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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex5/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex8/ex8/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | yuanxy92/ConvexOptimization-master | l1_ls_nonneg.m | .m | ConvexOptimization-master/3rd/l1_ls_matlab/l1_ls_nonneg.m | 7,985 | utf_8 | a65d1f604b6bb3e700f967b4eed0ba79 | function [x,status,history] = l1_ls_nonneg(A,varargin)
%
% l1-Regularized Least Squares Problem Solver
%
% l1_ls solves problems of the following form:
%
% minimize ||A*x-y||^2 + lambda*sum(x_i),
% subject to x_i >= 0, i=1,...,n
%
% where A and y are problem data and x is variable (described below).
%... |
github | yuanxy92/ConvexOptimization-master | l1_ls.m | .m | ConvexOptimization-master/3rd/l1_ls_matlab/l1_ls.m | 8,414 | utf_8 | 592cd5d633c7f3e474bcad9309e4ea07 | function [x,status,history] = l1_ls(A,varargin)
%
% l1-Regularized Least Squares Problem Solver
%
% l1_ls solves problems of the following form:
%
% minimize ||A*x-y||^2 + lambda*sum|x_i|,
%
% where A and y are problem data and x is variable (described below).
%
% CALLING SEQUENCES
% [x,status,history] = l1... |
github | yuanxy92/ConvexOptimization-master | l1_norm_ls_solver_pcg.m | .m | ConvexOptimization-master/homework4/l1_norm_ls_solver_pcg.m | 8,430 | utf_8 | 1c703e1d55264ccf9dfc48e4cce34520 | function [x,status,history] = l1_norm_ls_solver_pcg(A,varargin)
%
% l1-Regularized Least Squares Problem Solver
%
% l1_ls solves problems of the following form:
%
% minimize ||A*x-y||^2 + lambda*sum|x_i|,
%
% where A and y are problem data and x is variable (described below).
%
% CALLING SEQUENCES
% [x,stat... |
github | yuanxy92/ConvexOptimization-master | fast_deconv_bregman.m | .m | ConvexOptimization-master/MATLAB/blinddeconv/fast_deconv_bregman.m | 3,048 | utf_8 | 973e7fd7c8d796ae3710cba343daae82 | function [g] = fast_deconv_bregman(f, k, lambda, alpha)
%
% fast solver for the non-blind deconvolution problem: min_g \lambda/2 |g \oplus k
% - f|^2. We use a splitting trick as
% follows: introduce a (vector) variable w, and rewrite the original
% problem as: min_{g,w,b} \lambda/2 |g \oplus k - g|^2 + \beta/2 |w -
%... |
github | yuanxy92/ConvexOptimization-master | ms_blind_deconv.m | .m | ConvexOptimization-master/MATLAB/blinddeconv/ms_blind_deconv.m | 5,929 | utf_8 | 5c923da0f9819ccf3f8a120aed64b240 | function [yorig, deblur, kernel, opts] = ms_blind_deconv(fn, opts)
%
% Do multi-scale blind deconvolution given input file name and options
% structure opts. Returns a double deblurred image along with estimated
% kernel. Following the kernel estimation, a non-blind deconvolution is run.
%
% Copyright (2011): Dilip K... |
github | yuanxy92/ConvexOptimization-master | solve_image_bregman.m | .m | ConvexOptimization-master/MATLAB/blinddeconv/solve_image_bregman.m | 6,074 | utf_8 | 53ce7248ff591aab751e8787cbd2cdb7 | function [w] = solve_image_bregman(v, beta, alpha)
%
% solve the following component-wise separable problem
% min maskk .* |w|^\alpha + \frac{\beta}{2} (w - v).^2
%
% A LUT is used to solve the problem; when the function is first called
% for a new value of beta or alpha, a LUT is built for that beta/alpha
% combin... |
github | yuanxy92/ConvexOptimization-master | pcg_kernel_irls_conv.m | .m | ConvexOptimization-master/MATLAB/blinddeconv/pcg_kernel_irls_conv.m | 2,194 | utf_8 | 69320b59f1de28b4f4213c839c8e8eea | function k_out = pcg_kernel_irls_conv(k_init, X, Y, opts)
%
% Use Iterative Re-weighted Least Squares to solve l_1 regularized kernel
% update with sum to 1 and nonnegativity constraints. The problem that is
% being minimized is:
%
% min 1/2\|Xk - Y\|^2 + \lambda \|k\|_1
%
% Inputs:
% k_init = initial kernel, or s... |
github | yuanxy92/ConvexOptimization-master | sparse_deblur.m | .m | ConvexOptimization-master/MATLAB/code/sparse_deblur.m | 3,117 | utf_8 | 749ef9a032978645f074cec67e368742 | %% Motion Blurry Image Restoration using sparse image prior
