示例#1
0
	def __init__(self, loc, scale, skew):
		try:
			self._sknorm = pdf_moments((loc, scale**2, skew))
			self.st = (loc, scale**2, skew)
		except ValueError:
			self._sknorm = pdf_moments((loc, 1e-8, skew))
			self.st = (loc, 1e-8, skew)
		self.pdf = self._sknorm
示例#2
0
	def __init__(self, *args):
		"""
		Initializes the class.  Should be given a list of tuples:
		[('norm', (norm1_mean, norm1_std), 0.4), 
			('norm', (norm2_mean, norm2_std), 0.6)]
		Can be given:
			'norm', (mean, std), weight
			'skewnorm', (mean, std, skewness), weight
			'uniform', (min, max), weight
			
		"""
		self.dists = []
		self.weights = []
		self.cutoff = None
		self.use_bc = False
		if len(args) == 0:
			return
		
		for name, params, weight in args[0]:
			if name == 'norm':
				self.dists.append(norm(*params))
				self.weights.append(weight)
			elif name == 'skewnorm':
				#pdf_moments takes the VARIANCE ... so must square the std
				params = (params[0], params[1]**2, params[2])
				self.dists.append(pdf_moments(params))
				self.weights.append(weight)
			elif name == 'uniform':
				self.dists.append(uniform(*params))
				self.weights.append(weight)
			else:
				raise KeyError, 'Unknown distribution %s' % name
		assert N.sum(self.weights) == 1, 'sum(weight) != 1'