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Is there a generally accepted best approach to coding complex math? For example:
double someNumber = .123 + .456 * Math.Pow(Math.E, .789 * Math.Pow((homeIndex + .22), .012));
Is this a point where hard-coding the numbers is okay? Or should each number have a constant associated with it? Or is there even another way, like storing the calculations in config and invoking them somehow?
There will be a lot of code like this, and I'm trying to keep it maintainable.
Note: The example shown above is just one line. There would be tens or hundreds of these lines of code. And not only could the numbers change, but the formula could as well.
Generally, there are two kinds of constants - ones with the meaning to the implementation, and ones with the meaning to the business logic.
It is OK to hard-code the constants of the first kind: they are private to understanding your algorithm. For example, if you are using a ternary search and need to divide the interval in three parts, dividing by a hard-coded 3 is the right approach.
Constants with the meaning outside the code of your program, on the other hand, should not be hard-coded: giving them explicit names gives someone who maintains your code after you leave the company non-zero chances of making correct modifications without having to rewrite things from scratch or e-mailing you for help.
"Is it okay"? Sure. As far as I know, there's no paramilitary police force rounding up those who sin against the one true faith of programming. (Yet.).
Is it wise?
Well, there are all sorts of ways of deciding that - performance, scalability, extensibility, maintainability etc.
On the maintainability scale, this is pure evil. It make extensibility very hard; performance and scalability are probably not a huge concern.
If you left behind a single method with loads of lines similar to the above, your successor would have no chance maintaining the code. He'd be right to recommend a rewrite.
If you broke it down like
public float calculateTax(person)
float taxFreeAmount = calcTaxFreeAmount(person)
float taxableAmount = calcTaxableAmount(person, taxFreeAmount)
float taxAmount = calcTaxAmount(person, taxableAmount)
return taxAmount
end
and each of the inner methods is a few lines long, but you left some hardcoded values in there - well, not brilliant, but not terrible.
However, if some of those hardcoded values are likely to change over time (like the tax rate), leaving them as hardcoded values is not okay. It's awful.
The best advice I can give is:
Spend an afternoon with Resharper, and use its automatic refactoring tools.
Assume the guy picking this up from you is an axe-wielding maniac who knows where you live.
I usually ask myself whether I can maintain and fix the code at 3 AM being sleep deprived six months after writing the code. It has served me well. Looking at your formula, I'm not sure I can.
Ages ago I worked in the insurance industry. Some of my colleagues were tasked to convert the actuarial formulas into code, first FORTRAN and later C. Mathematical and programming skills varied from colleague to colleague. What I learned was the following reviewing their code:
document the actual formula in code; without it, years later you'll have trouble remember the actual formula. External documentation goes missing, become dated or simply may not be accessible.
break the formula into discrete components that can be documented, reused and tested.
use constants to document equations; magic numbers have very little context and often require existing knowledge for other developers to understand.
rely on the compiler to optimize code where possible. A good compiler will inline methods, reduce duplication and optimize the code for the particular architecture. In some cases it may duplicate portions of the formula for better performance.
That said, there are times where hard coding just simplify things, especially if those values are well understood within a particular context. For example, dividing (or multiplying) something by 100 or 1000 because you're converting a value to dollars. Another one is to multiply something by 3600 when you'd like to convert hours to seconds. Their meaning is often implied from the greater context. The following doesn't say much about magic number 100:
public static double a(double b, double c)
{
return (b - c) * 100;
}
but the following may give you a better hint:
public static double calculateAmountInCents(double amountDue, double amountPaid)
{
return (amountDue - amountPaid) * 100;
}
As the above comment states, this is far from complex.
You can however store the Magic numbers in constants/app.config values, so as to make it easier for the next developer to maitain your code.
When storing such constants, make sure to explain to the next developer (read yourself in 1 month) what your thoughts were, and what they need to keep in mind.
Also ewxplain what the actual calculation is for and what it is doing.
Do not leave in-line like this.
Constant so you can reuse, easily find, easily change and provides for better maintaining when someone comes looking at your code for the first time.
