def __init__(self, logger=None, verbosity=None, dtype=np.float32, labeltype=np.uint8): Progressable.__init__(self, logger, verbosity) self._exec = SerialExecutor(logger, verbosity) self._dtype = dtype self._labeltype = labeltype if dtype is np.float: self._rescaler = Rescaler() else: self._rescaler = MaxoutRescaler(dtype)
def __init__(self, coordinator, base_classifier): """ Construct a :class:`Classifier` Parameters ---------- coordinator : :class:`Coordinator` The coordinator responsible for the features extraction base_classifier : scikit-learn classifier (:meth:`predict_proba` required) The learning algorithm which will classify the data """ Progressable.__init__(self, coordinator.getLogger()) self._classifier = base_classifier self._coord = coordinator self._classifToUserLUT = [] self._userToClassifLUT = {}
def __init__(self, logger=None, verbosity=10): """ Creates a :class:`TaskExecutor` """ Progressable.__init__(self, logger, verbosity)
def __init__(self, logger=None, verbosity=None): Progressable.__init__(self, logger, verbosity) self._exec = SerialExecutor(logger, verbosity)