import numpy as np

# Pairwise similarity indices calculated over binary fingerprints

def indicators(x, y):
    """Calculating base descriptors
    a : number of common on bits
    d : number of common off bits
    dis = b + c : 1-0 mismatches
    p : len of fingerprint
    Check Table S1 in the SI of https://link.springer.com/article/10.1186/s13321-021-00505-3#Sec21
    """
    p = len(x)
    a = np.dot(x, y)
    d = np.dot(1 - x, 1 - y)
    dis = p - a - d
    return a, d, dis, p

# Indices
# BUB: Baroni-Urbani-Buser, Fai: Faith, Ja: Jaccard
# JT: Jaccard-Tanimoto, RT: Rogers-Tanimoto, RR: Russel-Rao
# SM: Sokal-Michener, SSn: Sokal-Sneath n

x = np.array([1, 0, 1, 0, 1])
y = np.array([1, 1, 1, 0, 0])

a, d, dis, p = indicators(x, y)

bub = (a * d)**0.5 + a)/((a * d)**0.5 + a + dis)

fai = (a + 0.5 * d)/p

ja = (3 * a)/(3 * a + dis)

jt = a/(a + dis)

rt = (a + d)/(p + dis)

rr = a/p

sm =(a + d)/p

ss1 = a/(a + 2 * dis)

ss2 = (2 * (a + d))/(p + (a + d))