% This code is written for ELEC5470 convex optimization project Fall 2017-2018
% @author: Shane Yuan
% @date: Dec 4, 2017
% I write this code basd on Jinshan Pan's open source code, which helps me
% a lot. Thanks to Jinshan Pan
%
function [Latent, k] = sparse... |
github | yuanxy92/ConvexOptimization-master | deblurring_adm_aniso.m | .m | ConvexOptimization-master/MATLAB/code/deblurring_adm_aniso.m | 2,406 | utf_8 | df3c7a21e133a0400474e324ac25aa1b | function [I] = deblurring_adm_aniso(B, k, lambda, alpha)
% Solving TV-\ell^2 deblurring problem via ADM/Split Bregman method
%
% This reference of this code is :Fast Image Deconvolution using Hyper-Laplacian Priors
% Original code is created by Dilip Krishnan
% Finally modified by Jinshan Pan 2011/12/25
% Note:
% In ... |
github | yuanxy92/ConvexOptimization-master | SparseRestorationIRLS.m | .m | ConvexOptimization-master/MATLAB/code/SparseRestorationIRLS.m | 5,712 | utf_8 | 6ccd0743e5ae878824f45f1320a0ef2c | function S = SparseRestorationIRLS(Im, kernel, lambda, kappa, type)
%% Image restoration with L1 prior without FFT
% The objective function:
% S^* = argmin ||I*k - B||^2 + lambda |\nabla I|_0 or
% S^* = argmin ||I*k - B||^2 + lambda |\nabla I|_1
% This code is written for ELEC5470 convex optimization project Fall 2017... |
github | yuanxy92/ConvexOptimization-master | aligned_psnr.m | .m | ConvexOptimization-master/MATLAB/code/aligned_psnr.m | 1,277 | utf_8 | e5c2fc5be4e03efc123052b8409accf0 | %% calculate aligned PSNR of two images
% This code is written for ELEC5470 convex optimization project Fall 2017-2018
% @author: Shane Yuan
% @date: Dec 4, 2017
% I write this code basd on Jinshan Pan's open source code, which helps me
% a lot. Thanks to Jinshan Pan
%
function [psnr_result] = aligned_psnr(ground, ima... |
github | yuanxy92/ConvexOptimization-master | estimate_psf.m | .m | ConvexOptimization-master/MATLAB/code/estimate_psf.m | 1,290 | utf_8 | f570f34e559fd6d11f20feb4f37963d9 | function psf = estimate_psf(blurred_x, blurred_y, latent_x, latent_y, weight, psf_size)
%----------------------------------------------------------------------
% these values can be pre-computed at the beginning of each level
% blurred_f = fft2(blurred);
% dx_f = psf2otf([1 -1 0], size(blurred));
% ... |
github | yuanxy92/ConvexOptimization-master | blind_deconv.m | .m | ConvexOptimization-master/MATLAB/code/blind_deconv.m | 5,338 | utf_8 | 7bb1cdb0addad885af945341306304dd | function [kernel, interim_latent] = blind_deconv(y, y_color, opts)
%% multiscale blind deblurring code
% This code is written for ELEC5470 convex optimization project Fall 2017-2018
% @author: Shane Yuan
% @date: Dec 4, 2017
% I write this code basd on Jinshan Pan's open source code. Thanks to
% Jinshan Pan
%% Input:
%... |
github | yuanxy92/ConvexOptimization-master | wrap_boundary_liu.m | .m | ConvexOptimization-master/MATLAB/code/utils/wrap_boundary_liu.m | 3,568 | utf_8 | 778eb4d6eeeb26991f536cb17154be69 | function ret = wrap_boundary_liu(img, img_size)
% wrap_boundary_liu.m
%
% pad image boundaries such that image boundaries are circularly smooth
%
% written by Sunghyun Cho (sodomau@postech.ac.kr)
%
% This is a variant of the method below:
% Reducing boundary artifacts in image deconvolution
% Renting Liu, J... |
github | yuanxy92/ConvexOptimization-master | adjust_psf_center.m | .m | ConvexOptimization-master/MATLAB/code/utils/adjust_psf_center.m | 1,453 | utf_8 | ffd7dc5a8dc7589030f98a822f6b7c9a | function psf = adjust_psf_center(psf)
[X Y] = meshgrid(1:size(psf,2), 1:size(psf,1));
xc1 = sum2(psf .* X);
yc1 = sum2(psf .* Y);
xc2 = (size(psf,2)+1) / 2;
yc2 = (size(psf,1)+1) / 2;
xshift = round(xc2 - xc1);
yshift = round(yc2 - yc1);
psf = warpimage(psf, [1 0 -xshift; 0 1 -yshift]);
function val = sum2(arr)
val =... |
