def regressionPrediction(self, transformedInstance: Instance,
selectedAttributes: List[bool],
coefficients: List[float]):
result = 0
column = 0
for j in range(transformedInstance.numAttributes()):
if self.m_ClassIndex != j and selectedAttributes[j]:
result += coefficients[column] * transformedInstance.value(j)
column += 1
result += coefficients[column]
return result
def convertInstance(self,instance:Instance):
inst=instance
hasMissing=instance.hasMissingValue()
if hasMissing:
vals=[0]*self.getInputFormat().numAttributes()
for j in range(instance.numAttributes()):
if instance.isMissing(j) and self.getInputFormat().classIndex()!=j \
and (self.getInputFormat().attribute(j).isNominal() or self.getInputFormat().attribute(j).isNumeric()):
vals[j]=self.m_ModesAndMeans[j]
else:
vals[j]=instance.value(j)
inst=Instance(instance.weight(),vals)
inst.setDataset(instance.dataset())
self.push(inst,not hasMissing)
