def testConnect4(setFunc = setEntropy, infoFunc = infoGain): """Correct classification averate rate is about 0.75""" examples,attrValues,labelName,labelValues = getConnect4Dataset() print 'Testing Connect4 dataset. Number of examples %d.'%len(examples) tree = makeTree(examples, attrValues, labelName, setFunc, infoFunc) f = open('connect4.out','w') print 'Tree size: %d.\n'%tree.count() print 'Entire tree written out to connect4.out in local directory\n' f.write(str(tree)) f.close() evaluation = getAverageClassificaionRate((examples,attrValues,labelName,labelValues),runs=10,testSize=2000) printDemarcation() return (tree,evaluation)
def testConnect4(setFunc = setEntropy, infoFunc = infoGain): """Correct classification averate rate is about 0.75""" examples,attrValues,labelName,labelValues = getConnect4Dataset() print 'Testing Connect4 dataset. Number of examples %d.'%len(examples) tree = makeTree(examples, attrValues, labelName, setFunc, infoFunc) f = open('connect4.out','w') print 'Tree size: %d.\n'%tree.count() print 'Entire tree written out to connect4.out in local directory\n' f.write(str(tree)) f.close() evaluation = getAverageClassificaionRate((examples,attrValues,labelName,labelValues),runs=10,testSize=2000) print 'Results for training set:\n%s\n'%str(evaluation) printDemarcation() return (tree,evaluation)