github | yuanxy92/ConvexOptimization-master | deblurring_adm_aniso.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/deblurring_adm_aniso.m | 2,406 | utf_8 | df3c7a21e133a0400474e324ac25aa1b | function [I] = deblurring_adm_aniso(B, k, lambda, alpha)
% Solving TV-\ell^2 deblurring problem via ADM/Split Bregman method
%
% This reference of this code is :Fast Image Deconvolution using Hyper-Laplacian Priors
% Original code is created by Dilip Krishnan
% Finally modified by Jinshan Pan 2011/12/25
% Note:
% In ... |
github | yuanxy92/ConvexOptimization-master | estimate_psf.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/estimate_psf.m | 1,290 | utf_8 | f570f34e559fd6d11f20feb4f37963d9 | function psf = estimate_psf(blurred_x, blurred_y, latent_x, latent_y, weight, psf_size)
%----------------------------------------------------------------------
% these values can be pre-computed at the beginning of each level
% blurred_f = fft2(blurred);
% dx_f = psf2otf([1 -1 0], size(blurred));
% ... |
github | yuanxy92/ConvexOptimization-master | blind_deconv.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/blind_deconv.m | 4,951 | utf_8 | b9995e1e5c0666707be466fdb39c522a | function [kernel, interim_latent] = blind_deconv(y, lambda_dark, lambda_grad, opts)
%
% Do multi-scale blind deconvolution
%
%% Input:
% @y : input blurred image (grayscale);
% @lambda_dark: the weight for the L0 regularization on intensity
% @lambda_grad: the weight for the L0 regularization on gradient
% @opts: see... |
github | yuanxy92/ConvexOptimization-master | wrap_boundary_liu.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/cho_code/wrap_boundary_liu.m | 3,568 | utf_8 | 778eb4d6eeeb26991f536cb17154be69 | function ret = wrap_boundary_liu(img, img_size)
% wrap_boundary_liu.m
%
% pad image boundaries such that image boundaries are circularly smooth
%
% written by Sunghyun Cho (sodomau@postech.ac.kr)
%
% This is a variant of the method below:
% Reducing boundary artifacts in image deconvolution
% Renting Liu, J... |
github | yuanxy92/ConvexOptimization-master | adjust_psf_center.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/cho_code/adjust_psf_center.m | 1,453 | utf_8 | ffd7dc5a8dc7589030f98a822f6b7c9a | function psf = adjust_psf_center(psf)
[X Y] = meshgrid(1:size(psf,2), 1:size(psf,1));
xc1 = sum2(psf .* X);
yc1 = sum2(psf .* Y);
xc2 = (size(psf,2)+1) / 2;
yc2 = (size(psf,1)+1) / 2;
xshift = round(xc2 - xc1);
yshift = round(yc2 - yc1);
psf = warpimage(psf, [1 0 -xshift; 0 1 -yshift]);
function val = sum2(arr)
val =... |
github | yuanxy92/ConvexOptimization-master | padImage.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/whyte_code/padImage.m | 906 | utf_8 | cbc0cf68a7e2e260cfccc8d64309d87f | % imPadded = padImage(im, padsize, padval)
% padsize = [top, bottom, left, right]
% padval = valid arguments for padval to padarray. e.g. 'replicate', or 0
%
% for negative padsize, undoes the padding
% Author: Oliver Whyte <oliver.whyte@ens.fr>
% Date: November 2011
% Copyright: 2011, Oliver Whyt... |
github | yuanxy92/ConvexOptimization-master | calculatePadding.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/whyte_code/calculatePadding.m | 2,718 | utf_8 | 620880ec6310f544654fe052967f1fe8 | % pad = calculatePadding(image_size,non_uniform = 0,kernel)
% pad = calculatePadding(image_size,non_uniform = 1,theta_list,Kinternal)
% where pad = [top, bottom, left, right]
% Author: Oliver Whyte <oliver.whyte@ens.fr>
% Date: November 2011
% Copyright: 2011, Oliver Whyte
% Reference: O. Whyte, J. Sivic and ... |
github | yuanxy92/ConvexOptimization-master | deconvRL.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/whyte_code/deconvRL.m | 7,302 | utf_8 | 1bf68715ed3c044f344431243ff2b907 | % i_rl = deconvRL(imblur, kernel, non_uniform, ...)
% for uniform blur, with non_uniform = 0
%
% i_rl = deconvRL(imblur, kernel, non_uniform, theta_list, Kblurry, ...)
