Least Squares source code in C++?

In summary, the conversation discusses finding source code for a simple and fast least squares solver written purely in C++. Suggestions include trying the C++ version of LAPACK, using code from "Numerical Recipes in C" in Chapter 15, and searching for algorithmic pseudocode. The conversation also mentions difficulties with using the Levenberg-Marquardt source code and finding the gaussj() function.
  • #1
SirTristan
12
0
Does anyone know where to find source code for a simple and fast least squares solver written purely in C++?
 
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  • #2
try the C++ version of LAPACK
 
  • #3
I actually already have LAPACK++ installed, however it's really pretty complicated. I'd like some simpler code that I can modify, for instance to implement regularization.
 
  • #4
you can find some code snippet in "Numerical Recipes in C book On-Line!"

http://www.fizyka.umk.pl/nrbook/bookcpdf.html

and they are in Chapter 15.

Good luck.
 
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  • #5
Thank you, that might be helpful.

Anyone know of algorithmic pseudocode for least squares anywhere, that I myself could use to create code? A http://en.wikipedia.org/wiki/Linear_regression#Example" can be estimated by solving for the (XTX)B=XTy normal equations, but that seems to be different than the decomposition and other approaches I've read about; and also I don't know how I'd be able to modify these normal equations for regularization purposes, as I don't see how the RSS can be modified. What I'm looking for is more of an iterative procedure.
 
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  • #6
I'm having a problem trying to use the Levenberg-Marquardt source code from Numerical Recipes in C. The code requires the gaussj(float**, int, float**, int) function, and according to:
http://www.fizyka.umk.pl/nrbook/c0-2.pdf

That is found in section 2.1. However, the http://www.fizyka.umk.pl/nrbook/bookcpdf.html" gives a "file is damaged and could not be repaired" error when trying to open it. Does anyone know where I could find the code for this same gaussj() function? Thanks :smile:
 
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1. What is Least Squares source code in C++?

Least Squares source code in C++ is a mathematical algorithm used for regression analysis. It is used to find the line of best fit for a set of data points and minimize the sum of the squared distances between the data points and the line.

2. How is Least Squares source code implemented in C++?

Least Squares source code in C++ is typically implemented using the vector and matrix data structures. The code involves calculating the coefficients of the line of best fit using the Least Squares formula and then using these coefficients to predict values for new data points.

3. What are the advantages of using Least Squares source code in C++?

One advantage of using Least Squares source code in C++ is that it is a well-established and widely used algorithm. It is also relatively easy to implement and can handle large datasets efficiently. Additionally, it provides a measure of the accuracy of the predicted values.

4. Are there any limitations to using Least Squares source code in C++?

One limitation of using Least Squares source code in C++ is that it assumes a linear relationship between the variables. This means that it may not be suitable for data with non-linear relationships. Additionally, it can be sensitive to outliers in the data, which can skew the results.

5. How can I learn more about Least Squares source code in C++?

There are many online resources and textbooks available for learning about Least Squares source code in C++. It is recommended to have a strong understanding of linear algebra and C++ programming before attempting to implement this algorithm. Some resources to get started include https://www.tutorialspoint.com/cplusplus/cpp_linear_least_squares.htm and https://www.geeksforgeeks.org/c-program-for-least-square-method/.

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