From the help for the Overflow Error in VBA, there's the following examples:
Dim x As Long
x = 2000 * 365 ' gives an error
Dim x As Long
x = CLng(2000) * 365 ' fine
I would have thought that, since the Long data type is supposed to be able to hold 32-bit numbers, that the first example would work fine.
I ask this because I have some code like this:
Dim Price as Long
Price = CLng(AnnualCost * Months / 12)
and this throws an Overflow Error when AnnualCost is 5000 and Months is 12.
What am I missing?
2000 and 365 are Integer values. In VBA, Integers are 16-bit signed types, when you perform arithmetic on 2 integers the arithmetic is carried out in 16-bits. Since the result of multiplying these two numbers exceeds the value that can be represented with 16 bits you get an exception. The second example works because the first number is first converted to a 32-bit type and the arithmetic is then carried out using 32-bit numbers. In your example, the arithmetic is being performed with 16-bit integers and the result is then being converted to long but at that point it is too late, the overflow has already occurred. The solution is to convert one of the operands in the multiplication to long first:
Dim Price as Long
Price = CLng(AnnualCost) * Months / 12
The problem is that the multiplication is happening inside the brackets, before the type conversion. That's why you need to convert at least one of the variables to Long first, before multiplying them.
Presumably you defined the variables as Integer. You might consider using Long instead of Integer, partly because you will have fewer overflow problems, but also because Longs calculate (a little) faster than Integers on 32 bit machines. Longs do take more memory, but in most cases this is not a problem.
In VBA, literals are integer by default (as mentioned). If you need to force a larger datatype on them you can recast them as in the example above or just append a type declaration character. (The list is here: http://support.microsoft.com/kb/191713) The type for Long is "&" so you could just do:
Price = CLng(AnnualCost * Months / 12&)
And the 12 would be recast as a long. However it is generally good practice to avoid literals and use constants. In which case you can type the constant in it's declaration.
Const lngMonths12_c as Long = 12
Price = CLng(AnnualCost * Months / lngMonths12_c)
Related
Disclaimer: I know that 0.025 cannot be represented exactly in IEEE floating-point variables and, thus, rounding might not return what one would expect. That is not my question!
Is it possible to simulate the behavior of the VBA arithmetic operators in .NET?
For example, in VBA, the following expression yields 3:
Dim myInt32 As Long
myInt32 = CLng(0.025 * 100) ' yields 3
However, in VB.NET, the following expression yields 2:
Dim myInt32 As Integer
myInt32 = CInt(0.025 * 100) ' yields 2
According to the specification, both should return the same value:
Long (VBA) and Integer (VB.NET) are 32-bit integer types.
According to the VBA specification, CLng performs Let-coercion to Long, and Let-coercion between numeric types uses Banker's rounding. The same is true for VB.NET's CInt.
0.025 is a double precision IEEE floating-point constant in both cases.
Thus, some implementation detail of the floating-point multiplication operator or the integer-conversion operator changed. However, for reasons of compatibility with a legacy VBA system, I'd need to replicate the mathematical behavior of VBA (however wrong it might be) in a .NET application.
Is there some way to do that? Did someone write a Microsoft.VBA.Math library? Or is the precise VBA algorithm documented somewhere so I can do that myself?
VBA and VB.NET behave differently because VBA uses 80-bit "extended" precision for intermediate floating-point calculations (even though Double is a 64-bit type), whereas VB.NET always uses 64-bit precision. When using 80-bit precision, the value of 0.025 * 100 is slightly greater than 2.5, so CLng(0.025 * 100) rounds up to 3.
Unfortunately, VB.NET doesn't seem to offer 80-bit precision arithmetic. As a workaround, you can create a native Win32 DLL using Visual C++ and call it via P/Invoke. For example:
#include <cmath>
#include <float.h>
#pragma comment(linker, "/EXPORT:MultiplyAndRound=_MultiplyAndRound#16")
extern "C" __int64 __stdcall MultiplyAndRound(double x, double y)
{
unsigned int cw = _controlfp(0, 0);
_controlfp(_PC_64, _MCW_PC); // use 80-bit precision (64-bit significand)
double result = floor(x * y + 0.5);
if (result - (x * y + 0.5) == 0 && fmod(result, 2))
result -= 1.0; // round down to even if halfway between even and odd
_controlfp(cw, _MCW_PC); // restore original precision
return (__int64)result;
}
And in VB.NET:
Declare Function MultiplyAndRound Lib "FPLib.dll" (ByVal x As Double, ByVal y As Double) As Long
Console.WriteLine(MultiplyAndRound(2.5, 1)) ' 2
Console.WriteLine(MultiplyAndRound(0.25, 10)) ' 2
Console.WriteLine(MultiplyAndRound(0.025, 100)) ' 3
Console.WriteLine(MultiplyAndRound(0.0025, 1000)) ' 3
Given that the VBA is supposed to use Banker's rounding, it seems clear to me at first glance that the bug is actually in the VBA side of things. Bankers rounding rounds at the midpoint (.5) so the result digit is even. Thus, to do correct Banker's rounding, 2.5 should round to 2, and not to 3. This matches the .Net result, rather than the VBA result.
