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So, basically, I have implemented a naive rarity system, where I have randomly generated byte values(0-255). From them I determine an enum variable as such:
case < 100:
return 0;
case > 100 and < 180:
return 1;
case >180 and <235:
return 2;
case > 235:
return 3;
So now I'm at the point, where I would like to improve some of my code and this method keeps bothering me. As you can see, values, corresponding to different enum numbers, are unequal. And I started wondering, is there a way to represent this kind of a cast with a formula? I fidgeted a bit with it, but I'm really no mathematician) So I'd appreciate some help
If you want those exact rarity thresholds, then I would write something like rarity1.
If you're a little flexible, you could do rarity2 or rarity3. For rarity2, in essence, we interpret b as a fixed point fraction x ∈ [0, 1) and return ⌊4x²⌋. The boundaries are x = 1/2, x = 1/√2, x = √3/2, i.e., b = 128, b = 182, b = 222.
The rarity3 alternative is not monotone. It takes the min of the upper and lower 2-bit quantities. The outcome probabilities are 7/16, 5/16, 3/16, 1/16, corresponding to decision boundaries 112, 192, 240.
def rarity1(b):
if b < 100:
return 0
elif b < 180:
return 1
elif b < 235:
return 2
else:
return 3
def rarity2(b):
return (b * b) >> 14
def rarity3(b):
return min(b >> 6, b & 3)
I am weak in math's hence the question I ask might be irrelevant to most here but the question is why are we dividing the Armstrong number by 10? I mean we can divide the number with any other number apart from 10.
I think the problem is that you don't understand what an Armstrong number is. From one web search:
An Armstrong number of three digits is an integer such that the sum of the cubes of its digits is equal to the number itself. For example, 371 is an Armstrong number since 33 + 73 + 13 = 371.
So, to check whether any random number is an Armstrong number: 243, say. Take the number as written and do 2×2×2 + 4×4×4 + 3×3×3 = 8 + 16 + 27, which is only 99, so 243 isn't an Armstrong number.
Now, there are two straightforward ways to get the individual digits of a number in a computer program. First, you can convert to a string.
std::string theString = to_string(243);
And then for each digit, convert back to a number. This is kind of gross.
Or you can do this:
int sumOfCubes = 0;
for (int newNumber = myNumber; newNumber > 0; newNumber = newNumber / 10) {
// This is the modulus operator, or the remainder. 243 % 10 = 3.
// 24 % 10 = 4. and 2 % 10 = 2.
int digit = newNumber % 10;
sumOfCubes += digit*digit*digit;
}
if (sumOfCubes == myNumber) {
cout << myNumber << " is an Armstrong number." << endl;
}
What happens in the loop:
newNumber is initialized to myNumber (243 in my example). digit
becomes 3 (243 divided by 10 has a remainder of 3). sumOfCubes +=
27;
Then it loops. newNumber becomes newNumber / 10 as an integer, which
is now 24. digit is 4. We add 64 to sumOfCubes.
Loops again. newNumber becomes 24 / 10 = 2. So we add 8.
Tries to loop. NewNumber becomes zero, which fails the condition, so
the loop ends.
Done.
So here's what I am stuck with:
Problem:
Find the minimum number of steps required to reach a target number x from 0 (zero), using only two operations: +1 (add 1 to the number) or *2 (multiply 2 with the number).
Here's what I thought/found out:
Method 1: Suppose the number is 29, start by adding 1 to 0
(current_ans: 1), then keep multiplying 2 to the current_ans till we
reach the closest value to 29, in this case it becomes 16 (operations:
+1 *2 *2 *2 *2). Then keep adding 1 to get the required number (operations: +1 *2 *2 *2 *2 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1).
But, this method does not give an efficient solution as the total
number of steps are 18, whereas it can be done in 8 steps using the
2nd method (mentioned below).
