def read(self, path):
self.mdef = s3mdef.open(os.path.join(path, "mdef"))
self.mean = s3gau.open(os.path.join(path, "means"))
self.var = s3gau.open(os.path.join(path, "variances"))
self.mixw = s3mixw.open(os.path.join(path, "mixture_weights"))
self.tmat = s3tmat.open(os.path.join(path, "transition_matrices"))
# Normalize transition matrices and mixture weights
for t in range(0, len(self.tmat)):
self.tmat[t] = (self.tmat[t].T / self.tmat[t].sum(1)).T
for t in range(0, len(self.mixw)):
self.mixw[t] = (self.mixw[t].T / self.mixw[t].sum(1)).T.clip(self.mwfloor, 1.0)
# Floor variances and precompute normalizing and inverse variance terms
self.norm = numpy.empty((len(self.var),
len(self.var[0]),
len(self.var[0][0])),'d')
for m,mgau in enumerate(self.var):
for f,feat in enumerate(mgau):
fvar = feat.clip(self.varfloor, numpy.inf)
# log of 1/sqrt((2*pi)^N * det(var))
det = numpy.log(fvar).sum(1)
lrd = -0.5 * (det + numpy.log(2 * numpy.pi) * feat.shape[1])
self.norm[m,f] = lrd
# "Invert" variances
feat[:] = (1 / (fvar * 2))
# Construct senone to codebook mapping
if os.access(os.path.join(path, "senmgau"), os.F_OK):
self.senmgau = s3file.S3File(os.path.join(path, "senmgau")).read1d()
elif len(self.mean) == 1:
self.senmgau = numpy.ones(len(self.mixw))
else:
self.senmgau = numpy.arange(0, len(self.mixw))
self.senscr = numpy.ones(len(self.mixw)) * WORSTSCORE
def read(self, path):
self.mdef = s3mdef.open(os.path.join(path, "mdef"))
self.mean = s3gau.open(os.path.join(path, "means"))
self.var = s3gau.open(os.path.join(path, "variances"))
self.mixw = s3mixw.open(os.path.join(path, "mixture_weights"))
self.tmat = s3tmat.open(os.path.join(path, "transition_matrices"))
# Normalize transition matrices and mixture weights
for t in range(0, len(self.tmat)):
self.tmat[t] = (self.tmat[t].T / self.tmat[t].sum(1)).T
for t in range(0, len(self.mixw)):
self.mixw[t] = (self.mixw[t].T / self.mixw[t].sum(1)).T.clip(self.mwfloor, 1.0)
# Floor variances and precompute normalizing and inverse variance terms
self.norm = numpy.empty((len(self.var),
len(self.var[0]),
len(self.var[0][0])),'d')
for m,mgau in enumerate(self.var):
for f,feat in enumerate(mgau):
fvar = feat.clip(self.varfloor, numpy.inf)
# log of 1/sqrt((2*pi)^N * det(var))
det = numpy.log(fvar).sum(1)
lrd = -0.5 * (det + numpy.log(2 * numpy.pi) * feat.shape[1])
self.norm[m,f] = lrd
# "Invert" variances
feat[:] = (1 / (fvar * 2))
# Construct senone to codebook mapping
if os.access(os.path.join(path, "senmgau"), os.F_OK):
self.senmgau = s3file.S3File(os.path.join(path, "senmgau")).read1d()
elif len(self.mean) == 1:
self.senmgau = numpy.ones(len(self.mixw))
else:
self.senmgau = numpy.arange(0, len(self.mixw))
self.senscr = numpy.ones(len(self.mixw)) * WORSTSCORE
def write(self, todir):
"""
Write GMM parameters to files in a directory.
@param todir: Directory to read parameters from. The files
'means', 'variances', and 'mixture_weights' will be created in
this directory.
@ptype todir: string
"""
s3gau.open(os.path.join(todir, "means"),'wb').writeall([[self.means]])
s3gau.open(os.path.join(todir, "variances"),'wb').writeall([[self.variances]])
s3mixw.open(os.path.join(todir, "mixture_weights"),'wb').writeall \
(self.mixw[numpy.newaxis,numpy.newaxis,:])
def write(self, todir):
"""
Write GMM parameters to files in a directory.
@param todir: Directory to read parameters from. The files
'means', 'variances', and 'mixture_weights' will be created in
this directory.
@ptype todir: string
"""
s3gau.open(os.path.join(todir, "means"), 'wb').writeall([[self.means]])
s3gau.open(os.path.join(todir, "variances"),
'wb').writeall([[self.variances]])
s3mixw.open(os.path.join(todir, "mixture_weights"),'wb').writeall \
(self.mixw[numpy.newaxis,numpy.newaxis,:])
def read(self, fromdir):
"""
Read GMM parameters from files in a directory.
