"\nData Set: ("
+ str(dataSet)
+ ") Run: "
+ str(run)
+ " Strategy: "
+ str(TS.TrainingStrategyType.desc(strat))
)
if run == 0:
p.initCombinedConfusionMatrix()
hiddenArchitecture = [
2 * len(p.patterns[0]["p"])
] # each hidden layer is a new index in this list, it's value = number of neurons in that layer
TS.Member.genomeTemplate = genomeTemplateFromArchitecture(
len(p.patterns[0]["p"]), hiddenArchitecture, len(p.targets)
)
Net.trainingStrategy = TS.TrainingStrategy.getTrainingStrategyOfType(strat)
Net.trainingStrategy.maxGenerations = maxGenerations
Net.trainingStrategy.initPopulation(populationSize, (-1.0, 1.0))
n = Net(p, hiddenArchitecture)
n.run(PatternType.Train, 0, int(p.count * trainPercentage))
n.run(PatternType.Test, int(p.count * trainPercentage), p.count)
# n.run(PatternType.Train, 0, p.count)
# n.run(PatternType.Test, 0, p.count)
p.printStats()
p.saveConfusionMatrix(
"records/" + str(dataSet.split("/")[1]) + "-" + str(TS.TrainingStrategyType.desc(strat)) + ".csv"
)
print("Done")
# 'data/zoo/zoo.json',
# 'data/iris/iris.json']
# Single:
# allDataTypes = ['data/ionosphere/ionosphere.json']
# allDataTypes = ['data/block/pageblocks.json']
# allDataTypes = ['data/heart/heart.json']
# allDataTypes = ['data/glass/glass.json']
# allDataTypes = ['data/car/car.json']
# allDataTypes = ['data/seeds/seeds.json']
allDataTypes = ['data/wine/wine.json']
# allDataTypes = ['data/yeast/yeast.json']
# allDataTypes = ['data/zoo/zoo.json']
# allDataTypes = ['data/iris/iris.json']
trainPercentage = 0.8
runsPerDataSet = 10
for dataset in allDataTypes:
p = PatternSet(dataset)
for run in range(runsPerDataSet):
if run == 0:
p.initCombinedConfusionMatrix()
n = Network(p)
n.run(PatternType.Train, 0, int(p.count*trainPercentage))
saveWeights(n)
n.run(PatternType.Test, int(p.count*trainPercentage), p.count)
p.printStats()
p.saveConfusionMatrix()
print("Done")
