def __init__(self, inferenceType=InferenceType.TemporalNextStep,
encoderParams=()):
super(TwoGramModel, self).__init__(inferenceType)
self._logger = opf_utils.initLogger(self)
self._reset = False
self._hashToValueDict = dict()
self._learningEnabled = True
self._encoder = encoders.MultiEncoder(encoderParams)
self._fieldNames = self._encoder.getScalarNames()
self._prevValues = [] * len(self._fieldNames)
self._twoGramDicts = [dict() for _ in xrange(len(self._fieldNames))]
def __init__(self, inferenceType=InferenceType.TemporalNextStep,
encoderParams=()):
""" Two-gram model constructor.
inferenceType: An opf_utils.InferenceType value that specifies what type of
inference (i.e. TemporalNextStep, Classification, etc.)
encoders: Sequence of encoder params dictionaries.
"""
super(TwoGramModel, self).__init__(inferenceType)
self._logger = opf_utils.initLogger(self)
self._reset = False
self._hashToValueDict = dict()
self._learningEnabled = True
self._encoder = encoders.MultiEncoder(encoderParams)
self._fieldNames = self._encoder.getScalarNames()
self._prevValues = [None] * len(self._fieldNames)
self._twoGramDicts = [dict() for _ in xrange(len(self._fieldNames))]