However, based on information pulled from a currently deleted answer, we can also see this result in VBA:
Dim myInt32 As Integer
myInt32 = CInt(2.5) ' 2
myInt32 = CInt(0.025 * 100) ' 3
This makes it seem like the rounding in VBA is correct, but the multiplication operation produces a result that is somehow greater than 2.5. Since we're no longer at a mid-point, the Banker's rule does not apply, and we round up to 3.
Therefore, to fix this issue, you'll need to figure out what that VBA code is really doing with that multiplication instruction. Regardless of what is documented, the observations prove that VBA is handling this part differently than .Net. Once you figure out exactly what's going on, with luck you'll be able to simulate that behavior.
One possible option is to go back to the old standby for floating point numbers: check whether you're within some small delta of a mid-point and, if so, just use the mid-point. Here's some (untested) naive code to do it:
Dim result As Double = 0.025 * 100
Dim delta As Double = Double.Epsilon
Dim floor As Integer = Math.Floor(result)
If Math.Abs(result - (CDbl(floor) + 0.5)) <= delta Then
result = floor + 0.5
End
I emphasize the untested, because at this point we're already dealing strange with results from small computer rounding errors. The naive implementation in this situation is unlikely to be good enough. At very least, you may want to use a factor of 3 or 4 epsilons for your delta. Also, the best you could hope for from this code is that it could force the VBA to match the .Net, when what you're really after is the reverse.
I am doing a simple calculation in VB.Net, following is the code.
Dim total As String = "192.04"
Dim paid As String = "200"
Dim change As String = "7.96"
'//This prints -7.99360577730113E-15 (Incorrect)
MsgBox((CDbl(total) - CDbl(paid)) + CDbl(change))
The answer I expect from this calculation is 0 but I get -7.99360577730113E-15. I don't know the exact reason for this, I couldn't quite understand the reason explained on MSDN. Any clarification would be really helpful.
And I did a Math.Round to 2 decimal places & the problem was solved, therefore do I need to use Math.Round everywhere I do a calculation with decimals ?
That's because of how double (or floating point numbers in general) is represented in memory. You should use decimal for financial calculations instead of double:
double total = 192.04;
double paid = 200;
double change = 7.96;
double result = (total - paid) + change; // -7.99360577730113E-15
decimal total = 192.04m;
decimal paid = 200m;
decimal change = 7.96m;
decimal result = (total - paid) + change; // 0.00
I know it's C#, but you should see the difference anyway.
You can use Decimal.Parse to get a decimal from string:
Dim total As String = "192.04"
Dim paid As String = "200"
Dim change As String = "7.96"
MsgBox((Decimal.Parse(total) - Decimal.Parse(paid)) + Decimal.Parse(change))
This precision error is due to the way floating point numbers work in computers (more specifically the IEEE standard, see WikiPedia). In short, floating point numbers are stored as "binary numbers with decimal points" then raised to some power of 2. Therefore it cannot store exact decimal digits. This problem is common to all programming languages and not just .NET alone.
To overcome this problem, the Decimal type is used when calculating payments that require base 10 precision. Outside of .NET, almost all modern programming environments also provide this variable type.
Right now I have a line of code like this:
float x = (([self.machine micSensitivity] - 0.0075f) / 0.00025f);
Where [self.machine micSensitivity] is a float containing the value 0.010000
So,
0.01 - 0.0075 = 0.0025
0.0025 / 0.00025 = 10.0
But in this case, it keeps returning 9.999999
I'm assuming there's some kind of rounding error but I can't seem to find a clean way of fixing it. micSensitivity is incremented/decremented by 0.00025 and that formula is meant to return a clean integer value for the user to reference so I'd rather get the programming right than just adding 0.000000000001.
Thanks.
that formula is meant to return a clean integer value for the user to reference
If that is really important to you, then why do you not multiply all the numbers in this story by 10000, coerce to int, and do integer arithmetic?
Or, if you know that the answer is arbitrarily close to an integer, round to that integer and present it.
