def main(*args, **kwargs):
""" NuPIC NLP main entry point. """
(options, args) = parser.parse_args()
if options.max_terms.lower() == 'all':
max_terms = sys.maxint
else:
max_terms = int(options.max_terms)
min_sparsity = float(options.min_sparsity)
prediction_start = int(options.prediction_start)
verbosity = 0
if options.verbose:
verbosity = 5
# Create the cache directory if necessary.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
reader = NLTK_Reader(os.path.join(cache_dir, 'text'), verbosity=verbosity)
builder = SDR_Builder(cept_app_key, cache_dir, verbosity=verbosity)
nupic = Nupic_Word_Client()
runner = Association_Runner(builder,
nupic,
max_terms,
min_sparsity,
prediction_start,
verbosity=verbosity)
noun_pairs = reader.get_noun_pairs_from_all_texts()[:max_terms]
runner.associate(noun_pairs)
def main(*args, **kwargs):
""" NuPIC NLP main entry point. """
(options, args) = parser.parse_args()
if options.max_terms.lower() == 'all':
max_terms = sys.maxint
else:
max_terms = int(options.max_terms)
min_sparsity = float(options.min_sparsity)
prediction_start = int(options.prediction_start)
verbosity = 0
if options.verbose:
verbosity = 1
# Create the cache directory if necessary.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
builder = SDR_Builder(cept_app_id, cept_app_key, cache_dir, verbosity=verbosity)
nupic = Nupic_Word_Client()
runner = Association_Runner(builder, nupic, max_terms, min_sparsity, prediction_start, verbosity=verbosity)
if len(args) is 0:
print 'no input file provided!'
exit(1)
elif len(args) == 1:
runner.direct_association(args[0])
else:
runner.random_dual_association(args[0], args[1])
def main(*args, **kwargs):
""" NuPIC NLP main entry point. """
(options, args) = parser.parse_args()
if options.max_terms.lower() == 'all':
max_terms = sys.maxint
else:
max_terms = int(options.max_terms)
min_sparsity = float(options.min_sparsity)
prediction_start = int(options.prediction_start)
verbosity = 0
if options.verbose:
verbosity = 5
# Create the cache directory if necessary.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
reader = NLTK_Reader(os.path.join(cache_dir, 'text'), verbosity=verbosity)
builder = SDR_Builder(cept_app_key, cache_dir, verbosity=verbosity)
nupic = Nupic_Word_Client()
runner = Association_Runner(builder, nupic, max_terms, min_sparsity, prediction_start, verbosity=verbosity)
noun_pairs = reader.get_noun_pairs_from_all_texts()[:max_terms]
runner.associate(noun_pairs)
def main(*args, **kwargs):
""" NuPIC NLP main entry point. """
(options, args) = parser.parse_args()
if options.max_terms.lower() == 'all':
max_terms = sys.maxint
else:
max_terms = int(options.max_terms)
min_sparsity = float(options.min_sparsity)
prediction_start = int(options.prediction_start)
verbosity = 0
if options.verbose:
verbosity = 1
retina = options.retina
# Create the cache directory if necessary.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
builder = SDR_Builder(cept_app_key, cache_dir,
verbosity=verbosity,
retina=retina)
def size_to_thresholds(sdr_size):
""" scale minThreshold and activationThreshold according to sdr_size """
factor = float(sdr_size) / (128*128)
return 80*factor, 100*factor
sdr_size = RETINA_SIZES[retina]['width'] * RETINA_SIZES[retina]['height']
minThreshold, activationThreshold = size_to_thresholds(sdr_size)
if options.predict_triples:
# Instantiate TP with parameters for Fox demo
nupic = Nupic_Word_Client(numberOfCols=sdr_size,
minThreshold=minThreshold,
activationThreshold=activationThreshold,
pamLength=10)
else:
nupic = Nupic_Word_Client(numberOfCols=sdr_size)
if options.verbose:
nupic.printParameters()
runner = Association_Runner(builder, nupic,
max_terms, min_sparsity,
prediction_start, verbosity=verbosity)
if len(args) is 0:
print 'no input file provided!'
exit(1)
elif len(args) == 1:
if options.predict_triples:
if options.verbose: print "Predicting triples!"
runner.direct_association_triples(args[0])
else:
runner.direct_association(args[0])
else:
if options.predict_triples:
print "Please specify exactly one input file containing triples"
else:
runner.random_dual_association(args[0], args[1])
def main(*args, **kwargs):
""" NuPIC NLP main entry point. """
(options, args) = parser.parse_args()
if options.max_terms.lower() == 'all':
max_terms = sys.maxint
else:
max_terms = int(options.max_terms)
min_sparsity = float(options.min_sparsity)
prediction_start = int(options.prediction_start)
verbosity = 0
if options.verbose:
verbosity = 1
# Create the cache directory if necessary.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
builder = SDR_Builder(cept_app_id, cept_app_key, cache_dir,
verbosity=verbosity)
if options.predict_triples:
# Instantiate TP with parameters for Fox demo
nupic = Nupic_Word_Client(
minThreshold=80, activationThreshold=100, pamLength=10)
else:
nupic = Nupic_Word_Client()
if options.verbose:
nupic.printParameters()
runner = Association_Runner(builder, nupic,
max_terms, min_sparsity,
prediction_start, verbosity=verbosity)
if len(args) is 0:
print 'no input file provided!'
exit(1)
elif len(args) == 1:
if options.predict_triples:
if options.verbose: print "Predicting triples!"
runner.direct_association_triples(args[0])
else:
runner.direct_association(args[0])
else:
if options.predict_triples:
print "Please specify exactly one input file containing triples"
else:
runner.random_dual_association(args[0], args[1])
def main(*args, **kwargs):
""" NuPIC NLP main entry point. """
(options, args) = parser.parse_args()
if options.max_terms.lower() == 'all':
max_terms = sys.maxint
else:
max_terms = int(options.max_terms)
min_sparsity = float(options.min_sparsity)
prediction_start = int(options.prediction_start)
verbosity = 0
if options.verbose:
verbosity = 1
retina = options.retina
# Create the cache directory if necessary.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
builder = SDR_Builder(cept_app_key,
cache_dir,
verbosity=verbosity,
retina=retina)
def size_to_thresholds(sdr_size):
""" scale minThreshold and activationThreshold according to sdr_size """
factor = float(sdr_size) / (128 * 128)
return 80 * factor, 100 * factor
sdr_size = RETINA_SIZES[retina]['width'] * RETINA_SIZES[retina]['height']
minThreshold, activationThreshold = size_to_thresholds(sdr_size)
if options.predict_triples:
# Instantiate TP with parameters for Fox demo
nupic = Nupic_Word_Client(numberOfCols=sdr_size,
minThreshold=minThreshold,
activationThreshold=activationThreshold,
pamLength=10)
else:
nupic = Nupic_Word_Client(numberOfCols=sdr_size)
if options.verbose:
nupic.printParameters()
runner = Association_Runner(builder,
nupic,
max_terms,
min_sparsity,
prediction_start,
verbosity=verbosity)
if len(args) is 0:
print 'no input file provided!'
exit(1)
elif len(args) == 1:
if options.predict_triples:
if options.verbose: print "Predicting triples!"
runner.direct_association_triples(args[0])
else:
runner.direct_association(args[0])
else:
if options.predict_triples:
print "Please specify exactly one input file containing triples"
else:
runner.random_dual_association(args[0], args[1])
