def get_state_machine():
"""Gets state transition probabilities for items."""
user_stats = get_global_rater_stats()
state_transitions = ConditionalFreqDist()
for user_data in user_stats:
state_transitions.add_counts(user_data['state_machine'])
return state_transitions
def _calculated_item_change(response_data):
"""
Calculates how many items were forgotten and how many were learned.
"""
fs = ConditionalFreqDist()
response_data = [(pid, pt, i, ic)
for (i, (pid, pt, ic)) in enumerate(response_data)]
response_data.sort()
for (pivot_id, pivot_type), responses in groupby(response_data, lambda r:
(r[0], r[1])):
responses = list(responses)
if len(responses) < 2:
continue
for i in xrange(len(responses) - 1):
from_state = responses[i][-1] and 'k' or 'u'
to_state = responses[i + 1][-1] and 'k' or 'u'
fs[from_state].inc(to_state)
return fs
