def __init__( self, k, mode = 0, distanceFunction = None) : self.k = k Classifier.__init__( self) self.logger.setDebugLevel( 0 ) self.logger.setFileDebugLevel( 3 ) self.distances = {} self.mode = mode self.dist = distanceFunction if(self.dist == None): self.dist = self.calculateDistance
def __init__(self, _data, _trans, _cv): Classifier.__init__(self, _cv) self.data = _data.copy(deep=True) self.names = list(OrderedDict.fromkeys(self.data['CATEGORIA'].values)) self.y = self.data['CATEGORIA'].astype("category").cat.codes.values self.data.drop(['CATEGORIA ESPECIFICA', 'CATEGORIA'], axis=1, inplace=True) self.X = self.data.values
def __init__(self, max_depth, min_group_size): """ Args: max_depth: Maximum Depth of Decision Tree min_group_size: Smallest number of samples before stopping splitting a node """ Classifier.__init__(self) self.min_group_size = min_group_size self.max_depth = max_depth self.root = None self.nodes = []
def __init__(self, training_data, nclassifiers=2): Classifier.__init__(self, training_data) self.nclassifiers = nclassifiers self.classifiers = [] self.classifier_weight = []
def __init__(self, one_hot=False): Classifier.__init__(self) self.one_hot = one_hot
def __init__(self, training_data): Classifier.__init__(self, training_data) # dictionary to count the occurrences of attributes self.attr_counter = defaultdict(lambda: (defaultdict(lambda: defaultdict(int)))) self.label_counter = defaultdict(int) self.max_feature_per_index = defaultdict(int)