How to use the orange.BayesLearner function in Orange

def __call__(self, examples, weight = 0,fulldata=0):
        if not(examples.domain.classVar.varType == 1 and len(examples.domain.classVar.values)==2):
            raise "BasicBayes learner only works with binary discrete class."
        for attr in examples.domain.attributes:
            if not(attr.varType == 1):
                raise "BasicBayes learner does not work with continuous attributes."
        translate = orng2Array.DomainTranslation(self.translation_mode_d,self.translation_mode_c)
        if fulldata != 0:
            translate.analyse(fulldata, weight)
        else:
            translate.analyse(examples, weight)
        translate.prepareLR()
        (beta, coeffs) = self._process(orange.BayesLearner(examples), examples)
        return BasicBayesClassifier(beta,coeffs,translate)

train_data = data.select(selection, test_fold, negate=1)
        test_data = data.select(selection, test_fold)
        classifiers = []
        for l in learners:
            classifiers.append(l(train_data))
        acc1 = accuracy(test_data, classifiers)
        print "%d: %s" % (test_fold + 1, ["%.6f" % a for a in acc1])
        for j in range(len(learners)):
            acc[j] += acc1[j]
    for j in range(len(learners)):
        acc[j] = acc[j] / k
    return acc

orange.setrandseed(0)
# set up the learners
bayes = orange.BayesLearner()
tree = orngTree.TreeLearner(mForPruning=2)

bayes.name = "bayes"
tree.name = "tree"
learners = [bayes, tree]

# compute accuracies on data
data = orange.ExampleTable("voting")
acc = cross_validation(data, learners, k=10)
print "Classification accuracies:"
for i in range(len(learners)):
    print learners[i].name, acc[i]

# Description: Read data, build naive Bayesian classifier, and output class probabilities for the first few instances
# Category:    modelling
# Uses:        voting.tab
# Referenced:  c_basics.htm

import orange
data = orange.ExampleTable("voting")
classifier = orange.BayesLearner(data)
print "Possible classes:", data.domain.classVar.values
print "Probabilities for democrats:"
for i in range(5):
    p = classifier(data[i], orange.GetProbabilities)
    print "%d: %5.3f (originally %s)" % (i+1, p[1], data[i].getclass())

# Description: Demostration of use of cross-validation as provided in orngEval module
# Category:    evaluation
# Uses:        voting.tab
# Classes:     orngTest.crossValidation
# Referenced:  c_performance.htm

import orange
import orngTest, orngStat, orngTree

# set up the learners
bayes = orange.BayesLearner()
tree = orngTree.TreeLearner(mForPruning=2)
bayes.name = "bayes"
tree.name = "tree"
learners = [bayes, tree]

# compute accuracies on data
data = orange.ExampleTable("voting")
res = orngTest.crossValidation(learners, data, folds=10)
cm = orngStat.computeConfusionMatrices(res,
        classIndex=data.domain.classVar.values.index('democrat'))

stat = (('CA', lambda res,cm: orngStat.CA(res)),
        ('Sens', lambda res,cm: orngStat.sens(cm)),
        ('Spec', lambda res,cm: orngStat.spec(cm)),
        ('AUC', lambda res,cm: orngStat.AUC(res)),
        ('IS', lambda res,cm: orngStat.IS(res)),

