@ -53,7 +53,7 @@ Should you stumble on weird English grammar or wording please do fix it - most o
Please avoid starting with a major refactoring or any code reformatting without talking to us first.
**Breaking Compatibility:**
We try to follow [semantic versioning](http://semver.org/), meaning that we cannot break compatibility until the next major version. Since Numerics intentionally permits straight access to raw algorithms, a lot of member declarations are public and thus cannot be modified. Instead of breaking compatibility, it is often possible to create a new better version side by side though and mark the original implementation as obsolete and scheduled for removal on the next major version.
We try to follow [semantic versioning](https://semver.org/), meaning that we cannot break compatibility until the next major version. Since Numerics intentionally permits straight access to raw algorithms, a lot of member declarations are public and thus cannot be modified. Instead of breaking compatibility, it is often possible to create a new better version side by side though and mark the original implementation as obsolete and scheduled for removal on the next major version.
**Merges:**
Please avoid merging mainline back into your pull request branch. If you need to leverage some changes recently added to mainline, consider to rebase instead. In other words, please make sure your commits sit directly on top of a recent mainline master.
* Update Vagrant setup to official Ubuntu 14.04 LTS box and proper apt-style Mono+F# provisioning.
### 3.0.0-beta01 - 2014-04-01
* See also: [Roadmap](https://sdrv.ms/17wPFlW) and [Towards Math.NET Numerics Version 3](http://christoph.ruegg.name/blog/towards-mathnet-numerics-v3.html).
* See also: [Roadmap](https://sdrv.ms/17wPFlW) and [Towards Math.NET Numerics Version 3](https://christoph.ruegg.name/blog/towards-mathnet-numerics-v3.html).
* **Major release with breaking changes**
* All obsolete code has been removed
* Reworked redundancies, inconsistencies and unfortunate past design choices.
@ -585,7 +585,7 @@
* BUG: fixing a bug in `ArrayStatistics.Variance` on arrays longer than 46341 entries.
### 2.6.0 - 2013-07-26
* See also: [What's New in Math.NET Numerics 2.6](http://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-6.html)
* See also: [What's New in Math.NET Numerics 2.6](https://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-6.html)
* Linear Curve Fitting: Linear least-squares fitting (regression) to lines, polynomials and linear combinations of arbitrary functions. Multi-dimensional fitting. Also works well in F# with the F# extensions.
* Root Finding:
* Brent's method. *~Candy Chiu, Alexander Täschner*
@ -617,7 +617,7 @@
* Repository now Vagrant-ready for easy testing against recent Mono on Debian.
### 2.5.0 - 2013-04-14
* See also: [What's New in Math.NET Numerics 2.5](http://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-5.html)
* See also: [What's New in Math.NET Numerics 2.5](https://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-5.html)
* Statistics: Empty statistics now return NaN instead of either 0 or throwing an exception. *This may break code in case you relied upon the previous unusual and inconsistent behavior.*
* Linear Algebra: More reasonable ToString behavior for matrices and vectors. *This may break code if you relied upon ToString to export your full data to text form intended to be parsed again later. Note that the classes in the MathNet.Numerics.IO library are more appropriate for storing and loading data.*
This algorithm calculates the abscissas and weights for a given order and integration interval. For efficiency, pre-computed abscissas and weights for the orders $ N = 2 - 20, \, 32, \, 64, \, 96, 100, \, 128, \, 256, \, 512, \, 1024$ are used. Otherwise, they are calculated on the fly using Newton's method. For more information on the algorithm see [[Holoborodko, Pavel] ](http://www.holoborodko.com/pavel/numerical-methods/numerical-integration/).
This algorithm calculates the abscissas and weights for a given order and integration interval. For efficiency, pre-computed abscissas and weights for the orders $ N = 2 - 20, \, 32, \, 64, \, 96, 100, \, 128, \, 256, \, 512, \, 1024$ are used. Otherwise, they are calculated on the fly using Newton's method. For more information on the algorithm see [[Holoborodko, Pavel] ](https://www.holoborodko.com/pavel/numerical-methods/numerical-integration/).
