def runTest(self):
for model in ['EvenOdd', 'FOL', 'Magnetism.Simple', 'Magnetism.Complex',
'NAND', 'Number', 'RegularExpression', 'RationalRules',
'StochasticGrammarInduction', 'SymbolicRegression.Galileo',
'SymbolicRegression.Symbolic', 'Prolog', 'PureLambda', 'Lua']:
print "# Testing loading of example", model
make_hypothesis, make_data = load_example(model)
d = make_data()
d = make_data(10) # require an amount
# Let's just try initializing a bunch of times
for _ in xrange(100):
h0 = make_hypothesis()
# and ensure that the samplign will run
for _ in MHSampler(h0, d, steps=100):
pass
def runTest(self):
for model in [
'EvenOdd', 'FOL', 'Magnetism.Simple', 'Magnetism.Complex',
'NAND', 'Number', 'RegularExpression', 'RationalRules',
'StochasticGrammarInduction', 'SymbolicRegression.Galileo',
'SymbolicRegression.Symbolic', 'Prolog', 'PureLambda', 'Lua'
]:
print "# Testing loading of example", model
make_hypothesis, make_data = load_example(model)
d = make_data()
d = make_data(10) # require an amount
# Let's just try initializing a bunch of times
for _ in xrange(100):
h0 = make_hypothesis()
# and ensure that the samplign will run
for _ in MHSampler(h0, d, steps=100):
pass
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
# This is just a wrapper that nicely prints information
standard_sample(make_hypothesis,
make_data,
alsoprint=options.ALSO_PRINT,
steps=options.STEPS,
skip=options.SKIP)
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("--steps", dest="STEPS", type="int", default=Infinity, help="Draw this many samples")
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()
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
# This is just a wrapper that nicely prints information
standard_sample(make_hypothesis, make_data, alsoprint=options.ALSO_PRINT, steps=options.STEPS, skip=options.SKIP)
