def predict(statement=[]):
sentence = "".join(statement).split(" ")
choices = []
one = None
two = None
if len(sentence) > 1:
length = len(
autocomplete.predict_currword_given_lastword(
sentence[-2], sentence[-1]))
if length >= 1:
one = autocomplete.predict_currword_given_lastword(
sentence[-2], sentence[-1])[0]
if length >= 2:
two = autocomplete.predict_currword_given_lastword(
sentence[-2], sentence[-1])[1]
else:
length = len(autocomplete.predict_currword(sentence[-1]))
if length >= 1: one = autocomplete.predict_currword(sentence[-1])[0]
if length >= 2: two = autocomplete.predict_currword(sentence[-1])[1]
if one is not None: one = extract(one)
if two is not None: two = extract(two)
if one is not None: choices.append(one)
else: choices.append("N/A")
choices.append(sentence[-1])
if two is not None: choices.append(two)
else: choices.append("N/A")
print " - ".join(choices)
return choices
def predict_word(text, max_suggestions=10):
if " " not in text:
results = [
x[0] for x in autocomplete.predict_currword(text.lower(),
top_n=max_suggestions)
]
else:
words = text.lower().split(" ")
results = [
x[0] for x in autocomplete.predict_currword_given_lastword(
words[-2], words[-1], top_n=max_suggestions)
]
return results
def __init__(self, word, prev_word, num_choices): if prev_word is None: word_list = autocomplete.predict_currword(word,num_choice) else: tmp_wordlist1 = autocomplete.predict_currword(word,num_choice) tmp_wordlist2 = autocomplete.predict_currword_given_lastword(prev_word, word, num_choice) if len(tmp_wordlist1) > len(tmp_wordlist2): word_list = tmp_wordlist1 else: word_list = tmp_wordlist2 self.w_size = len(word_list) for i in range(0, len(word_list)-1): (word_list[i])[1] = (len(word_list)/2 - 1)+((-1)^(i+1))*i self.make_wordTree(word_list)
def predict_character(text, max_suggestions=27):
if " " not in text:
results = autocomplete.predict_currword(text.lower(),
top_n=max_suggestions)
characters = [
x[0][len(text)] if not x[0][len(text):] == "" else " "
for x in results
]
else:
words = text.lower().split(" ")
results = autocomplete.predict_currword_given_lastword(
words[-2], words[-1], top_n=max_suggestions)
characters = [
x[0][len(words[-1])] if not x[0][len(words[-1]):] == "" else " "
for x in results
]
probabilities = [x[1] for x in results]
character_dict = {}
for character in range(len(characters)):
character_dict[characters[character]] = sum([
probabilities[x] for x in range(len(characters))
if characters[character] == characters[x]
])
return sorted(character_dict, key=character_dict.__getitem__, reverse=True)