## HNN-0.1 has been released !

Hi,

I just released the 0.1 version of my **H**askell **N**eural **N**etwork library on Hackage.

Instead of writing a long blog post, I created a page on the Haskell wiki that you can find here : HNN describing what is HNN, how to get it, showing a sample and all.

There is an online version of the documentation here : hnn documentation

You can also consult hnn’s hackage page : hnn at hackage (the documentation should be generated soon there)

Here is a sample showing how you can use HNN :

module Main where

import AI.HNN.Net

import AI.HNN.Layer

import AI.HNN.Neuron

import Data.Array.Vector

import Control.Arrow

import Data.List

alpha = 0.8 :: Double — learning ratio

epsilon = 0.001 :: Double — desired maximal bound for the quad error

layer1, layer2 :: [Neuron]

layer1 = createSigmoidLayer 4 0.5 [0.5, 0.5, 0.5] — the hidden layer

layer2 = createSigmoidLayer 1 0.5 [0.5, 0.4, 0.6, 0.3] — the output layer

net = [layer1, layer2] — the neural network

finalnet = train alpha epsilon net [([1, 1, 1],[0]), ([1, 0, 1],[1]), ([1, 1, 0],[1]), ([1, 0, 0],[0])] — the trained neural network

good111 = computeNet finalnet [1, 1, 1]

good101 = computeNet finalnet [1, 0, 1]

good110 = computeNet finalnet [1, 1, 0]

good100 = computeNet finalnet [1, 0, 0]

main = do

putStrLn $ "Final neural network : \n" ++ show finalnet

putStrLn " —- "

putStrLn $ "Output for [1, 1, 1] (~ 0): " ++ show good111

putStrLn $ "Output for [1, 0, 1] (~ 1): " ++ show good101

putStrLn $ "Output for [1, 1, 0] (~ 1): " ++ show good110

putStrLn $ "Output for [1, 0, 0] (~ 0): " ++ show good100

Output :

$ ./xor-3inputs

Final neural network :

[[Threshold : 0.5

Weights : toU [1.30887603787326,1.7689534867644316,2.2908214981696453],Threshold : 0.5

Weights : toU [-2.4792430791673947,4.6447786039112655,-4.932860802255383],Threshold : 0.5

Weights : toU [2.613377735822592,6.793687725768354,-5.324081206358496],Threshold : 0.5

Weights : toU [-2.5134194114492585,4.730152273922408,-5.021321916827272]],[Threshold : 0.5

Weights : toU [4.525235803191061,4.994126671590998,-8.2102354168462,5.147655509585701]]]

—-

Output for [1, 1, 1] (~ 0): [2.5784449476436315e-2]

Output for [1, 0, 1] (~ 1): [0.9711209812630944]

Output for [1, 1, 0] (~ 1): [0.9830499812666017]

Output for [1, 0, 0] (~ 0): [1.4605247804272069e-2]

Don’t hesitate to try it, play with it and give some feedback ! For any feedback or question, see the end of the HNN wiki page.

Thanks, and enjoy !

## HNN : a Haskell Neural Network library

Hi,

Few months ago, I started working on a neural network library, in Haskell. The result wasn’t that bad, but needed some additional work. Past days, I’ve worked a bit on that code again to get a releasable and usable 0.1 version of HNN up and working. For example, the weights, inputs and outputs are of type *UArr Double* now (they were of type [Double] before).

You can find the source code there : http://github.com/alpmestan/HNN.

Also, I’ve built a minimal documentation. You can find it here : http://mestan.fr/haskell/hnn/.

To get the current code and test it :

$ git clone git://github.com/alpmestan/HNN.git

$ cd HNN

$ cabal configure

$ cabal build

$ cabal install (to add it in your ghc package list, etc)

$ cabal haddock (to generate the documentation)

I plan to put this on Hackage soon, but I would like to get some feedback & reviews about it before.

Thank you !

## Functional compile-time templates based type lists in C++

Hi,

Have you ever heard about typelists in C++ ? That just consists in using the functional way of defining lists, but with templates.

It looks like that :

template <typename Head, typename Tail> struct TypeList { typedef Head head; typedef Tail tail; };

However, we’ll need a type representing an empty type list. Ours will be the following.

struct EmptyList { };

How to write metafunctions (compile-time functions, working over types and not values — actually, the types are in this context like “values”) for these type lists now ?

Let’s start with a metafunction computing the length of a type list :

// declaration template <typename Typelist> struct Length; // the normal case : the head element of the list (it's a type), and the tail, which is itself a type list template <typename H, typename T> struct Length<TypeList<H, T> > { static const int value = 1 + Length<T>::value ; }; // the terminal case : our typelist is the empty list, we're at the end of the list, so we won't add 1 neither we will go on with the recursion template <> struct Length<EmptyList> { static const int value = 0 ; };

Now, calling it on a given typelist will return us the good result :

Length< TypeList<int, TypeList<char, TypeList<bool, EmptyList> > > >::value // equals 3

Do you want more ? I guess you do, of course. Let’s tackle a more complicated one : Map. It maps a type list to another one, computing the result of the application of a metafunction on each type of the type list. Ok, let’s start with the declaration.

template <typename TL, template <typename> class Func> struct Map;

Now, the basical case, with a head element and the tail, will consist in computing the result of the application of the metafunction of the head element, and appending to it the result of Map on the tail. Looks like that :

template <typename H, typename T, template <typename> class Func> struct Map<TypeList<H, T>, Func> { typedef TypeList< typename Func<H>::type, typename Map<T, Func>::type > type; };

And the trivial case, on the empty list :

template <template <typename> class Func> struct Map<EmptyList, Func> { typedef EmptyList type; };

And we’re done with Map !

Let’s see one more interesting function over type lists : Filter. It’ll filter (really ?!) the type list according to a compile-time predicate, and return the original type list without the types that didn’t match the predicate.

Here we go !

template <typename TypeList, template <typename> class Pred> struct Filter; template <typename H, typename T, template <typename> class Pred, bool result> struct FilterAux { typedef typename Filter<T, Pred>::type type; }; template <typename H, typename T, template <typename> class Pred> struct FilterAux<H, T, Pred, true> { typedef TypeList<H, typename Filter<T, Pred>::type> type; }; template <typename H, typename T, template <typename> class Pred> struct Filter<TypeList<H, T>, Pred> { typedef typename FilterAux<H, T, Pred, Pred<H>::value>::type type; }; template <template <typename> class Pred> struct Filter<EmptyList, Pred> { typedef EmptyList type; };

This one was trickier, because we needed an auxiliary template structure to have a bool against which we could specialize, to either go on without keeping the type in the type list (in case it doesn’t match the predicate) or keeping it.

Now, I’ll leave as exercise the following functions :

– Repeat : takes a type, a number, and returns a type list containing n times the given type

– Take : takes a type list and a number, and returns a type list contaning the first n elements of the typelist if possible, less otherwise.

– Interleave : it takes 2 two lists, say l1 = [T1, T2, T3] and l2 = [U1, U2, U3] and returns the list [T1, U1, T2, U2, T3, U3]

– Zip : takes two lists and returns a list of component-wise pairs of the types

– ZipWith : takes two lists and a function itself taking two types and returning a type, and returns a list compound of the component-wise application of the given function on both lists simultaneously

I’ll try to post these during the upcoming days.

Good functional metaprogramming to all đ

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