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()