@ -7,18 +7,18 @@ Feel free to [add, edit or remove your own work](https://github.com/mathnet/math
Open Source
-----------
* [MyMediaLite Recommender System Library](http://www.ismll.uni-hildesheim.de/mymedialite/)
* [MyMediaLite Recommender System Library](https://www.ismll.uni-hildesheim.de/mymedialite/)
* [FermiSim, studying potential solutions to the Fermi paradox via computational simulation of models for space colonisation](https://launchpad.net/fermisim)
* [Three-Dimensional Model Shape Description and Retrieval Based on LightField Descriptors](https://code.google.com/p/lightfieldretrieval/)
* [Yin Zhu: Tutorial: Using Math.NET Numerics in F#](http://msdn.microsoft.com/en-us/library/hh304363.aspx)
* [Don Syme: Getting Started with Math.NET and F# Programming](http://blogs.msdn.com/b/dsyme/archive/2012/07/06/getting-started-with-math-net-and-f-programming.aspx)
* [Libor Tinka: Linear and Nonlinear Least-Squares with Math.NET](http://www.imagingshop.com/articles/least-squares)
* [Carl Nolan: Co-occurrence Approach to an Item Based Recommender](http://code.msdn.microsoft.com/Co-occurrence-Approach-to-57027db7)
* [Gustavo Guerra: F# as a Octave/Matlab Replacement for Machine Learning](http://functionalflow.co.uk/blog/2011/10/27/f-as-a-octavematlab-replacement-for-machine-learning/)
* [Mathias Brandewinder: Simplify data with SVD and Math.NET in F#](http://clear-lines.com/blog/post/Simplify-data-with-SVD-and-MathNET-in-FSharp.aspx)
* [Mathias Brandewinder: Recommendation Engine using Math.NET, SVD and F#](http://clear-lines.com/blog/post/Recommendation-Engine-with-SVD-and-MathNET-in-FSharp.aspx)
* [Thomas Jungblut: Stochastic Logistic Regression in F#](http://codingwiththomas.blogspot.ch/2014/05/stochastic-logistic-regression-in-f.html)
* [Calvin Bottoms: Set-Based Operations: They’re Not Just For Databases](http://calvinbottoms.blogspot.ch/2012/01/set-based-operations-theyre-not-just.html)
* [Chao-Jen Chen: F#: K-S test on final prices of GBM paths ](http://programmingcradle.blogspot.ch/2012/09/f-k-s-test-on-final-prices-of-gbm-paths.html)
* [Dawid Kowalski: F#, Deedle and Computational Investing](http://dkowalski.com/blog/archive/2014/01/11/f-deedle-and-computational-investing.aspx)
* [Jason Fossen: PowerShell for Math.NET Numerics](http://cyber-defense.sans.org/blog/2015/06/27/powershell-for-math-net-numerics)
* [Yin Zhu: Tutorial: Using Math.NET Numerics in F#](https://msdn.microsoft.com/en-us/library/hh304363.aspx)
* [Don Syme: Getting Started with Math.NET and F# Programming](https://blogs.msdn.com/b/dsyme/archive/2012/07/06/getting-started-with-math-net-and-f-programming.aspx)
* [Libor Tinka: Linear and Nonlinear Least-Squares with Math.NET](https://www.imagingshop.com/articles/least-squares)
* [Carl Nolan: Co-occurrence Approach to an Item Based Recommender](https://code.msdn.microsoft.com/Co-occurrence-Approach-to-57027db7)
* [Gustavo Guerra: F# as a Octave/Matlab Replacement for Machine Learning](https://functionalflow.co.uk/blog/2011/10/27/f-as-a-octavematlab-replacement-for-machine-learning/)
* [Mathias Brandewinder: Simplify data with SVD and Math.NET in F#](https://clear-lines.com/blog/post/Simplify-data-with-SVD-and-MathNET-in-FSharp.aspx)
* [Mathias Brandewinder: Recommendation Engine using Math.NET, SVD and F#](https://clear-lines.com/blog/post/Recommendation-Engine-with-SVD-and-MathNET-in-FSharp.aspx)
* [Thomas Jungblut: Stochastic Logistic Regression in F#](https://codingwiththomas.blogspot.ch/2014/05/stochastic-logistic-regression-in-f.html)
* [Calvin Bottoms: Set-Based Operations: They’re Not Just For Databases](https://calvinbottoms.blogspot.ch/2012/01/set-based-operations-theyre-not-just.html)
* [Chao-Jen Chen: F#: K-S test on final prices of GBM paths ](https://programmingcradle.blogspot.ch/2012/09/f-k-s-test-on-final-prices-of-gbm-paths.html)
* [Dawid Kowalski: F#, Deedle and Computational Investing](https://dkowalski.com/blog/archive/2014/01/11/f-deedle-and-computational-investing.aspx)
* [Jason Fossen: PowerShell for Math.NET Numerics](https://cyber-defense.sans.org/blog/2015/06/27/powershell-for-math-net-numerics)
* [Jason Fossen: TrueRNG Random Numbers with PowerShell and Math.NET Numerics](https://cyber-defense.sans.org/blog/2015/07/24/truerng-usb-random-numbers-powershell-mathnet-numerics)
* [Thomasz Jaskula: Data Science tools in F# through univariante linear regression](http://jaskula.fr/blog/2015/12-02-data-science-tools-in-f-through-univariante-linear-regression/)
* [Christoph Rüegg: Linear Regression With Math.NET Numerics](http://christoph.ruegg.name/blog/linear-regression-mathnet-numerics.html)
* [Thomasz Jaskula: Data Science tools in F# through univariante linear regression](https://jaskula.fr/blog/2015/12-02-data-science-tools-in-f-through-univariante-linear-regression/)
* [Christoph Rüegg: Linear Regression With Math.NET Numerics](https://christoph.ruegg.name/blog/linear-regression-mathnet-numerics.html)
Christoph Ruegg
6 years ago