def buildClassifier(self, instances):
"""
builds the ZeroR classifier with the given data
Parameter(s):
'instances' -- the data to build the classifier from
"""
self.getCapabilities().testWithFail(instances)
# remove instances with missing class
instances = Instances(instances)
instances.deleteWithMissingClass()
sumOfWeights = 0
self.__Class = instances.classAttribute()
self.__ClassValue = 0
self.__Counts = None
if (instances.classAttribute().isNumeric()):
self.__Counts = None
elif (instances.classAttribute().isNominal()):
self.__Counts = jarray.zeros(instances.numClasses(), 'd')
for i in range(len(self.__Counts)):
self.__Counts[i] = 1
sumOfWeights = instances.numClasses()
enu = instances.enumerateInstances()
while (enu.hasMoreElements()):
instance = enu.nextElement()
if (not instance.classIsMissing()):
if (instances.classAttribute().isNominal()):
self.__Counts[int(
instance.classValue())] += instance.weight()
else:
self.__ClassValue += instance.weight(
) * instance.classValue()
sumOfWeights += instance.weight()
if (instances.classAttribute().isNumeric()):
if (Utils.gr(sumOfWeights, 0)):
self.__ClassValue /= sumOfWeights
else:
self.__ClassValue = Utils.maxIndex(self.__Counts)
Utils.normalize(self.__Counts, sumOfWeights)
return
def buildClassifier(self, instances):
"""
builds the ZeroR classifier with the given data
Parameter(s):
'instances' -- the data to build the classifier from
"""
self.getCapabilities().testWithFail(instances)
# remove instances with missing class
instances = Instances(instances)
instances.deleteWithMissingClass()
sumOfWeights = 0
self.__Class = instances.classAttribute()
self.__ClassValue = 0
self.__Counts = None
if (instances.classAttribute().isNumeric()):
self.__Counts = None
elif (instances.classAttribute().isNominal()):
self.__Counts = jarray.zeros(instances.numClasses(), 'd')
for i in range(len(self.__Counts)):
self.__Counts[i] = 1
sumOfWeights = instances.numClasses()
enu = instances.enumerateInstances()
while (enu.hasMoreElements()):
instance = enu.nextElement()
if (not instance.classIsMissing()):
if (instances.classAttribute().isNominal()):
self.__Counts[int(instance.classValue())] += instance.weight()
else:
self.__ClassValue += instance.weight() * instance.classValue()
sumOfWeights += instance.weight()
if (instances.classAttribute().isNumeric()):
if (Utils.gr(sumOfWeights, 0)):
self.__ClassValue /= sumOfWeights
else:
self.__ClassValue = Utils.maxIndex(self.__Counts)
Utils.normalize(self.__Counts, sumOfWeights)
return
