from scipy.stats import wilcoxon, chisquare # import matplotlib.pyplot as plt # from hcluster import * import Assurance as ass import Parser as parser import Experiment1 as exp1 import Experiment5 as exp5 import Experiment6 as exp6 import CrossValidation as cross import SVMValidatedExperiment as exp7 def initialize(): trials = parser.parse() atributos = ["type", "orientation", "age", "hairColour", "hasBeard", "hasHair", "hasGlasses", "hasShirt", "hasTie", "hasSuit", "x-dimension", "y-dimension"] return trials, atributos if __name__ == '__main__': trials, atributos = initialize() # trials = exp1.run(trials, atributos) folds = cross.crossValidation(10, trials) # exp5.run(folds, atributos, 0.7) # exp6.run(folds, atributos, 0.7) # exp7.run(trials, folds, atributos, {}, False) exp7.run(trials, folds, atributos, {}, True)
if __name__ == '__main__': dominios, anotacoes, atributos, targets = initialize() #anotacoes = exp1.run(dominios, anotacoes, atributos, targets) folds = cross.crossValidation(10, anotacoes) #folds = exp5.run(dominios, folds, atributos, targets, 0.7) # folds = exp6.run(dominios, folds, atributos, targets, 0.7) print "Machine Learning sem ID" # exp7.run(dominios, targets, folds, atributos, {}, False) print "Machine Learning com ID" exp7.run(dominios, targets, folds, atributos, {}, True) # dice = [] # diceGlobal = [] # dicePersonalizado = [] # # diceGlobalSuperespecificado = [] # dicePersonalizadoSuperespecificado = [] # print "DICE \t DICE GLOBAL \t DICE PERSONALIZADO \t" # for fold in folds: # for participante in folds[fold].keys(): # dice.append(folds[fold][participante]["dice"]) # diceGlobal.append(folds[fold][participante]["dice_global"]) # dicePersonalizado.append(folds[fold][participante]["dice_personalizado"]) # # diceGlobalSuperespecificado.append(folds[fold][participante]["dice_global_superespecificado"])
# from hcluster import * import ParserStars as parser import CrossValidation as cross import Experiment1 as exp1 import SVMValidatedExperiment as exp2 import SVMValidatedExperiment2 as exp3 import ExperimentDecisionTree as exp4 import ValidatedExperimentIndividual as exp5 def initialize(): anotacoes = parser.parseAnnotation() dominios = parser.parseDominio() participantes = {} atributos = ["type", "size", "colour", "hpos", "vpos", "near", "left", "right", "below", "above", "in-front-of"] targets = {"01f-t1n":"h", "01f-t1r":"h", "01f-t2n":"h", "01f-t2r":"h", "01o-t1n":"h", "01o-t1r":"h", "01o-t2n":"h", "01o-t2r":"h", "02f-t1n":"o", "02f-t1r":"o", "02f-t2n":"o", "02f-t2r":"o", "02o-t1n":"o", "02o-t1r":"o", "02o-t2n":"o", "02o-t2r":"o", "03f-t1n":"m", "03f-t1r":"m", "03f-t2n":"m", "03f-t2r":"m", "03o-t1n":"m", "03o-t1r":"m", "03o-t2n":"m", "03o-t2r":"m", "04f-t1n":"a", "04f-t1r":"a", "04f-t2n":"a", "04f-t2r":"a", "04o-t1n":"a", "04o-t1r":"a", "04o-t2n":"a", "04o-t2r":"a", "05f-t1n":"m", "05f-t2n":"m", "05f-t1r":"m", "05f-t2r":"m", "05o-t1n":"m", "05o-t1r":"m", "05o-t2n":"m", "05o-t2r":"m", "06f-t1n":"h", "06f-t1r":"h", "06f-t2n":"h", "06f-t2r":"h", "06o-t1n":"h", "06o-t1r":"h", "06o-t2n":"h", "06o-t2r":"h", "07f-t1n":"i", "07f-t1r":"i", "07f-t2n":"i", "07f-t2r":"i", "07o-t1n":"i", "07o-t1r":"i", "07o-t2n":"i", "07o-t2r":"i", "08f-t1n":"a", "08f-t1r":"a", "08f-t2n":"a", "08f-t2r":"a", "08o-t1n":"a", "08o-t1r":"a", "08o-t2n":"a", "08o-t2r":"a" } return dominios, targets, anotacoes, atributos, participantes if __name__ == '__main__': dominios, targets, anotacoes, atributos, participantes = initialize() folds = cross.crossValidation(10, anotacoes) print "Machine Learning sem ID" # exp5.run(dominios, targets, anotacoes, atributos, False) exp2.run(dominios, targets, folds, atributos, {}, False) print "\n\n" print "Machine Learning com ID" # exp5.run(dominios, targets, anotacoes, atributos, True) exp2.run(dominios, targets, folds, atributos, {}, True)
# import matplotlib.pyplot as plt # from hcluster import * import Assurance as ass import ParserGRE3D as parser import CrossValidation as cross import Experiment1 as exp1 import SVMValidatedExperiment as exp2 import ExperimentDecisionTree as exp4 import ValidatedExperimentIndividual as exp5 def initialize(): anotacoes = parser.parseAnnotation() dominios = parser.parseDominio() participantes = parser.parseParticipantes() targets = {"1":"b3","2":"b2","3":"b3","4":"b2","5":"b3","6":"b1","7":"b3","8":"b1","9":"b2","10":"b1","11":"b2","12":"b1","13":"b3","14":"b1","15":"b3","16":"b1","17":"b1","18":"b1","19":"b1","20":"b1","21":"b1","22":"b1","23":"b1","24":"b1","25":"b4","26":"b3","27":"b4","28":"b3","29":"b3","30":"b3","31":"b3","32":"b3"} atributos = ['loc', 'left-of', 'next-to', 'on-top-of', 'right-of', 'type', 'col', 'size'] return dominios, targets, anotacoes, participantes, atributos if __name__ == '__main__': dominios, targets, anotacoes, participantes, atributos = initialize() folds = cross.crossValidation(10, anotacoes) print "Machine Learning sem ID" # exp5.run(dominios, targets, anotacoes, atributos, participantes, False) exp2.run(dominios, targets, folds, atributos, participantes, False) print "\n\n" print "Machine Learning com ID" # exp5.run(dominios, targets, anotacoes, atributos, participantes, True) # exp2.run(dominios, targets, folds, atributos, participantes, True)