def __init__(self,
	             priors,
	             sigma=10,
	             scale_type='none',
	             prior_power=1):
		'''
		Parameters
		----------
		priors : pd.Series
			term -> prior count

		sigma : np.float
			prior scale

		scale_type : str
			'none': Don't scale prior. Jurafsky approach.
			'class-size': Scale prior st the sum of the priors is the same as the word count
			  in the document-class being scaled
			'corpus-size': Scale prior to the size of the corpus
			'word': Original formulation from MCQ. Sum of priors will be sigma.
			'background-corpus-size': Scale corpus size to multiple of background-corpus.

		prior_power : numeric
			Exponent to apply to prior
			> 1 will shrink frequent words

		'''
		assert scale_type in ['none', 'class-size', 'corpus-size',
		                      'background-corpus-size', 'word']
		self._priors = priors
		self._scale_type = scale_type
		self._prior_power = prior_power
		self._scale = sigma
		LogOddsRatioUninformativeDirichletPrior.__init__(self, sigma)

		Parameters
		----------
		priors : pd.Series
			term -> prior count

		sigma : np.float
			prior scale

		scale_type : str
			'none': Don't scale prior. Jurafsky approach.
			'class-size': Scale prior st the sum of the priors is the same as the word count
			  in the document-class being scaled
			'corpus-size': Scale prior to the size of the corpus
			'word': Original formulation from MCQ. Sum of priors will be sigma.
			'background-corpus-size': Scale corpus size to multiple of background-corpus.

		prior_power : numeric
			Exponent to apply to prior
			> 1 will shrink frequent words

		
scattertext/termsignificance/LogOddsRatioInformativeDirichletPiror.py