def generateVantagePointsWithPattern(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
majorPattern = [[] for i in xrange(numberOfVP)]
minorPattern = [[] for i in xrange(numberOfVP)]
for j in xrange(dim):
d = {}
for i in xrange(numberOfData):
if datas[i][j] in d:
d[datas[i][j]] += 1
else:
d[datas[i][j]] = 0
d = sorted(d.items(), key=lambda x: x[1], reverse=True)
for k in xrange(numberOfVP):
majorPattern[k].append(d[k][0])
minorPattern[k].append(d[-1 - k][0])
utils.writeDataToFile(
'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality, 'major'),
majorPattern)
utils.writeDataToFile(
'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality, 'minor'),
minorPattern)
def generateVantagePointsWithManyAlgorithm(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
vps = []
vps.append(datas[0])
while len(vps) < numberOfVP:
print len(vps)
ans, ansDataIndex = -1, -1
for i in xrange(len(datas)):
ok = False
for j in xrange(len(vps)):
if datas[i] == vps[j]:
ok = True
if ok:
continue
vps.append(datas[i])
cur = calculateMany(vps)
if cur > ans:
ans, ansDataIndex = cur, i
vps = vps[:-1]
vps.append(datas[ansDataIndex])
utils.writeDataToFile(
'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality, typeOfVP),
vps)
def generateVantagePointsWithManyAlgorithm(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
vps = []
vps.append(datas[0])
while len(vps) < numberOfVP:
print len(vps)
ans,ansDataIndex = -1,-1
for i in xrange(len(datas)):
ok = False
for j in xrange(len(vps)):
if datas[i] == vps[j]:
ok = True
if ok:
continue
vps.append(datas[i])
cur = calculateMany(vps)
if cur > ans:
ans,ansDataIndex = cur,i
vps = vps[:-1]
vps.append(datas[ansDataIndex])
utils.writeDataToFile('vp/vp_%d_%d_%d_%s.txt'%(dim,numberOfVP,cardinality,typeOfVP),vps)
def convertNDDSToCDS(options):
size = options['numberOfData']
dim = options['numberOfDimension']
distribution = options['distribution']
cardinality = options['numberOfAlphabet']
numberOfVP = options['numberOfVP']
typeOfVP = options['typeOfVP']
dataFileName = 'data/data_%d_%d_%s_%d.txt' % (size, dim, distribution,
cardinality)
queryFileName = 'query/query_%d_%d_%s_%d.txt' % (size, dim, distribution,
cardinality)
vpFileName = 'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality,
typeOfVP)
# cdsDataFileName = utils.getCDSDataFileName(options)
# cdsQueryFileName= utils.getCDSQueryFileName(options)
datas = utils.getDataInFile(dataFileName)
querys = utils.readDataFromFile(queryFileName)
vps = utils.readDataFromFile(vpFileName)
print len(datas), len(querys), len(vps)
d = [{} for i in xrange(dim)]
for i in xrange(len(datas)):
for j in xrange(dim):
if datas[i][j] in d[j]:
d[j][datas[i][j]] += 1
else:
d[j][datas[i][j]] = 1
for i in xrange(dim):
for key in d[i]:
d[i][key] = 1.0 - float(d[i][key]) / float(len(datas))
def geh(a, b):
ret = 0.0
for i in xrange(len(a)):
if a[i] != b[i]:
ret += 1.0
else:
ret += d[i][a[i]] / float(dim)
return ret / float(dim)
datas = datas[:1000]
average = 0.0
for i in xrange(len(datas)):
cur = 98765432
for j in xrange(len(datas)):
if i != j:
cur = min(cur, geh(datas[i], datas[j]))
average += cur
average /= len(datas)
print average
def generateRandomVP(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
vps = []
for i in xrange(numberOfVP):
vps.append(datas[random.randint(0, numberOfData)])
utils.writeDataToFile(
'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality, typeOfVP),
vps)
def generateVantagePointsWithManyAlgorithm(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
majorPattern = [[] for i in xrange(numberOfVP + 1)]
for j in xrange(dim):
d = {}
for i in xrange(numberOfData):
if datas[i][j] in d:
d[datas[i][j]] += 1
else:
d[datas[i][j]] = 0
d = sorted(d.items(), key=lambda x: x[1], reverse=True)
for k in xrange(1):
majorPattern[k].append(d[k][0])
vps = []
vps.append(majorPattern[0])