You can do a config if it can/should be customized. What is the impact of a customer altering the value(s)? Sometimes it is best to not give them that option. They could change it on their own then blame you when things don't work. Then again, maybe they have it in flux more often than your release schedules.
Its worth noting that the C# compiler (or is it the CLR) will automatically inline 1 line methods so if you can extract certain formulas into one liners you can just extract them as methods without any performance loss.
EDIT:
Constants and such more or less depends on the team and the quantity of use. Obviously if you're using the same hard-coded number more than once, constant it. However if you're writing a formula that its likely only you will ever edit (small team) then hard coding the values is fine. It all depends on your teams views on documentation and maintenance.
If the calculation in your line explains something for the next developer then you can leave it, otherwise its better to have calculated constant value in your code or configuration files.
I found one line in production code which was like:
int interval = 1 * 60 * 60 * 1000;
Without any comment, it wasn't hard that the original developer meant 1 hour in milliseconds, rather than seeing a value of 3600000.
IMO May be leaving out calculations is better for scenarios like that.
Names can be added for documentation purposes. The amount of documentation needed depends largely on the purpose.
Consider following code:
float e = m * 8.98755179e16;
And contrast it with the following one:
const float c = 299792458;
float e = m * c * c;
Even though the variable names are not very 'descriptive' in the latter you'll have much better idea what the code is doing the the first one - arguably there is no need to rename the c to speedOfLight, m to mass and e to energy as the names are explanatory in their domains.
const float speedOfLight = 299792458;
float energy = mass * speedOfLight * speedOfLight;
I would argue that the second code is the clearest one - especially if programmer can expect to find STR in the code (LHC simulator or something similar). To sum up - you need to find an optimal point. The more verbose code the more context you provide - which might both help to understand the meaning (what is e and c vs. we do something with mass and speed of light) and obscure the big picture (we square c and multiply by m vs. need of scanning whole line to get equation).
Most constants have some deeper meening and/or established notation so I would consider at least naming it by the convention (c for speed of light, R for gas constant, sPerH for seconds in hour). If notation is not clear the longer names should be used (sPerH in class named Date or Time is probably fine while it is not in Paginator). The really obvious constants could be hardcoded (say - division by 2 in calculating new array length in merge sort).
I have a 64-bit long that I want to round down to the nearest 10,000, so I am doing a simple:
long myLong = 123456789
long rounded = (myLong / 10000) * 10000; //rounded = 123450000
This appears to do what I expect, but as I'm not 100% on the internals of how integer types get divided, I am just slightly concerned that there may be situations where this doesn't work as expected.
Will this still work at very large numbers / edge cases?
Yes, it will work, so long as no result, intermediate or otherwise, exceeds long.MaxValue.
To be explicit about your constants you could use the L specifier at the end, e.g. 123456789L.
For straightforward calculations like this, can I suggest Pex from Microsoft ( http://research.microsoft.com/en-us/projects/pex/ ), which looks for edge cases and tests them. This is a clean-cut example, but if you were building up lots of logic based on things you are unsure of, it's a great tool.
Yes, it will work. The semantics of integer division guarantee what you expect.
However it may be good to write some tests for your specific use case, including edge cases. This will reassure you.
I've been playing with Script#, and I was wondering how the C# numbers were converted to Javascript. I wrote this little bit of code
int a = 3 / 2;
and looked at the relevant bit of compiled Javascript:
var $0=3/2;
In C#, the result of 3 / 2 assigned to an int is 1, but in Javascript, which only has one number type, is 1.5.
Because of this disparity between the C# and Javascript behaviour, and since the compiled code doesn't seem to compensate for it, should I assume that my numeric calculations written in C# might behave incorrectly when compiled to Javascript?
Should I assume that my numeric calculations written in C# might behave incorrectly when compiled to Javascript?
Yes.
Like you said, "the compiled code doesn't seem to compensate for it" - though for the case you mention where a was declared as an int it would be easy enough to compensate by using var $0 = Math.floor(3/2);. But if you don't control how the "compiler" works you're in a pickle. (You could correct the JavaScript manually, but you'd have to do that every time you regenerated it. Yuck.)
