Python hamming示例

示例#1

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

示例#2

文件： minimum_hamming_distance.py 项目： cnu-cse-datacom/hammingdistance-omj9803

import random
import numpy as np
import pandas as pd
from hamming_practice import hamming

df = pd.read_csv('sample.csv', names=['word', 'bin'])

count = 0
min = 10

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])
        print(count, "( ", df.iloc[i, 0], df.iloc[j, 0],
              " )hamming_distance: ", hd)
        if min > hd:
            min = hd
        count = count + 1

print("min hamming distance", min)

示例#3

文件： dc.py 项目： cnu-cse-datacom/hammingdistance-Kylin-1q84

import random
import numpy as np
import pandas as pd
from hamming_practice import hamming

df = pd.read_csv('sample.csv', names=['word', 'bin'])

count = 0
xor_lambda = lambda a, b: hamming(a, b)
count_lambda = lambda x: x == 1
minimum = 32

for i in range(0, len(df)):
    for j in range(i + 1, len(df)):
        hd = xor_lambda(df.iloc[i, 1], df.iloc[j, 1])
        print(count, "(", df.iloc[i, 0], df.iloc[j, 0], ")",
              "hamming_distance: ", hd)
        if (hd < minimum):
            minimum = hd
        count += 1

print("min hamming distance", minimum)

示例#4

import random
import numpy as np
import pandas as pd
from hamming_practice import hamming

df = pd.read_csv('sample.csv', names=['word', 'bin'])

count = 1
min_hd = hamming(df.iloc[0, 1], df.iloc[1, 1])
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 > hd:
            min_hd = hd
        count = count + 1

print("min hamming distance", min_hd)

示例#5

import random
import numpy as np
import pandas as pd
from hamming_practice import hamming

df = pd.read_csv('sample.csv', names=['word', 'bin'])

count = 0
min = hamming(max(df.iloc[:, 1]), min(df.iloc[:, 1]))
for i in range(0, len(df) - 1):
    for j in range(i + 1, len(df)):
        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

print("min hamming distance", min)

示例#6

import random
import numpy as np
import pandas as pd
from hamming_practice import hamming

df = pd.read_csv('sample.csv', names=['word','bin'])

count = 0
minimum = 0
for i in range(0, 99):
    for j in range(i+1, 100):
        hd = hamming(df.iloc[i,1], df.iloc[j,1]) # hamming_practice
        print(count,"(", df.iloc[i,0], df.iloc[j,0], ")", hd)
        if i == 0:
            minimum = hd
        if hd < minimum:
            minimum = hd
        count = count + 1
   

print("min hamming distnace", minimum)