d = [0 for i in xrange(dim + 1)]
one_pass = True
threshold = 0
while len(vps) < numberOfVP:
print len(vps)
ans, ansDataIndex = -1, -1
for i in xrange(len(datas)):
ok = False
for j in xrange(len(vps)):
dist = utils.hammingDistance(datas[i], vps[j])
if d[dist] > threshold:
ok = True
if datas[i] == vps[j]:
ok = True
if ok:
continue
for j in xrange(len(vps)):
dist = utils.hammingDistance(datas[i], vps[j])
d[dist] += 1
vps.append(datas[i])
threshold += 1
utils.writeDataToFile(
'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality, typeOfVP),
vps)
def getMajorPattern():
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
rs = [random.randint(0, numberOfData - 1) for i in xrange(100)]
majorPattern = ''
for j in xrange(dim):
d = {}
for i in rs:
if datas[i][j] in d:
d[datas[i][j]] += 1
else:
d[datas[i][j]] = 0
d = sorted(d.items(), key=lambda x: x[1], reverse=True)
majorPattern += d[0][0]
return majorPattern
def generateVantagePointsWithManyAlgorithm(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
majorPattern = [[] for i in xrange(numberOfVP + 1)]
for j in xrange(dim):
d = {}
for i in xrange(numberOfData):
if datas[i][j] in d:
d[datas[i][j]] += 1
else:
d[datas[i][j]] = 0
d = sorted(d.items(), key=lambda x: x[1], reverse=True)
for k in xrange(1):
majorPattern[k].append(d[k][0])
vps = []
vps.append(majorPattern[0])
while len(vps) < numberOfVP:
print len(vps)
ans, ansDataIndex = -1, -1
for i in xrange(len(datas)):
ok = False
for j in xrange(len(vps)):
if datas[i] == vps[j]:
ok = True
if ok:
continue
vps.append(datas[i])
cur = calculateMany(vps)
if cur > ans:
ans, ansDataIndex = cur, i
vps = vps[:-1]
vps.append(datas[ansDataIndex])
utils.writeDataToFile(
'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality, typeOfVP),
vps)
def convertNDDSToCDS(options):
size = options['numberOfData']
dim = options['numberOfDimension']
distribution = options['distribution']
cardinality = options['numberOfAlphabet']
numberOfVP = options['numberOfVP']
typeOfVP = options['typeOfVP']
dataFileName = 'data/data_%d_%d_%s_%d.txt' % (size, dim, distribution,
cardinality)
queryFileName = 'query/query_%d_%d_%s_%d.txt' % (size, dim, distribution,
cardinality)
vpFileName = 'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality,
typeOfVP)
cdsDataFileName = utils.getCDSDataFileName(options)
cdsQueryFileName = utils.getCDSQueryFileName(options)
# cdsDataFileName = 'cds_data/data_%d_%d_%s_%d_%s.txt'%(size,numberOfVP,distribution,cardinality,typeOfVP)
# cdsQueryFileName = 'cds_query/query_%d_%d_%s_%d_%s.txt'%(size,numberOfVP,distribution,cardinality,typeOfVP)
datas = utils.getDataInFile(dataFileName)
querys = utils.readDataFromFile(queryFileName)
vps = utils.readDataFromFile(vpFileName)
print len(datas), len(querys), len(vps)
cdsDatas = []
for i in xrange(len(datas)):
t = []
for j in xrange(len(vps)):
t.append(utils.hammingDistance(datas[i], vps[j]))
cdsDatas.append(t)
utils.writeDataToFile(cdsDataFileName, cdsDatas)
cdsQuerys = []
for i in xrange(len(querys)):
t = []
for j in xrange(len(vps)):
t.append(utils.hammingDistance(querys[i], vps[j]))
cdsQuerys.append(t)
utils.writeDataToFile(cdsQueryFileName, cdsQuerys)
print cdsDataFileName, cdsQueryFileName
def generateVantagePointsWithPattern(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
majorPattern = [ [] for i in xrange(numberOfVP) ]
minorPattern = [ [] for i in xrange(numberOfVP) ]
for j in xrange(dim):
d = {}
for i in xrange(numberOfData):
if datas[i][j] in d:
d[datas[i][j]] += 1
else :
d[datas[i][j]] = 0
d = sorted(d.items(), key=lambda x: x[1], reverse=True)
for k in xrange(numberOfVP):
majorPattern[k].append(d[k][0])
minorPattern[k].append(d[-1-k][0])
utils.writeDataToFile('vp/vp_%d_%d_%d_%s.txt'%(dim,numberOfVP,cardinality,'major'),majorPattern)
utils.writeDataToFile('vp/vp_%d_%d_%d_%s.txt'%(dim,numberOfVP,cardinality,'minor'),minorPattern)
