import numpy as np
from sam.math_util import avk, deriv_avk
from sam.optimize import check_grad
from sam.corpus.corpus import CorpusReader
from sam.vem.model import VEMModel
import sam.log as log
CORPUS_FILENAME = r"./testdata/20news-all.h5"
reader = CorpusReader(CORPUS_FILENAME, data_series='sam')
model = VEMModel(reader)
while True:
model.run_one_iteration()
#model.write_topics()
model.write_parameters()
def check_grads(model):
assert np.isfinite(model.l_alpha())
assert np.isfinite(model.l_valpha())
x = model.grad_l_vmu()
assert np.isfinite(x).all()
import pdb
try:
# Main update rules
log.info('xi update:',
def run(self, options):
if os.path.exists(options.model):
log.info('Loading model snapshot from %s' % options.model)
model = VEMModel.load(options.model)
else:
# Initialize a model from scratch
log.info('Initializing new model on %s [T=%d]' % (options.corpus, options.T))
reader = CorpusReader(options.corpus, data_series='sam')
model = VEMModel(reader=reader, T=options.T)
while model.iteration < options.iterations:
log.info('** Iteration %d / %d **' % (model.iteration + 1, options.iterations))
model.run_one_iteration()
if model.iteration % SAVE_MODEL_INTERVAL == 0:
log.info('Saving model snapshot...')
model.save(options.model)
if model.iteration % SAVE_TOPICS_INTERVAL == 0:
if options.write_topics:
log.info('Saving topics to %s' % options.write_topics)
with open(options.write_topics, 'w') as f:
model.write_topics(f)
if options.write_topic_weights:
log.info('Saving topic weights to %s' % options.write_topic_weights)
with open(options.write_topic_weights, 'w') as f:
model.write_topic_weights_arff(f)
if options.write_topics:
log.info('Saving topics to %s' % options.write_topics)
with open(options.write_topics, 'w') as f:
model.write_topics(f)
if options.write_topic_weights:
log.info('Saving topic weights to %s' % options.write_topic_weights)
with open(options.write_topic_weights, 'w') as f:
model.write_topic_weights_arff(f)
model.save(options.model)
import numpy as np
from sam.math_util import avk, deriv_avk
from sam.optimize import check_grad
from sam.corpus.corpus import CorpusReader
from sam.vem.model import VEMModel
import sam.log as log
CORPUS_FILENAME = 'nips-425D.h5'
reader = CorpusReader(CORPUS_FILENAME, data_series='sam')
model = VEMModel(reader)
while True:
model.run_one_iteration()
model.print_topics()
def check_grads(model):
assert np.isfinite(model.l_alpha())
assert np.isfinite(model.l_valpha())
x = model.grad_l_vmu()
assert np.isfinite(x).all()
import pdb
try:
# Main update rules
log.info('xi update:', check_grad(model, 'xi', model.l_xi, model.grad_l_xi))
def run(self, options):
if os.path.exists(options.model):
log.info('Loading model snapshot from %s' % options.model)
model = VEMModel.load(options.model)
else:
# Initialize a model from scratch
log.info('Initializing new model on %s [T=%d]' %
(options.corpus, options.T))
reader = CorpusReader(options.corpus, data_series='sam')
model = VEMModel(reader=reader, T=options.T)
while model.iteration < options.iterations:
log.info('** Iteration %d / %d **' %
(model.iteration + 1, options.iterations))
model.run_one_iteration()
if model.iteration % SAVE_MODEL_INTERVAL == 0:
log.info('Saving model snapshot...')
model.save(options.model)
if model.iteration % SAVE_TOPICS_INTERVAL == 0:
if options.write_topics:
log.info('Saving topics to %s' % options.write_topics)
with open(options.write_topics, 'w') as f:
model.write_topics(f)
if options.write_topic_weights:
log.info('Saving topic weights to %s' %
options.write_topic_weights)
with open(options.write_topic_weights, 'w') as f:
model.write_topic_weights_arff(f)
if options.write_topics:
log.info('Saving topics to %s' % options.write_topics)
with open(options.write_topics, 'w') as f:
model.write_topics(f)
if options.write_topic_weights:
log.info('Saving topic weights to %s' %
options.write_topic_weights)
with open(options.write_topic_weights, 'w') as f:
model.write_topic_weights_arff(f)
model.save(options.model)
