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I have a float for totalPoints which is being automatically increased by the Timer, but after totalPoints reaches certain number, it doesn't seem to increase no more. I did not put any limits so I'm not sure why is it happening.
So, totalPoints stops increasing when it reaches "2097152" value.
Here's part of my code:
public float totalPoins;
void AccumulatePoints()
{
timer += Time.deltaTime * miningPowerValue;
if (timer > 1f)
{
totalPoints += someValue;
timer = 0;
}
}
So basically it accumulates points depending on miningPowerValue. If its low, it will accumulate on a slower rate, higher - faster. Please help. I'm confused.
Floating-point numbers get less precise as they get bigger. You've ran into a case where they're big enough that the amount you're trying to add is smaller than the smallest possible difference between numbers of that size. Switch to double or something else with more precision.
To expand on Joseph Sible's answer in a way that I can't simply through a comment.
A single-precision floating-point value is a method of specifying a floating-point value, and in most programming languages is defined by the specification of IEEE 754. We can assume that C#'s floating-point values conform to IEEE 754. Floating point values are given a certain amount of bytes, which they use in three chunks: The sign (positive or negative), the exponent ('y' in x * 10^y), and the value ('x' in x * 10^y). (Note: The definition of these three chunks is a little more elaborate than stated here, but for this example, it suffices).
In other words, a floating point value would encode the value "2,000.00" as "positive, 3, 2". That is, positive 2 * 10^3, or 2 * 1000, or 2,000.
This means that a floating point value can represent a very large value, but it also means that very large values tend to encounter errors. At a certain point, floats need to start "lopping off" the end of their information, in order to get the best approximation for the space they have.
For example, assume we were trying to use a very small version of a floating point value to define the value 1,234,567, and it only has the space for 4 digits in its x. 1,234,567 would become "positive, 6, 1.234"; 1.234 + 10 ^ 6, which calculates to 1.234 * 1,000,000, which calculates to 1,234,000 - the last three digits are removed, because they are the "least significant" for the purposes of estimation. Assume we store this value to the variable Foo.
Now, say you try to add 1 to this value, via Foo += 1. The value would become 1,234,001. But since our floating-point value can only store the 4 largest digits, that 1 gets ignored. 1,234,000 is a good enough approximation for 1,234,001. Even if you added 1 a thousand times, it wouldn't change anything because it gets rounded off every time you add. Meanwhile, adding 1,000 directly would, in fact, have an effect.
This is what is happening in your code, on a much larger scale, and what Joseph Sible was trying to convey. This also explains why totalPoints += 0.5f will work when 0.1136f wont - 0.5 is a larger number than 0.1136, which means that 0.1136 stops being "significant" before 0.5 does. He recommended you use double, which is more precise. A double-precision floating-point value is similar to a single-precision floating point value, but considerably larger; that is, a double can store more bits than a float (about twice as many, in fact), so smaller numbers won't be lost as easily. This would solve your problem, at least up until the point where double needs to start lopping off small numbers again!
The problem is that you are exceeding the precision of a `float' which is only 7 digits.
Look at the following simple code:
void Main()
{
float f = 0.0f;
float i = 0.1136f;
int j = 0;
while (f <= 3000000.0f) //I arbitrarily chose 3 million here for illustration
{
f += i;
//Only print after every 1000th iteration of the loop
if (j % 1000 == 0)
{
Console.WriteLine(f);
}
j++;
}
}
At the beginning of this code you will see values like the following:
0.1136
113.7148
227.3164
340.9067
454.4931
568.0796
Then, after a bit, the decimal portion starts shrinking as the whole number portion grows:
9430.525
9543.807
9657.088
9770.369
9883.65
9996.932
10110.21
10223.49
10336.78
10450.06
10563.34
But as the value gets higher and higher, it eventually just outputs this:
999657.9
999782.9
999907.9
1000033
1000158
1000283
1000408
1000533
1000658
1000783
Notice how the part after decimal shrinks but the part before it increases? Since the float data type only has 7 digits of precision, the least significant digits after the decimal point are chopped off. double on the other hand has 16 digits of precision.
I hope this helps to explain what is happening.
Assume I have an array of bytes which are truly random (e.g. captured from an entropy source).
byte[] myTrulyRandomBytes = MyEntropyHardwareEngine.GetBytes(8);
Now, I want to get a random double precision floating point value, but between the values of 0 and positive 1 (like the Random.NextDouble() function performs).
