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Mushroom Data Set (UCI ML repo) Python implementation
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README.md

MUSHPy

This repository contains tests of the AlignmentRepaPy repository using data from the UCI Machine Learning Repository Mushroom Data Set. The AlignmentRepaPy repository is a fast Python implementation of some of the practicable inducers described in the paper The Theory and Practice of Induction by Alignment at https://greenlake.co.uk/.

Documentation

There is an analysis of this dataset here, with sections (a) predicting edibility without modelling, (b) predicting odor without modelling, (c) manual modelling of edibility and (d) induced modelling of edibility.

Installation

The MUSH executables require the AlignmentRepa module which is in the AlignmentRepaPy repository. See the AlignmentRepaPy repository for installation instructions of the Python compiler and libraries.

Then download the zip files or use git to get the MUSHPy repository and the underlying AlignmentPy and AlignmentRepaPy repositories -

cd
git clone https://github.com/caiks/AlignmentPy.git
git clone https://github.com/caiks/AlignmentRepaPy.git
git clone https://github.com/caiks/MUSHPy.git

Usage

To experiment with the dataset in the interpreter,

cd MUSHPy
export PYTHONPATH=../AlignmentPy:../AlignmentRepaPy
python3
from MUSHDev import *

(uu,aa) = mushIO()
vv = uvars(uu)
vvl = sset([VarStr("edible")])
vvk = vv - vvl

hr = aahr(uu,aa)

(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed) = ((9*9*10), 8, (9*9*10), 10, (10*3), 3, (9*9*10), 1, 3, 3, 5)

(uu1,df1) = decomperIO(uu,vvk,hr,wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed)

open("df.json","w").write(decompFudsPersistentsEncode(decompFudsPersistent(df1)))

summation(mult,seed,uu1,df1,hr)
(61302.44944051167, 26729.333546815153)
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