Floating-point arithmetic is binary, not decimal. It will almost always give rounding errors. You need to take that into account. "float" has about six digit precision. "double" has about 15 digits precision. You throw away nine digits precision for no reason.
Now think: What do you want to display? What do you want to display if the result of your calculation is 9.999999999? What would you want to display if the result is 9.538105712?
None of the numbers in your question, except 10.0, can be exactly represented in a float or a double on iOS. If you want to do float math with those numbers, you will have rounding errors.
You can round your result to the nearest integer easily enough:
float x = rintf((self.machine.micSensitivity - 0.0075f) / 0.00025f);
Or you can just multiply all your numbers, including the allowed values of micSensitivity, by 4000 (which is 1/0.00025), and thus work entirely with integers.
Or you can change the allowed values of micSensitivity so that its increment is a fraction whose denominator is a power of 2. For example, if you use an increment of 0.000244140625 (which is 2-12), and change 0.0075 to 0.00732421875 (which is 30 * 2-12), you should get exact results, as long as your micSensitivity is within the range ±4096 (since 4096 is 212 and a float has 24 bits of significand).
The code you have posted is correct and functioning properly. This is a known side effect of using floating point arithmetic. See the wiki on floating point accuracy problems for a dull explanation as to why.
There are several ways to work around the problem depending on what you need to use the number for.
If you need to compare two floats, then most everything works OK: less than and greater than do what you would expect. The only trouble is testing if two floats are equal.
// If x and y are within a very small number from each other then they are equal.
if (fabs(x - y) < verySmallNumber) { // verySmallNumber is usually called epsilon.
// x and y are equal (or at least close enough)
}
If you want to print a float, then you can specify a precision to round to.
// Get a string of the x rounded to five digits of precision.
NSString *xAsAString = [NSString stringWithFormat:#"%.5f", x];
9.999999 is equal 10. there is prove:
9.999999 = x then 10x = 99.999999 then 10x-x = 9x = 90 then x = 10
first post!
I have a problem with a program that i'm writing for a numerical simulation and I have a problem with the multiplication. Basically, I am trying to calculate:
result1 = (a + b)*c
and this loops thousands of times. I need to expand this code to be
result2 = a*c + b*c
However, when I do that I start to get significant errors in my results. I used a high precision library, which did improve things, but the simulation ran horribly slow (the simulation took 50 times longer) and it really isn't a practical solution. From this I realised that it isn't really the precision of the variables a, b, & c that is hurting me, but something in the way the multiplication is done.
My question is: how can I multiply out these brackets in way so that result1 = result2?
Thanks.
SOLVED!!!!!!!!!
It was a problem with the addition. So i reordered the terms and applied Kahan addition by writing the following piece of code:
double Modelsimple::sum(double a, double b, double c, double d) {
//reorder the variables in order from smallest to greatest
double tempone = (a<b?a:b);
double temptwo = (c<d?c:d);
double tempthree = (a>b?a:b);
double tempfour = (c>d?c:d);
double one = (tempone<temptwo?tempone:temptwo);
double four = (tempthree>tempfour?tempthree:tempfour);
double tempfive = (tempone>temptwo?tempone:temptwo);
double tempsix = (tempthree<tempfour?tempthree:tempfour);
double two = (tempfive<tempsix?tempfive:tempsix);
double three = (tempfive>tempsix?tempfive:tempsix);
//kahan addition
double total = one;
double tempsum = one + two;
double error = (tempsum - one) - two;
total = tempsum;
// first iteration complete
double tempadd = three - error;
tempsum = total + tempadd;
error = (tempsum - total) - tempadd;
total = tempsum;
//second iteration complete
tempadd = four - error;
total += tempadd;
return total;
}
This gives me results that are as close to the precise answer as makes no difference. However, in a fictitious simulation of a mine collapse, the code with the Kahan addition takes 2 minutes whereas the high precision library takes over a day to finish!!
Thanks to all the help here. This problem was really a pain in the a$$.
I am presuming your numbers are all floating point values.
You should not expect result1 to equal result2 due to limitations in the scale of the numbers and precision in the calculations. Which one to use will depend upon the numbers you are dealing with. More important than result1 and result2 being the same is that they are close enough to the real answer (eg that you would have calculated by hand) for your application.
Imagine that a and b are both very large, and c much less than 1. (a + b) might overflow so that result1 will be incorrect. result2 would not overflow because it scales everything down before adding.
There are also problems with loss of precision when combining numbers of widely differing size, as the smaller number has significant digits reduced when it is converted to use the same exponent as the larger number it is added to.