Simply passing an array of 8 random bytes into BitConverter.ToDouble() can yield strange results, but most importantly, the results will almost never be less than 1.
I am fine with bit-manipulation, but the formatting of floating point numbers has always been mysterious to me. I tried many combinations of bits to apply randomness to and always ended up finding the numbers were either just over 1, always VERY close to 0, or very large.
Can someone explain which bits should be made random in a double in order to make it random within the range 0 and 1?
Though working answers have been given, I'll give an other one, that looks worse but isn't:
long asLong = BitConverter.ToInt64(myTrulyRandomBytes, 0);
double number = (double)(asLong & long.MaxValue) / long.MaxValue;
The issue with casting from an ulong to double is that it's not directly supported by hardware, so it compiles to this:
vxorps xmm0,xmm0,xmm0
vcvtsi2sd xmm0,xmm0,rcx ; interpret ulong as long and convert it to double
test rcx,rcx ; add fixup if it was "negative"
jge 000000000000001D
vaddsd xmm0,xmm0,mmword ptr [00000060h]
vdivsd xmm0,xmm0,mmword ptr [00000068h]
Whereas with my suggestion it will compile more nicely:
vxorps xmm0,xmm0,xmm0
vcvtsi2sd xmm0,xmm0,rcx
vdivsd xmm0,xmm0,mmword ptr [00000060h]
Both tested with the x64 JIT in .NET 4, but this applies in general, there just isn't a nice way to convert an ulong to a double.
Don't worry about the bit of entropy being lost: there are only 262 doubles between 0.0 and 1.0 in the first place, and most of the smaller doubles cannot be chosen so the number of possible results is even less.
Note that this as well as the presented ulong examples can result in exactly 1.0 and distribute the values with slightly differing gaps between adjacent results because they don't divide by a power of two. You can change them exclude 1.0 and get a slightly more uniform spacing (but see the first plot below, there is a bunch of different gaps, but this way it is very regular) like this:
long asLong = BitConverter.ToInt64(myTrulyRandomBytes, 0);
double number = (double)(asLong & long.MaxValue) / ((double)long.MaxValue + 1);
As a really nice bonus, you can now change the division to a multiplication (powers of two usually have inverses)
long asLong = BitConverter.ToInt64(myTrulyRandomBytes, 0);
double number = (double)(asLong & long.MaxValue) * 1.08420217248550443400745280086994171142578125E-19;
Same idea for ulong, if you really want to use that.
Since you also seemed interested specifically in how to do it with double-bits trickery, I can show that too.
Because of the whole significand/exponent deal, it can't really be done in a super direct way (just reinterpreting the bits and that's it), mainly because choosing the exponent uniformly spells trouble (with a uniform exponent, the numbers are necessarily clumped preferentially near 0 since most exponents are there).
But if the exponent is fixed, it's easy to make a double that's uniform in that region. That cannot be 0 to 1 because that spans a lot of exponents, but it can be 1 to 2 and then we can subtract 1.
So first mask away the bits that won't be part of the significand:
x &= (1L << 52) - 1;
Put in the exponent (1.0 - 2.0 range, excluding 2)
x |= 0x3ff0000000000000;
Reinterpret and adjust for the offset of 1:
return BitConverter.Int64BitsToDouble(x) - 1;
Should be pretty fast, too. An unfortunate side effect is that this time it really does cost a bit of entropy, because there are only 52 but there could have been 53. This way always leaves the least significant bit zero (the implicit bit steals a bit).
There were some concerns about the distributions, which I will address now.
The approach of choosing a random (u)long and dividing it by the maximum value clearly has a uniformly chosen (u)long, and what happens after that is actually interesting. The result can justifiably be called a uniform distribution, but if you look at it as a discrete distribution (which it actually is) it looks (qualitatively) like this: (all examples for minifloats)
Ignore the "thicker" lines and wider gaps, that's just the histogram being funny. These plots used division by a power of two, so there is no spacing problem in reality, it's only plotted strangely.
Top is what happens when you use too many bits, as happens when dividing a complete (u)long by its max value. This gives the lower floats a better resolution, but lots of different (u)longs get mapped onto the same float in the higher regions. That's not necessarily a bad thing, if you "zoom out" the density is the same everywhere.
The bottom is what happens when the resolution is limited to the worst case (0.5 to 1.0 region) everywhere, which you can do by limiting the number of bits first and then doing the "scale the integer" deal. My second suggesting with the bit hacks does not achieve this, it's limited to half that resolution.
