F# Bloggers

Blog articles of F# Bloggers

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on 3/22/2014 7:11 PM
A couple of days ago, I got into the following Twitter exchange: @brandewinder do you have any link to get in touch with this combo? #fsharp— Max Malook (@max_malook) March 21, 2014   So why do I think FsCheck + XUnit = The Bomb? I have a long history with Test-Driven Development; to this day, I consider Kent Beck’s “Test-Driven Development by Example” one of the biggest influences in the way I write code (any terrible code I might have written is, of course, to be blamed entirely on me, and not on the[...]
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on 3/10/2014 8:21 PM
In the last post, we talked about techniques to implement tail-recursion in F#. We also learned that to write pure functional code we can only use immutable data structures which means we have to implement loop using recursion. Writing recursive function can be cumbersome (e.g., the function has to have one or more base cases) … Continue reading →
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on 3/3/2014 9:59 PM
I was recently a guest on the .NET Rocks podcast and one of the things that came up was this notion in F# of “if it compiles – it works”. Given the constraints of time on the show (and technical … Continue reading →
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on 3/3/2014 8:25 AM
You may encounter an error which looks like this: MSB4006: There is a circular dependency in the target dependency graph involving target "ResolveProjectReferences" [MyProjectName\MyProjectName.csproj] …when running MSBuild from the command line. This error happens when: You run MSBuild on a machine with .NET 4.5 installed and You build a project referencing a solution where the SLN file contains dependencies in [...]
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on 3/1/2014 7:32 PM
During some recent meanderings through the confines of the internet, I ended up discovering the Winnow Algorithm. The simplicity of the approach intrigued me, so I thought it would be interesting to try and implement it in F# and see how well it worked. The purpose of the algorithm is to train a binary classifier, based on binary features. In other words, the goal is to predict one of two states, using a collection of features which are all binary. The prediction model assigns weights to each feature; to [...]
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