def fit_to_feat(self, word_v, wchoice): # fits an ad to a feature vector, returns a weight vector vec = [] words = self.ad_to_words() stemmed_words = common.stem_low_wvec(words) words = common.strip_vec(words) # print words for word in word_v: if(wchoice == NUM): vec.append(float(words.count(word))) elif(wchoice == LOG_NUM): vec.append(math.log(float(words.count(word)))) return vec
def gen_word_vec(self, word_v, wchoice=W_CHOICE): # check generates a vector of words from AdVector, fits it to word_v
vec = []
words = self.advec_to_words()
stemmed_words = common.stem_low_wvec(words)
words = common.strip_vec(words)
# print words
for word in word_v:
if(wchoice == NUM):
vec.append(float(words.count(word)))
elif(wchoice == LOG_NUM):
vec.append(math.log(float(words.count(word))))
return vec
def fit_to_feat(self, word_v, wchoice): # fits an news to a feature vector, returns a weight vector vec = [] words = self.news_to_words() stemmed_words = common.stem_low_wvec(words) words = common.strip_vec(words) # print words for word in word_v: if(wchoice == NUM): vec.append(float(words.count(word))) elif(wchoice == LOG_NUM): vec.append(math.log(float(words.count(word)))) return vec
def gen_word_vec(self, word_v, wchoice=W_CHOICE): # check generates a vector of words from NewsVector, fits it to word_v vec = [] words = self.newsvec_to_words() stemmed_words = common.stem_low_wvec(words) words = common.strip_vec(words) # print words for word in word_v: if(wchoice == NUM): vec.append(float(words.count(word))) elif(wchoice == LOG_NUM): vec.append(math.log(float(words.count(word)))) return vec
def word_vectors(list): # returns a frequency vector of words, when input a list of adVecs
ad_union = adVector.AdVector()
for ads in list:
ad_union = ad_union.union(ads)
words = ad_union.advec_to_words()
stemmed_words = common.stem_low_wvec(words)
filtered_words = [w for w in stemmed_words if not w in stopwords.words('english')]
word_v = common.unique_words(filtered_words)
word_v = common.strip_vec(word_v)
wv_list = []
labels = []
for ads in list:
wv_list.append(ads.gen_word_vec(word_v))
labels.append(ads.label)
return wv_list, labels, word_v ## Returns word_v as feature
def word_vectors(
list
): # returns a frequency vector of words, when input a list of adVecs
ad_union = adVector.AdVector()
for ads in list:
ad_union = ad_union.union(ads)
words = ad_union.advec_to_words()
stemmed_words = common.stem_low_wvec(words)
filtered_words = [
w for w in stemmed_words if not w in stopwords.words('english')
]
word_v = common.unique_words(filtered_words)
word_v = common.strip_vec(word_v)
wv_list = []
labels = []
for ads in list:
wv_list.append(ads.gen_word_vec(word_v))
labels.append(ads.label)
return wv_list, labels, word_v ## Returns word_v as feature