% for non-uniform blur, with non_uniform = 1
%
% Additional arguments, in any order:
% ... , 'forward_saturation', ... use forward mo... |
github | yuanxy92/ConvexOptimization-master | crossmatrix.m | .m | ConvexOptimization-master/MATLAB/cvpr16_deblurring_code_v1/whyte_code/crossmatrix.m | 417 | utf_8 | cac6f7c9c7414f240720e5a918c7c776 |
% Author: Oliver Whyte <oliver.whyte@ens.fr>
% Date: November 2011
% Copyright: 2011, Oliver Whyte
% Reference: O. Whyte, J. Sivic and A. Zisserman. "Deblurring Shaken and Partially Saturated Images". In Proc. CPCV Workshop at ICCV, 2011.
% URL: http://www.di.ens.fr/willow/research/saturation/
function vx = cross... |
github | yuanxy92/ConvexOptimization-master | deblurring_adm_aniso.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/deblurring_adm_aniso.m | 2,406 | utf_8 | df3c7a21e133a0400474e324ac25aa1b | function [I] = deblurring_adm_aniso(B, k, lambda, alpha)
% Solving TV-\ell^2 deblurring problem via ADM/Split Bregman method
%
% This reference of this code is :Fast Image Deconvolution using Hyper-Laplacian Priors
% Original code is created by Dilip Krishnan
% Finally modified by Jinshan Pan 2011/12/25
% Note:
% In ... |
github | yuanxy92/ConvexOptimization-master | estimate_psf.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/estimate_psf.m | 1,290 | utf_8 | f570f34e559fd6d11f20feb4f37963d9 | function psf = estimate_psf(blurred_x, blurred_y, latent_x, latent_y, weight, psf_size)
%----------------------------------------------------------------------
% these values can be pre-computed at the beginning of each level
% blurred_f = fft2(blurred);
% dx_f = psf2otf([1 -1 0], size(blurred));
% ... |
github | yuanxy92/ConvexOptimization-master | blind_deconv.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/blind_deconv.m | 4,802 | utf_8 | 3f6f793caaf4b9bb62ab9c91970af2ea | function [kernel, interim_latent] = blind_deconv(y, lambda_pixel, lambda_grad, opts)
%
% Do multi-scale blind deconvolution
%
%% Input:
% @y : input blurred image (grayscale);
% @lambda_pixel: the weight for the L0 regularization on intensity
% @lambda_grad: the weight for the L0 regularization on gradient
% @opts: s... |
github | yuanxy92/ConvexOptimization-master | wrap_boundary_liu.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/cho_code/wrap_boundary_liu.m | 3,568 | utf_8 | 778eb4d6eeeb26991f536cb17154be69 | function ret = wrap_boundary_liu(img, img_size)
% wrap_boundary_liu.m
%
% pad image boundaries such that image boundaries are circularly smooth
%
% written by Sunghyun Cho (sodomau@postech.ac.kr)
%
% This is a variant of the method below:
% Reducing boundary artifacts in image deconvolution
% Renting Liu, J... |
github | yuanxy92/ConvexOptimization-master | adjust_psf_center.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/cho_code/adjust_psf_center.m | 1,453 | utf_8 | ffd7dc5a8dc7589030f98a822f6b7c9a | function psf = adjust_psf_center(psf)
[X Y] = meshgrid(1:size(psf,2), 1:size(psf,1));
xc1 = sum2(psf .* X);
yc1 = sum2(psf .* Y);
xc2 = (size(psf,2)+1) / 2;
yc2 = (size(psf,1)+1) / 2;
xshift = round(xc2 - xc1);
yshift = round(yc2 - yc1);
psf = warpimage(psf, [1 0 -xshift; 0 1 -yshift]);
function val = sum2(arr)
val =... |
github | yuanxy92/ConvexOptimization-master | padImage.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/whyte_code/padImage.m | 906 | utf_8 | cbc0cf68a7e2e260cfccc8d64309d87f | % imPadded = padImage(im, padsize, padval)
% padsize = [top, bottom, left, right]
% padval = valid arguments for padval to padarray. e.g. 'replicate', or 0
%
% for negative padsize, undoes the padding
% Author: Oliver Whyte <oliver.whyte@ens.fr>
% Date: November 2011
% Copyright: 2011, Oliver Whyt... |
github | yuanxy92/ConvexOptimization-master | calculatePadding.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/whyte_code/calculatePadding.m | 2,718 | utf_8 | 620880ec6310f544654fe052967f1fe8 | % pad = calculatePadding(image_size,non_uniform = 0,kernel)
% pad = calculatePadding(image_size,non_uniform = 1,theta_list,Kinternal)
% where pad = [top, bottom, left, right]
% Author: Oliver Whyte <oliver.whyte@ens.fr>
% Date: November 2011
% Copyright: 2011, Oliver Whyte
% Reference: O. Whyte, J. Sivic and ... |
github | yuanxy92/ConvexOptimization-master | deconvRL.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/whyte_code/deconvRL.m | 7,302 | utf_8 | 1bf68715ed3c044f344431243ff2b907 | % i_rl = deconvRL(imblur, kernel, non_uniform, ...)