Here's the code snippet to do the same:
int count = 0, current_ans = 0, old_value, x = 29; //x is the number to reach
while(current_ans != x)
{
old_value = current_ans;
if(current_ans == 0)
{
current_ans++;
count++;
}
if(current_ans < x)
{
current_ans *= 2;
count++;
continue;
}
if(current_ans > x)
{
current_ans = old_value;
count += x - current_ans;
current_ans = x;
}
}
Method 2: Let us take same number 29 and convert it to binary, which
is 11101. So starting from 0, we take the first step as +1
(current_ans: 1). Next, we multiply by 2 and convert it to binary
(current_ans: 10). Similarly, other operations include +1
(current_ans: 1) *2 (current_ans: 10) +1 (current_ans: 11) *2
(current_ans: 110) +1 (current_ans: 111) *2 (current_ans: 1110) *2
(current_ans: 11100) +1 (current_ans: 11101) and we get the required
answer in 8 steps, which is the right answer.
Here's the code snippet to do the same:
int count = 0, current_ans = 0, old_value, x = 29; //x is the number to reach
while(current_ans != x)
{
old_value = current_ans;
if(current_ans == 0)
{
current_ans++;
count++;
}
if(current_ans < x) //I am not really sure of this condition
{
current_ans = current_ans << 1;
count++;
continue;
}
}
So, I don't know how to convert my logic to a working code in C/C++/Java/Python. If anyone has a better solution or a code snippet towards solving the same would be really helpful. I checked out other related questions too, but they didn't really help me with my query. For Method 2, I know I can use left shift operation, but how exactly do I use it if there's no other better solution?
Thanks in advance :)
EDIT: Added the code snippets :)
The best way is to work backwards. Start from the number you need:
Subtract 1 if the number is odd.
Divide by 2 if the number if even.
Stop when you get to zero.
For example, for 29, you get 28, 14, 7, 6, 3, 2, 1, 0.
C code, intentionally undocumented:
int main() {
int steps = 0;
int x = 29;
for (; x; x = x % 2 ? x - 1 : x / 2, ++steps);
return steps;
}
One way to look at your problem is to say that each bit in your number requires an operation and each 1 bit requires another operation. So count the number of bits total and add the number of 1 bits, then subtract one to normalize the result to what is wanted. Another way to say this is to add 1 for a 0 bit and add 2 for a 1 bit. There are multiple ways to do that counting or adding. One way is with a loop over the bits.
Here is a pythonic way to do this.
cntsteps = sum(2 if c == '1' else 1 for c in bin(x)[2:]) - 1
The bin function converts the value to it binary string with a prefix 0x, the [2:] removes that prefix, the sum( adds the counts for the bits, and the final -1 normalizes the result.
If you prefer a method that is more applicable to other languages,
def cnt(x):
if x <= 0: # handle special case of zero or negative
return 0
cntsteps = 0
while x > 0:
if x & 1: # x has a 1 for the least significant bit
cntsteps += 2
else:
cntsteps += 1
x >>= 1
return cntsteps - 1
I'm looking for an algorithm that places tick marks on an axis, given a range to display, a width to display it in, and a function to measure a string width for a tick mark.
For example, given that I need to display between 1e-6 and 5e-6 and a width to display in pixels, the algorithm would determine that I should put tickmarks (for example) at 1e-6, 2e-6, 3e-6, 4e-6, and 5e-6. Given a smaller width, it might decide that the optimal placement is only at the even positions, i.e. 2e-6 and 4e-6 (since putting more tickmarks would cause them to overlap).
A smart algorithm would give preference to tickmarks at multiples of 10, 5, and 2. Also, a smart algorithm would be symmetric around zero.
As I didn't like any of the solutions I've found so far, I implemented my own. It's in C# but it can be easily translated into any other language.
