def kpca(Y, k, params):
"""KPCA driver.
Runs KPCA on the input data matrix and UPDATES the KPCA parameters given
by the user. See
[1] B. Schoelkopf, A. Smola, and K. R. Muller. "Nonlinear component analysis
as a kernel eigenvalue problem", Neural Computation, vol. 10,
pp. 1299-1319, 1998
for technical details on KPCA.
Parameters:
-----------
Y : numpy array, shape = (N, D)
Input matrix of D N-dimensional signals.
k : int
Compute k KPCA components.
params : KPCAParam instance
KPCA parameters.
Upon completion, the params is updated. The following fields
are set:
_data : numpy.array, shape = (N, D) - Original data
_A : numpy.array, shape = (N, k) - KPCA weight matrix
_l : numpy.array, shape = (k,) - Eigenvalues of kernel matrix
The following fields need to be set already:
_kPar : Kernel parameters (depends on the kernel)
_kFun : Kernel function (depends on the kernel)
Since the kernel will be called interally, the kernel parameters
will also be updated (see kernel documentation).
Returns:
--------
Xhat : numpy array, shape (k, D)
NLDS state parameters.
"""
if not isinstance(params, KPCAParam):
raise ErrorDS('wrong KCPA parameters!')
if (params._kPar is None or params._kFun is None):
raise ErrorDS('KPCA not properly configured!')
# save data
params._data = Y
# calls kernel fun
params._kFun(Y, Y, params._kPar)
kpcaObj = KernelPCA(kernel="precomputed")
kpcaObj.fit(params._kPar._kMat)
params._A = kpcaObj.alphas_[:,0:k]
params._l = kpcaObj.lambdas_[0:k]
if np.any(np.where(kpcaObj.lambdas_ <= 0)[0]):
dsinfo.warn("some unselected eigenvalues are negative!")
if np.any(np.where(params._l < 0)[0]):
dsinfo.warn("some eigenvalues are negative!")
# normalize KPCA weight vectors
normalize(params._A, params._l)
return params._A.T*params._kPar._kMat
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-s", dest="inFile")
parser.add_option("-d", dest="dbFile")
parser.add_option("-m", dest="models")
parser.add_option("-v", dest="videos")
parser.add_option("-c", dest="config")
parser.add_option("-o", dest="mdFile")
parser.add_option("-h", dest="doUsage", action="store_true", default=False)
parser.add_option("-x", dest="verbose", action="store_true", default=False)
options, args = parser.parse_args()
if options.doUsage:
usage()
# read config file
config = json.load(open(options.config))
# get DS config settings
dynType = config["dynType"]
shiftMe = config["shiftMe"]
numIter = config["numIter"]
verbose = options.verbose
# I/O configuration
inFile = options.inFile
dbFile = options.dbFile
models = options.models
videos = options.videos
mdFile = options.mdFile
# check if the required options are present
if (inFile is None or dbFile is None or models is None or videos is None):
dsinfo.warn('Options missing!')
usage()
inVideo, inVideoSize = dsutil.loadDataFromVideoFile(inFile)
if verbose:
dsinfo.info("Loaded source video with %d frames!" % inVideo.shape[1])
(db, winSize, nStates, dynType) = loadDB(videos, models, dbFile)
if verbose:
dsinfo.info("#Templates: %d #States: %d, WinSize: %d, Shift: %d" %
(len(db), nStates, winSize, shiftMe))
if dynType.__name__ == "LinearDS":
# create online version of LinearDS
ds = OnlineLinearDS(nStates, winSize, shiftMe, False, verbose)
elif dynType.__name__ == "NonLinearDS":
kpcaP = KPCAParam()
# select kernel
if config["kdtKern"] == "rbf":
kpcaP._kPar = RBFParam()
kpcaP._kFun = rbfK
else:
dsinfo.fail("Kernel %s not supported!" % kdtKern)
return -1
# configure kernel
if config["kCenter"] == 1:
kpcaP._kPar._kCen = True
else:
kpcaP._kPar._kCen = False
# create online version of KDT
ds = OnlineNonLinearDS(nStates, kpcaP, winSize, shiftMe, verbose)
else:
dsinfo.fail('System type %s not supported!' % options.dsType)
return -1
dList = []
for f in range(inVideo.shape[1]):
ds.update(inVideo[:, f])
if ds.check() and ds.hasChanged():
dists = np.zeros((len(db), ))
for j, dbentry in enumerate(db):
dists[j] = {
"LinearDS": dsdist.ldsMartinDistance,
"NonLinearDS": dsdist.nldsMartinDistance
}[dynType.__name__](ds, dbentry["model"], numIter)
dList.append(dists)
# write distance matrix
if not mdFile is None:
np.savetxt(mdFile, np.asmatrix(dList), fmt='%.5f', delimiter=' ')
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-s", dest="inFile")
parser.add_option("-d", dest="dbFile")
parser.add_option("-m", dest="models")
parser.add_option("-v", dest="videos")
parser.add_option("-c", dest="config")
parser.add_option("-o", dest="mdFile")
parser.add_option("-h", dest="doUsage", action="store_true", default=False)
parser.add_option("-x", dest="verbose", action="store_true", default=False)
options, args = parser.parse_args()
if options.doUsage:
usage()
# read config file
config = json.load(open(options.config))
# get DS config settings
dynType = config["dynType"]
shiftMe = config["shiftMe"]
numIter = config["numIter"]
verbose = options.verbose
# I/O configuration
inFile = options.inFile
dbFile = options.dbFile
models = options.models
videos = options.videos
mdFile = options.mdFile
# check if the required options are present
if inFile is None or dbFile is None or models is None or videos is None:
dsinfo.warn("Options missing!")
usage()
inVideo, inVideoSize = dsutil.loadDataFromVideoFile(inFile)
if verbose:
dsinfo.info("Loaded source video with %d frames!" % inVideo.shape[1])
(db, winSize, nStates, dynType) = loadDB(videos, models, dbFile)
if verbose:
dsinfo.info("#Templates: %d #States: %d, WinSize: %d, Shift: %d" % (len(db), nStates, winSize, shiftMe))
if dynType.__name__ == "LinearDS":
# create online version of LinearDS
ds = OnlineLinearDS(nStates, winSize, shiftMe, False, verbose)
elif dynType.__name__ == "NonLinearDS":
kpcaP = KPCAParam()
# select kernel
if config["kdtKern"] == "rbf":
kpcaP._kPar = RBFParam()
kpcaP._kFun = rbfK
else:
dsinfo.fail("Kernel %s not supported!" % kdtKern)
return -1
# configure kernel
if config["kCenter"] == 1:
kpcaP._kPar._kCen = True
else:
kpcaP._kPar._kCen = False
# create online version of KDT
ds = OnlineNonLinearDS(nStates, kpcaP, winSize, shiftMe, verbose)
else:
dsinfo.fail("System type %s not supported!" % options.dsType)
return -1
dList = []
for f in range(inVideo.shape[1]):
ds.update(inVideo[:, f])
if ds.check() and ds.hasChanged():
dists = np.zeros((len(db),))
for j, dbentry in enumerate(db):
dists[j] = {"LinearDS": dsdist.ldsMartinDistance, "NonLinearDS": dsdist.nldsMartinDistance}[
dynType.__name__
](ds, dbentry["model"], numIter)
dList.append(dists)
# write distance matrix
if not mdFile is None:
np.savetxt(mdFile, np.asmatrix(dList), fmt="%.5f", delimiter=" ")
