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)