# Data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def make_data(size=options.datasize):
return [FunctionData(input=[],
output={'h e s': size, 'm e s': size, 'm e g': size, 'h e g': size, 'm e n': size, 'h e m': size, 'm e k': size, 'k e s': size, 'h e k': size, 'k e N': size, 'k e g': size, 'h e n': size, 'm e N': size, 'k e n': size, 'h e N': size, 'f e N': size, 'g e N': size, 'n e N': size, 'n e s': size, 'f e n': size, 'g e n': size, 'g e m': size, 'f e m': size, 'g e k': size, 'f e k': size, 'f e g': size, 'f e s': size, 'n e g': size, 'k e m': size, 'n e m': size, 'g e s': size, 'n e k': size})]
import LOTlib.Miscellaneous
from LOTlib.Grammar import Grammar
from LOTlib.Miscellaneous import q
from LOTlib.Eval import register_primitive
register_primitive(LOTlib.Miscellaneous.flatten2str)
# # # # # # # # # # # # # # # # # # # # # # # # # # # #
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Grammar
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
TERMINAL_WEIGHT = 15
grammar = Grammar()
# flattern2str lives at the top, and it takes a cons, cdr, car structure and projects it to a string
grammar.add_rule('START', 'flatten2str', ['EXPR'], 1.0)
grammar.add_rule('EXPR', 'sample_', ['SET'], 1.)
from LOTlib.Eval import register_primitive from LOTlib.Miscellaneous import flatten2str, logsumexp, qq from LOTlib.TopN import TopN from Model.Hypothesis import MyHypothesis from Language import * from mpi4py import MPI comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() fff = sys.stdout.flush from copy import deepcopy from LOTlib.Projects.FormalLanguageTheory.Model.Grammar import base_grammar # passed in as kwargs register_primitive(flatten2str) global prefix global suffix prefix = "" suffix = "" LARGE_SAMPLE = 10000 # sample this many and then re-normalize to fractional counts from math import log from LOTlib.Miscellaneous import attrmem from Levenshtein import distance from LOTlib.Hypotheses.RecursiveLOTHypothesis import RecursiveLOTHypothesis, RecursionDepthException from LOTlib.Hypotheses.Proposers.Sandbox.InverseInlineProposer import InverseInlineProposer from LOTlib.Hypotheses.Proposers.InsertDeleteProposal import InsertDeleteProposal from LOTlib.Hypotheses.Likelihoods.StochasticLikelihood import StochasticLikelihood
'D N V N': size,
'D N V D N': size
})
]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Grammar
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import LOTlib.Miscellaneous
from LOTlib.Grammar import Grammar
from LOTlib.Miscellaneous import q
from LOTlib.Eval import register_primitive
register_primitive(LOTlib.Miscellaneous.flatten2str)
# # # # # # # # # # # # # # # # # # # # # # # # # # # #
TERMINAL_WEIGHT = 2.
grammar = Grammar()
# flattern2str lives at the top, and it takes a cons, cdr, car structure and projects it to a string
grammar.add_rule('START', 'flatten2str', ['EXPR'], 1.0)
grammar.add_rule('BOOL', 'and_', ['BOOL', 'BOOL'], 1.)
grammar.add_rule('BOOL', 'or_', ['BOOL', 'BOOL'], 1.)
grammar.add_rule('BOOL', 'not_', ['BOOL'], 1.)
grammar.add_rule('EXPR', 'if_', ['BOOL', 'EXPR', 'EXPR'], 1.)
from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler
from LOTlib.Eval import register_primitive
from LOTlib.Miscellaneous import flatten2str, logsumexp, qq
from LOTlib.TopN import TopN
from Model.Hypothesis import MyHypothesis
from Language import *
from mpi4py import MPI
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
fff = sys.stdout.flush
from copy import deepcopy
from LOTlib.Projects.FormalLanguageTheory.Model.Grammar import base_grammar # passed in as kwargs
register_primitive(flatten2str)
global prefix
global suffix
prefix = ""
suffix = ""
LARGE_SAMPLE = 100000 # sample this many and then re-normalize to fractional counts
def run(options, ndata):
"""
This out on the DATA_RANGE amounts of data and returns all hypotheses in top count
"""
if LOTlib.SIG_INTERRUPTED:
return set()
# Math rules (30-40 of these)
# ---------------------------
# Odd numbers
mix_grammar.add_rule('EXPR', 'plus_', ['ODD', str(1)], 1.)
mix_grammar.add_rule('ODD', 'times_', ['X', str(2)], 1.)
# Primes
mix_grammar.add_rule('EXPR', 'isprime_', ['X'], 1.)
# Squares, cubes
mix_grammar.add_rule('EXPR', 'ipowf_', ['X', str(2)], 1.)
mix_grammar.add_rule('EXPR', 'ipowf_', ['X', str(3)], 1.)
# { 2^n - 32 }
register_primitive(lambda x: x if x in (2, 4, 8, 16, 64) else 0, name='pow2n_d32_')
mix_grammar.add_rule('EXPR', 'pow2n_d32_', ['X'], 1.)
# { 2^n & 37 }
register_primitive(lambda x: x if x in (2, 4, 8, 16, 32, 37, 64) else 0, name='pow2n_u37_')
mix_grammar.add_rule('EXPR', 'pow2n_u37_', ['X'], 1.)
# [2,12] * n
for i in range(2, 13):
mix_grammar.add_rule('EXPR', 'times_', ['X', str(i)], 1.)
# [2,10] ^ m
for i in range(2, 11):
mix_grammar.add_rule('EXPR', 'ipowf_', [str(i), 'X'], 1.)
# Ends in [0,9]
for i in range(0, 10):