#Preprocessor name replacement rules
    REPLACE = {preprocess.Discretize: "Discretize ({0.method})",
               preprocess.DiscretizeEntropy: "Discretize (entropy)",
               preprocess.RemoveContinuous: "Discretize (remove continuous)",
               preprocess.Continuize: "Continuize ({0.multinomialTreatment})",
               preprocess.RemoveDiscrete: "Continuize (remove discrete)",
               preprocess.Impute: "Impute ({0.model})",
               preprocess.ImputeByLearner: "Impute ({0.learner})",
               preprocess.DropMissing: "Remove missing",
               preprocess.FeatureSelection: "Feature selection ({0.measure}, {0.filter}, {0.limit})",
               preprocess.Sample: "Sample ({0.filter}, {0.limit})",
               orange.EntropyDiscretization: "entropy",
               orange.EquiNDiscretization: "freq, {0.numberOfIntervals}",
               orange.EquiDistDiscretization: "width, {0.numberOfIntervals}",
               orange.RandomLearner: "random",  
               orange.BayesLearner: "bayes  model",
               orange.MajorityLearner: "average",
               orange.MeasureAttribute_relief: "ReliefF",
               orange.MeasureAttribute_info: "Info gain",
               orange.MeasureAttribute_gainRatio: "Gain ratio",
               orange.MeasureAttribute_gini: "Gini",
               orange.MeasureAttribute_logOddsRatio: "Log Odds",
               orngSVM.MeasureAttribute_SVMWeights: "Linear SVM weights",
               type(lambda : None): _funcName}
    
    import re
    INSERT_RE = re.compile(r"{0\.(\w+)}")
    
    def __init__(self, parent=None):
        QStyledItemDelegate.__init__(self, parent)
        
    def displayText(self, value, locale):

p = max(maxp)  # max class probability
    classifier_index = maxp.index(p)
    c = pmatrix[classifier_index].modus()
    
    if resultType == orange.GetValue:
      return c
    elif resultType == orange.getClassDistribution:
      return pmatrix[classifier_index]
    else:
      return (c, pmatrix[classifier_index])


tree = orngTree.TreeLearner(mForPruning=5.0)
tree.name = 'class. tree'
bayes = orange.BayesLearner()
bayes.name = 'naive bayes'
winner = WinnerLearner(learners=[tree, bayes])
winner.name = 'winner'

majority = orange.MajorityLearner()
majority.name = 'default'
learners = [majority, tree, bayes, winner]

data = orange.ExampleTable("promoters")

results = orngTest.crossValidation(learners, data)
print "Classification Accuracy:"
for i in range(len(learners)):
    print ("%15s: %5.3f") % (learners[i].name, orngStat.CA(results)[i])

import orange, orngTree

def accuracy(test_data, classifiers):
    correct = [0.0]*len(classifiers)
    for ex in test_data:
        for i in range(len(classifiers)):
            if classifiers[i](ex) == ex.getclass():
                correct[i] += 1
    for i in range(len(correct)):
        correct[i] = correct[i] / len(test_data)
    return correct

# set up the classifiers
data = orange.ExampleTable("voting")
bayes = orange.BayesLearner(data)
bayes.name = "bayes"
tree = orngTree.TreeLearner(data);
tree.name = "tree"
classifiers = [bayes, tree]

# compute accuracies
acc = accuracy(data, classifiers)
print "Classification accuracies:"
for i in range(len(classifiers)):
    print classifiers[i].name, acc[i]

# Description: Demostration of use of cross-validation as provided in orngEval module
# Category:    evaluation
# Uses:        voting.tab
# Classes:     orngTest.crossValidation
# Referenced:  c_performance.htm

import orange
import orngTest, orngStat, orngTree

# set up the learners
bayes = orange.BayesLearner()
tree = orngTree.TreeLearner(mForPruning=2)
bayes.name = "bayes"
tree.name = "tree"
learners = [bayes, tree]

# compute accuracies on data
data = orange.ExampleTable("voting")
res = orngTest.crossValidation(learners, data, folds=10)
cm = orngStat.computeConfusionMatrices(res,
        classIndex=data.domain.classVar.values.index('democrat'))

stat = (('CA', lambda res,cm: orngStat.CA(res)),
        ('Sens', lambda res,cm: orngStat.sens(cm)),
        ('Spec', lambda res,cm: orngStat.spec(cm)),
        ('AUC', lambda res,cm: orngStat.AUC(res)),
        ('IS', lambda res,cm: orngStat.IS(res)),