LCMFH-python-version/hammingDist.py at main · binny1446/LCMFH-python-version · GitHub

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import numpy as np

def hammingDist(B1=None,B2=None,*args,**kwargs):
    varargin = hammingDist.varargin
    nargin = hammingDist.nargin


    # Compute hamming distance between two sets of samples (B1, B2)

    # Dh=hammingDist(B1, B2);

    # Input
#    B1, B2: compact bit vectors. Each datapoint is one row.
#    size(B1) = [ndatapoints1, nwords]
#    size(B2) = [ndatapoints2, nwords]
#    It is faster if ndatapoints1 < ndatapoints2
#
# Output
#    Dh = hamming distance.
#    size(Dh) = [ndatapoints1, ndatapoints2]

    # example query
# Dhamm = hammingDist(B2, B1);
# this will give the same result than:
#    Dhamm = distMat(U2>0, U1>0).^2;
# the size of the distance matrix is:
#    size(Dhamm) = [Ntest x Ntraining]

    # loop-up table:
    bit_in_char=np.uint16([0,1,1,2,1,2,2,3,1,2,2,3,2,3,3,4,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,4,5,5,6,5,6,6,7,5,6,6,7,6,7,7,8])

    n1=np.shape(B1,1)

    n2,nwords=np.shape(B2)

    Dh=np.zeros(([n1,n2]), dtype=np.uint16)

    for j in np.arange(1,n1).reshape(-1):
        for n in np.arange(1,nwords).reshape(-1):
            y=np.bitwise_xor(B1(j,n),B2(np.arange(),n))

            Dh[j,np.arange()]=Dh(j,np.arange()) + bit_in_char(y + 1)