def computeAndShowFilters(datapmatfile,
img_height,
img_width,
filtertype='PCA',
lambd=1e-6,
nu=0,
centered=True,
displaytranspose=False):
"""
Input is considered to be the first img_height x img_width columns of datapmatfile.
centered indicates whether we compte centered covariance (default) or uncentered covariance.
Covariance matrix will get lambd*I added to its diagonal, and its off-diagonal terms multiplied by (1-nu).
Filtertype can be 'PCA' or 'denoising' or 'denoising_eig'.
Original version of linear denoising with zeroing noise (with probability of zeroing equal to nu)
is obtained for centered=False and lambda=0
"""
data = load_pmat_as_array(datapmatfile)
inputs = data[:, 0:img_height * img_width]
C = mycov(inputs, centered)
if (filtertype == "PCA"):
filters = computePCAFiltersFromCovariance(C, lambd, nu)
elif (filtertype == "denoising"):
filters = computeDenoisingFiltersFromCovariance(C, lambd, nu)
elif (filtertype == "denoising_eig"):
filters = computeDenoisingEigenFiltersFromCovariance(C, lambd, nu)
else:
raise ValueError("Invalid filtertype " + filtertype)
if displaytranspose:
filters = filters.T
showRowsAsImages(filters, img_height, img_width, figtitle="Filters")
def computeAndShowFilters(datapmatfile, img_height, img_width, filtertype='PCA', lambd=1e-6, nu=0, centered=True, displaytranspose=False):
"""
Input is considered to be the first img_height x img_width columns of datapmatfile.
centered indicates whether we compte centered covariance (default) or uncentered covariance.
Covariance matrix will get lambd*I added to its diagonal, and its off-diagonal terms multiplied by (1-nu).
Filtertype can be 'PCA' or 'denoising' or 'denoising_eig'.
Original version of linear denoising with zeroing noise (with probability of zeroing equal to nu)
is obtained for centered=False and lambda=0
"""
data = load_pmat_as_array(datapmatfile)
inputs = data[:,0:img_height*img_width]
C = mycov(inputs, centered)
if(filtertype=="PCA"):
filters = computePCAFiltersFromCovariance(C, lambd, nu)
elif(filtertype=="denoising"):
filters = computeDenoisingFiltersFromCovariance(C, lambd, nu)
elif(filtertype=="denoising_eig"):
filters = computeDenoisingEigenFiltersFromCovariance(C, lambd, nu)
else:
raise ValueError("Invalid filtertype "+filtertype)
if displaytranspose:
filters = filters.T
showRowsAsImages(filters, img_height, img_width, figtitle="Filters")
showBihistRows(m2,
nbins,
nbins,
nrows=10,
ncols=20,
startidx=0,
figtitle="",
luminance_scale_mode=1,
vmin=-1.,
vmax=1.,
transpose_img=False)
def print_usage_and_exit():
print "Usage : displaybihist.py bihists.pmat"
print " will graphically display the bivariate histograms in the pmat."
sys.exit()
############
### main ###
############
if len(sys.argv) < 2:
print_usage_and_exit()
pmatfname = sys.argv[1]
m = load_pmat_as_array(pmatfname)
display_bihists(m)
if i==j:
bihist.fill(1.)
else:
bihist[:,:] = bihist-outer(hists[i],hists[j])
showBihistRows(m2, nbins, nbins,
nrows = 10, ncols = 20,
startidx = 0,
figtitle="",
luminance_scale_mode=1,
vmin = -1., vmax = 1.,
transpose_img=False)
def print_usage_and_exit():
print "Usage : displaybihist.py bihists.pmat"
print " will graphically display the bivariate histograms in the pmat."
sys.exit()
############
### main ###
############
if len(sys.argv)<2:
print_usage_and_exit()
pmatfname = sys.argv[1]
m = load_pmat_as_array(pmatfname)
display_bihists(m)
