Thanks Hernán!
On Thu, Sep 25, 2014 at 8:59 PM, Hernán Morales Durand <
hernan.mora...@gmail.com> wrote:
> In BioSmalltalk you can do something like this:
>
> | cluster classifier trainedData observations|
> cluster := BioGroupOrganization forSimilarityOn: #value.
> trainedData := { 'Polaromonas naphthalenivorans CJ2' .
> 'Polaromonas sp. JS666' .
> 'Planctomyces limnophilus DSM 3776' .
> 'Nautilia' .
> 'Lactobacillus crispatus ST1' .
> 'Acidithiobacillus ferrooxidans' }.
> trainedData do: [ : feature | cluster addOrganization: (BioOrganization
> new feature: feature) ].
> classifier := BioClassifier new organization: cluster.
> observations := 'Acidithiobacillus ferrooxidans ATCC 53993 chromosome,
> complete genome
> Lactobacillus crispatus ST1, complete genome
> Nautilia profundicola AmH chromosome, complete genome
> Planctomyces limnophilus DSM 3776 plasmid pPLIM01, complete sequence
> Planctomyces limnophilus DSM 3776 chromosome, complete genome
> Polaromonas sp. JS666 plasmid 2, complete sequence
> Polaromonas sp. JS666 plasmid 1, complete sequence
> Polaromonas sp. JS666, complete genome
> Polaromonas naphthalenivorans CJ2, complete genome
> Polaromonas naphthalenivorans CJ2 plasmid pPNAP08, complete sequence
> Polaromonas naphthalenivorans CJ2 plasmid pPNAP07, complete sequence
> Polaromonas naphthalenivorans CJ2 plasmid pPNAP06, complete sequence
> Polaromonas naphthalenivorans CJ2 plasmid pPNAP05, complete sequence
> Polaromonas naphthalenivorans CJ2 plasmid pPNAP04, complete sequence
> ' lines.
> observations do: [ : obs | classifier classify: obs ].
> classifier classesSize = 6.
> classifier maxClasses = 6.
> classifier maxClass feature = 'Polaromonas naphthalenivorans CJ2'.
> classifier minClasses = 1.
> classifier minClass feature = 'Nautilia'.
>
> Hernán
>
> 2014-09-25 7:08 GMT-03:00 Martin Dias <tinchod...@gmail.com>:
>
> Hi all,
>>
>> I'm playing with weka [1] for classifying data using data mining/machine
>> learning. I'm really new on this field so I want to give you a concrete
>> example of my use case to make it clear:
>>
>> I have elements that can be classified as true or false. In a training
>> phase, I set up a bayesian network with elements that I manually
>> classified. Then, I can use such network for predicting the classification
>> of new elements.
>>
>> ---> my question is:
>> Do we have a package for replacing weka-in-my-use-case in Pharo? It
>> doesn't need to be exactly a bayesian network, it could be simpler.
>>
>>
>> I looked a bit in Moose-Algos and in BioSmalltalk but I think they don't
>> have what I need.
>>
>> I would appreciate any help. Cheers.
>> Martín
>>
>>
>> [1]: http://www.cs.waikato.ac.nz/ml/weka/
>>
>>
>>
>