For what it's worth, NextDouble in System.Random scales a non-negative int into the 0.0 .. 1.0 range. The resolution of that is obviously a lot lower than it could be. It also uses an int that cannot be int.MaxValue and therefore scales by approximately 1/(231-1) (cannot be represented by a double, so slightly rounded), so there are actually 33 slightly different gaps between adjacent possible results, though the majority of the gaps is the same distance.
Since int.MaxValue is small compared to what can be brute-forced these days, you can easily generate all possible results of NextDouble and examine them, for example I ran this:
const double scale = 4.6566128752458E-10;
double prev = 0;
Dictionary<long, int> hist = new Dictionary<long, int>();
for (int i = 0; i < int.MaxValue; i++)
{
long bits = BitConverter.DoubleToInt64Bits(i * scale - prev);
if (!hist.ContainsKey(bits))
hist[bits] = 1;
else
hist[bits]++;
prev = i * scale;
if ((i & 0xFFFFFF) == 0)
Console.WriteLine("{0:0.00}%", 100.0 * i / int.MaxValue);
}
This is easier than you think; its all about scaling (also true when going from a 0-1 range to some other range).
Basically, if you know that you have 64 truly random bits (8 bytes) then just do this:
double zeroToOneDouble = (double)(BitConverter.ToUInt64(bytes) / (decimal)ulong.MaxValue);
The trouble with this kind of algorithm comes when your "random" bits aren't actually uniformally random. That's when you need a specialized algorithm, such as a Mersenne Twister.
I don't know wether it's the best solution for this, but it should do the job:
ulong asLong = BitConverter.ToUInt64(myTrulyRandomBytes, 0);
double number = (double)asLong / ulong.MaxValue;
All I'm doing is converting the byte array to a ulong which is then divided by it's max value, so that the result is between 0 and 1.
To make sure the long value is within the range from 0 to 1, you can apply the following mask:
long longValue = BitConverter.ToInt64(myTrulyRandomBytes, 0);
longValue &= 0x3fefffffffffffff;
The resulting value is guaranteed to lay in the range [0, 1).
Remark. The 0x3fefffffffffffff value is very-very close to 1 and will be printed as 1, but it is really a bit less than 1.
If you want to make the generated values greater, you could set a number higher bits of an exponent to 1. For instance:
longValue |= 0x03c00000000000000;
Summarizing: example on dotnetfiddle.
If you care about the quality of the random numbers generated, be very suspicious of the answers that have appeared so far.
Those answers that use Int64BitsToDouble directly will definitely have problems with NaNs and infinities. For example, 0x7ff0000000000001, a perfectly good random bit pattern, converts to NaN (and so do thousands of others).
Those that try to convert to a ulong and then scale, or convert to a double after ensuring that various bit-pattern constraints are met, won't have NaN problems, but they are very likely to have distributional problems. Representable floating point numbers are not distributed uniformly over (0, 1), so any scheme that randomly picks among all representable values will not produce values with the required uniformity.
To be safe, just use ToInt32 and use that int as a seed for Random. (To be extra safe, reject 0.) This won't be as fast as the other schemes, but it will be much safer. A lot of research and effort has gone into making RNGs good in ways that are not immediately obvious.
Simple piece of code to print the bits out for you.
for (double i = 0; i < 1.0; i+=0.05)
{
var doubleToInt64Bits = BitConverter.DoubleToInt64Bits(i);
Console.WriteLine("{0}:\t{1}", i, Convert.ToString(doubleToInt64Bits, 2));
}
0.05: 11111110101001100110011001100110011001100110011001100110011010
0.1: 11111110111001100110011001100110011001100110011001100110011010
0.15: 11111111000011001100110011001100110011001100110011001100110100
0.2: 11111111001001100110011001100110011001100110011001100110011010
0.25: 11111111010000000000000000000000000000000000000000000000000000
0.3: 11111111010011001100110011001100110011001100110011001100110011
0.35: 11111111010110011001100110011001100110011001100110011001100110
0.4: 11111111011001100110011001100110011001100110011001100110011001
0.45: 11111111011100110011001100110011001100110011001100110011001100
0.5: 11111111011111111111111111111111111111111111111111111111111111
0.55: 11111111100001100110011001100110011001100110011001100110011001
0.6: 11111111100011001100110011001100110011001100110011001100110011
0.65: 11111111100100110011001100110011001100110011001100110011001101
0.7: 11111111100110011001100110011001100110011001100110011001100111
0.75: 11111111101000000000000000000000000000000000000000000000000001
0.8: 11111111101001100110011001100110011001100110011001100110011011
0.85: 11111111101011001100110011001100110011001100110011001100110101
0.9: 11111111101100110011001100110011001100110011001100110011001111
0.95: 11111111101110011001100110011001100110011001100110011001101001
I need to add a very small value to a floating point value to make it insignificantly different so that it fails an equality test.
