def test_ldsMartinDistance():
"""Test Martin distance computation for LDS's (5 states).
Config: 5 states (observation centering - default).
"""
fileA = os.path.join(TESTBASE, "data/data1.txt")
fileB = os.path.join(TESTBASE, "data/data2.txt")
# load data files
dataA, _ = loadDataFromASCIIFile(fileA)
dataB, _ = loadDataFromASCIIFile(fileB)
ldsA = LinearDS(5, False, False)
ldsB = LinearDS(5, False, False)
# estimate LDS's
ldsA.suboptimalSysID(dataA)
ldsB.suboptimalSysID(dataB)
# compute distances A<->A, A<->B
dAA = ldsMartinDistance(ldsA, ldsA, 20)
dAB = ldsMartinDistance(ldsA, ldsB, 20)
truth = np.genfromtxt(os.path.join(TESTBASE, "data/ldsMartinDistanceData1Data2.txt"))
np.testing.assert_almost_equal(dAB, truth, 2)
np.testing.assert_almost_equal(dAA, 0, 2)
def test_LinearDS_suboptimalSysID():
"""Test LinearDS system identification.
"""
dataFile = os.path.join(TESTBASE, "data/data1.txt")
data, _ = loadDataFromASCIIFile(dataFile)
lds = LinearDS(5, False, False)
lds.suboptimalSysID(data)
baseLDSFile = os.path.join(TESTBASE, "data/data1-dt-5c-center.pkl")
baseLDS = pickle.load(open(baseLDSFile))
err = ldsMartinDistance(lds, baseLDS, N=20)
np.testing.assert_almost_equal(err, 0, 2)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-s", dest="model1File")
parser.add_option("-r", dest="model2File")
parser.add_option("-n", dest="iterations", type="int", default=20)
parser.add_option("-h", dest="shoHelp", action="store_true", default=False)
parser.add_option("-v", dest="verbose", action="store_true", default=False)
opt, args = parser.parse_args()
if opt.shoHelp:
usage()
with open(opt.model1File, 'r') as fid:
dt1 = pickle.load(fid)
with open(opt.model2File, 'r') as fid:
dt2 = pickle.load(fid)
martinD = dsdist.ldsMartinDistance(dt1, dt2, opt.iterations)
dsinfo.info('D(%s,%s) = %.4f' % (opt.model1File, opt.model2File, martinD))
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-i", dest="iListFile")
parser.add_option("-o", dest="oListFile")
parser.add_option("-d", dest="dMatFile")
parser.add_option("-b", dest="iBase")
parser.add_option("-k", dest="kCenter", type="int", default=5)
parser.add_option("-h", dest="shoHelp", action="store_true", default=False)
parser.add_option("-v", dest="verbose", action="store_true", default=False)
options, args = parser.parse_args()
if options.shoHelp:
usage()
iBase = options.iBase
dMatFile = options.dMatFile
iListFile = options.iListFile
oListFile = options.oListFile
kCenter = options.kCenter
verbose = options.verbose
assert kCenter > 0, "Oops (kCenter < 1) ..."
iList = pickle.load(open(iListFile))
if verbose:
dsinfo.info("Loaded list with %d DT models!" % len(iList))
# load DT's
dts = []
for dtFile in iList:
dts.append(pickle.load(open(os.path.join(iBase, dtFile))))
if verbose:
dsinfo.info("Running DT clustering with %d clusters ..." % kCenter)
D = None
if not dMatFile is None:
if os.path.exists(dMatFile):
if verbose:
dsinfo.info("Try loading distance matrix %s!" % dMatFile)
D = pickle.load(open(dMatFile))
if D is None:
if verbose:
dsinfo.info("Computing pairwise distances ...")
nDTs = len(dts)
D = np.zeros((nDTs, nDTs))
for i in range(nDTs):
for j in range(nDTs):
D[i,j] = ldsMartinDistance(dts[i], dts[j], 50)
# run clustering
(eData, ids) = LinearDS.cluster(D, kCenter, verbose)
ids = list(ids)
#write list of DT representatives
oList = []
for j, dtFile in enumerate(iList):
if j in ids:
oList.append(dtFile)
else:
oList.append("Dummy")
pickle.dump(oList, open(oListFile, "w"))
if verbose:
dsinfo.info("Wrote list of representative DS's to %s!" % oListFile)
if not dMatFile is None:
if verbose:
dsinfo.info("Writing distance matrix to %s!" % dMatFile)
pickle.dump(D, open(dMatFile, "w"))
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-i", dest="iListFile")
parser.add_option("-o", dest="oListFile")
parser.add_option("-d", dest="dMatFile")
parser.add_option("-b", dest="iBase")
parser.add_option("-k", dest="kCenter", type="int", default=5)
parser.add_option("-h", dest="shoHelp", action="store_true", default=False)
parser.add_option("-v", dest="verbose", action="store_true", default=False)
options, args = parser.parse_args()
if options.shoHelp:
usage()
iBase = options.iBase
dMatFile = options.dMatFile
iListFile = options.iListFile
oListFile = options.oListFile
kCenter = options.kCenter
verbose = options.verbose
assert kCenter > 0, "Oops (kCenter < 1) ..."
iList = pickle.load(open(iListFile))
if verbose:
dsinfo.info("Loaded list with %d DT models!" % len(iList))
# load DT's
dts = []
for dtFile in iList:
dts.append(pickle.load(open(os.path.join(iBase, dtFile))))
if verbose:
dsinfo.info("Running DT clustering with %d clusters ..." % kCenter)
D = None
if not dMatFile is None:
if os.path.exists(dMatFile):
if verbose:
dsinfo.info("Try loading distance matrix %s!" % dMatFile)
D = pickle.load(open(dMatFile))
if D is None:
if verbose:
dsinfo.info("Computing pairwise distances ...")
nDTs = len(dts)
D = np.zeros((nDTs, nDTs))
for i in range(nDTs):
for j in range(nDTs):
D[i, j] = ldsMartinDistance(dts[i], dts[j], 50)
# run clustering
(eData, ids) = LinearDS.cluster(D, kCenter, verbose)
ids = list(ids)
#write list of DT representatives
oList = []
for j, dtFile in enumerate(iList):
if j in ids:
oList.append(dtFile)
else:
oList.append("Dummy")
pickle.dump(oList, open(oListFile, "w"))
if verbose:
dsinfo.info("Wrote list of representative DS's to %s!" % oListFile)
if not dMatFile is None:
if verbose:
dsinfo.info("Writing distance matrix to %s!" % dMatFile)
pickle.dump(D, open(dMatFile, "w"))