% for uniform blur, with non_uniform = 0
%
% i_rl = deconvRL(imblur, kernel, non_uniform, theta_list, Kblurry, ...)
% for non-uniform blur, with non_uniform = 1
%
% Additional arguments, in any order:
% ... , 'forward_saturation', ... use forward mo... |
github | yuanxy92/ConvexOptimization-master | crossmatrix.m | .m | ConvexOptimization-master/MATLAB/text_deblurring_code/whyte_code/crossmatrix.m | 417 | utf_8 | cac6f7c9c7414f240720e5a918c7c776 |
% Author: Oliver Whyte <oliver.whyte@ens.fr>
% Date: November 2011
% Copyright: 2011, Oliver Whyte
% Reference: O. Whyte, J. Sivic and A. Zisserman. "Deblurring Shaken and Partially Saturated Images". In Proc. CPCV Workshop at ICCV, 2011.
% URL: http://www.di.ens.fr/willow/research/saturation/
function vx = cross... |
github | nqanh/affordance-net-master | voc_eval.m | .m | affordance-net-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | nqanh/affordance-net-master | classification_demo.m | .m | affordance-net-master/caffe-affordance-net/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | lipan00123/InHclustering-master | missclassf.m | .m | InHclustering-master/motionsegmentation/missclassf.m | 252 | utf_8 | 7cb914bb3bf5de5f05e90eb0e3d2887b |
function [err,assignment]=missclassf(estlabel,label)
c = max(label);
cost = zeros(c,c);
for i = 1:c,
for j = 1:c,
cost(i,j)=sum(estlabel(find(label==i))~=j);
end
end
[assignment,err] = munkres(cost);
end |
github | lipan00123/InHclustering-master | munkres.m | .m | InHclustering-master/motionsegmentation/munkres.m | 7,171 | utf_8 | b44ad4f1a20fc5d03db019c44a65bac3 | function [assignment,cost] = munkres(costMat)
% MUNKRES Munkres (Hungarian) Algorithm for Linear Assignment Problem.
%
% [ASSIGN,COST] = munkres(COSTMAT) returns the optimal column indices,
% ASSIGN assigned to each row and the minimum COST based on the assignment
% problem represented by the COSTMAT, where the... |
github | lijunzh/fd_elastic-master | guiSurvey.m | .m | fd_elastic-master/gui/guiSurvey.m | 64,340 | utf_8 | eef780e9025802e1419a3e9d0b8ca138 | function varargout = guiSurvey(varargin)
% GUISURVEY MATLAB code for guiSurvey.fig
% GUISURVEY, by itself, creates a new GUISURVEY or raises the existing
% singleton*.
%
% H = GUISURVEY returns the handle to a new GUISURVEY or the handle to
% the existing singleton*.
%
% GUISURVEY('CALLBACK',hO... |
github | lijunzh/fd_elastic-master | guiAbout.m | .m | fd_elastic-master/gui/guiAbout.m | 3,151 | utf_8 | bb20ce2f08ae9a9280a307509fe67ae0 | function varargout = guiAbout(varargin)
% GUIABOUT MATLAB code for guiAbout.fig
% GUIABOUT, by itself, creates a new GUIABOUT or raises the existing
% singleton*.
%
% H = GUIABOUT returns the handle to a new GUIABOUT or the handle to
% the existing singleton*.
%
% GUIABOUT('CALLBACK',hObject,ev... |
github | lijunzh/fd_elastic-master | plotTrace.m | .m | fd_elastic-master/src/plotTrace.m | 2,573 | utf_8 | b553ce3346283fa24804133d6d91d982 | function plotTrace(data)
% PLOTTRACE plot seismic data traces
%
%
% This matlab source file is free for use in academic research.
% All rights reserved.
%
% Written by Lingchen Zhu (zhulingchen@gmail.com)
% Center for Signal and Information Processing, Center for Energy & Geo Processing
% Georgia Institute of Technolog... |
github | lijunzh/fd_elastic-master | createSampler.m | .m | fd_elastic-master/src/createSampler.m | 1,662 | utf_8 | 0c0ef6672c2fefa3f2dadab892a78123 | % createSampler
% create sampler matrix for each branch in parallel structured sampler
% Author: Lingchen Zhu
% Creation Date: 11/07/2013
function Phi = createSampler(N, M, method, repeat, seed)
if (nargin < 4)
repeat = false;
end
if (nargin < 5)
seed = 0;
end
if (repeat)
rng(seed);
end
switch lower(m... |
github | lijunzh/fd_elastic-master | wiggle.m | .m | fd_elastic-master/src/wiggle.m | 17,037 | utf_8 | f32a3676d5f7f98124d16c4873f0a96a | %WIGGLE Display data as wiggles.