@param fromdir: Directory to read parameters from. The files
'means', 'variances', and 'mixture_weights' will be read from
this directory.
@ptype fromdir: string
"""
self.means = s3gau.open(os.path.join(fromdir, "means"))
self.variances = s3gau.open(os.path.join(fromdir, "variances"))
self.mixw = s3mixw.open(os.path.join(fromdir, "mixture_weights"))
self.featlen = self.means.veclen[0]
self.ndensity = self.means.density
def read(self, fromdir):
"""
Read GMM parameters from files in a directory.
@param fromdir: Directory to read parameters from. The files
'means', 'variances', and 'mixture_weights' will be read from
this directory.
@ptype fromdir: string
"""
self.means = s3gau.open(os.path.join(fromdir, "means"))
self.variances = s3gau.open(os.path.join(fromdir, "variances"))
self.mixw = s3mixw.open(os.path.join(fromdir, "mixture_weights"))
self.featlen = self.means.veclen[0]
self.ndensity = self.means.density
return Hs
if __name__ == '__main__':
def usage():
sys.stderr.write("Usage: %s INMEAN INVAR ACCUMDIRS...\n" % sys.argv[0])
try:
opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
except getopt.GetoptError:
usage()
sys.exit(2)
if len(args) < 3:
usage()
sys.exit(2)
ldafn = None
for o, a in opts:
if o in ('-h', '--help'):
usage()
sys.exit()
inmean = s3gau.open(args[0])
invar = s3gau.open(args[1])
accumdirs = args[2:]
stats = s3gaucnt.accumdirs(accumdirs)
Ws = estimate_mllr_mean(stats, inmean, invar)
Hs = estimate_mllr_variance(stats, inmean, invar, Ws)
write_mllr(sys.stdout, Ws, Hs)
sys.argv[0])
try:
opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
except getopt.GetoptError:
usage()
sys.exit(2)
if len(args) < 3:
usage()
sys.exit(2)
ldafn = None
for o, a in opts:
if o in ('-h', '--help'):
usage()
sys.exit()
outmean = 'means.cmllr'
outvar = 'variances.cmllr'
inmean = s3gau.open(args[0])
invar = s3gau.open(args[1])
mdef = s3mdef.open(args[2])
accumdirs = args[3:]
stats = s3gaucnt.accumdirs_full(accumdirs)
Ws = estimate_cmllr(stats, inmean, invar, mdef)
write_mllr(open("cmllr_matrix", "w"), Ws)
Wp = solve_transform(Ws)
param = solve_mllr(Wp, inmean, invar, mdef)
om = s3gau.open(outmean, "wb")
om.writeall(param[0])
om = s3gau.open(outvar, "wb")
om.writeall(param[1])
try:
opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
except getopt.GetoptError:
usage()
sys.exit(2)
if len(args) < 3:
usage()
sys.exit(2)
ldafn = None
for o, a in opts:
if o in ('-h', '--help'):
usage()
sys.exit()
outmean = 'means.cmllr'
outvar = 'variances.cmllr'
print 'begin'
inmean = s3gau.open(args[0])
invar = s3gau.open(args[1])
mdef = s3mdef.open(args[2])
accumdirs = args[3:]
stats = s3gaucnt.accumdirs(accumdirs)
Ws = estimate_cmllr(stats, inmean, invar, mdef)
Wp = solve_transform(Ws)
write_mllr(sys.stdout, Wp)
param = solve_mllr(Wp, inmean, invar, mdef)
om = s3gau.open(outmean,"wb")
om.writeall(param[0])
om = s3gau.open(outvar,"wb")
om.writeall(param[1])
print 'end'
norm += stats.dnom[j][i][k]
Hs.append(H / norm)
return Hs
if __name__ == '__main__':
def usage():
sys.stderr.write("Usage: %s INMEAN INVAR ACCUMDIRS...\n" % sys.argv[0])
try:
opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
except getopt.GetoptError:
usage()
sys.exit(2)
if len(args) < 3:
usage()
sys.exit(2)
ldafn = None
for o, a in opts:
if o in ('-h', '--help'):
usage()
sys.exit()
inmean = s3gau.open(args[0])
invar = s3gau.open(args[1])
accumdirs = args[2:]
stats = s3gaucnt.accumdirs(accumdirs)
Ws = estimate_mllr_mean(stats, inmean, invar)
Hs = estimate_mllr_variance(stats, inmean, invar, Ws)
write_mllr(sys.stdout, Ws, Hs)