def test_hamming_core(self):
Q = (np.random.random((100, 4)) * 255).astype(np.uint8)
D = (np.random.random((1000, 4)) * 255).astype(np.uint8)
D1 = cext.hamming(Q, D)
D2 = sh.hammingDist(Q, D)
D3 = sh.hammingDist2(Q, D)
self.assertEqual(self.ndarray_diff(D1, D2), 0)
self.assertEqual(self.ndarray_diff(D1, D3), 0)
def hammingDist2(B1, B2):
"""
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]
"""
if B1.ndim == 1:
B1 = B1.reshape((1, -1))
if B2.ndim == 1:
B2 = B2.reshape((1, -1))
npt1, dim1 = B1.shape
npt2, dim2 = B2.shape
if dim1 != dim2:
raise Exception("Dimension not consists: %d, %d" % (dim1, dim2))
Dh = cext.hamming(B1, B2)
return Dh
def hammingDist2(B1, B2):
"""
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]
"""
if B1.ndim == 1:
B1 = B1.reshape((1, -1))
if B2.ndim == 1:
B2 = B2.reshape((1, -1))
npt1, dim1 = B1.shape
npt2, dim2 = B2.shape
if dim1 != dim2:
raise Exception("Dimension not consists: %d, %d" % (dim1, dim2))
Dh = cext.hamming(B1, B2)
return Dh
