noob here wants to calculate compound interest on iPhone.
float principal;
float rate;
int compoundPerYear;
int years;
float amount;
formula should be: amount = principal*(1+rate/compoundPerYear)^(rate*years)
I get slightly incorrect answer with:
amount = principal*pow((1+(rate/compoundPerYear)), (compoundPerYear*years));
I'm testing it with rate of .1, but debugger reports .100000001 .
Am I doing it wrong? Should I use doubles or special class (e.g., NSNumber)?
Thanks for any other ideas!
After further research it seems that the NSDecimalNumber class may be just what I need. Now I just have to figure out how to use this bad boy.
double will get you closer, but you can't represent 1/10 exactly in binary (using IEEE floating point notation, anyway).
If you're really interested, you can look at What Every Computer Scientist Should Know About Floating-Point Arithmetic. Link shamefully stolen from another SO thread.
The quick and dirty explanation is that floating point is stored in binary with bits that represents fractional powers of 2 (1/2, 1/4, 1/8, ...). There is simply no mathematical way to add up these fractions to exactly 1/10, thus 0.1 is not able to be exactly represented in IEEE floating point notation.
double extends the accuracy of the number by giving you more numerals before/after the radix, but it does not change the format of the binary in a way that can compensate for this. You'll just get the extra bit somewhere later down the line, most likely.
See also:
Why can’t decimal numbers be represented exactly in binary?
What’s wrong with using == to compare floats in Java?
and other similar threads.
Further expansion that I mulled over on the drive home from work: one way you could conceivably handle this is by just representing all of the monetary values in cents (as an int), then converting to a dollars.cents format when displaying the data. This is actually pretty easy, too, since you can take advantage of integer division's truncating when you convert:
int interest, dollars, cents;
interest = 16034; //$160.34, in cents
dollars = value / 100; //The 34 gets truncated: dollars == 160
cents = value % 100; //cents == 34
printf("Interest earned to date: $%d.%d\n", dollars, cents);
I don't know Objective-C, but hopefully this C example makes sense, too. Again, this is just one way to handle it. It would also be improved by having a function that does the string formatting whenever you need to show the data.
You can obviously come up with your own (even better!) way to do it, but maybe this will help get you started. If anyone else has suggestions on this one, I'd like to hear them, too!
Short answer: Never use floating point numbers for money.
The easy way that works across most platforms is to represent money as integer amounts of its smallest unit. The smallest unit is often something like a cent, although often 1/10 or 1/100 of a cent are the real base units.
On many platforms, there are also number types available that can represent fixed-point decimals.
Be sure to get the rounding right. Financial bookkeeping often uses banker's rounding.
Related
Quick Aside: I'm going to use the word "Float" to refer to both a .Net float and a SQL float with only 7 significant digits. I will use the word "Double" to refer to a .Net double and a SQL float with 15 significant digits. I also realize that this is very similar to some other posts regarding decimals/doubles, but the answers on those posts are really inconsistent, and I really want some recommendations for my specific circumstance...
I am part of a team that is rewriting an old application. The original app used floats (7 digits). This of course caused issues since the app conducted a lot of calculations and rounding errors accumulated very quickly. At some point, many of these floats were changed to decimals. Later, the floats (7) in the database all became doubles (15). After that we had several more errors with calculations involving doubles, and they too were changed to decimals.
Today about 1/3 of all of our floating point numbers in the database are decimals, the rest are doubles. My team wants to "standardize" all of our floating-point numbers in the database (and the new .Net code) to use either exclusively decimals or doubles except in cases where the other MUST be used. The majority of the team is set on using decimals; I'm the only person on my team advocating using doubles instead of decimals. Here's why...
Most of the numbers in the database are still doubles (though much of the application code still uses floats), and it would be a lot more effort to change all of the floats/doubles to decimals
For our app, none of the fields stored are "exact" decimal quantities. None of them are monetary quantities, and most represent some sort of "natural" measurement (e.g. mass, length, volume, etc.), so a double's 16 significant digits are already way more precise than even our initial measurements.
Many tables have measurements stored in two columns: 1 for the value; 1 for the unit of measure. This can lead to a HUGE difference in scale between the values in a single column. For example, one column can store a value in terms of pCi/g or Ci/m3 (1 Ci = 1000000000000 pCi). Since all the values in a single decimal columns must have the same scale (that is... an allocated number of digits both before and after the decimal point), I'm concerned that we will have overflow and rounding issues.
My teammates argue that:
Doubles are not as accurate nor as precise as decimals due to their inability to exactly represent 1/10 and that they only have 16 significant digits.
