def validateModel(self, testFile):
testdata = LibsvmFileImporter(testFile).get_dataSet()
self.__inst_test = testdata.get_numInstances()
## --- statistics
correct = 0.
sum_error = 0
for i in testdata.get_targets():
if i == 1: #correct
correct += 1.
else:
sum_error += math.pow(1 - i, 2)
# percent correct
self.__pct_correct = 100 * (correct / self.__inst_test)
# root mean squared error
self.__rmse = math.sqrt(sum_error / self.__inst_test)
def validateModel(self, testFile):
testdata = LibsvmFileImporter(testFile).get_dataSet()
self.__inst_test = testdata.get_numInstances()
## --- statistics
correct = 0.
sum_error = 0
for i in testdata.get_targets():
if i == 1: #correct
correct += 1.
else:
sum_error += math.pow(1 - i, 2)
# percent correct
self.__pct_correct = 100 * (correct/self.__inst_test)
# root mean squared error
self.__rmse = math.sqrt(sum_error / self.__inst_test)
def buildClassifier(self, trainFile):
'''"builds" a classification model returning always 1 for each instance'''
train = LibsvmFileImporter(trainFile).get_dataSet()
self.__inst_train = train.get_numInstances()
def buildClassifier(self, trainFile):
'''"builds" a classification model returning always 1 for each instance'''
train = LibsvmFileImporter(trainFile).get_dataSet()
self.__inst_train = train.get_numInstances()