aravindpai

#merge the frequent pair in corpus
corpus = merge_vocab(best, corpus)
print("Updated Corpus (After Merge operation)": corpus)

#convert a tuple to a string
best = "".join(list(best))

#append to merge and vocabulary
merges = []
merges.append(best)

aravindpai

#compute the best pair
best = max(pairs, key=pairs.get)
print("Most Frequent pair:",best)

aravindpai

#compute frequency of bigrams in a corpus
pairs = get_stats(corpus)
print(pairs)

aravindpai

#merges the most frequent pair in the corpus
#accepts the corpus and best pair
#returns the modified corpus 
import re
def merge_vocab(pair, corpus_in):
    corpus_out = {}
    bigram = re.escape(' '.join(pair))
    p = re.compile(r'(?<!\S)' + bigram + r'(?!\S)')
    
    for word in corpus_in:

aravindpai

#computer frequency of a pair of characters or character sequences
#accepts corpus and return frequency of each pair
def get_stats(corpus):
    pairs = collections.defaultdict(int)
    for word, freq in corpus.items():
        symbols = word.split()
        for i in range(len(symbols)-1):
            pairs[symbols[i],symbols[i+1]] += freq
    return pairs

aravindpai

import collections

#returns frequency of each word
corpus = collections.Counter(corpus)

#convert counter object to dictionary
corpus = dict(corpus)
print("Corpus:",corpus)

aravindpai

#initlialize the vocabulary
vocab = list(set(" ".join(corpus)))
vocab.remove(' ')

#split the word into characters
corpus = [" ".join(token) for token in corpus]

#appending </w>
corpus=[token+' </w>' for token in corpus]

aravindpai

#importing library
import pandas as pd

#reading .txt file
text = pd.read_csv("sample.txt",header=None)

#converting a dataframe into a single list 
corpus=[]
for row in text.values:
    tokens = row[0].split(" ")

aravindpai

dist= compute_distance(midpoints,num)

aravindpai

x1,y1,x2,y2 = person[0]
print(x1,y1,x2,y2)