Even though we are not tracking money, the app is a inventory system that keeps track of material (mostly gram quantities) and it needs to be "as accurate as possible".
Even after the floats were changed to doubles, we continued to have bad results from calculations that used doubles. Changing these columns (and the application code) to decimals caused these calculations to produce the expected results.
It is my strong belief that the original issues where caused due to floats only having 7 significant digits and that simple arithmetic (e.g. 10001 * 10001) caused them to the data to quickly use up the few significant digits that they had. I do not believe this had anything to do with how binary-floating point numbers can only approximate decimal values, and I believe that using doubles would have fixed this issue.
I believe that the issue with doubles arose because doubles were used along side decimals in calculations that values were be converted back and forth between data types. Many of these calculations would round between intermediary steps in the calculation!
I'm trying to convince my team not to make everything under the sun into a decimal. Most values in the database don't have more than 5 or 6 significant digits anyway. Unfortunately, I am out-ranked by other members of my team that see things rather differently.
So, my question then is...
Am I worrying over nothing? Is there any real harm done by using almost exclusively decimals instead of doubles in an application with nearly 200 database tables, hundreds of transactions, and a rewrite schedule of 5 to 6 years?
Is using decimals actually solving an issue that doubles could not? From my research, both decimals and doubles are susceptible to rounding errors involving arbitrary fractions (adding 1/3 for example) and that the only way to account for this is to consider any value within a certain tolerance as being "equal" when comparing doubles and/or decimals.
If it is more appropriate to use doubles, what arguments could I make (other than what I have already made) could convince my team to not change everything to decimals?
Use decimal when you need perfect accuracy as a base-10 number (financial data, grades)
Use double or float when you are storing naturally imprecise data (measurements, temperature), want much faster mathematical operations, and can sacrifice a minute amount of imprecision.
Since you seem to be only storing various measurements (which have some precision anyways), float would be the logical choice (or double if you need more than 7 digits of precision).
Is using decimals actually solving an issue that doubles could not?
Not really - The data is only going to be as accurate as the measurements used to generate the data. Can you really say that a measured quantity is 123.4567 grams? Does the equipment used to measure it have that level of precision?
To deal with "rounding errors" I would argue that you can't really say whether a measurement of 1234.5 grams is exactly halfway - it could just as easily be 1234.49 grams, which would round down anyways.
What you need to decide is "what level of precision is acceptable" and always round to that precision as a last step. Don't round your data or intermediate calculations.
If it is more appropriate to use doubles, what arguments could I make (other than what I have already made) could convince my team to not change everything to decimals?
Other than the time spent switching, the only thing you're really sacrificing is speed. The only way to know how much speed is to try it both ways and measure the difference.
You'd better try your best not to lose precision. I guess my fault may convince you to choose double.
===> I did some wrong arithmetic, and it returns something very weird:
given 0.60, it returns 5
int get_index(double value) {
if (value < 0 || value > 1.00)
return -1;
return value / 0.10;
}
and I fixed it:
int get_index(double value) {
if (value < 0 || value > 1.00)
return -1;
return (value * 100000000) / (0.10 * 100000000);
}
I'm literately just doing a multiplication of two floats. How come these statements produce different results ? Should I even be using floats ?
500,000.00 * 0.001660 = 830
How come these statements produce different results ?
Because floating-point arithmetic is not exact and apparently you were not printing the multiplier precisely enough (i. e. with sufficient number of decimal digits). And it wasn't .00166 but something that seemed 0.00166 rounded.
Should I even be using floats ?
No. For money, use integers and treat them as fixed-point rational numbers. (They still aren't exact, but significantly better and less error-prone.)
You didn't show how you initialized periodicInterest, and presumably you think you set it to 0.00166, but in fact the error in your output is large enough that you must not have explicitly initialized it as periodicInterest = 0.00166. It must be closer to 0.00165975, and the difference between 0.00166 and 0.00165975 is definitely large enough not to just be a single floating-point rounding error.
Assuming you are working with monetary quantities, you should use NSDecimalNumber or NSDecimal.
One non-obvious benefit of using NSDecimalNumber is that it works with NSNumberFormatter, so you can let Apple take care of formatting currencies for all sorts of foreign locales.
UPDATE
In response to the comments:
“periodicInterest is clearly not a monetary quantity” and “decimal is no more free of error when dividing by 12 than binary is” - for inexact quantities, I can think of two concerns:
One concern is using sufficient precision to give accurate results. NSDecimalNumber is a floating-point number with 38 digits of precision and an exponent in the range -128…127. This is more than twice the number of decimal digits an IEEE 'double' can store. The exponent range is less than that of a double, but that's unlikely to matter in financial computing. So NSDecimalNumbers can definitely result in smaller error than floats or doubles, even though none of them can store 1/12 exactly.