def convertNDDSToCDS(options):
size = options['numberOfData']
dim = options['numberOfDimension']
distribution = options['distribution']
cardinality = options['numberOfAlphabet']
numberOfVP = options['numberOfVP']
typeOfVP = options['typeOfVP']
dataFileName = 'data/data_%d_%d_%s_%d.txt'%(size,dim,distribution,cardinality)
queryFileName = 'query/query_%d_%d_%s_%d.txt'%(size,dim,distribution,cardinality)
vpFileName = 'vp/vp_%d_%d_%d_%s.txt'%(dim,numberOfVP,cardinality,typeOfVP)
cdsDataFileName = utils.getCDSDataFileName(options)
cdsQueryFileName= utils.getCDSQueryFileName(options)
# cdsDataFileName = 'cds_data/data_%d_%d_%s_%d_%s.txt'%(size,numberOfVP,distribution,cardinality,typeOfVP)
# cdsQueryFileName = 'cds_query/query_%d_%d_%s_%d_%s.txt'%(size,numberOfVP,distribution,cardinality,typeOfVP)
datas = utils.getDataInFile(dataFileName)
querys = utils.readDataFromFile(queryFileName)
vps = utils.readDataFromFile(vpFileName)
print len(datas),len(querys),len(vps)
cdsDatas = []
for i in xrange(len(datas)):
t = []
for j in xrange(len(vps)):
t.append(utils.hammingDistance(datas[i],vps[j]))
cdsDatas.append(t)
utils.writeDataToFile(cdsDataFileName,cdsDatas)
cdsQuerys = []
for i in xrange(len(querys)):
t = []
for j in xrange(len(vps)):
t.append(utils.hammingDistance(querys[i],vps[j]))
cdsQuerys.append(t)
utils.writeDataToFile(cdsQueryFileName,cdsQuerys)
print cdsDataFileName, cdsQueryFileName
if __name__ == '__main__':
utils.createDirectory('ndt_data')
utils.createDirectory('ndt_query')
dictionary = makeDictionaryKeyIsAlphabet()
dataFileNames = glob.glob('data/*.txt')
for dataFileName in dataFileNames:
print dataFileName
onlyFileName = dataFileName.split('.')[0].split('/')[1]
size = onlyFileName.split('_')[1]
dim = onlyFileName.split('_')[2]
vptype = onlyFileName.split('_')[3]
cardinality = onlyFileName.split('_')[4]
queryFileName = 'query/query_%s_%s_%s_%s.txt'%(size,dim,vptype,cardinality)
datas = utils.getDataInFile(dataFileName)
querys = utils.readDataFromFile(queryFileName)
ndtDataFileName = 'ndt_data/data_%s_%s_%s_%s.txt'%(size,dim,vptype,cardinality)
ndtQueryFileName = 'ndt_query/query_%s_%s_%s_%s.txt'%(size,dim,vptype,cardinality)
if os.path.exists(ndtDataFileName):
print '%s is exists'%ndtDataFileName
else :
with open(ndtDataFileName,'w') as fp:
for i in xrange(len(datas)):
for j in xrange(len(datas[i])):
fp.write(str(dictionary[datas[i][j]])+' ')
fp.write('\n')
if os.path.exists(ndtQueryFileName):
print '%s is exsts'%ndtQueryFileName
def convertNDDSToCDS(options):
size = options['numberOfData']
dim = options['numberOfDimension']
distribution = options['distribution']
cardinality = options['numberOfAlphabet']
numberOfVP = options['numberOfVP']
typeOfVP = options['typeOfVP']
dataFileName = 'data/data_%d_%d_%s_%d.txt' % (size, dim, distribution,
cardinality)
queryFileName = 'query/query_%d_%d_%s_%d.txt' % (size, dim, distribution,
cardinality)
vpFileName = 'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality,
typeOfVP)
# cdsDataFileName = utils.getCDSDataFileName(options)
# cdsQueryFileName= utils.getCDSQueryFileName(options)
cdsDataFileName = 'cds_data/data_%d_%d_%d_%s_%d_%sgeh.txt' % (
size, dim, numberOfVP, distribution, cardinality, typeOfVP)
cdsQueryFileName = 'cds_query/query_%d_%d_%d_%s_%d_%sgeh.txt' % (
size, dim, numberOfVP, distribution, cardinality, typeOfVP)
datas = utils.getDataInFile(dataFileName)
querys = utils.readDataFromFile(queryFileName)
vps = utils.readDataFromFile(vpFileName)
print len(datas), len(querys), len(vps)
d = [{} for i in xrange(dim)]
for i in xrange(len(datas)):
for j in xrange(dim):
if datas[i][j] in d[j]:
d[j][datas[i][j]] += 1
else:
d[j][datas[i][j]] = 1
for i in xrange(dim):
for key in d[i]:
d[i][key] = 1.0 - float(d[i][key]) / float(len(datas))
def geh(a, b):
ret = 0.0
for i in xrange(len(a)):
if a[i] != b[i]:
