nrows=10, ncols=10, figtitle=""): """Will open a .pmat .dmat .amat or .vmat file and consider the beginning of each row a imgheight x imgwidth imagette. These images will be interactively displayed in a nrows x ncols grid of imagettes.""" data = None if figtitle == "": figtitle = matfile if matfile.endswith(".pmat"): # Use pure python implementation of pmat (faster loading) from plearn.vmat.PMat import PMat data = PMat(matfile) else: # Use of VMat through the Python-bridge from plearn.pyext import AutoVMatrix data = AutoVMatrix(filename=matfile) showRowsAsImages(data, img_height=imgheight, img_width=imgwidth, nrows=nrows, ncols=ncols, figtitle=figtitle) #################### ### main program ### if __name__ == '__main__': from plearn.utilities.autoscript import autoscript autoscript(show_rows_as_images, True)
# library, go to the PLearn Web site at www.plearn.org # Author: Pascal Vincent from plearn.plotting.netplot import showRowsAsImages def show_rows_as_images(matfile, imgheight, imgwidth, nrows=10, ncols=10, figtitle=""): """Will open a .pmat .dmat .amat or .vmat file and consider the beginning of each row a imgheight x imgwidth imagette. These images will be interactively displayed in a nrows x ncols grid of imagettes.""" data = None if figtitle=="": figtitle = matfile if matfile.endswith(".pmat"): # Use pure python implementation of pmat (faster loading) from plearn.vmat.PMat import PMat data = PMat(matfile) else: # Use of VMat through the Python-bridge from plearn.pyext import AutoVMatrix data = AutoVMatrix(filename=matfile) showRowsAsImages(data, img_height=imgheight, img_width=imgwidth, nrows=nrows, ncols=ncols, figtitle=figtitle) #################### ### main program ### if __name__ == '__main__': from plearn.utilities.autoscript import autoscript autoscript(show_rows_as_images, True)
for i in range(len(Cd)): C2[i, i] = Cd[i] eigvals, eigvecs = eig(C2) return real(eigvecs.T) def computeDenoisingFiltersFromCovariance(C, lambd=1e-6, nu=0.10): C = C + diag(len(C) * [lambd]) Cd = C.diagonal() C2 = C * (1.0 - nu) # copy back intial diagonal for i in range(len(Cd)): C2[i, i] = Cd[i] WW = dot(inv(C2), C) return WW.T def computeDenoisingEigenFiltersFromCovariance(C, lambd=1e-6, nu=0.10): WW = computeDenoisingFiltersFromCovariance(C, lambd, nu).T eigvals, eigvecs = eig(WW) return real(eigvecs.T) # return real(inv(eigvecs).T) #################### ### main program ### if __name__ == "__main__": from plearn.utilities.autoscript import autoscript autoscript(computeAndShowFilters, True)
C2 = C*(1.0-nu) # copy back intial diagonal for i in range(len(Cd)): C2[i,i] = Cd[i] eigvals, eigvecs = eig(C2) return real(eigvecs.T) def computeDenoisingFiltersFromCovariance(C, lambd=1e-6, nu=0.10): C = C+diag(len(C)*[lambd]) Cd = C.diagonal() C2 = C*(1.0-nu) # copy back intial diagonal for i in range(len(Cd)): C2[i,i] = Cd[i] WW = dot(inv(C2),C) return WW.T def computeDenoisingEigenFiltersFromCovariance(C, lambd=1e-6, nu=0.10): WW = computeDenoisingFiltersFromCovariance(C, lambd, nu).T eigvals, eigvecs = eig(WW) return real(eigvecs.T) # return real(inv(eigvecs).T) #################### ### main program ### if __name__ == "__main__": from plearn.utilities.autoscript import autoscript autoscript(computeAndShowFilters, True)
# ncomponents=ncomponents, # constrain_norm_type=constrain_norm_type, # cov_transformation_type=cov_transformation_type, # diag_weight = diag_weight, # diag_nonlinearity = diag_nonlinearity, # diag_premul = diag_premul, # offdiag_weight = offdiag_weight, # offdiag_nonlinearity = offdiag_nonlinearity, # offdiag_premul = offdiag_premul, # force_zero_mean = force_zero_mean, # lr=0.01, nsteps=1, optimizer_nsteps=10) if __name__ == "__main__": from plearn.utilities.autoscript import autoscript helptext = """ OLDEXAMPLE: dcaexperiment.py 123:1 123 2 -2 cov -1 square 1 1 square 1 0" OLDEXAMPLE: dcaexperiment.py 121:-2 123 4 -2 squaredist 0 exp 1 1 exp -1.6 0" OLDEXAMPLE: dcaexperiment.py /data/icml07data/mnist_basic/plearn/mnist_basic2_train.pmat 125 400 -2 squaredist 0 exp -1 1 exp -1 0 Ex: data_set=123:1 seed=1827 ncomponents=2 nonlinearity=none constrain_norm_type=-2 cov_transformation_type=cov diag_add=0. diag_weight=0. diag_nonlinearity=square diag_premul=1.0 offdiag_weight=1.0 offdiag_nonlinearity=exp offdiag_premul=1.0 lr=0.01 nsteps=1 optimizer_nsteps=1 force_zero_mean=False """ autoscript(DCAExperiment,True,helptext=helptext)