def __init__(self, modeldir=None):
BaseClassifier.__init__(self, modeldir)
self.classes = 46
self.class_sample_num = 5
self.sample_size = 15#15*15
self.model = None
self.model_type = 1 #1=knearest, 2=svm
self.k_neighbor_num = 5
def __init__(self, classes=24, samplepercls=10, modeltype=1, modeldir=None):
BaseClassifier.__init__(self, modeldir)
self.classes = classes
self.class_sample_num = samplepercls
self.model_type = modeltype #1=knearest, 2=svm
self.k_neighbor_num = 5
self.sample_size = 15#15*15
self.model = None
def __init__(self, features, target_names, bins=default_bins, postprocess=None):
"""
Initialize the classifier.
@param features: see `BaseClassifier.__init__()`.
@param target_names: see `BaseClassifier.__init__()`.
@param bins: A list of interval borders as generated by `initialize_bins`.
@param postprocess: A function that can be called to postprocess the generated recommendations in some manner.
At the moment, static cutoff and dynamic cutoff are defined as postprocessing methods.
@return:
"""
BaseClassifier.__init__(self, features, target_names)
self.bins = bins
self.postprocess = postprocess
#make an index that allows fast lookup of index of the correct timedelta column for each sensor
self.timedelta_column_for_sensor = {sensor: self.timedelta_columns.index("%s_timedelta" % sensor)
for sensor, value in self.settings_columns}
def __init__(self,
features,
target_names,
bins=default_bins,
postprocess=None):
"""
Initialize the classifier.
@param features: see `BaseClassifier.__init__()`.
@param target_names: see `BaseClassifier.__init__()`.
@param bins: A list of interval borders as generated by `initialize_bins`.
@param postprocess: A function that can be called to postprocess the generated recommendations in some manner.
At the moment, static cutoff and dynamic cutoff are defined as postprocessing methods.
@return:
"""
BaseClassifier.__init__(self, features, target_names)
self.bins = bins
self.postprocess = postprocess
#make an index that allows fast lookup of index of the correct timedelta column for each sensor
self.timedelta_column_for_sensor = {
sensor: self.timedelta_columns.index("%s_timedelta" % sensor)
for sensor, value in self.settings_columns
}
def __init__(self, features, target_names):
BaseClassifier.__init__(self, features, target_names)
def __init__(self, features, target_names):
BaseClassifier.__init__(self, features, target_names)