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slice_variable.py
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slice_variable.py
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"""
:Authors: - Iason
"""
import numpy
import math
from scipy.stats import beta
from collections import defaultdict
import logging
class SliceVariable(object):
def __init__(self, slice_variables={}, conditions={}, a=0.1, b=1):
self.slice_variables = defaultdict(None, slice_variables)
self.conditions = defaultdict(None, conditions)
self.a = a
self.b = b
def get(self, sym, start, end):
"""
Returns a slice variable if it exists, otherwise calculates one based on the condition of the
previous derivations or if none, on a beta distribution
"""
# slice variables are indexed by the annotated LHS symbol as shown below
state = (sym, start, end)
# try to retrieve an assignment of the slice variable
u = self.slice_variables.get(state, None)
if u is None: # if we have never computed such an assignment
theta = self.conditions.get(state, None) # first we try to retrieve a condition
if theta is None: # if there is none
u = math.log(numpy.random.beta(self.a, self.b)) # the option is to sample u from a beta
else: # otherwise
u = math.log(numpy.random.uniform(0, math.exp(theta))) # we must sample u uniformly in the interval [0, theta)
self.slice_variables[state] = u # finally we store u for next time
return u
def reset(self, conditions=None, a=None, b=None):
"""
"""
self.slice_variables = defaultdict(None) # the actual slice variables always get reset
if conditions is not None: # we overwrite conditions only if necessary
self.conditions = defaultdict(None, conditions)
# similarly for the parameters
if a is not None:
self.a = a
if b is not None:
self.b = b
def weight(self, sym, start, end, theta):
state = (sym, start, end)
try:
u = self.slice_variables[state]
except:
raise ValueError('I do not expect to reweight a rule for an unseen state: %s' % str(state))
if theta > u:
return - beta.logpdf(math.exp(u), self.a, self.b)
else:
raise ValueError('I do not expect to reweight rules scoring less than the threshold')