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 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 __init__(self):
ConditionalFreqDist.__init__(self)
kanji_script = scripts.Script.Kanji
i_stream = sopen(_edict_aligned_file, 'r')
for line in i_stream:
alignment = Alignment.from_line(line)
for (g, p) in alignment:
if scripts.contains_script(kanji_script, g):
self[g].inc(scripts.to_hiragana(p))
i_stream.close()
return
def __init__(self):
ConditionalFreqDist.__init__(self)
kanji_script = scripts.Script.Kanji
i_stream = sopen(_edict_aligned_file, 'r')
for line in i_stream:
alignment = Alignment.from_line(line)
for (g, p) in alignment:
if scripts.contains_script(kanji_script, g):
self[g].inc(scripts.to_hiragana(p))
i_stream.close()
return
def __init__(self):
# Loads up the frequency distribution for P(r*|k).
self.normalized_freq_dist = ConditionalFreqDist.from_file(
_reading_counts_file)
# Load up the alternation probabilities P(r|r*).
self.alternation_dist = self._load_alternation_dist(
_reading_counts_map_file)
# Build a mapping from (k, r) to r*.
self.from_canonical_reading = self._build_alternation_map()
# Build a frequency distribution for P(r|k).
self.raw_freq_dist = raw_reading_model.RawReadingModel()
self.reverse_mapping = self.get_reverse_mapping()
return
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
