From lemming at henning-thielemann.de  Wed Aug 19 10:46:55 2015
From: lemming at henning-thielemann.de (Henning Thielemann)
Date: Wed, 19 Aug 2015 12:46:55 +0200 (CEST)
Subject: [Numeric] ANN: apportionment
Message-ID: <alpine.DEB.2.02.1508191238520.10078@sputnik>


I have uploaded a new package to Hackage implementing two methods for 
rounding a set of numbers while maintaining their sum. Useful applications 
are elections of parliaments and rounding percentages. E.g.

Prelude> Apportionment.largestRemainderScaled 100 [1,2,3 :: Double]
[17,33,50]
Prelude> Apportionment.largestRemainderScaled 100 [1,1,1 :: Double]
[34,33,33]


The package is very simple, yet useful. It's plain Haskell 98.


https://hackage.haskell.org/package/apportionment

From lemming at henning-thielemann.de  Wed Aug 19 10:53:11 2015
From: lemming at henning-thielemann.de (Henning Thielemann)
Date: Wed, 19 Aug 2015 12:53:11 +0200 (CEST)
Subject: [Numeric] ANN: set-cover with new examples for Nonogram, Pangram,
 Mastermind, Lonpos pyramid
Message-ID: <alpine.DEB.2.02.1508191247120.10078@sputnik>


The package 'set-cover' solves exact set cover problems:
    https://en.wikipedia.org/wiki/Set_cover_problem

I have added new examples: Nonogram, Pangram, Mastermind, Lonpos pyramid.

From lemming at henning-thielemann.de  Wed Aug 19 11:06:27 2015
From: lemming at henning-thielemann.de (Henning Thielemann)
Date: Wed, 19 Aug 2015 13:06:27 +0200 (CEST)
Subject: [Numeric] ANN: hmm-hmatrix - efficient Hidden Markov Model
	implementation
Message-ID: <alpine.DEB.2.02.1508191255580.10078@sputnik>


My HMM implementation is on Hackage and has already proven to be useful:
    https://hackage.haskell.org/package/hmm-hmatrix

It supports Discrete and Gaussian models and can be extended to other 
moduls using the type classes from Distribution module. The package 
implements supervised and unsupervised training, as well as a training 
using a predefined distribution and patterns. It also supports mixing of 
trainings. Trained models can be read from and written to CSV. The 
features are demonstrated by three simple examples.


https://en.wikipedia.org/wiki/Hidden_Markov_Model

From dominic at steinitz.org  Wed Aug 19 11:41:52 2015
From: dominic at steinitz.org (Dominic Steinitz)
Date: Wed, 19 Aug 2015 12:41:52 +0100
Subject: [Numeric] ANN: hmm-hmatrix - efficient Hidden Markov Model
	implementation
In-Reply-To: <alpine.DEB.2.02.1508191255580.10078@sputnik>
References: <alpine.DEB.2.02.1508191255580.10078@sputnik>
Message-ID: <B73288FA-DB75-4E04-ADB3-4D1B31660D71@steinitz.org>

Hi Henning,

Thanks for this. I look forward to trying it.

How general are the models that it can fit? For example, a Kalman filter is an example of a hidden Markov model in which the updates are linear and the errors are Gaussian. I wrote a few notes on such models although I have yet to follow this up: https://idontgetoutmuch.wordpress.com/2015/06/20/some-background-on-hidden-markov-models/ <https://idontgetoutmuch.wordpress.com/2015/06/20/some-background-on-hidden-markov-models/>. As you can see, I don?t think the wikipedia definition is correct but maybe this is a matter of taste / definition although I often find wikipedia slightly misses the point on some mathematical topics.

I also uploaded an extended Kalman filter package here: https://hackage.haskell.org/package/Kalman <https://hackage.haskell.org/package/Kalman>.

You might also be interested in a version of haddock I have which renders mathematics correctly. For example, see here: https://hackage.haskell.org/package/Kalman-0.1.0.1/docs/Kalman.html <https://hackage.haskell.org/package/Kalman-0.1.0.1/docs/Kalman.html>.

Dominic Steinitz
dominic at steinitz.org
http://idontgetoutmuch.wordpress.com

> On 19 Aug 2015, at 12:06, Henning Thielemann <lemming at henning-thielemann.de> wrote:
> 
> 
> My HMM implementation is on Hackage and has already proven to be useful:
>   https://hackage.haskell.org/package/hmm-hmatrix
> 
> It supports Discrete and Gaussian models and can be extended to other moduls using the type classes from Distribution module. The package implements supervised and unsupervised training, as well as a training using a predefined distribution and patterns. It also supports mixing of trainings. Trained models can be read from and written to CSV. The features are demonstrated by three simple examples.
> 
> 
> https://en.wikipedia.org/wiki/Hidden_Markov_Model
> _______________________________________________
> Numeric mailing list
> Numeric at haskell.org
> http://mail.haskell.org/cgi-bin/mailman/listinfo/numeric

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From lemming at henning-thielemann.de  Wed Aug 19 12:49:09 2015
From: lemming at henning-thielemann.de (Henning Thielemann)
Date: Wed, 19 Aug 2015 14:49:09 +0200 (CEST)
Subject: [Numeric] ANN: hmm-hmatrix - efficient Hidden Markov Model
 implementation
In-Reply-To: <B73288FA-DB75-4E04-ADB3-4D1B31660D71@steinitz.org>
References: <alpine.DEB.2.02.1508191255580.10078@sputnik>
 <B73288FA-DB75-4E04-ADB3-4D1B31660D71@steinitz.org>
Message-ID: <alpine.DEB.2.02.1508191446420.10078@sputnik>


On Wed, 19 Aug 2015, Dominic Steinitz wrote:

> How general are the models that it can fit? For example, a Kalman filter 
> is an example of a hidden Markov model in which the updates are linear 
> and the errors are Gaussian.

My package is limited to a finite set of states. I don't think you can use 
it for Kalman filtering and so far I do not know, whether I can generalize 
it that way. Unfortunately, I have no experience with Kalman filters.

From lemming at henning-thielemann.de  Wed Aug 26 20:00:10 2015
From: lemming at henning-thielemann.de (Henning Thielemann)
Date: Wed, 26 Aug 2015 22:00:10 +0200 (CEST)
Subject: [Numeric] ANN: linear-circuit, comfort-graph, resistor-cube
Message-ID: <alpine.DEB.2.02.1508262154060.7560@sputnik>


I have uploaded the package linear-circuit that computes the resistance of 
a network of resistors using linear algebra from hmatrix:
    https://hackage.haskell.org/package/linear-circuit

This package also demonstrates the use of a graph structure that is an 
alternative to fgl:
    https://hackage.haskell.org/package/comfort-graph

The resistance computation is a generalization of a small example for 
computing the resistance of a cube of resistors:
    https://hackage.haskell.org/package/resistor-cube