To avoid issues with precision, instead of adding a very small number, I have opted to add a relatively small number. Is this a good solution? or is there a reliable way to add an even smaller number?
matrix.m00 += matrix.m00 * 0.0000001f;
matrix.m11 += matrix.m11 * 0.0000001f;
matrix.m22 += matrix.m22 * 0.0000001f;
From reading I have found that the best solution is to use the next representable floating point number. Though in C# the process of doing this either a) requires unmanaged/unsafe code, or b) uses BitConverter which is too slow. So I figured that the above solution would work, but I would like to know if there are any gotchyas.
You can add an ulp to any double (depends on the double); that is the smallest number that you can add or subtract to it that will change its value.
Calculate the unit in the last place (ULP) for doubles
next higher/lower IEEE double precision number
Though, those posts all use BitConverter. I discovered a post that discusses how to add an ulp without unsafe code or BitConverter, though:
http://realtimemadness.blogspot.com/2012/06/nextafter-in-c-without-allocations-of.html
Sure there's a gotcha. If any of these values is 0, then you'll be adding exactly 0, i.e. not modifying the value at all.
Is there any reason why you couldn't use unsafe code to do this?
The minimum number you can add to a floating point number such that a different number is produced is a function of the original number, it's not some constant. Call this function Epsilon(x).
Epsilon(0), i.e. the minimum floating point number you can add to floating point 0 such that a distinguishable number is produced, can be found in the static value Double.Epsilon.
Even using a "large" epsilon like 1 will eventually fail, though. For example, this returns true in C#:
var big = 10000000000000000.0;
Console.WriteLine(big == (big + 1.0));
So unless you are sure that your input is in some fixed range of magnitude (e.g. all close to 0), you can't just fudge it with a single constant.
For a clever and complicated reason that I don't really want to explain (because it involves making a timer in an extremely ugly and hacky way), I wrote some C# code sort of like this:
int i = 0;
while (i >= 0) i++; //Should increment forever
Console.Write(i);
I expected the program to hang forever or crash or something, but, to my surprise, after waiting for about 20 seconds or so, I get this ouput:
-2147483648
Well, programming has taught me many things, but I still cannot grasp why continually incrementing a number causes it to eventually be negative...what's going on here?
In C#, the built-in integers are represented by a sequence of bit values of a predefined length. For the basic int datatype that length is 32 bits. Since 32 bits can only represent 4,294,967,296 different possible values (since that is 2^32), clearly your code will not loop forever with continually increasing values.
Since int can hold both positive and negative numbers, the sign of the number must be encoded somehow. This is done with first bit. If the first bit is 1, then the number is negative.
Here are the int values laid out on a number-line in hexadecimal and decimal:
Hexadecimal Decimal
----------- -----------
0x80000000 -2147483648
0x80000001 -2147483647
0x80000002 -2147483646
... ...
0xFFFFFFFE -2
0xFFFFFFFF -1
0x00000000 0
0x00000001 1
0x00000002 2
... ...
0x7FFFFFFE 2147483646
0x7FFFFFFF 2147483647
As you can see from this chart, the bits that represent the smallest possible value are what you would get by adding one to the largest possible value, while ignoring the interpretation of the sign bit. When a signed number is added in this way, it is called "integer overflow". Whether or not an integer overflow is allowed or treated as an error is configurable with the checked and unchecked statements in C#. The default is unchecked, which is why no error occured, but you got that crazy small number in your program.
This representation is called 2's Complement.
The value is overflowing the positive range of 32 bit integer storage going to 0xFFFFFFFF which is -2147483648 in decimal. This means you overflow at 31 bit integers.
It's been pointed out else where that if you use an unsigned int you'll get different behaviour as the 32nd bit isn't being used to store the sign of of the number.
What you are experiencing is Integer Overflow.
