{'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
bestPhenotype = ge.getBestPhenotype(5, 0)
for phenotype in bestPhenotype:
print(phenotype)
all_extracted_features = []
all_original_features = []
all_targets = []
trainingHeader = trainingSet['header']
for trainingData in trainingSet['data']:
sandbox = {}
extracted_inputs = []
],
'<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}
]
}
gfcs._trainingSet = {
'header' : ['otsu', 'stdev', 'mean', 'minOtsu', 't'],
'target' : 't',
'data' : thresholdingData
}
gfcs.train()
gfcs.printAllPhenotype()
best = gfcs.getBestPhenotype(10,0.0)
trainingSet = {}
trainingSet['target'] = gfcs._trainingSet['target']
trainingSet['header'] = []
trainingSet['data'] = []
for individu in best:
print(individu)