Note also that you are likely to have problems with decimal numbers too due to the way JavaScript represents decimal places. Most people are surprised the first time they find out that JavaScript will tell you that 0.4 * 3 works out to be 1.2000000000000002. For more details see one of the many other questions on this issue, e.g., How to deal with floating point number precision in JavaScript?. (Actually I think C# handles decimals the same way, so maybe this issue won't be such a surprise. Still, it can be a trap for new players...)
So far it looks like Fabrice Bellard's base 2 equation is the way to go
Ironically this will require a BigReal type; do we have this for .Net? .Net 4.0 has BigInteger.
Anyone have a Haskell version?
Since you're asking for a Haskell version, here is a paper by Jerzy Karczmarczuk, called "The Most Unreliable Technique in the World to compute π":
This paper is an atypical exercice in
lazy functional coding, written for
fun and instruction. It can be read
and understood by anybody who
understands the programming language
Haskell. We show how to implement the
Bailey-Borwein-Ploue formula for π
in a co-recursive, incremental way
which produces the digits 3, 1, 4, 1,
5, 9. . . until the memory
exhaustion. This is not a way to
proceed if somebody needs many
digits! Our coding strategy is
perverse and dangerous, and it
provably breaks down. It is based on
the arithmetics over the domain of
infinite sequences of digits
representing proper fractions expanded
in an integer base. We show how to
manipulate: add, multiply by an
integer, etc. such sequences from the
left to the right ad infinitum,
which obviously cannot work in all
cases because of ambiguities. Some
deep philosophical consequences are
discussed in the conclusions.
It doesn't really solve the problem in an efficient or very practical way, but is entertaining and shows some of the problems with lazy infinite precision arithmetic.
Then there's also this paper by Jeremy Gibbons.
By far my favorite Haskell spigot for pi comes from Jeremy Gibbons:
pi = g(1,0,1,1,3,3) where
g(q,r,t,k,n,l) =
if 4*q+r-t<n*t
then n : g(10*q,10*(r-n*t),t,k,div(10*(3*q+r))t-10*n,l)
else g(q*k,(2*q+r)*l,t*l,k+1,div(q*(7*k+2)+r*l)(t*l),l+2)
The mathematical background that justifies that implementation can be found in:
A Spigot Algorithm for the Digits of Pi
Wikipedia details a lot of ways to get numerical approximations of pi here. They also give some sample pseudo-code
Edit : If you're interested in this kind of mathematical problems without having any related real-world problem to solve (which is definitely a good attitude to have, IMHO), you could visit the Euler Project page
There exists such possibility to process big rational numbers in DLR-based dynamic languages (e.g. IronPython). Or you can use any portable C/C++ implementation of big real numbers through P/Invoke.
I was wondering if anyone had any suggestions for minimizing a function, f(x,y), where x and y are integers. I have researched lots of minimization and optimization techniques, like BFGS and others out of GSL, and things out of Numerical Recipes. So far, I have tried implenting a couple of different schemes. The first works by picking the direction of largest descent f(x+1,y),f(x-1,y),f(x,y+1),f(x,y-1), and follow that direction with line minimization. I have also tried using a downhill simplex (Nelder-Mead) method. Both methods get stuck far away from a minimum. They both appear to work on simpler functions, like finding the minimum of a paraboloid, but I think that both, and especially the former, are designed for functions where x and y are real-valued (doubles). One more problem is that I need to call f(x,y) as few times as possible. It talks to external hardware, and takes a couple of seconds for each call. Any ideas for this would be greatly appreciated.