It basically chooses from a list of possible steps the smallest one that displays all values, without leaving any value exactly in the edge, lets you easily select which possible steps you want to use (without having to edit ugly if-else if blocks), and supports any range of values. I used a C# Tuple to return three values just for a quick and simple demonstration.
private static Tuple<decimal, decimal, decimal> GetScaleDetails(decimal min, decimal max)
{
// Minimal increment to avoid round extreme values to be on the edge of the chart
decimal epsilon = (max - min) / 1e6m;
max += epsilon;
min -= epsilon;
decimal range = max - min;
// Target number of values to be displayed on the Y axis (it may be less)
int stepCount = 20;
// First approximation
decimal roughStep = range / (stepCount - 1);
// Set best step for the range
decimal[] goodNormalizedSteps = { 1, 1.5m, 2, 2.5m, 5, 7.5m, 10 }; // keep the 10 at the end
// Or use these if you prefer: { 1, 2, 5, 10 };
// Normalize rough step to find the normalized one that fits best
decimal stepPower = (decimal)Math.Pow(10, -Math.Floor(Math.Log10((double)Math.Abs(roughStep))));
var normalizedStep = roughStep * stepPower;
var goodNormalizedStep = goodNormalizedSteps.First(n => n >= normalizedStep);
decimal step = goodNormalizedStep / stepPower;
// Determine the scale limits based on the chosen step.
decimal scaleMax = Math.Ceiling(max / step) * step;
decimal scaleMin = Math.Floor(min / step) * step;
return new Tuple<decimal, decimal, decimal>(scaleMin, scaleMax, step);
}
static void Main()
{
// Dummy code to show a usage example.
var minimumValue = data.Min();
var maximumValue = data.Max();
var results = GetScaleDetails(minimumValue, maximumValue);
chart.YAxis.MinValue = results.Item1;
chart.YAxis.MaxValue = results.Item2;
chart.YAxis.Step = results.Item3;
}
Take the longest of the segments about zero (or the whole graph, if zero is not in the range) - for example, if you have something on the range [-5, 1], take [-5,0].
Figure out approximately how long this segment will be, in ticks. This is just dividing the length by the width of a tick. So suppose the method says that we can put 11 ticks in from -5 to 0. This is our upper bound. For the shorter side, we'll just mirror the result on the longer side.
Now try to put in as many (up to 11) ticks in, such that the marker for each tick in the form i*10*10^n, i*5*10^n, i*2*10^n, where n is an integer, and i is the index of the tick. Now it's an optimization problem - we want to maximize the number of ticks we can put in, while at the same time minimizing the distance between the last tick and the end of the result. So assign a score for getting as many ticks as we can, less than our upper bound, and assign a score to getting the last tick close to n - you'll have to experiment here.
In the above example, try n = 1. We get 1 tick (at i=0). n = 2 gives us 1 tick, and we're further from the lower bound, so we know that we have to go the other way. n = 0 gives us 6 ticks, at each integer point point. n = -1 gives us 12 ticks (0, -0.5, ..., -5.0). n = -2 gives us 24 ticks, and so on. The scoring algorithm will give them each a score - higher means a better method.
Do this again for the i * 5 * 10^n, and i*2*10^n, and take the one with the best score.
(as an example scoring algorithm, say that the score is the distance to the last tick times the maximum number of ticks minus the number needed. This will likely be bad, but it'll serve as a decent starting point).
Funnily enough, just over a week ago I came here looking for an answer to the same question, but went away again and decided to come up with my own algorithm. I am here to share, in case it is of any use.
I wrote the code in Python to try and bust out a solution as quickly as possible, but it can easily be ported to any other language.
The function below calculates the appropriate interval (which I have allowed to be either 10**n, 2*10**n, 4*10**n or 5*10**n) for a given range of data, and then calculates the locations at which to place the ticks (based on which numbers within the range are divisble by the interval). I have not used the modulo % operator, since it does not work properly with floating-point numbers due to floating-point arithmetic rounding errors.