The other concern is matching the results computed by some other system, like your bank or your broker or the NYSE. In that case, you need to figure out how that other system is storing numbers and computing with them. If the other system is using a decimal format (which is likely in the financial sector), then NSDecimalNumber will probably be useful.
“Wouldn't it be more efficient to use primitive types to do floating point arithmetic, specially thousands in real time.” Arithmetic on primitive types is far faster than arithmetic on NSDecimalNumbers. I haven't measured it, but a factor of 100 would not surprise me.
You have to strike a balance between your requirements. If decimal accuracy is paramount (as it often is in financial programming), you must sacrifice performance for accuracy. If decimal accuracy is not so important, you can consider carefully using a primitive type, but you should be aware of the accuracy you're sacrificing. Even then, the size of a float is so small (usually only 7 significant decimal digits) that you should probably be using double (at least 15, usually 16 significant decimal digits).
If you need to perform millions of arithmetic operations per second with true decimal accuracy, you might be able to do it using doubles, if you are an IEEE 754 expert capable of analyzing your code to figure out where errors are introduced and how to eliminate them. Few people have this level of expertise. (I don't claim to.) You must also understand how your compiler turns your Objective-C code into machine instructions.
Anyway, perhaps you are just writing a casual app to compute a rough estimate of net present value or future value. In that case, using double would probably suffice, but using NSDecimalNumber would probably also be sufficiently fast. Without knowing more about the app you're writing, I can't give you more specific advice.
When you run a VBA macro that performs numeric calculations which result in a decimal value, the result that is returned may be incorrect.
Here are a couple of examples:
Dim me_wrong as Double
me_wrong = 1000 - 999.59
RESULT = 0.409999999999968
Dim me_wrong_too as Double
me_wrong_too = 301.84 - 301
RESULT = 0.839999999999975
I have never ever noticed this before. What on earth is going on???
I have seen the following article about Office 97 but can't find anything about a bug in Excel 2007:
http://support.microsoft.com/default.aspx?scid=kb;en-us;165373
Plus it doesn't explain why I have never seen it before.
Please help!
The explanation for the problem from Office 97 and VBA is equally applicable going forward into Excel 2007. The core VBA system is largely unchanged despite the migration into later versions, hence the same kinds of accuracy gremlins that plague older VBA macros will persist.
The fundamental problem lies with the inherent inaccuracy in the representation of fractional numbers in binary, and how at least some effort to mitigate that inaccuracy was made with IEEE floating point representations. There is a very decent treatment of the subject of IEEE representation at this location.
*Edit: Just a minor bit of extra info for detail. *
For a very simple example that illustrates this issue in a trivial case, consider a situation in which decimals are represented as sums of inverse powers of two, eg 2-1, 2-2, 2-3, and so on. That ends up looking like .5, .25, .125, and so on. If you're representing exactly those numbers, all is good. However, consider a number like .761; 2-1+2-2 gets you to .750, but now you need .011. 2-3 (.125) is too big, but 2-4 (.0625) is too small...so you keep going to smaller powers of two, realizing you'll never quite represent the number precisely.
The choice becomes where you stop resolving and accept the inherent inaccuracy as being "good enough" for the problem you're solving/modeling.
It is, unfortunately, not a bug.
Double representation follows a fixed point notation, where the mantissa is a number "1,x" with "1" being implicit. There's an exponent and a sign, which makes the full representation in Base 2.
The pertinent issue is Base=2 which makes "x" in "1,x" to be a finite-precision (53bits of it) fractional binary. Think x= a52*1/2+a51*1/4+a50*1/8+...+a*1**1/(2^52)+a0*1/(2^53), where a< i > are the bits in the mantissa.
Try attaining 1,4 with this representation, and you hit the precision wall... there is no finite decomposition of 0.4 in binary weights. So the norm specifies you should represent the number immediately before the real one, which leaves you with 0,39999..9997346 (or whatever the tail is).
The "good" news is, and I've just burned four "c" coding days last week on that subject, you can do without Doubles if you represent your number using a very small scale (say 10^-9), store then in very large variables (long64), and do your display functions using nothing but integers (mathematically slicing away integral and fractional parts through integer division and their remainders). A treat, I tell you... not.
