def __init__(self):
#self.db = {}
self.engine_type = 'randomdb'
self.db = RandomChoiceDict()
self.db_noise = {}
self.log = []
# ALWAYS WORKING WITH LOG PROBABILITIES
self.uneval_p = 0
self.eval_p = 0
self.p = 0
self.env = Environment()
self.assumes = {}
self.observes = {}
self.predicts = {}
self.vars = {}
def __init__(self):
self.engine_type = 'reduced traces'
self.assumes = {} # id -> evalnode
self.observes = {} # id -> evalnode
self.predicts = {} # id -> evalnode
self.directives = []
self.db = RandomChoiceDict()
self.weighted_db = WeightedRandomChoiceDict()
self.choices = {} # hash -> evalnode
self.xrps = {} # hash -> (xrp, set of application nodes)
self.env = EnvironmentNode()
self.p = 0
self.uneval_p = 0
self.eval_p = 0
self.new_to_old_q = 0
self.old_to_new_q = 0
self.debug = False
# necessary because of the new XRP interface requiring some state kept while doing inference
self.application_reflip = False
self.reflip_node = ReducedEvalNode(self, self.env, VarExpression(''))
self.nodes = []
self.old_vals = [Value()]
self.new_vals = [Value()]
self.old_val = Value()
self.new_val = Value()
self.reflip_xrp = XRP()
self.mhstats_details = False
self.mhstats = {}
self.made_proposals = 0
self.accepted_proposals = 0
self.hashval = rrandom.random.randbelow()
return
