],
'<DIGIT>' : [
{'become' : '<DIGIT><DIGIT>', 'p' : 1},
{'become' : '0', 'p' : 1},
{'become' : '1', 'p' : 1},
{'become' : '2', 'p' : 1},
{'become' : '3', 'p' : 1},
{'become' : '4', 'p' : 1},
{'become' : '5', 'p' : 1},
{'become' : '6', 'p' : 1},
{'become' : '7', 'p' : 1},
{'become' : '8', 'p' : 1},
{'become' : '9', 'p' : 1}
]
}
ge.startExpr = '<EXPR>'
trainingSet = {
'header' : ['otsu', 'stdev', 'mean', 't', 'minOtsu'],
'target' : 't',
'data' : thresholdingData
}
ge.trainingSet = trainingSet
ge.train()
ge.printAllPhenotype()
#good features should have correlation with the output (done),
#good features should not be correlated each other
