def __init__(self, *args, **kwargs): name = kwargs.pop('name', None) if name is None: self.name = 'NaiveBayes'+ '_' + get_ts() else: self.name = name super(NBMatcher, self).__init__() self.clf = GaussianNB(*args, **kwargs)
def __init__(self, *args, **kwargs): super(LinRegMatcher, self).__init__() name = kwargs.pop('name', None) if name is None: self.name = 'LinearRegression' + '_' + get_ts() else: self.name = name self.clf = LinRegClassifierSKLearn(*args, **kwargs)
def __init__(self, *args, **kwargs): name = kwargs.pop('name', None) if name is None: self.name = 'LogisticRegression'+ '_' + get_ts() else: self.name = name super(LogRegMatcher, self).__init__() self.clf = LogisticRegression(*args, **kwargs)
def __init__(self, *args, **kwargs): super(RFMatcher, self).__init__() name = kwargs.pop('name', None) if name is None: self.name = 'RandomForest'+ '_' + get_ts() else: self.name = name self.clf = RandomForestClassifier(*args, **kwargs)
def __init__(self, *args, **kwargs): super(SVMMatcher, self).__init__() name = kwargs.pop('name', None) if name is None: self.name = 'SVM'+ '_' + get_ts() else: self.name = name self.clf = SVC(*args, **kwargs)
def __init__(self, *args, **kwargs): name = kwargs.pop('name', None) if name is None: self.name = 'NaiveBayes' + '_' + get_ts() else: self.name = name super(NBMatcher, self).__init__() self.clf = GaussianNB(*args, **kwargs)
def __init__(self, *args, **kwargs): super(SVMMatcher, self).__init__() name = kwargs.pop('name', None) if name is None: self.name = 'SVM' + '_' + get_ts() else: self.name = name self.clf = SVC(*args, **kwargs)
def __init__(self, *args, **kwargs): super(DTMatcher, self).__init__() name = kwargs.pop('name', None) if name is None: self.name = 'DecisionTree' + '_' + get_ts() else: self.name = name self.clf = DecisionTreeClassifier(*args, **kwargs)
def __init__(self, *args, **kwargs): name = kwargs.pop('name', None) if name is None: self.name = 'BooleanRuleMatcher' + '_' + get_ts() else: self.name = name self.rules = OrderedDict() self.rule_source = OrderedDict() self.rule_conjunct_list = OrderedDict() self.rule_cnt = 0