ret += 1.0
else:
ret += d[i][a[i]] / float(dim)
return ret / float(dim)
cdsDatas = []
for i in xrange(len(datas)):
t = []
for j in xrange(len(vps)):
t.append(geh(datas[i], vps[j]))
cdsDatas.append(t)
utils.writeDataToFile(cdsDataFileName, cdsDatas)
cdsQuerys = []
for i in xrange(len(querys)):
t = []
for j in xrange(len(vps)):
t.append(geh(querys[i], vps[j]))
cdsQuerys.append(t)
utils.writeDataToFile(cdsQueryFileName, cdsQuerys)
print cdsDataFileName, cdsQueryFileName
def generateVantagePointsWithHybridAlgorithm(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
majorPattern = [[] for i in xrange(numberOfVP + 1)]
for j in xrange(dim):
d = {}
for i in xrange(numberOfData):
if datas[i][j] in d:
d[datas[i][j]] += 1
else:
d[datas[i][j]] = 0
d = sorted(d.items(), key=lambda x: x[1], reverse=True)
for k in xrange(1):
majorPattern[k].append(d[k][0])
vps = []
vps.append(majorPattern[0])
d = [0 for i in xrange(dim + 1)]
one_pass = False
while len(vps) < numberOfVP:
print len(vps)
ans, ansDataIndex = -1, -1
if one_pass:
for i in xrange(len(datas)):
ok = False
for j in xrange(len(vps)):
dist = utils.hammingDistance(datas[i], vps[j])
if d[dist] > 1:
ok = True
if datas[i] == vps[j]:
ok = True
if ok:
continue
for j in xrange(len(vps)):
dist = utils.hammingDistance(datas[i], vps[j])
d[dist] += 1
vps.append(datas[i])
one_pass = False
else:
change = False
for i in xrange(dim + 1):
if d[i] == 0:
change = True
ans, ans_vp = -1, ''
fx = 987654321
for j in xrange(len(vps)):
cur_vp = generateVpWithDist(dim, cardinality, vps[j],
i)
vps.append(cur_vp)
cur, dists = calculateMany(vps)
if cur > ans:
ans, ans_vp = cur, cur_vp
fx = max(dists)
elif cur == ans:
if max(dists) < fx:
ans, ans_vp = cur, cur_vp
fx = max(dists)
vps = vps[:-1]
for j in xrange(len(vps)):
dist = utils.hammingDistance(vps[j], ans_vp)
d[dist] += 1
vps.append(ans_vp)
break
if not change:
vps.append(datas[random.randrange(0, numberOfData)])
utils.writeDataToFile(
'vp/vp_%d_%d_%d_%s.txt' % (dim, numberOfVP, cardinality, typeOfVP),
vps)
def generateVantagePointsWithManyAlgorithm(options):
numberOfData = options['numberOfData']
dim = options['numberOfDimension']
numberOfVP = options['numberOfVP']
cardinality = options['numberOfAlphabet']
typeOfVP = options['typeOfVP']
datas = utils.getDataInFile(utils.getDataFileName(options))
threshold = 2
majorPattern = []
for j in xrange(dim):
d = {}
for i in xrange(numberOfData):
if datas[i][j] in d:
d[datas[i][j]] += 1
else:
d[datas[i][j]] = 0
d = sorted(d.items(), key=lambda x: x[1], reverse=True)
majorPattern.append(d[0][0])
vps = []
vps.append(majorPattern)
d = [0 for i in xrange(dim + 1)]
isSelected = [False for i in xrange(len(datas))]
for i in xrange(len(datas)):
if datas[i] == majorPattern:
isSelected[i] = True
notChangedCount = 0
while len(vps) < numberOfVP:
print len(vps)
changed = False
for i in xrange(len(datas)):
if isSelected[i]:
continue
is_pass = False
for j in xrange(len(vps)):
if datas[i] == vps[j]:
is_pass = True
dist = utils.hammingDistance(datas[i], vps[j])
if d[dist] > threshold:
is_pass = True
if is_pass:
continue
for j in xrange(len(vps)):
dist = utils.hammingDistance(datas[i], vps[j])
d[dist] += 1
vps.append(datas[i])
isSelected[i] = True
changed = True
if not changed:
print 'not changed so pop worst (%d)' % notChangedCount
worstIdx = getWorstVP(vps)
for j in xrange(len(vps)):
if j == worstIdx: continue
dist = utils.hammingDistance(vps[j], vps[worstIdx])
d[dist] -= 1
nextVPS = popDataAtIndex(vps, worstIdx)
vps = nextVPS
notChangedCount += 1
if notChangedCount > numberOfVP / 2:
notChangedCount = 0
for k in xrange(notChangedCount - 1):
worstIdx = getWorstVP(vps)
for j in xrange(len(vps)):
if j == worstIdx: continue
dist = utils.hammingDistance(vps[j], vps[worstIdx])
d[dist] -= 1
nextVPS = popDataAtIndex(vps, worstIdx)
vps = nextVPS
print len(vps)
print d