Python: improve performance of some function with a lot of for-loops and if-statements

I am new to Python and I wrote the following code to generate some matrix A. Is there a way to improve the performance of this function? Can I avoid some or all of the for-loops and if-statements?

import numpy as np
# input: some other matrix B and a threshold epsilon
def generate_A(epsilon, n, B):

    A = np.zeros((n,n))

    for v in range(n):
        for w in [x for x in range(n) if x != v]:
            if B[v,w] >= B[w,v]:
                A[w,v] = 1

    for i in range(n):
        for j in range(n):
            if i != j:
                for k in [x for x in range(n) if x != i and x !=j]:
                    if np.abs(B[i,j] - B[i,k] * B[k,j]) < epsilon:
                        if A[i,j] == 0 and A [i,k] * A[k,j] == 1:
                            A[i,j] = 1
                        if A[k,i] == 0 and A[k,j] * A[j,i] == 1:
                            A[k,i] = 1
                        if A[j,k] == 0 and A[j,i] * A[i,k] == 1:
                            A[j,k] = 1

    return(A)