Code:
import math
def get_tick_positions(data: list):
if len(data) == 0:
return []
retpoints = []
data_range = max(data) - min(data)
lower_bound = min(data) - data_range/10
upper_bound = max(data) + data_range/10
view_range = upper_bound - lower_bound
num = lower_bound
n = math.floor(math.log10(view_range) - 1)
interval = 10**n
num_ticks = 1
while num <= upper_bound:
num += interval
num_ticks += 1
if num_ticks > 10:
if interval == 10 ** n:
interval = 2 * 10 ** n
elif interval == 2 * 10 ** n:
interval = 4 * 10 ** n
elif interval == 4 * 10 ** n:
interval = 5 * 10 ** n
else:
n += 1
interval = 10 ** n
num = lower_bound
num_ticks = 1
if view_range >= 10:
copy_interval = interval
else:
if interval == 10 ** n:
copy_interval = 1
elif interval == 2 * 10 ** n:
copy_interval = 2
elif interval == 4 * 10 ** n:
copy_interval = 4
else:
copy_interval = 5
first_val = 0
prev_val = 0
times = 0
temp_log = math.log10(interval)
if math.isclose(lower_bound, 0):
first_val = 0
elif lower_bound < 0:
if upper_bound < -2*interval:
if n < 0:
copy_ub = round(upper_bound*10**(abs(temp_log) + 1))
times = copy_ub // round(interval*10**(abs(temp_log) + 1)) + 2
else:
times = upper_bound // round(interval) + 2
while first_val >= lower_bound:
prev_val = first_val
first_val = times * copy_interval
if n < 0:
first_val *= (10**n)
times -= 1
first_val = prev_val
times += 3
else:
if lower_bound > 2*interval:
if n < 0:
copy_ub = round(lower_bound*10**(abs(temp_log) + 1))
times = copy_ub // round(interval*10**(abs(temp_log) + 1)) - 2
else:
times = lower_bound // round(interval) - 2
while first_val < lower_bound:
first_val = times*copy_interval
if n < 0:
first_val *= (10**n)
times += 1
if n < 0:
retpoints.append(first_val)
else:
retpoints.append(round(first_val))
val = first_val
times = 1
while val <= upper_bound:
val = first_val + times * interval
if n < 0:
retpoints.append(val)
else:
retpoints.append(round(val))
times += 1
retpoints.pop()
return retpoints
When passing in the following three data-points to the function
points = [-0.00493, -0.0003892, -0.00003292]
... the output I get (as a list) is as follows:
[-0.005, -0.004, -0.003, -0.002, -0.001, 0.0]
When passing this:
points = [1.399, 38.23823, 8309.33, 112990.12]
... I get:
[0, 20000, 40000, 60000, 80000, 100000, 120000]
When passing this:
points = [-54, -32, -19, -17, -13, -11, -8, -4, 12, 15, 68]
... I get:
[-60, -40, -20, 0, 20, 40, 60, 80]
... which all seem to be a decent choice of positions for placing ticks.
The function is written to allow 5-10 ticks, but that could easily be changed if you so please.
Whether the list of data supplied contains ordered or unordered data it does not matter, since it is only the minimum and maximum data points within the list that matter.
This simple algorithm yields an interval that is multiple of 1, 2, or 5 times a power of 10. And the axis range gets divided in at least 5 intervals. The code sample is in java language:
protected double calculateInterval(double range) {
double x = Math.pow(10.0, Math.floor(Math.log10(range)));
if (range / x >= 5)
return x;
else if (range / (x / 2.0) >= 5)
return x / 2.0;
else
return x / 5.0;
}
This is an alternative, for minimum 10 intervals:
protected double calculateInterval(double range) {
double x = Math.pow(10.0, Math.floor(Math.log10(range)));
if (range / (x / 2.0) >= 10)
return x / 2.0;
else if (range / (x / 5.0) >= 10)
return x / 5.0;
else
return x / 10.0;
}
I've been using the jQuery flot graph library. It's open source and does axis/tick generation quite well. I'd suggest looking at it's code and pinching some ideas from there.
