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
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)
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)
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)
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)
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)