def main(database, table, smooth_func, lambda_, min_tag_freq):
with AnnotReader(database) as reader:
reader.change_table(table)
#Builds value calculator
estimator = SmoothEstimator(smooth_func, lambda_, reader.iterate())
calculator = ValueCalculator(estimator)
#Determine tags which will be considered
tags_to_consider = []
if min_tag_freq < 0: #All tags
tags_to_consider = range(estimator.num_tags())
else:
counter = Counter(annot['tag'] for annot in reader.iterate())
for tag, pop in counter.iteritems():
if pop >= min_tag_freq:
tags_to_consider.append(tag)
#Dumps probabilities
connection = None
database = None
try:
items = np.arange(estimator.num_items())
for tag in tags_to_consider:
v_prob_it = calculator.rnorm_prob_items_given_tag(tag, items)
for item in xrange(len(v_prob_it)):
prob = float(v_prob_it[item])
print({'tag':tag, 'item':item, 'prob_it':prob})
finally:
if connection:
connection.disconnect()
