def meanInClusterDistances(cluster):
overlaps = []
perms = list(permutations(cluster, 2))
for (sdr1, sdr2) in perms:
overlap = percentOverlap(sdr1, sdr2)
overlaps.append(overlap)
return sum(overlaps) / len(overlaps)
def computeDistanceMat(sdrs):
"""
Compute distance matrix between SDRs
:param sdrs: (array of arrays) array of SDRs
:return: distance matrix
"""
numSDRs = len(sdrs)
# calculate pairwise distance
distanceMat = np.zeros((numSDRs, numSDRs), dtype=np.float64)
for i in range(numSDRs):
for j in range(numSDRs):
distanceMat[i, j] = 1 - percentOverlap(sdrs[i], sdrs[j])
return distanceMat
def computeDistanceMat(sdrs):
"""
Compute distance matrix between SDRs
:param sdrs: (array of arrays) array of SDRs
:return: distance matrix
"""
numSDRs = len(sdrs)
# calculate pairwise distance
distanceMat = np.zeros((numSDRs, numSDRs), dtype=np.float64)
for i in range(numSDRs):
for j in range(numSDRs):
distanceMat[i, j] = 1 - percentOverlap(sdrs[i], sdrs[j])
return distanceMat