type="int",
default=0,
help="Skip this many steps between samples")
parser.add_option(
"--alsoprint",
dest="ALSO_PRINT",
type="string",
default="None",
help=
"A function of a hypothesis we can also print at the start of a line to see things we "
"want. E.g. --alsoprint='lambda h: h.get_knower_pattern()' ")
(options, args) = parser.parse_args()
from LOTlib.Miscellaneous import display_option_summary
display_option_summary(options)
# ========================================================================================================
# Load the model specified on the command line
# ========================================================================================================
from LOTlib.Examples import load_example
make_hypothesis, make_data = load_example(options.MODEL)
# ========================================================================================================
# Run the example's standard sampler with these parameters
# ========================================================================================================
from LOTlib.Inference.Samplers.StandardSample import standard_sample
# ========================================================================================================
# Process command line arguments
# ========================================================================================================
from optparse import OptionParser
parser = OptionParser()
parser.add_option("--model", dest="MODEL", type="string", default="Number",
help="Which model do we run? (e.g. 'Number', 'Magnetism.Simple', etc.")
parser.add_option("--skip", dest="SKIP", type="int", default=0, help="Skip this many steps between samples")
parser.add_option("--alsoprint", dest="ALSO_PRINT", type="string", default="None",
help="A function of a hypothesis we can also print at the start of a line to see things we "
"want. E.g. --alsoprint='lambda h: h.get_knower_pattern()' ")
(options, args) = parser.parse_args()
display_option_summary(options)
# ========================================================================================================
# Load the model specified on the command line
# ========================================================================================================
from LOTlib.Examples.ExampleLoader import load_example
make_hypothesis, make_data = load_example(options.MODEL)
# ========================================================================================================
# Run the example's standard sampler with these parameters
# ========================================================================================================
# This is just a wrapper that nicely prints information
standard_sample(make_hypothesis, make_data, alsoprint=options.ALSO_PRINT, skip=options.SKIP)
parser.add_option("--top", dest="TOP_COUNT", type="int", default=20, help="Top number of hypotheses to store")
parser.add_option("--finite", dest="FINITE", type="int", default=10, help="specify the max_length to make language finite")
parser.add_option("--name", dest="NAME", type="string", default='', help="name of file")
parser.add_option("--N", dest="N", type="int", default=3, help="number of inner hypotheses")
parser.add_option("--terminal", dest="TERMINALS", type="string", default='', help="extra terminals")
parser.add_option("--bound", dest="BOUND", type="int", default=5, help="recursion bound")
(options, args) = parser.parse_args()
prefix = 'out/'
# prefix = '/home/lijm/WORK/yuan/lot/'
suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime())
# set the output codec -- needed to display lambda to stdout
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
if rank == 0:
display_option_summary(options); fff()
# you need to run 12 machine on that
DATA_RANGE = np.arange(0, 70, 6)
# DATA_RANGE = np.arange(70, 140, 6)
language = instance(options.LANG, options.FINITE)
args = list(itertools.product([make_hypothesis], [language], DATA_RANGE))
# run on MPI
# results = MPI_map(run, args)
hypotheses = simple_mpi_map(run, args)
# ========================================================================================================
# Get stats
# ========================================================================================================
mpiexec -n 12 python my_search_stp.py --language=Dyck --finite=8 --N=2 --terminal=b --bound=7 --steps=100000
mpiexec -n 12 python my_search_stp.py --language=SimpleEnglish --finite=8 --N=3 --bound=5 --steps=100000
"""
# ========================================================================================================
# Process command line arguments /
# ========================================================================================================
fff = sys.stdout.flush
parser = OptionParser()
parser.add_option("--language", dest="LANG", type="string", default='An', help="name of a language")
parser.add_option("--steps", dest="STEPS", type="int", default=40000, help="Number of samples to run")
parser.add_option("--top", dest="TOP_COUNT", type="int", default=20, help="Top number of hypotheses to store")
parser.add_option("--finite", dest="FINITE", type="int", default=10, help="specify the max_length to make language finite")
parser.add_option("--name", dest="NAME", type="string", default='', help="name of file")
parser.add_option("--N", dest="N", type="int", default=3, help="number of inner hypotheses")
parser.add_option("--terminal", dest="TERMINALS", type="string", default='', help="extra terminals")
parser.add_option("--bound", dest="BOUND", type="int", default=5, help="recursion bound")
(options, args) = parser.parse_args()
prefix = 'out/'
# prefix = '/home/lijm/WORK/yuan/lot/'
suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime())
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
if rank == 0: display_option_summary(options); fff()
DATA_RANGE = np.arange(0, 70, 6)
language = instance(options.LANG, options.FINITE)
args = list(itertools.product([make_hypothesis], [language], DATA_RANGE))
hypotheses = simple_mpi_map(run, args)
fff = sys.stdout.flush
parser = OptionParser()
parser.add_option("--language", dest="LANG", type="string", default='An', help="name of a language")
parser.add_option("--steps", dest="STEPS", type="int", default=40000, help="Number of samples to run")
parser.add_option("--skip", dest="SKIP", type="int", default=100, help="Print out every this many")
parser.add_option("--top", dest="TOP_COUNT", type="int", default=20, help="Top number of hypotheses to store")
parser.add_option("--name", dest="NAME", type="string", default='', help="name of file")
parser.add_option("--N", dest="N", type="int", default=3, help="number of inner hypotheses")
parser.add_option("--terminal", dest="TERMINALS", type="string", default='', help="extra terminals")
parser.add_option("--bound", dest="BOUND", type="int", default=5, help="recursion bound")
parser.add_option("--out", dest="OUT", type="str", default="out/", help="Output directory")
(options, args) = parser.parse_args()
global prefix
prefix = options.OUT
global suffix
suffix = time.strftime('_' + options.NAME + '_%m%d_%H%M%S', time.localtime())
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
if rank == 0: display_option_summary(options); fff()
#DATA_RANGE = np.arange(120, 264, 12)
DATA_RANGE = np.arange(100, 50000, 100)
random.shuffle(DATA_RANGE) # run in random order
args = list(itertools.product([options], DATA_RANGE))
# run(options, 1000)
hypotheses = simple_mpi_map(run, args)
