def _searchFactoid(self, channel, key):
"""Try to typo-match input to possible factoids.
Assume first letter is correct, to reduce processing time.
First, try a simple wildcard search.
If that fails, use the Damerau-Levenshtein edit-distance metric.
"""
# if you made a typo in a two-character key, boo on you.
if len(key) < 3:
return []
db = self.getDb(channel)
cursor = db.cursor()
cursor.execute("""SELECT key FROM keys WHERE key LIKE ?""", ('%' + key + '%',))
wildcardkeys = cursor.fetchall()
if len(wildcardkeys) > 0:
return [line[0] for line in wildcardkeys]
cursor.execute("""SELECT key FROM keys WHERE key LIKE ?""", (key[0] + '%',))
flkeys = cursor.fetchall()
if len(flkeys) == 0:
return []
flkeys = [line[0] for line in flkeys]
dl_metrics = [dameraulevenshtein(key, sourcekey) for sourcekey in flkeys]
dict_metrics = dict(list(zip(flkeys, dl_metrics)))
if min(dl_metrics) <= 2:
return [key for key,item in list(dict_metrics.items()) if item <= 2]
if min(dl_metrics) <= 3:
return [key for key,item in list(dict_metrics.items()) if item <= 3]
return []
def _searchFactoid(self, channel, key):
"""Try to typo-match input to possible factoids.
Assume first letter is correct, to reduce processing time.
First, try a simple wildcard search.
If that fails, use the Damerau-Levenshtein edit-distance metric.
"""
# if you made a typo in a two-character key, boo on you.
if len(key) < 3:
return []
db = self.getDb(channel)
cursor = db.cursor()
cursor.execute("""SELECT key FROM keys WHERE key LIKE ?""", ('%' + key + '%',))
wildcardkeys = cursor.fetchall()
if len(wildcardkeys) > 0:
return [line[0] for line in wildcardkeys]
cursor.execute("""SELECT key FROM keys WHERE key LIKE ?""", (key[0] + '%',))
flkeys = cursor.fetchall()
if len(flkeys) == 0:
return []
flkeys = [line[0] for line in flkeys]
dl_metrics = [dameraulevenshtein(key, sourcekey) for sourcekey in flkeys]
dict_metrics = dict(list(zip(flkeys, dl_metrics)))
if min(dl_metrics) <= 2:
return [key for key,item in dict_metrics.iteritems() if item <= 2]
if min(dl_metrics) <= 3:
return [key for key,item in dict_metrics.iteritems() if item <= 3]
return []
def _getMarketInfo(self, input, action='ticker'):
sm = getattr(self, action + '_supported_markets')
sml = sm.keys()+sm.values()
dl = [dameraulevenshtein(input.lower(), i.lower()) for i in sml]
if (min(dl) <= 2):
mkt = (sml)[dl.index(min(dl))]
else:
return None
if mkt.lower() in sm.keys():
return [mkt.lower(), sm[mkt.lower()],
getattr(self, '_get' + mkt.capitalize() + action.capitalize()),]
r = filter(lambda x: sm[x].lower() == mkt.lower(), sm)
if len(r) == 1:
return [r[0], sm[r[0]],
getattr(self, '_get' + r[0].capitalize() + action.capitalize()),]
return None
