def corpus_statistics(root_path, categories):
statistics = dc(dict)
for category in categories:
path = os.path.join(root_path, category)
for corpus in os.listdir(path):
if corpus.endswith(suffix):
statistics[category], statistics[category]["total"] = \
count_tweets(os.path.join(path, corpus))
return dict(statistics)
def __init__(self, no_of_topics=10, scan=False):
self.file_path = None
self.text = None
self.url = None
self.no_of_topics = no_of_topics
self.scan = scan
#self.no_of_sent=None
#self.positive_count
stopword_ = pd.read_pickle(directory +
'\\Text\\dictionary\\text_stopwords.pkl')
positive_dict = pd.read_pickle(directory +
'\\Text\\dictionary\\text_pos.pkl')
negative_dict = pd.read_pickle(directory +
'\\Text\\dictionary\\text_neg.pkl')
constraints = pd.read_pickle(
directory + '\\Text\\dictionary\\text_constraints.pkl')
uncertain = pd.read_pickle(directory +
'\\Text\\dictionary\\text_uncertain.pkl')
# stopword_=pd.read_pickle('C:\\Users\\vishu\\OneDrive\\Desktop\\Vishal\\final year project\\Text-analysis~\\Final_project_Server-main\\Text\\dictionary\\text_stopwords.pkl')
# positive_dict=pd.read_pickle('C:\\Users\\vishu\\OneDrive\\Desktop\\Vishal\\final year project\\Text-analysis~\\Final_project_Server-main\\Text\\dictionary\\text_pos.pkl')
# negative_dict=pd.read_pickle('C:\\Users\\vishu\\OneDrive\\Desktop\\Vishal\\final year project\\Text-analysis~\\Final_project_Server-main\\Text\\dictionary\\text_neg.pkl')
# constraints=pd.read_pickle('C:\\Users\\vishu\\OneDrive\\Desktop\\Vishal\\final year project\\Text-analysis~\\Final_project_Server-main\\Text\\dictionary\\text_constraints.pkl')
# uncertain=pd.read_pickle('C:\\Users\\vishu\\OneDrive\\Desktop\\Vishal\\final year project\\Text-analysis~\\Final_project_Server-main\\Text\\dictionary\\text_uncertain.pkl')
self.stopwords = dc(int)
self.positive_dict_13 = dc(int)
self.negative_dict_13 = dc(int)
self.constraints_dict = dc(int)
self.uncertain_dict = dc(int)
for i in stopword_:
self.stopwords[i] = 1
for i in positive_dict:
self.positive_dict_13[i] = 1
for i in negative_dict:
self.negative_dict_13[i] = 1
for i in constraints:
self.constraints_dict[i] = 1
for i in uncertain:
self.uncertain_dict[i] = 1
from collections import Counter as cs
from collections import defaultdict as dc
x = input().split(" ")
x = list(map(int, x))
for i in range(len(x)):
l1 = []
d = dc()
for j in range(x[i]):
y = input()
d[j + 1] = y
l1.append(y)
print(l1)
print(d)
print(cs(l1))
a = cs(l1)
for k, v in a.items():
if v != 1:
for i in d.keys():
if d[i] == k:
print(i, end="")
print()
'''5 2
a
b
a
a
b
['a', 'b', 'a', 'a', 'b']
defaultdict(None, {1: 'a', 2: 'b', 3: 'a', 4: 'a', 5: 'b'})
Counter({'a': 3, 'b': 2})
from collections import defaultdict as dc
t=lambda:map(int,input().split())
n,x=t()
a=list(t())
d=dc(list)
for i in range(n):
d[a[i]]+=[i+1]
for i in d:
for j in d:
a=b=0
k=x-i-j
if k not in d:
continue
if i==k:
if len(d[i])==1:
continue
b=1
if j==k:
if len(d[j])==1:
continue
b=1
if i==j:
if len(d[i])==1 or (len(d[i])==2 and i==k):
continue
elif i==k:
b=2
a=1
print(d[i][0],d[j][a],d[k][b])
exit()