def solution():
count = 1
min = 100000
for i in range(0, 100):
for j in range(i + 1, 100):
hd = hamming(df.iloc[i, 1], df.iloc[j, 1])
print(count, "(", df.iloc[i, 0], df.iloc[j, 0],
")hamming_distance: ", hd)
if min > hd:
min = hd
count += 1
return min
def sol():
df = pd.read_csv('sample.csv', names=['word', 'bin'])
min = 1000000000
count = 1
for i in range(0, len(df)):
for j in range(i + 1, len(df)):
hd = hamming(df.iloc[i, 1], df.iloc[j, 1])
if min > hd:
min = hd
count += 1
return min
def main():
df = pd.read_csv('sample.csv', names=['word', bin])
print()
min = -1
count = 1
length = df.shape[0]
#length = 10
#print(df.size)
for i in range(length - 1):
for j in range(i + 1, length):
#print(i,j)
hd = hamming(df.iloc[i, 1], df.iloc[j, 1])
print(count, "(", df.iloc[i, 0], df.iloc[j, 0],
")hamming_distance:", hd)
if min == -1 or min > hd:
min = hd
count += 1
print("min hamming distance", min)
import random
import numpy as np
import pandas as pd
from hamming_parctice import hamming
df = pd.read_csv('sample.csv', names=['word', 'bin'])
m_d = hamming(df.iloc[0, 1], df.iloc[1, 1])
count = 1
for i in range(0, len(df)):
for j in range(0, len(df)):
if i < j:
dist = hamming(df.iloc[i, 1], df.iloc[j, 1])
print(count, "(", df.iloc[i, 0], df.iloc[j, 0],
") hamming_distance :", dist)
if m_d > dist:
m_d = dist
count = count + 1
print("min hamming distance", m_d)
import random
import numpy as np
import pandas as pd
from hamming_parctice import hamming
df = pd.read_csv('sample.csv', names = ['word', 'bin'])
min = hamming(df.iloc[1,1], df.iloc[2,1])
count = 1;
for i in range(0,len(df)):
for j in range(0,len(df)):
if i < j:
hd = hamming(df.iloc[i,1],df.iloc[j,1])
print(count,"(",df.iloc[i,0],df.iloc[j,0],")hamming_distance: ",hd)
count = count + 1;
if min > hd:
min = hd
print("min hamming distance", min)
import random
import numpy as np
import pandas as pd
from hamming_parctice import hamming
df = pd.read_csv('sample.csv', names=['word', 'bin'])
leng = df.shape
count = 1
for i in range(0, leng[0]):
for j in range(i + 1, leng[0]):
hd = hamming(df.iloc[i, 1], df.iloc[j, 1])
print(count, "(", df.iloc[i, 0], df.iloc[j, 0], ")hamming_distance:",
hd)
if count == 1:
min_ = hd
if hd < min_:
min_ = hd
count = count + 1
print("min hamming distance", min_)
import random
import numpy as np
import pandas as pd
from hamming_parctice import hamming
from itertools import combinations
df = pd.read_csv('sample.csv', names=['word', 'bin'])
words = [df.iloc[i, 0] for i in range(0, len(df))]
bins = [df.iloc[i, 1] for i in range(0, len(df))]
count = 0
minimum_hd = 32
for alpha, beta in combinations(zip(words, bins), 2):
hd = hamming(alpha[1], beta[1])
print(count, "(", alpha[0], beta[0], ")", "hamming_distance: ", hd)
if minimum_hd > hd:
minimum_hd = hd
count += 1
print("min hamming distance", minimum_hd)
