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):
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(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):
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):
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
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