% WIGGLE(C) displays matrix C as wiggles plus filled lobes, which is a
% common display for seismic data or any oscillatory data. A WIGGLE
% display is similar to WATERFALL, except that the Z heights are
% projected onto the horizontal plane, meaning that a WIGGLE display is... |
github | lijunzh/fd_elastic-master | lbfgs.m | .m | fd_elastic-master/src/lbfgs.m | 3,948 | utf_8 | 4fafb45e1f656cdb02aa7dc9fc971fe2 | function [x, f] = lbfgs(fh,x0,options)
% Simple L-BFGS method with Wolfe linesearch
%
% use:
% [xn,info] = lbfgs(fh,x0,options)
%
% input:
% fh - function handle to misfit of the form [f,g] = fh(x)
% where f is the function value, g is the gradient of the same size
% as the input vector x.
% x0 - i... |
github | lijunzh/fd_elastic-master | fdct_wrapping_dispcoef.m | .m | fd_elastic-master/src/CurveLab-2.1.3/fdct_wrapping_matlab/fdct_wrapping_dispcoef.m | 1,919 | utf_8 | 2af5a55f76ce583e6879244514db1b37 | function img = fdct_wrapping_dispcoef(C)
% fdct_wrapping_dispcoef - returns an image containing all the curvelet coefficients
%
% Inputs
% C Curvelet coefficients
%
% Outputs
% img Image containing all the curvelet coefficients. The coefficents are rescaled so that
% the largest coefficent... |
github | lijunzh/fd_elastic-master | spgdemo.m | .m | fd_elastic-master/src/spgl1-1.8/spgdemo.m | 16,195 | utf_8 | 629972a6bc0f55788ac56dda78d403a2 | function spgdemo(interactive)
%DEMO Demonstrates the use of the SPGL1 solver
%
% See also SPGL1.
% demo.m
% $Id: spgdemo.m 1079 2008-08-20 21:34:15Z ewout78 $
%
% ----------------------------------------------------------------------
% This file is part of SPGL1 (Spectral Projected Gradient for L1).
%
% ... |
github | lijunzh/fd_elastic-master | spg_mmv.m | .m | fd_elastic-master/src/spgl1-1.8/spg_mmv.m | 2,853 | utf_8 | d6de8533593624586e911b8b26de8f3b | function [x,r,g,info] = spg_mmv( A, B, sigma, options )
%SPG_MMV Solve multi-measurement basis pursuit denoise (BPDN)
%
% SPG_MMV is designed to solve the basis pursuit denoise problem
%
% (BPDN) minimize ||X||_1,2 subject to ||A X - B||_2,2 <= SIGMA,
%
% where A is an M-by-N matrix, B is an M-by-G matrix, a... |
github | lijunzh/fd_elastic-master | spgl1.m | .m | fd_elastic-master/src/spgl1-1.8/spgl1.m | 31,061 | utf_8 | ba9dfd0ef199543c9289ed4fd0d301bd | function [x,r,g,info] = spgl1( A, b, tau, sigma, x, options )
%SPGL1 Solve basis pursuit, basis pursuit denoise, and LASSO
%
% [x, r, g, info] = spgl1(A, b, tau, sigma, x0, options)
%
% ---------------------------------------------------------------------
% Solve the basis pursuit denoise (BPDN) problem
%
% (BPDN) m... |
github | lijunzh/fd_elastic-master | oneProjectorMex.m | .m | fd_elastic-master/src/spgl1-1.8/private/oneProjectorMex.m | 3,797 | utf_8 | df5afe507062bc6b713674d862bf73cd | function [x, itn] = oneProjectorMex(b,d,tau)
% [x, itn] = oneProjectorMex(b,d,tau)
% Return the orthogonal projection of the vector b >=0 onto the
% (weighted) L1 ball. In case vector d is specified, matrix D is
% defined as diag(d), otherwise the identity matrix is used.
%
% On exit,
% x solves minimize ||b-x... |
github | lijunzh/fd_elastic-master | lsqr.m | .m | fd_elastic-master/src/spgl1-1.8/private/lsqr.m | 11,849 | utf_8 | b60925c5944249161e00049c67d30868 | function [ x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var ]...
= lsqr( m, n, A, b, damp, atol, btol, conlim, itnlim, show )
%
% [ x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var ]...
