def sampleQueryPairs(fileName, weightFile, featFile):
weightMatrix = readWeightMatrix(weightFile)
featMan = FeatureManager()
featMan.loadQueries(featFile)
idDict = featMan.returnIdDict()
qDict = featMan.returnQueryDict()
clusters = loadClustersWithQueryFile(fileName, idDict)
done = {}
for entry in clusters:
minPair = None
maxPair = None
minDist = 1000
maxDist = 0
#print entry
if len(entry) > 3:
sentry = sorted(entry)
for i in range(len(sentry) - 1):
for j in range(i + 1, len(sentry)):
try:
if weightMatrix[sentry[i]][sentry[j]] < minDist:
minDist = weightMatrix[sentry[i]][sentry[j]]
minPair = (qDict[sentry[i]], qDict[sentry[j]])
if weightMatrix[sentry[i]][sentry[j]] > maxDist:
maxDist = weightMatrix[sentry[i]][sentry[j]]
maxPair = (qDict[sentry[i]], qDict[sentry[j]])
except:
dist = random.uniform(0.8, 1.0)
if dist < minDist:
minDist = dist
minPair = (qDict[sentry[i]], qDict[sentry[j]])
if dist > maxDist:
maxDist = dist
maxPair = (qDict[sentry[i]], qDict[sentry[j]])
if minPair and minPair[0] not in done and minPair[1] not in done:
print 'Min\t' + minPair[0] + '\t' + minPair[1]
if maxPair and maxPair[0] not in done and maxPair[1] not in done:
print 'Max\t' + maxPair[0] + '\t' + maxPair[1]
if minPair:
done[minPair[0]] = 1
done[minPair[1]] = 1
if maxPair:
done[maxPair[0]] = 1
done[maxPair[1]] = 1
avg_inter_ij[i] = min(avg_inter_ij[i], score)
avg_inter_ij[j] = min(avg_inter_ij[j], score)
fmin_i = []
for i, mini in avg_inter_ij.items():
#print i, vals.values()
fmin_i.append(mini / maxDiam)
print 'FMIN ', fmin_i
print 'Dunn index ', min(fmin_i)
if __name__ == '__main__':
argv = sys.argv
lbreak = False
weightMatrix = readWeightMatrix(argv[2])
featMan = FeatureManager()
featMan.loadQueries(argv[3])
for ifile in os.listdir(argv[1]):
clusters = loadClustersWithQueryFile(argv[1] + '/' + ifile,
featMan.returnIdDict())
print len(clusters), len(featMan.returnIdDict()), len(weightMatrix)
#load the cluster-assignments and points
Dunn(clusters, weightMatrix)
#DB(clusters,weightMatrix)
#load the weight matrix
#load the centers