def applyLearner(self):
self.warning(0)
if float(self.m_estimator.m) < 0:
self.warning(0, "Parameter m should be positive")
self.learner = None
elif float(self.windowProportion) < 0 or float(
self.windowProportion) > 1:
self.warning(
0, "Window proportion for LOESS should be between 0.0 and 1.0")
self.learner = None
else:
self.learner = orange.BayesLearner(
name=self.name, adjustThreshold=self.adjustThreshold)
self.learner.estimatorConstructor = self.estMethods[
self.probEstimation][1]
if self.condProbEstimation:
self.learner.conditionalEstimatorConstructor = self.condEstMethods[
self.condProbEstimation][1]
self.learner.conditionalEstimatorConstructorContinuous = orange.ConditionalProbabilityEstimatorConstructor_loess(
windowProportion=self.windowProportion,
nPoints=self.loessPoints)
if self.preprocessor:
self.learner = self.preprocessor.wrapLearner(self.learner)
self.send("Learner", self.learner)
self.applyData()
self.changed = False
def setData(self, attr, data):
self.clearAll()
self.attr, self.data = attr, data
self.curCutPoints = []
if not data or not attr:
self.snapDecimals = 1
self.probDist = None
return
if data.domain.classVar:
self.contingency = orange.ContingencyAttrClass(attr, data)
try:
self.condProb = orange.ConditionalProbabilityEstimatorConstructor_loess(
self.contingency,
nPoints=50)
except:
self.condProb = None
self.probDist = None
attrValues = self.contingency.keys()
else:
self.condProb = self.contingency = None
self.probDist = orange.Distribution(attr, data)
attrValues = self.probDist.keys()
if attrValues:
self.minVal, self.maxVal = min(attrValues), max(attrValues)
else:
self.minVal, self.maxVal = 0, 1
mdist = self.maxVal - self.minVal
if mdist > 1e-30:
self.snapDecimals = -int(math.ceil(math.log(mdist, 10)) -2)
else:
self.snapDecimals = 1
self.baseCurveX = None
self.plotRug(True)
self.plotProbCurve(True)
self.plotCutLines(True)
self.updateLayout()
self.replot()
j += 1
print("beta:", -m.beta)
#t = orange.ExampleTable('c:/proj/domains/voting.tab') # discrete
t = orange.ExampleTable(
r"E:\Development\Orange Datasets\UCI\shuttle.tab") # discrete
#t = orange.ExampleTable('c_cmc.tab') # continuous
print("NAIVE BAYES")
print("===========")
bl = orange.BayesLearner()
bl.estimatorConstructor = orange.ProbabilityEstimatorConstructor_Laplace()
# prevent too many estimation points
# increase the smoothing level
bl.conditionalEstimatorConstructorContinuous = orange.ConditionalProbabilityEstimatorConstructor_loess(
windowProportion=0.5, nPoints=10)
c = bl(t)
printmodel(t, c, printexamples=0)
print("\n\nLOGISTIC REGRESSION")
print("===================")
c = orngLR_Jakulin.BasicLogisticLearner()(t)
printmodel(t, c, printexamples=0)
print("\n\nLINEAR SVM")
print("==========")
l = orngSVM.BasicSVMLearner()
l.kernel = 0 # linear SVM
l.for_nomogram = 1
c = l(t)
printmodel(t, c, printexamples=0)