dariodip / rfd-discovery Goto Github PK
View Code? Open in Web Editor NEWThis project, written in Python and Cython, deals with Discovery of Relaxed Functional Dependencies(RFDs) using a bottom-up approach.
This project, written in Python and Cython, deals with Discovery of Relaxed Functional Dependencies(RFDs) using a bottom-up approach.
Dobbiamo implementare almeno il primo passo dell'algoritmo naive e vedere come si comporta.
Qua ho letto che si può selezionare un subdataframe dal dataframe. Se riuscissimo ad implementare l'algoritmo selezionando direttamente i subdataframe per le diverse distanze, secondo me, stiamo a cavallo.
Es: partiamo dalla distanza massima, selezioniamo il subdataframe per quella distanza sull'attributo RHS e da quello selezioniamo le righe che non sono dominate (con una funzione sparata da una factory in modo da rendere tutto molto versatile); poi andiamo alla distanza precedente e facciamo i dovuti confronti ecc.
Realizziamo un test che controlla se il contenuto della matrice delle distanze create da noi è esattamente lo stesso di quello di una matrice delle distanze oracolo (e.g. quella della presentazione PP)
In order to extract statistics from our data, we put some invalid tests in our tests directory.
We should delete them.
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