% = lsqr( m, n, A, b, damp, atol, btol, conlim, itnlim, show );
%
% LSQR solves Ax = b or mi... |
github | lijunzh/fd_elastic-master | wfb2rec.m | .m | fd_elastic-master/src/contourlet_toolbox/wfb2rec.m | 1,419 | utf_8 | a8eb98892d022925b472758e34d4640d | function x = wfb2rec(x_LL, x_LH, x_HL, x_HH, h, g)
% WFB2REC 2-D Wavelet Filter Bank Decomposition
%
% x = wfb2rec(x_LL, x_LH, x_HL, x_HH, h, g)
%
% Input:
% x_LL, x_LH, x_HL, x_HH: Four 2-D wavelet subbands
% h, g: lowpass analysis and synthesis wavelet filters
%
% Output:
% x: reconst... |
github | lijunzh/fd_elastic-master | wfb2dec.m | .m | fd_elastic-master/src/contourlet_toolbox/wfb2dec.m | 1,359 | utf_8 | cf0a7abcc9abae631039550460b07a48 | function [x_LL, x_LH, x_HL, x_HH] = wfb2dec(x, h, g)
% WFB2DEC 2-D Wavelet Filter Bank Decomposition
%
% y = wfb2dec(x, h, g)
%
% Input:
% x: input image
% h, g: lowpass analysis and synthesis wavelet filters
%
% Output:
% x_LL, x_LH, x_HL, x_HH: Four 2-D wavelet subbands
% Make sure... |
github | lijunzh/fd_elastic-master | extend2.m | .m | fd_elastic-master/src/contourlet_toolbox/extend2.m | 1,861 | utf_8 | 40bc6d67909280efd214bb2536a4a46f | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per... |
github | lijunzh/fd_elastic-master | Meyer_sf_vkbook.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/Meyer_sf_vkbook.m | 664 | utf_8 | c34c2143df4bcd9c3d5d9f2588e4550d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Meyer_sf_vkbook.m
%
% First Created: 08-26-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | rcos.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/rcos.m | 476 | utf_8 | e62db4d444bbc10be5c8478b7b671042 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% rcos.m
%
% First Created: 08-26-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | PyrNDDec_mm.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/PyrNDDec_mm.m | 3,663 | utf_8 | 2f1cda7f9c0e6816ff309802f6040e9e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% PyrNDDec_mm.m
%
% First Created: 10-11-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | PyrNDRec_mm.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/PyrNDRec_mm.m | 3,341 | utf_8 | 626e7e143d4a6d7138676e79f47b8b04 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% PyrNDRec_mm.m
%
% First Created: 10-11-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | PSNR.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/PSNR.m | 448 | utf_8 | e453f5dc8f9837e471e9bcab2c65c239 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% PSNR.m
%
% First Created: 09-23-06
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | ccsym.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/ccsym.m | 1,229 | utf_8 | 466eff5ba10dd1882486bc8bb8b773fc | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% ccsym.m
%
% First created: 08-14-05
% Last modified: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | ContourletSDDec.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/ContourletSDDec.m | 2,103 | utf_8 | ca02dc7beab42188367dfff90105a5fe | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% ContourletSDDec.m
%
% First Created: 10-13-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | PrySDdec_onestep.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/PrySDdec_onestep.m | 3,399 | utf_8 | 6c4ba7db35e6e5fc98bf07e16f46ddb7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% PrySDdec_onestep.m
%
% First Created: 10-11-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | ContourletSDRec.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/ContourletSDRec.m | 1,936 | utf_8 | dff2ea8a87a784ea1c51d735fd9d59ab | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% ContourletSDRec.m
%
% First Created: 10-13-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | PrySDrec_onestep.m | .m | fd_elastic-master/src/contourlet_toolbox/contourlet_sfl/PrySDrec_onestep.m | 3,291 | utf_8 | 177fd547ae5154f355724cff80c2656a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Yue M. Lu and Minh N. Do
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% PrySDrec_onestep.m
