def main():
'''
Clustering and data mining resources.
'''
opts = options.options()
# Set document database
document = motor.MotorClient(opts.mongo_host, opts.mongo_port).mango
# Set default database
db = document
if opts.ensure_indexes:
logging.info('Ensuring indexes...')
indexes.ensure_indexes(db)
logging.info('DONE.')
# base url
base_url = opts.base_url
# Simon
application = web.Application(
[
# clustering model dataset
(r'/clusters/(?P<cluster_uuid>.+)/trainers/(?P<trainer_uuid>.+)/?', kmeans.TrainerHandler),
(r'/clusters/(?P<cluster_uuid>.+)/trainers/?', kmeans.TrainerHandler),
# clustering model prediction
(r'/clusters/(?P<cluster_uuid>.+)/predictions/(?P<prediction_uuid>.+)/?', kmeans.PredictionHandler),
(r'/clusters/(?P<cluster_uuid>.+)/predictions/?', kmeans.PredictionHandler),
# clustering models
(r'/clusters/(?P<cluster_uuid>.+)/?', kmeans.Handler),
(r'/clusters/?', kmeans.Handler)
],
# system database
db=db,
# application timezone
tz=arrow.now(opts.timezone),
# application domain
domain=opts.domain,
# Latent Semantic Analysis (number of components)
lsa=opts.lsa,
# Number of centroid seed
number_seeds=opts.number_seeds,
# Maximum number of iterations over the complete dataset
max_iter=opts.max_iterations,
# Number of random initializations that are tried.
number_init=opts.number_init,
# Control early stopping based on the consecutive number of mini batches
# that does not yield an improvement on the smoothed inertia.
max_no_improvement=opts.max_no_improvement,
# Size of the mini batches.
batch_size=opts.batch_size,
# Use ordinary k-means algorithm (in batch mode)
minibatch=opts.minibatch,
# Print progress reports inside k-means algorithm
verbose=opts.verbose,
# Inverse Document Frequency feature weighting
idf=opts.idf,
# Use a hashing feature vectorizer
use_hashing=opts.use_hashing,
# Maximum number of features (dimensions) to extract from text
max_features=opts.max_features,
# Pagination page size
page_size=opts.page_size
)
# Set sql database
# application.sql = momoko.Pool(
# dsn='dbname=simon user=postgres',
# size=1
# )
# Set graph database
# application.graph = Neo4J?
# Set key-value database
# application.kvalue = Riak?
# Setting up simon server process
application.listen(opts.port)
logging.info('Listening daemon swarm on http://%s:%s' % (
opts.host, opts.port))
IOLoop.instance().start()
'''
if __name__ == '__main__':
'''
Clustering and data mining system.
'''
_options = options.options()
# Set document database
document = motor.MotorClient().open_sync().simon
# Set default database
db = document
if _options.ensure_indexes:
logging.info('Ensuring indexes...')
indexes.ensure_indexes(db)
logging.info('DONE.')
# base url
base_url = _options.base_url
# Simon
application = web.Application(
[
# system status
(r'/', IndexHandler),
# clustering model dataset
(r'/clusters/(?P<cluster_uuid>.+)/trainers/(?P<trainer_uuid>.+)/?', kmeans.TrainerHandler),
(r'/clusters/(?P<cluster_uuid>.+)/trainers/?', kmeans.TrainerHandler),
# clustering model prediction
