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utils.py
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utils.py
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import torch
from torch import nn
import math
import nltk
import pandas as pd
def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
def init_weights(m):
if type(m)==nn.Linear:
nn.init.kaiming_uniform_(m.weight)
def count_each_char(string):
res = {}
for i in string:
if i not in res:
res[i] = 1
else:
res[i] += 1
return res
def entropy(string):
length = len(string)
h=0
freq=count_each_char(string)
for i in string:
p_i=freq[i]/length
h-=p_i*math.log(p_i,2)
return h/length
def bigrams_freq(string):
C=0
bigrams_unique=set(nltk.bigrams(string))
K=len(bigrams_unique)
freq=dict(pd.DataFrame(nltk.bigrams(string)).value_counts())
for each in bigrams_unique:
C+=freq[each]
return C/K