficient.
Note (Off Topic):
It's a good example, why sometimes it is wise to define intervals with the upper bound being being the first element not in the range (such as for STL iterators...). In this case, both "+1" would vanish.
Fastest solution, least flexible: Take advantage of native datatypes that will do wrapping in the hardware.
The absolute fastest method for wrapping integers would be to make sure your data is scaled to int8/int16/int32 or whatever native datatype. Then when you need your data to wrap the native data type will be done in hardware! Very painless and orders of magnitude faster than any software wrapping implementation seen here.
As an example case study:
I have found this to be very useful when I need a fast implementation of sin/cos implemented using a look-up-table for a sin/cos implementation. Basically you make scale your data such that INT16_MAX is pi and INT16_MIN is -pi. Then have you are set to go.
As a side note, scaling your data will add some up front finite computation cost that usually looks something like:
int fixedPoint = (int)( floatingPoint * SCALING_FACTOR + 0.5 )
Feel free to exchange int for something else you want like int8_t / int16_t / int32_t.
Next fastest solution, more flexible: The mod operation is slow instead if possible try to use bit masks!
Most of the solutions I skimmed are functionally correct... but they are dependent on the mod operation.
The mod operation is very slow because it is essentially doing a hardware division. The laymans explanation of why mod and division are slow is to equate the division operation to some pseudo-code for(quotient = 0;inputNum> 0;inputNum -= divisor) { quotient++; } ( def of quotient and divisor ). As you can see, the hardware division can be fast if it is a low number relative to the divisor... but division can also be horribly slow if it is much greater than the divisor.
If you can scale your data to a power of two then you can use a bit mask which will execute in one cycle ( on 99% of all platforms ) and your speed improvement will be approximately one order of magnitude ( at the very least 2 or 3 times faster ).
C code to implement wrapping:
#define BIT_MASK (0xFFFF)
int wrappedAddition(int a, int b) {
return ( a + b ) & BIT_MASK;
}
int wrappedSubtraction(int a, int b) {
return ( a - b ) & BIT_MASK;
}
Feel free to make the #define something that is run time. And feel free to adjust the bit mask to be whatever power of two that you need. Like 0xFFFFFFFF or power of two you decide on implementing.
p.s. I strongly suggest reading about fixed point processing when messing with wrapping/overflow conditions. I suggest reading:
Fixed-Point Arithmetic: An Introduction by Randy Yates August 23, 2007
Please do not overlook this post. :)
Is this any good?
int Wrap(N,L,H){
H=H-L+1; return (N-L+(N<L)*H)%H+L;
}
This works for negative inputs, and all arguments can be negative so long as L is less than H.
Background... (Note that H here is the reused variable, set to original H-L+1).
I had been using (N-L)%H+L when incrementing, but unlike in Lua, which I used before starting to learn C a few months back, this would NOT work if I used inputs below the lower bound, never mind negative inputs. (Lua is built in C, but I don't know what it's doing, and it likely wouldn't be fast...)
I decided to add +(N<L)*H to make (N-L+(N<L)*H)%H+L, as C seems to be defined such that true=1 and false=0. It works well enough for me, and seems to answer the original question neatly. If anyone knows how to do it without the MOD operator % to make it dazzlingly fast, please do it. I don't need speed right now, but some time I will, no doubt.
EDIT:
That function fails if N is lower than L by more than H-L+1 but this doesn't:
int Wrap(N,L,H){
H-=L; return (N-L+(N<L)*((L-N)/H+1)*++H)%H+L;
}
I think it would break at the negative extreme of the integer range in any system, but should work for most practical situations. It adds an extra multiplication and a division, but is still fairly compact.
(This edit is just for completion, because I came up with a much better way, in a newer post in this thread.)
Crow.
Personally I've found solutions to these types of functions to be cleaner if range is exclusive and divisor is restricted to positive values.
int ifloordiv(int x, int y)
{
if (x > 0)
return x / y;
if (x < 0)
return (x + 1) / y - 1;
return 0
}
int iwrap(int x, int y)
{ return x - y * ifloordiv(x, y);
}
Integrated.
int iwrap(int x, int y)
{
if (x > 0)
return x % y;
if (x < 0)
return (x + 1) % y + y - 1;
return 0;
}
Same family. Why not?
int ireflect(int x, int y)
{
int z = iwrap(x, y*2);
if (z < y)
return z;
return y*2-1 - z;
}
int ibandy(int x, int y)
{
if (y != 1)
return ireflect(abs(x + x / (y - 1)), y);
return 0;
}
Ranged functionality can be implemented for all functions with,
// output is in the range [min, max).
int func2(int x, int min, int max)
{
// increment max for inclusive behavior.
assert(min < max);
return func(x - min, max - min) + min;
}
Actually, since -1 % 4 returns -1 on every system I've even been on, the simple mod solution doesn't work. I would try:
int range = kUpperBound - kLowerBound +1;
kx = ((kx - kLowerBound) % range) + range;
return (kx % range) + kLowerBound;
if kx is positive, you mod, add range, and mod back, undoing the add. If kx is negative, you mod, add range which makes it positive, then mod again, which doesn't do anything.