I have an sql:
SELECT Sum(Field1), Sum(Field2), Sum(Field1)+Sum(Field2)
FROM Table
GROUP BY DateField
HAVING Sum(Field1)+Sum(Field2)<>0;
Problem is sometimes Sum of field1 and field2 is value like: 9.5-10.3 and the result is -0,800000000000001. Could anybody explain why this happens and how to solve it?
Problem is sometimes Sum of field1 and
field2 is value like: 9.5-10.3 and the
result is -0.800000000000001. Could
anybody explain why this happens and
how to solve it?
Why this happens
The float and double types store numbers in base 2, not in base 10. Sometimes, a number can be exactly represented in a finite number of bits.
9.5 → 1001.1
And sometimes it can't.
10.3 → 1010.0 1001 1001 1001 1001 1001 1001 1001 1001...
In the latter case, the number will get rounded to the closest value that can be represented as a double:
1010.0100110011001100110011001100110011001100110011010 base 2
= 10.300000000000000710542735760100185871124267578125 base 10
When the subtraction is done in binary, you get:
-0.11001100110011001100110011001100110011001100110100000
= -0.800000000000000710542735760100185871124267578125
Output routines will usually hide most of the "noise" digits.
Python 3.1 rounds it to -0.8000000000000007
SQLite 3.6 rounds it to -0.800000000000001.
printf %g rounds it to -0.8.
Note that, even on systems that display the value as -0.8, it's not the same as the best double approximation of -0.8, which is:
- 0.11001100110011001100110011001100110011001100110011010
= -0.8000000000000000444089209850062616169452667236328125
So, in any programming language using double, the expression 9.5 - 10.3 == -0.8 will be false.
The decimal non-solution
With questions like these, the most common answer is "use decimal arithmetic". This does indeed get better output in this particular example. Using Python's decimal.Decimal class:
>>> Decimal('9.5') - Decimal('10.3')
Decimal('-0.8')
However, you'll still have to deal with
>>> Decimal(1) / 3 * 3
Decimal('0.9999999999999999999999999999')
>>> Decimal(2).sqrt() ** 2
Decimal('1.999999999999999999999999999')
These may be more familiar rounding errors than the ones binary numbers have, but that doesn't make them less important.
In fact, binary fractions are more accurate than decimal fractions with the same number of bits, because of a combination of:
The hidden bit unique to base 2, and
The suboptimal radix economy of decimal.
It's also much faster (on PCs) because it has dedicated hardware.
There is nothing special about base ten. It's just an arbitrary choice based on the number of fingers we have.
It would be just as accurate to say that a newborn baby weighs 0x7.5 lb (in more familiar terms, 7 lb 5 oz) as to say that it weighs 7.3 lb. (Yes, there's a 0.2 oz difference between the two, but it's within tolerance.) In general, decimal provides no advantage in representing physical measurements.
Money is different
Unlike physical quantities which are measured to a certain level of precision, money is counted and thus an exact quantity. The quirk is that it's counted in multiples of 0.01 instead of multiples of 1 like most other discrete quantities.
If your "10.3" really means $10.30, then you should use a decimal number type to represent the value exactly.
(Unless you're working with historical stock prices from the days when they were in 1/16ths of a dollar, in which case binary is adequate anyway ;-) )
Otherwise, it's just a display issue.
You got an answer correct to 15 significant digits. That's correct for all practical purposes. If you just want to hide the "noise", use the SQL ROUND function.
I'm certain it is because the float data type (aka Double or Single in MS Access) is inexact. It is not like decimal which is a simple value scaled by a power of 10. If I'm remembering correctly, float values can have different denominators which means that they don't always convert back to base 10 exactly.
The cure is to change Field1 and Field2 from float/single/double to decimal or currency. If you give examples of the smallest and largest values you need to store, including the smallest and largest fractions needed such as 0.0001 or 0.9999, we can possibly advise you better.
Be aware that versions of Access before 2007 can have problems with ORDER BY on decimal values. Please read the comments on this post for some more perspective on this. In many cases, this would not be an issue for people, but in other cases it might be.
In general, float should be used for values that can end up being extremely small or large (smaller or larger than a decimal can hold). You need to understand that float maintains more accurate scale at the cost of some precision. That is, a decimal will overflow or underflow where a float can just keep on going. But the float only has a limited number of significant digits, whereas a decimal's digits are all significant.
If you can't change the column types, then in the meantime you can work around the problem by rounding your final calculation. Don't round until the very last possible moment.
Update
A criticism of my recommendation to use decimal has been leveled, not the point about unexpected ORDER BY results, but that float is overall more accurate with the same number of bits.
