Esempi in Python per load_human_data

Esempio n. 1

File: RunStan.py Progetto: joshrule/LOTlib

print "# Loaded hypotheses: ", len(hypotheses)

# - - logging - - - - - - - -
with open(LOG+"/hypotheses.txt", 'w') as f:
    for i, h in enumerate(hypotheses):
        print >>f, i, qq(h)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load the human data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Load the concepts from the human data
from Data import load_human_data

human_nyes, human_ntrials = load_human_data()
print "# Loaded human data"

observed_sets = set([ k[0] for k in human_nyes.keys() ])

## TRIM TO FEWER
# observed_sets = set(list(observed_sets)[:100])

print "# Loaded %s observed sets" % len(observed_sets)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Get the rule count matrices
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

from LOTlib.GrammarInference.GrammarInference import create_counts

Esempio n. 2

File: MakeHypotheses.py Progetto: wrongu/LOTlib

    print "# Finished %s" % str(observed_set)

    return set(tn.get_all())


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# define the running function
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if __name__ == "__main__":

    # Load the concepts from the human data
    from Data import load_human_data

    human_nyes, _ = load_human_data()
    print "# Loaded human data"

    observed_sets = set([k[0] for k in human_nyes.keys()])

    # And now map
    from LOTlib.MPI.MPI_map import MPI_unorderedmap, is_master_process
    from LOTlib.Miscellaneous import display_option_summary

    if is_master_process():
        display_option_summary(options)

    hypotheses = set()
    for s in MPI_unorderedmap(myrun,
                              [[s] for s in observed_sets] * options.CHAINS):
        hypotheses.update(s)

Esempio n. 3

print "# Loaded hypotheses: ", len(hypotheses)

# - - logging - - - - - - - -
with open(LOG + "/hypotheses.txt", 'w') as f:
    for i, h in enumerate(hypotheses):
        print >> f, i, qq(h)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load the human data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Load the concepts from the human data
from Data import load_human_data

human_nyes, human_ntrials = load_human_data()
print "# Loaded human data"

observed_sets = set([k[0] for k in human_nyes.keys()])

## TRIM TO FEWER
# observed_sets = set(list(observed_sets)[:100])

print "# Loaded %s observed sets" % len(observed_sets)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Get the rule count matrices
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

from LOTlib.GrammarInference.GrammarInference import create_counts

Esempio n. 4

File: MakeHypotheses.py Progetto: piantado/LOTlib

        # print "#", h

    print "# Finished %s" % str(observed_set)

    return set(tn.get_all())

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# define the running function
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if __name__ == "__main__":

    # Load the concepts from the human data
    from Data import load_human_data

    human_nyes, _ = load_human_data()
    print "# Loaded human data"

    observed_sets = set([ k[0] for k in human_nyes.keys() ])

    # And now map
    from LOTlib.MPI.MPI_map import MPI_unorderedmap, is_master_process
    from LOTlib.Miscellaneous import display_option_summary

    if is_master_process():
        display_option_summary(options)

    hypotheses = set()
    for s in MPI_unorderedmap(myrun, [ [s] for s in observed_sets ]*options.CHAINS ):
        hypotheses.update(s)