My other post got nasty, all that 'corrective' multiplication and division got out of hand. After looking at Martin Stettner's post, and at my own starting conditions of (N-L)%H+L, I came up with this:
int Wrap(N,L,H){
H=H-L+1; N=(N-L)%H+L; if(N<L)N+=H; return N;
}
At the extreme negative end of the integer range it breaks as my other one would, but it will be faster, and is a lot easier to read, and avoids the other nastiness that crept in to it.
Crow.
I would suggest this solution:
int Wrap(int const kX, int const kLowerBound, int const kUpperBound)
{
int d = kUpperBound - kLowerBound + 1;
return kLowerBound + (kX >= 0 ? kX % d : -kX % d ? d - (-kX % d) : 0);
}
The if-then-else logic of the ?: operator makes sure that both operands of % are nonnegative.
I would give an entry point to the most common case lowerBound=0, upperBound=N-1. And call this function in the general case. No mod computation is done where I is already in range. It assumes upper>=lower, or n>0.
int wrapN(int i,int n)
{
if (i<0) return (n-1)-(-1-i)%n; // -1-i is >=0
if (i>=n) return i%n;
return i; // In range, no mod
}
int wrapLU(int i,int lower,int upper)
{
return lower+wrapN(i-lower,1+upper-lower);
}
An answer that has some symmetry and also makes it obvious that when kX is in range, it is returned unmodified.
int Wrap(int const kX, int const kLowerBound, int const kUpperBound)
{
int range_size = kUpperBound - kLowerBound + 1;
if (kX < kLowerBound)
return kX + range_size * ((kLowerBound - kX) / range_size + 1);
if (kX > kUpperBound)
return kX - range_size * ((kX - kUpperBound) / range_size + 1);
return kX;
}
I've faced this problem as well. This is my solution.
template <> int mod(const int &x, const int &y) {
return x % y;
}
template <class T> T mod(const T &x, const T &y) {
return ::fmod((T)x, (T)y);
}
template <class T> T wrap(const T &x, const T &max, const T &min = 0) {
if(max < min)
return x;
if(x > max)
return min + mod(x - min, max - min + 1);
if(x < min)
return max - mod(min - x, max - min + 1);
return x;
}
I don't know if it's good, but I'd thought I'd share since I got directed here when doing a Google search on this problem and found the above solutions lacking to my needs. =)
In the special case where the lower bound is zero, this code avoids division, modulus and multiplication. The upper bound does not have to be a power of two. This code is overly verbose and looks bloated, but compiles into 3 instructions: subtract, shift (by constant), and 'and'.
#include <climits> // CHAR_BIT
// -------------------------------------------------------------- allBits
// sign extend a signed integer into an unsigned mask:
// return all zero bits (+0) if arg is positive,
// or all one bits (-0) for negative arg
template <typename SNum>
static inline auto allBits (SNum arg) {
static constexpr auto argBits = CHAR_BIT * sizeof( arg);
static_assert( argBits < 256, "allBits() sign extension may fail");
static_assert( std::is_signed< SNum>::value, "SNum must be signed");
typedef typename std::make_unsigned< SNum>::type UNum;
// signed shift required, but need unsigned result
const UNum mask = UNum( arg >> (argBits - 1));
return mask;
}
// -------------------------------------------------------------- boolWrap
// wrap reset a counter without conditionals:
// return arg >= limit? 0 : arg
template <typename UNum>
static inline auto boolWrap (const UNum arg, const UNum limit) {
static_assert( ! std::is_signed< UNum>::value, "UNum assumed unsigned");
typedef typename std::make_signed< UNum>::type SNum;
const SNum negX = SNum( arg) - SNum( limit);
const auto signX = allBits( negX); // +0 or -0
return arg & signX;
}
// example usage:
for (int j= 0; j < 15; ++j) {
cout << j << boolWrap( j, 11);
}
For negative kX, you can add:
int temp = kUpperBound - kLowerBound + 1;
while (kX < 0) kX += temp;
return kX%temp + kLowerBound;
Why not using Extension methods.
public static class IntExtensions
{
public static int Wrap(this int kX, int kLowerBound, int kUpperBound)
{
int range_size = kUpperBound - kLowerBound + 1;
if (kX < kLowerBound)
kX += range_size * ((kLowerBound - kX) / range_size + 1);
return kLowerBound + (kX - kLowerBound) % range_size;
}
}
Usage: currentInt = (++currentInt).Wrap(0, 2);