In computer programming, an integer overflow occurs when an arithmetic operation attempts to create a numeric value that is larger than can be represented within the available storage space. For instance, adding 1 to the largest value that can be represented constitutes an integer overflow. The most common result in these cases is for the least significant representable bits of the result to be stored (the result is said to wrap).
int is a signed integer. Once past the max value, it starts from the min value (large negative) and marches towards 0.
Try again with uint and see what is different.
Try it like this:
int i = 0;
while (i >= 0)
checked{ i++; } //Should increment forever
Console.Write(i);
And explain the results
What the others have been saying. If you want something that can go on forever (and I wont remark on why you would need something of this sort), use the BigInteger class in the System.Numerics namespace (.NET 4+). You can do the comparison to an arbitrarily large number.
It has a lot to do with how positive numbers and negative numbers are really stored in memory (at bit level).
If you're interested, check this video: Programming Paradigms at 12:25 and onwards. Pretty interesting and you will understand why your code behaves the way it does.
This happens because when the variable "i" reaches the maximum int limit, the next value will be a negative one.
I hope this does not sound like smart-ass advice, because its well meant, and not meant to be snarky.
What you are asking is for us to describe that which is pretty fundamental behaviour for integer datatypes.
There is a reason why datatypes are covered in the 1st year of any computer science course, its really very fundamental to understanding how and where things can go wrong (you can probably already see how the behaviour above if unexpected causes unexpected behaviour i.e. a bug in your application).
My advice is get hold of the reading material for 1st year computer science + Knuth's seminal work "The art of computer pragramming" and for ~ $500 you will have everything you need to become a great programmer, much cheaper than a whole Uni course ;-)
I have a double value that equals 1.212E+25
When I throw it out to text I do myVar.ToString("0000000000000000000000")
The problem is even if I do myVar++ 3 or 4 times the value seems to stay the same.
Why is that?
That is because the precision of a double is not sufficient. It simply can't hold that many significant digits.
It will not fit into a long, but probably into a Decimal.
But... do you really need this level of precision?
To expand on the other answer the smallest increase you can make to a double is one Unit in the Last Place, or ULP, as double is a floating point type then the size of an ULP changes, at 1E+25 it will be about 1E+10.
as you can see compared to 1E+10 incrementing by 1 really might as well be adding nothing. which is exactly what double will do, so it wouldnt matter if you tried it 10^25 times it still won't increase unless you try to increase by at least 1 ULP
if incrementing by an ULP is useful you can do this by casting the bits to long and back here is a quick extension method to do that
public static double UlpChange(this double val, int ulp)
{
if (!double.IsInfinity(val) && !double.IsNaN(val))
{
//should probably do something if we are at max or min values
//but its not clear what
long bits = BitConverter.DoubleToInt64Bits(val);
return BitConverter.Int64BitsToDouble(bits + ulp);
}
return val;
}
double (Double) holds about 16 digits of precision and long (Int64) about 18 digits.
Neither of these appear to have sufficient precision for your needs.
However decimal (Decimal) holds up to 30 digits of precision. Although this appears to be great enough for your needs I'd recommend caution in case your requirement grows even larger. In that case you may need a third party numeric library.
Related StackOverflow entries are:
How can I represent a very large integer in .NET?
Big integers in C#
You may want to read the Floating-Point Guide to understand how doubles work.
Basically, a double only has about 16 decimal digits of precision. At a magnitude of 10^25, an increase of 1.0 is below the threshold of precision and gets lost. Due to the binary representation, this may not be obvious.
The smallest increment that'll work is 2^30+1 which will actually increment the double by 2^31. You can test this kind of thing easily enough with LINQPad:
double inc = 1.0;
double num = 1.212e25;
while(num+inc == num) inc*=2;
inc.Dump(); //2147483648 == 2^31
(num+inc == num).Dump(); //false due to loop invariant
(num+(inc/2.0) == num).Dump();//true due to loop invariant
(num+(inc/2.0+1.0) == num).Dump();//false - 2^30+1 suffices to change the number
(num+(inc/2.0+1.0) == num + inc).Dump();//true - 2^30+1 and 2^31 are equiv. increments
((num+(inc/2.0+1.0)) - num == inc ).Dump();//true - the effective increment is 2^31
Since a double is essentially a binary number with limited precision, that means that the smallest possible increment will itself always be a power of two (this increment can be determined directly from the bit-pattern of the double, but it's probably clearer to do so with a loop as above since that's portable across float, double and other floating point representations (which don't exist in .NET).