I am trying to find an algorithm to count from 0 to 2n-1 but their bit pattern reversed. I care about only n LSB of a word. As you may have guessed I failed.
For n=3:
000 -> 0
100 -> 4
010 -> 2
110 -> 6
001 -> 1
101 -> 5
011 -> 3
111 -> 7
You get the idea.
Answers in pseudo-code is great. Code fragments in any language are welcome, answers without bit operations are preferred.
Please don't just post a fragment without even a short explanation or a pointer to a source.
Edit: I forgot to add, I already have a naive implementation which just bit-reverses a count variable. In a sense, this method is not really counting.
This is, I think easiest with bit operations, even though you said this wasn't preferred
Assuming 32 bit ints, here's a nifty chunk of code that can reverse all of the bits without doing it in 32 steps:
unsigned int i;
i = (i & 0x55555555) << 1 | (i & 0xaaaaaaaa) >> 1;
i = (i & 0x33333333) << 2 | (i & 0xcccccccc) >> 2;
i = (i & 0x0f0f0f0f) << 4 | (i & 0xf0f0f0f0) >> 4;
i = (i & 0x00ff00ff) << 8 | (i & 0xff00ff00) >> 8;
i = (i & 0x0000ffff) << 16 | (i & 0xffff0000) >> 16;
i >>= (32 - n);
Essentially this does an interleaved shuffle of all of the bits. Each time around half of the bits in the value are swapped with the other half.
The last line is necessary to realign the bits so that bin "n" is the most significant bit.
Shorter versions of this are possible if "n" is <= 16, or <= 8
At each step, find the leftmost 0 digit of your value. Set it, and clear all digits to the left of it. If you don't find a 0 digit, then you've overflowed: return 0, or stop, or crash, or whatever you want.
This is what happens on a normal binary increment (by which I mean it's the effect, not how it's implemented in hardware), but we're doing it on the left instead of the right.
Whether you do this in bit ops, strings, or whatever, is up to you. If you do it in bitops, then a clz (or call to an equivalent hibit-style function) on ~value might be the most efficient way: __builtin_clz where available. But that's an implementation detail.
This solution was originally in binary and converted to conventional math as the requester specified.
It would make more sense as binary, at least the multiply by 2 and divide by 2 should be << 1 and >> 1 for speed, the additions and subtractions probably don't matter one way or the other.
If you pass in mask instead of nBits, and use bitshifting instead of multiplying or dividing, and change the tail recursion to a loop, this will probably be the most performant solution you'll find since every other call it will be nothing but a single add, it would only be as slow as Alnitak's solution once every 4, maybe even 8 calls.
int incrementBizarre(int initial, int nBits)
// in the 3 bit example, this should create 100
mask=2^(nBits-1)
// This should only return true if the first (least significant) bit is not set
// if initial is 011 and mask is 100
// 3 4, bit is not set
if(initial < mask)
// If it was not, just set it and bail.
return initial+ mask // 011 (3) + 100 (4) = 111 (7)
else
// it was set, are we at the most significant bit yet?
// mask 100 (4) / 2 = 010 (2), 001/2 = 0 indicating overflow
if(mask / 2) > 0
// No, we were't, so unset it (initial-mask) and increment the next bit
return incrementBizarre(initial - mask, mask/2)
else
// Whoops we were at the most significant bit. Error condition
throw new OverflowedMyBitsException()
Wow, that turned out kinda cool. I didn't figure in the recursion until the last second there.
It feels wrong--like there are some operations that should not work, but they do because of the nature of what you are doing (like it feels like you should get into trouble when you are operating on a bit and some bits to the left are non-zero, but it turns out you can't ever be operating on a bit unless all the bits to the left are zero--which is a very strange condition, but true.
