I need to compare two arrays made only by 0 and 1:

A = [0,1,0,0,1]
B = [1,1,0,0,1]

I want to get the number of elements that aren't equals and the percentage, i tried this code:

print(numpy.mean(A == B))

but sometimes i get 0 when some elements are equal...

CodePudding user response：

To get the number of elements that aren't equal in the two lists index wise, you can do this :

noneq = sum(i==j for i, j in zip(A, B))

To get the percentage, you can simple calculate noneq/len(A) or noneq/len(B)

CodePudding user response：

You could use list comprehension to create list of whether the values in indexes are equal and then calculate the mean of the said list.

numpy.mean([x == y for x, y in zip(A, B)])

CodePudding user response：

import numpy as np

A = np.array([0,1,0,0,1])

B = np.array([1,1,0,0,1])

# number of
(A!=B).sum() #1

# percentage
(A!=B).sum()*100 / A.size

CodePudding user response：

use count_nonzero to Count the number of elements satisfying the condition

A = np.array([0,1,0,0,1]) 
B = np.array([1,1,0,0,1])
print(np.count_nonzero((A==B) == True))

for the percentage use np.count_nonzero/shape

CodePudding user response：

While list comprehension is always the most elegant solution - also esthetically, in my view - I found the following slightly faster:

def calc(v1, v2):
    s = 0
    for i, x in enumerate(v1):
        s  = x == v2[i]
    return s / len(v1)

Clearly, in all cases, you should always check/be aware of what happens if A and B hace different lengths (for example, the code I have just share would return an IndexError if len(v1) > len(v2); in that respect, zip behaves differently in different version of python)