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 = 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):
"""POS Experiment main entry point."""
(options, args) = parser.parse_args()
verbosity = NLTK_Reader.WARN
if options.verbose:
verbosity = NLTK_Reader.DEBUG
reader = NLTK_Reader(input='./resources/text',
cache_dir='./cache/text',
verbosity=verbosity)
simple_tags = not options.full_tagging
if options.text_info:
reader.text_report()
if options.list_texts:
print 'Available texts:'
for t in reader.available_texts():
print '\t%s' % t
if options.input_text:
target_text = options.input_text
else:
target_text = None
if target_text is not None:
if options.pos_report:
print 'Parts of Speech found in %s:' % target_text
for pos in reader.get_parts_of_speech(target_text,
simplify_tags=simple_tags):
tag_description = reader.describe_tag(pos)
print '\t%6s %s (%s)' % (pos, tag_description[0],
tag_description[1])
else:
output_dir = options.output_dir
model = ModelFactory.create(run_pos_model_params.MODEL_PARAMS)
model.enableInference({'predictedField': 'pos'})
if output_dir:
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_file_path = os.path.join(output_dir,
'pos_out_' + target_text)
# Clear the output file with a header.
with open(output_file_path, 'w') as output_file:
output_file.write('%10s%10s%20s\n' %
('input', 'pos', 'predicted_pos'))
# Append each result to output file.
with open(output_file_path, 'a') as output_file:
run_pos_experiment(model, reader, target_text, simple_tags,
output_file)
else:
run_pos_experiment(model, reader, target_text, simple_tags)
def main(*args, **kwargs):
"""POS Experiment main entry point."""
(options, args) = parser.parse_args()
verbosity = NLTK_Reader.WARN
if options.verbose:
verbosity = NLTK_Reader.DEBUG
reader = NLTK_Reader(input="./resources/text", cache_dir="./cache/text", verbosity=verbosity)
simple_tags = not options.full_tagging
if options.text_info:
reader.text_report()
if options.list_texts:
print "Available texts:"
for t in reader.available_texts():
print "\t%s" % t
if options.input_text:
target_text = options.input_text
else:
target_text = None
if target_text is not None:
if options.pos_report:
print "Parts of Speech found in %s:" % target_text
for pos in reader.get_parts_of_speech(target_text, simplify_tags=simple_tags):
tag_description = reader.describe_tag(pos)
print "\t%6s %s (%s)" % (pos, tag_description[0], tag_description[1])
else:
output_dir = options.output_dir
model = ModelFactory.create(run_pos_model_params.MODEL_PARAMS)
model.enableInference({"predictedField": "pos"})
if output_dir:
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_file_path = os.path.join(output_dir, "pos_out_" + target_text)
# Clear the output file with a header.
with open(output_file_path, "w") as output_file:
output_file.write("%10s%10s%20s\n" % ("input", "pos", "predicted_pos"))
# Append each result to output file.
with open(output_file_path, "a") as output_file:
run_pos_experiment(model, reader, target_text, simple_tags, output_file)
else:
run_pos_experiment(model, reader, target_text, simple_tags)