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Least Squares Data Fit Implementation...

Posted on 2004-04-24
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Hello Experts,

I am developing a mathematical module for my application and I am required to implement a Least Squares Data Fit algorithm in C#. Can anyone direct me to resources on line that have an implementation or can anyone with a strong mathematical background guide me to the solution.

Basically I have a set of coordinates that define a curve and I would like to use these points to a derive an equation of nth order (could be 2nd, 3rd, 4th etc) that represent these points.

Please cany anyone help.

Thanking you in advance.

Imran
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Question by:imran89
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NTAC earned 150 total points
ID: 10908914
Hi Imran

Do you want to implement it yourself?  There are many free packages out there that can do this for you, such as:
Mapack for .NET:  http://www.aisto.com/roeder/dotnet/  (scroll down)

Or if you have to implement it, it isn't too bad.

Here is a great example in C++: http://home.wxs.nl/~ammeraal/stlcpp.html
Download the source code and you can follow it very easily.  
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by:imran89
ID: 10913272
Thankyou for responding NTAC.

Ideally I would like to implement it myself but I assume the task is too daunting. Do you have any experience with this type of algorithm?

Is it easy to integrate Mapack with an application?

regards Imran
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Author Comment

by:imran89
ID: 10913464
NTAC,

Another point that I forgot to mention was that how difficult the algorithm would become if I required the facility to select the order of the derived equation and whether to perform a straight line fit or a curve fit.

regards Imran
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by:NTAC
ID: 10914757
Imran

Sorry for the delay in responding

It is very easy to use the Mapack with your application.  If you are using Visual Studio--you can add the mapack dll to your project--then have the line: using Mapack; at the top of yoru file.  Then you can call any of the functions their dll has implemented.

As for your second question, I'm not sure if mappack has the flexibility to do that or not.  You can check the readme--I did but I don't understand the math (its been a few years out of school for me).  

Good luck
NTAC
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Author Comment

by:imran89
ID: 10916015
Thanks for responding NTAC.

Do you have any experience in using the least squares functionality as it is with Mapack. If there are a few mathematical commands to process, are you aware of them?


Thanks

regards Imran
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Assisted Solution

by:_TAD_
_TAD_ earned 100 total points
ID: 10921531
based on your description (derive an equation of nth order), I am going to assume that you are looking for something more complex than a simple linear regression analysis.



Visually, it is exceptionally easy to view a scatter plot and draw a best fit curve.  To mathematically generate this curve requires multi-variable calculus and partial derivatives.

Here's a complete description:
http://mathworld.wolfram.com/LeastSquaresFitting.html



However... if you are just looking for a rough estimate, there are some simple calculations that will get you close.

Here's something to get you started ({R} represents a real  number (1.0, 1.5, 12.723, etc)

Gather all of your data points (x's and y's).

{we'll use these points as samples:  (.5, 2); (.25, 3); (.75,5); (1,11) }

Now take a look at all of your Y values where 0<X<1

Y Calc1:  first find the population mean of all of the Y values and then for the final sample mean we'll use the least squares method.

[(2 + 3 + 5 + 11)/4]  = 5.25

Now we are going to re-calculate using least squares method, but we are going to exclude points that are too extreme.  To determine extreme points we will take our population mean calculation and multiply it by 0.66 (this is two standard deviations from the mean)

5.25 * 0.66 = 3.465   --->  Recalc using least squares, but only include values betwwen (5.25 + 3.465) and (5.25 - 3.465)


Since Y = 11 is out of scope, we will call this an extreme point and ignore it.

SqrRt[(2*2 + 3*3 + 5*5)/3]  =  3.55

This is now our new Y value that we can place at the X = 0.5 position


Following the same procedure for each set of x values you will calculate sets of coordinates that follow a best fit curve.

The caviat is that this really only works well with a lot of points.  With few points the estimate become very inaccurate compared to the calculus version.

Also, for goo estimators you may want to find groupings of approximately 10 points.  If you only have 6 points between 0<X<1 then you may want to increase the range to 2 and do the same calculations for 0<X<2.  Then you would place your point at the middle of the range (X=1).




This comment does not answer your question in a way you want, but I'm afraid that a better explanation requires you to be well versed in advanced calculus and also requires a better forum in which to discuss this (ASCII characters and HTML is not adequate).

HTH
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Author Comment

by:imran89
ID: 10922226
Thankyou for responding TAD and thanks for the theory.

A pure solution for least squares seems a very involved task and my math is very weak to tackle such a thing. I was hoping the algorithm was not as involved.

I will then need to focus on finding an exisitng implementation of least squares in C#. I know libraries like Mapack exist (thanks to NTAC) but I dont know if it will do the job.

I am dealing with around 50 coordinates at a time and require to find an equation most probably at cubic or quartet level hence having the option to choose the nth degree.

Are you aware of C# implementation of least squares upto quartet level?

Thanking you in advance

regards Imran
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Expert Comment

by:_TAD_
ID: 10922700


I am not aware of any .Net package that does what you are asking, although they may exist.


Something that may help you is to look at some applications geared for mathematical computations (like MatLab) and see if you can find what you are looing for there and borrow some of the logic of their code.

Sorry I can't be of more help.
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Author Comment

by:imran89
ID: 11071023
Thankyou very much for both of your assistance.

NTAC and _TAD_, both of you have provided me with pointers to start research on.

regards Imran
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