Here's an example of the error function. Sorry I didn't post this before. This function takes a couple of seconds to evaluate. Also, the information we query from the device does not add to the error if it is below our desired value, only if it is above
double Error(x,y)
{
SetDeviceParams(x,y);
double a = QueryParamA();
double b = QueryParamB();
double c = QueryParamC();
double _fReturnable = 0;
if(a>=A_desired)
{
_fReturnable+=(A_desired-a)*(A_desired-a);
}
if(b>=B_desired)
{
_fReturnable+=(B_desired-b)*(B_desired-b);
}
if(c>=C_desired)
{
_fReturnable+=(C_desired-c)*(C_desired-c);
}
return Math.sqrt(_fReturnable)
}
There are many, many solutions here. In fact, there are entire books and academic disciplines based on the subject. I am reading an excellent one right now: How to Solve It: Modern Heuristics.
There is no one solution that is correct - different solutions have different advantages based on specific knowledge of your function. It has even been proven that there is no one heuristic that performs the best at all optimization tasks.
If you know that your function is quadratic, you can use Newton-Gauss to find the minimum in one step. A genetic algorithm can be a great general-purpose tool, or you can try simulated annealing, which is less complicated.
Have you looked at genetic algorithms? They are very, very good at finding minimums and maximums, while avoiding local minimum/maximums.
How do you define f(x,y) ? Minimisation is a hard problem, depending on the complexity of your function.
Genetic Algorithms could be a good candidate.
Resources:
Genetic Algorithms in Search, Optimization, and Machine Learning
Implementing a Genetic Algorithms in C#
Simple C# GA
If it's an arbitrary function, there's no neat way of doing this.
Suppose we have a function defined as:
f(x, y) = 0 for x==100, y==100
100 otherwise
How could any algorithm realistically find (100, 100) as the minimum? It could be any possible combination of values.
Do you know anything about the function you're testing?
What you are generally looking for is called an optimisation technique in mathematics. In general, they apply to real-valued functions, but many can be adapted for integral-valued functions.
In particular, I would recommend looking into non-linear programming and gradient descent. Both would seem quite suitable for your application.
If you could perhaps provide any more details, I might be able to suggest somethign a little more specific.
Jon Skeet's answer is correct. You really do need information about f and it's derivatives even if f is everywhere continuous.
The easiest way to appreciate the difficulties of what you ask(minimization of f at integer values only) is just to think about an f: R->R (f is a real valued function of the reals) of one variable that makes large excursions between individual integers. You can easily construct such a function so that there is NO correllation between the local minimums on the real line and the minimums at the integers as well as having no relationship to the first derivative.
For an arbitrary function I see no way except brute force.
So let's look at your problem in math-speak. This is all assuming I understand
your problem fully. Feel free to correct me if I am mistaken.
we want to minimize the following:
\sqrt((a-a_desired)^2 + (b-b_desired)^2 + (c-c_desired)^2)
or in other notation
||Pos(x - x_desired)||_2
where x = (a,b,c) and Pos(y) = max(y, 0) means we want the "positive part"(this accounts
for your if statements). Finally, we wish to restrict ourself
to solutions where x is integer valued.
Unlike the above posters, I don't think genetic algorithms are what you want at all.
In fact, I think the solution is much easier (assuming I am understanding your problem).
1) Run any optimization routine on the function above. THis will give you
the solution x^* = (a^*, b^*,c^*). As this function is increasing with respect
to the variables, the best integer solution you can hope for is
(ceil(a^*),ceil(b^*),ceil(c^*)).
Now you say that your function is possibly hard to evaluate. There exist tools
for this which are not based on heuristics. The go under the name Derivative-Free
Optimization. People use these tools to optimize objective based on simulations (I have
even heard of a case where the objective function is based on crop crowing yields!)
Each of these methods have different properties, but in general they attempt to
minimize not only the objective, but the number of objective function evaluations.
Sorry the formatting was so bad previously. Here's an example of the error function
double Error(x,y)
{
SetDeviceParams(x,y);
double a = QueryParamA();
double b = QueryParamB();
double c = QueryParamC();
double _fReturnable = 0;
if(a>=A_desired)
{
_fReturnable+=(A_desired-a)*(A_desired-a);
}
if(b>=B_desired)
{
_fReturnable+=(B_desired-b)*(B_desired-b);
}
if(c>=C_desired)
{
_fReturnable+=(C_desired-c)*(C_desired-c);
}
return Math.sqrt(_fReturnable)
}