No contest to this fact. However, I think it is more common for people to be working with values that are in fact counted or are expected to be expressed in base ten. I see questions over and over in forums about what's wrong with their floating-point data types, and I don't see these same questions about decimal. That means to me that people should start off with decimal, and when they're ready for the leap to how and when to use float they can study up on it and start using it when they're competent.
In the meantime, while it may be a tad frustrating to have people always recommending decimal when you know it's not as accurate, don't let yourself get divorced from the real world where having more familiar rounding errors at the expense of very slightly reduced accuracy is of value.
Let me point out to my detractors that the example
Decimal(1) / 3 * 3 yielding 1.999999999999999999999999999
is, in what should be familiar words, "correct to 27 significant digits" which is "correct for all practical purposes."
So if we have two ways of doing what is practically speaking the same thing, and both of them can represent numbers very precisely out to a ludicrous number of significant digits, and both require rounding but one of them has markedly more familiar rounding errors than the other, I can't accept that recommending the more familiar one is in any way bad. What is a beginner to make of a system that can perform a - a and not get 0 as an answer? He's going to get confusion, and be stopped in his work while he tries to fathom it. Then he'll go ask for help on a message board, and get told the pat answer "use decimal". Then he'll be just fine for five more years, until he has grown enough to get curious one day and finally studies and really grasps what float is doing and becomes able to use it properly.
That said, in the final analysis I have to say that slamming me for recommending decimal seems just a little bit off in outer space.
Last, I would like to point out that the following statement is not strictly true, since it overgeneralizes:
The float and double types store numbers in base 2, not in base 10.
To be accurate, most modern systems store floating-point data types with a base of 2. But not all! Some use or have used base 10. For all I know, there are systems which use base 3 which is closer to e and thus has a more optimal radix economy than base 2 representations (as if that really mattered to 99.999% of all computer users). Additionally, saying "float and double types" could be a little misleading, since double IS float, but float isn't double. Float is short for floating-point, but Single and Double are float(ing point) subtypes which connote the total precision available. There are also the Single-Extended and Double-Extended floating point data types.
It is probably an effect of floating point number implementations. Sometimes numbers cannot be exactly represented, and sometimes the result of operations is slightly off what we may expect for the same reason.
The fix would be to use a rounding function on the values to cut off the extraneous digits. Like this (I've simply rounded to 4 significant digits after the decimal, but of course you should use whatever precision is appropriate for your data):
SELECT Sum(Field1), Sum(Field2), Round(Sum(Field1)+Sum(Field2), 4)
FROM Table
GROUP BY DateField
HAVING Round(Sum(Field1)+Sum(Field2), 4)<>0;
Someone mentioned in this decimal vs double! - Which one should I use and when? post that it's best to use double for physical science computations. Does this apply to measurements such as flash point, viscosity, weight and volume? Can someone explain further?
Decimal = exact. That is, I have £1.23 in my pocket and applying VAT, say, is a known and accepted rounding issue.
When you measure something, you can never be exact. If something is 123 centimeters long then strictly speaking it's somewhere between 122.5 and 123.49999 centimeters long....
You are dealing with 2 different kinds of quantities.
So, use double for this.
The question you link to is about the data types in C#, not SQL, and the answers reflect that.
Decimal is a datatype used and created for dealing with currency, making sure calculations balance out (no lost cents or fractions of, for example).
Physical scientific computations rarely deal with money values, hence, you should use double whenever you need the accuracy.
Generally you would always use doubles for recording physical measurements unless you have a good reason to do otherwise. There is potentially a miniscule loss of accuracy when using any floating point number since binary is unable to perfectly represent certain decimal numbers (0.1 being the most obvious example) but the inaccuracy in the double representation is going to be many orders of magnitude smaller than the error in the measurements you take.
Decimal is used where it's very important that numbers are represented exactly so typically only when dealing with money (yes, it would seem we care more about money than science!)
Knowing which database this is for would make things easier...
Oracle only has NUMBER, which if you omit the two optional parameters precision and scale - is a float. Using both parameters is DECIMAL, and only specifying the precision is INTEGER. Can't remember how to get REAL...
MySQL Numeric data type info: http://dev.mysql.com/doc/refman/5.0/en/numeric-types.html
SQL Server Numeric data type info: http://msdn.microsoft.com/en-us/library/aa258271(SQL.80).aspx
I haven't dealt with float and real much, but I've heard they aren't great for mathematical computations. I've used DECIMAL for varying precision, not just for monetary values.
What data type to use depends on the data, and how you intend to use that data.