If you give some specific examples of a, b and c which are causing you issues it might be possible to suggest further improvements.
I have been using the following program as a test, using values for a and b between 10^5 and 10^10, and c around 10^-5, but so far cannot find any differences.
Thinking about the storage of 10^5 vs 10^10, I think it requires about 13 bits vs 33 bits, so you may lose about 20 bits of precision when you add a and b together in result1.
But multiplying them by the same value c essentially reduces the exponent but leaves the significand the same, so it should also lose about 20 bits of precision in result2.
A double significand usually stores 53 bits, so I suspect your results will still retain 33 bits, or about 10 decimal digits of precision.
#include <stdio.h>
int main()
{
double a = 13584.9484893449;
double b = 43719848748.3911;
double c = 0.00001483394434;
double result1 = (a+b)*c;
double result2 = a*c + b*c;
double diff = result1 - result2;
printf("size of double is %d\n", sizeof(double));
printf("a=%f\nb=%f\nc=%f\nr1=%f\nr2=%f\ndiff=%f\n",a,b,c,result1,result2,diff);
}
However I do find a difference if I change all the doubles to float and use c=0.00001083394434. Are you sure that you are using 64 (or 80) bit doubles when doing your calculations?
Usually "loss of precision" in these kinds of calculations can be traced to "poorly formulated problem". For example, when you have to add a series of numbers of very different sizes, you will get a different answer depending on the order in which you sum them. The problem is even more acute when you subtract numbers.
The best approach in your case above is to look not simply at this one line, but at the way that result1 is used in your subsequent calculations. In principle, an engineering calculation should not require precision in the final result beyond about three significant figures; but in many instances (for example, finite element methods) you end up subtracting two numbers that are very similar in magnitude - in which case you may lose many significant figures and get a meaningless answer. Given that you are talking about "materials properties" and "strain", I am suspecting that is actually at the heart of your problem.
One approach is to look at places where you compute a difference, and see if you can reformulate your problem (for example, if you can differentiate your function, you can replace Y(x+dx)-Y(x) with dx * Y(x)'.
There are many excellent references on the subject of numerical stability. It is a complicated subject. Just "throwing more significant figures at the problem" is almost never the best solution.
I want to perform a basic calculation with fractional numbers using vb.net.
Dim a As Single= 7200.5
Dim b As Single= 7150.3
Dim c As Single= a - b
'Expected result = 50.2
MsgBox(a.ToString + " - " + b.ToString + " = " + c.ToString.Trim)
'Produced result is: 50.2002
Dim single1 As Single
Dim single2 As Single
Dim single3 As Single
single1 = 425000
single2 = 352922.2
single3 = single1 - single2
'Expected result is: 72077.8
MsgBox(single3.ToString)
'Produced result is: 72077.81
How can the results be so inaccurate for such a simple calculation? The problem is solved when I change the data type to Decimal, but Decimal objects consume more memory (16 bytes). Is there any alternative data type that i can use to perform simple fractional calculations with accurate results?
This is to do with the way floating point numbers are stored in memory, and a Single in .Net is a single precision floating point number, which is much less accurate than a Decimal or a Double for storing decimal numbers.
When the computer calculates your number, it only has binary fractions to use and in a single precision floating point number, they're not very accurate.
See http://en.wikipedia.org/wiki/Single-precision_floating-point_format for more information.
EDIT: There's some more information specific to VB.Net here: http://msdn.microsoft.com/en-us/library/ae382yt8(v=vs.110).aspx
The Single and Double data types are not precise. They use the floating point method to store their values. Floating points use less memory and allow for faster calculations, but they are imprecise. That is the trade-off that you have to accept if you are going to use them. If precision is important, then they are not an option for you. Decimal is precise (to a certain number of fractional digits, that is), so usually, that is the best choice for precise fractional numbers in most cases. If you really need to save memory, and you are guaranteed that your numbers will be within a certain range, then you could use an Int16, Int32, or Int64 instead. For instance, if you only care about two fractional digits, you could simply multiply everything by 100 and then just divide by 100 (using Decimal types for the division) before displaying it. In that way, you can store many numbers and perform many operations using less memory, and only need to use the Decimal data type when you need to display a result.
Dim a As Integer = 720050 '7200.5
Dim b As Integer = 715030 '7150.3
Dim c As Integer = a - b
Dim cDisplay As Decimal = CDec(c) / CDec(100)
MessageBox.Display(String.Format("{0} - {1} = {2}", a, b, c))
You can use the Decimal data type instead. It will work great! This is because Decimal is a fixed point value, whereas Single and Double are floating point values (with loss of precision).