Example of flow to get from 110 to 001 (backwards 3 to backwards 4):
mask 100 (4), initial 110 (6); initial < mask=false; initial-mask = 010 (2), now try on the next bit
mask 010 (2), initial 010 (2); initial < mask=false; initial-mask = 000 (0), now inc the next bit
mask 001 (1), initial 000 (0); initial < mask=true; initial + mask = 001--correct answer
Here's a solution from my answer to a different question that computes the next bit-reversed index without looping. It relies heavily on bit operations, though.
The key idea is that incrementing a number simply flips a sequence of least-significant bits, for example from nnnn0111 to nnnn1000. So in order to compute the next bit-reversed index, you have to flip a sequence of most-significant bits. If your target platform has a CTZ ("count trailing zeros") instruction, this can be done efficiently.
Example in C using GCC's __builtin_ctz:
void iter_reversed(unsigned bits) {
unsigned n = 1 << bits;
for (unsigned i = 0, j = 0; i < n; i++) {
printf("%x\n", j);
// Compute a mask of LSBs.
unsigned mask = i ^ (i + 1);
// Length of the mask.
unsigned len = __builtin_ctz(~mask);
// Align the mask to MSB of n.
mask <<= bits - len;
// XOR with mask.
j ^= mask;
}
}
Without a CTZ instruction, you can also use integer division:
void iter_reversed(unsigned bits) {
unsigned n = 1 << bits;
for (unsigned i = 0, j = 0; i < n; i++) {
printf("%x\n", j);
// Find least significant zero bit.
unsigned bit = ~i & (i + 1);
// Using division to bit-reverse a single bit.
unsigned rev = (n / 2) / bit;
// XOR with mask.
j ^= (n - 1) & ~(rev - 1);
}
}
void reverse(int nMaxVal, int nBits)
{
int thisVal, bit, out;
// Calculate for each value from 0 to nMaxVal.
for (thisVal=0; thisVal<=nMaxVal; ++thisVal)
{
out = 0;
// Shift each bit from thisVal into out, in reverse order.
for (bit=0; bit<nBits; ++bit)
out = (out<<1) + ((thisVal>>bit) & 1)
}
printf("%d -> %d\n", thisVal, out);
}
Maybe increment from 0 to N (the "usual" way") and do ReverseBitOrder() for each iteration. You can find several implementations here (I like the LUT one the best).
Should be really quick.
Here's an answer in Perl. You don't say what comes after the all ones pattern, so I just return zero. I took out the bitwise operations so that it should be easy to translate into another language.
sub reverse_increment {
my($n, $bits) = #_;
my $carry = 2**$bits;
while($carry > 1) {
$carry /= 2;
if($carry > $n) {
return $carry + $n;
} else {
$n -= $carry;
}
}
return 0;
}
Here's a solution which doesn't actually try to do any addition, but exploits the on/off pattern of the seqence (most sig bit alternates every time, next most sig bit alternates every other time, etc), adjust n as desired:
#define FLIP(x, i) do { (x) ^= (1 << (i)); } while(0)
int main() {
int n = 3;
int max = (1 << n);
int x = 0;
for(int i = 1; i <= max; ++i) {
std::cout << x << std::endl;
/* if n == 3, this next part is functionally equivalent to this:
*
* if((i % 1) == 0) FLIP(x, n - 1);
* if((i % 2) == 0) FLIP(x, n - 2);
* if((i % 4) == 0) FLIP(x, n - 3);
*/
for(int j = 0; j < n; ++j) {
if((i % (1 << j)) == 0) FLIP(x, n - (j + 1));
}
}
}
How about adding 1 to the most significant bit, then carrying to the next (less significant) bit, if necessary. You could speed this up by operating on bytes:
Precompute a lookup table for counting in bit-reverse from 0 to 256 (00000000 -> 10000000, 10000000 -> 01000000, ..., 11111111 -> 00000000).
Set all bytes in your multi-byte number to zero.
Increment the most significant byte using the lookup table. If the byte is 0, increment the next byte using the lookup table. If the byte is 0, increment the next byte...
Go to step 3.
With n as your power of 2 and x the variable you want to step:
(defun inv-step (x n) ; the following is a function declaration
"returns a bit-inverse step of x, bounded by 2^n" ; documentation
(do ((i (expt 2 (- n 1)) ; loop, init of i
(/ i 2)) ; stepping of i
(s x)) ; init of s as x
((not (integerp i)) ; breaking condition
s) ; returned value if all bits are 1 (is 0 then)
(if (< s i) ; the loop's body: if s < i
(return-from inv-step (+ s i)) ; -> add i to s and return the result
(decf s i)))) ; else: reduce s by i
I commented it thoroughly as you may not be familiar with this syntax.
edit: here is the tail recursive version. It seems to be a little faster, provided that you have a compiler with tail call optimization.
(defun inv-step (x n)
(let ((i (expt 2 (- n 1))))
(cond ((= n 1)
(if (zerop x) 1 0)) ; this is really (logxor x 1)
((< x i)
(+ x i))
(t
(inv-step (- x i) (- n 1))))))
When you reverse 0 to 2^n-1 but their bit pattern reversed, you pretty much cover the entire 0-2^n-1 sequence
Sum = 2^n * (2^n+1)/2
O(1) operation. No need to do bit reversals
Edit: Of course original poster's question was about to do increment by (reversed) one, which makes things more simple than adding two random values. So nwellnhof's answer contains the algorithm already.
Summing two bit-reversal values
Here is one solution in php:
function RevSum ($a,$b) {
// loop until our adder, $b, is zero
while ($b) {
// get carry (aka overflow) bit for every bit-location by AND-operation
// 0 + 0 --> 00 no overflow, carry is "0"
// 0 + 1 --> 01 no overflow, carry is "0"
// 1 + 0 --> 01 no overflow, carry is "0"
// 1 + 1 --> 10 overflow! carry is "1"
$c = $a & $b;
// do 1-bit addition for every bit location at once by XOR-operation
// 0 + 0 --> 00 result = 0
// 0 + 1 --> 01 result = 1
// 1 + 0 --> 01 result = 1
// 1 + 1 --> 10 result = 0 (ignored that "1", already taken care above)
$a ^= $b;
// now: shift carry bits to the next bit-locations to be added to $a in
// next iteration.
// PHP_INT_MAX here is used to ensure that the most-significant bit of the
// $b will be cleared after shifting. see link in the side note below.
$b = ($c >> 1) & PHP_INT_MAX;
}
return $a;
}
Side note: See this question about shifting negative values.
And as for test; start from zero and increment value by 8-bit reversed one (10000000):
$value = 0;
$add = 0x80; // 10000000 <-- "one" as bit reversed
for ($count = 20; $count--;) { // loop 20 times
printf("%08b\n", $value); // show value as 8-bit binary
$value = RevSum($value, $add); // do addition
}
... will output:
00000000
10000000
01000000
11000000
00100000
10100000
01100000
11100000
00010000
10010000
01010000
11010000
00110000
10110000
01110000
11110000
00001000
10001000
01001000
11001000
Let assume number 1110101 and our task is to find next one.
1) Find zero on highest position and mark position as index.
11101010 (4th position, so index = 4)
2) Set to zero all bits on position higher than index.
00001010
3) Change founded zero from step 1) to '1'
00011010
That's it. This is by far the fastest algorithm since most of cpu's has instructions to achieve this very efficiently. Here is a C++ implementation which increment 64bit number in reversed patern.
#include <intrin.h>
unsigned __int64 reversed_increment(unsigned __int64 number)
{
unsigned long index, result;
_BitScanReverse64(&index, ~number); // returns index of the highest '1' on bit-reverse number (trick to find the highest '0')
result = _bzhi_u64(number, index); // set to '0' all bits at number higher than index position
result |= (unsigned __int64) 1 << index; // changes to '1' bit on index position
return result;
}
Its not hit your requirements to have "no bits" operations, however i fear there is now way how to achieve something similar without them.