%
% First Created: 10-11-05
% Last Revision: 07-13-09
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | lijunzh/fd_elastic-master | dynamicimage.m | .m | fd_elastic-master/src/deblocking_filter/dynamicimage.m | 3,024 | utf_8 | f1ac482da624a445cd56661d94ef410f | function ttbin=dynamicimage(stackeddataM2int,nrpixels,imag_col)
if nargin==1
%nrpixels=16;
nrpixels=30;
%nrpixels = 64;
%nrpixels=128;
%imag_col=255;
imag_col = 255;
end
if nargin==2
imag_col=64;
end
sc=size(stackeddataM2int,2);
begin_im=zeros(1,sc);end_im=zeros(1,sc);
for ... |
github | lijunzh/fd_elastic-master | example_PQN_Lasso_Complex.m | .m | fd_elastic-master/src/PQN/example_PQN_Lasso_Complex.m | 1,568 | utf_8 | e16a33e8a000fd7b25dbc0b78fce3c6d | function example_PQN_Lasso_Complex
% solve min_x ||R*fft(x)-b||^2 s.t. ||x||_1 <= tau
close all;
clear;
clc;
addpath(genpath('./'));
m = 128;
n = 512;
R = randn(m, n) + 1j * randn(m, n);
R = R / sqrt(m);
x = 10 * randn(n,1).*(rand(n,1) > 0.9);
X = 1/sqrt(n) * fft(x, n);
F = 1/sqrt(n) * fft(eye(n, n));
b = R * X;
... |
github | lijunzh/fd_elastic-master | prettyPlot.m | .m | fd_elastic-master/src/PQN/misc/prettyPlot.m | 4,994 | utf_8 | b170f4e05629a1115ee65f9f364039b1 | function [] = prettyPlot(xData,yData,legendStr,plotTitle,plotXlabel,plotYlabel,type,style,errors)
% prettyPlot(xData,yData,legendStr,plotTitle,plotXlabel,plotYlabel,type,style,errors)
%
% type 0: plot
% type 1: semilogx
%
% style -1: matlab style
% style 0: use line styles
% style 1: use markers
%
% Save as i... |
github | lijunzh/fd_elastic-master | myProcessOptions.m | .m | fd_elastic-master/src/PQN/misc/myProcessOptions.m | 674 | utf_8 | b94d252a960faa95a3074129247619e6 | function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
... |
github | lijunzh/fd_elastic-master | auxGroupLinfProject.m | .m | fd_elastic-master/src/PQN/project/auxGroupLinfProject.m | 1,001 | utf_8 | beb66218882b76d74e58a8e4e86a0591 | function w = groupLinfProject(w,p,groupStart,groupPtr)
alpha = w(p+1:end);
w = w(1:p);
for i = 1:length(groupStart)-1
groupInd = groupPtr(groupStart(i):groupStart(i+1)-1);
[w(groupInd) alpha(i)] = projectAuxSort(w(groupInd),alpha(i));
end
w = [w;alpha];
end
%% Function to solve the projection f... |
github | lijunzh/fd_elastic-master | Algorithm3BlockMatrix.m | .m | fd_elastic-master/src/PQN/DuchiEtAl_UAI2008/Algorithm3BlockMatrix.m | 2,981 | utf_8 | abb9e934001a060787e9cd1c805a7a70 | function [K,W,f] = Algorithm3(Sigma, groups, lambda,normtype)
% normtype = {2,Inf}
% Groups is a vector containing the group numbers for each row
% construct cell-array with indices for faster projection
groups = groups(:);
nGroups = max(groups);
indices = cell(nGroups,1);
for i=1:nGroups
indices{i} = fin... |
github | lijunzh/fd_elastic-master | Algorithm1.m | .m | fd_elastic-master/src/PQN/DuchiEtAl_UAI2008/Algorithm1.m | 2,282 | utf_8 | c242f4201c7825c86e7951b13c83425a | function [K,W] = Algorithm1(Sigma, lambda)
% Get problem size
n = size(Sigma,1);
% Find initial W, using lemma 1 and diag(W) = lambda
W = initialW(Sigma,diag(lambda));
K = inv(Sigma + W);
% Print header
fprintf('%4s %11s %9s %9s\n','Iter','Objective','Gap','Step');
% Main loop
i = 0; maxiter = 1200; e... |
github | lijunzh/fd_elastic-master | minConF_PQN.m | .m | fd_elastic-master/src/PQN/minConF/minConF_PQN.m | 8,435 | utf_8 | 9af951891988336bc11af977e11f33f2 | function [x,f,funEvals] = minConF_PQN(funObj,x,funProj,options)
% function [x,f] = minConF_PQN(funObj,funProj,x,options)
%
% Function for using a limited-memory projected quasi-Newton to solve problems of the form
% min funObj(x) s.t. x in C
%
% The projected quasi-Newton sub-problems are solved the spectral pr... |
github | lijunzh/fd_elastic-master | minConF_PQN_new.m | .m | fd_elastic-master/src/PQN/minConF/minConF_PQN_new.m | 9,327 | utf_8 | 9a3b5e452f461865773d72fe89f5373f | function [x,f,funEvals] = minConF_PQN_new(funObj,x,funProj,options)
% function [x,f] = minConF_PQN(funObj,funProj,x,options)
%
% Function for using a limited-memory projected quasi-Newton to solve problems of the form
% min funObj(x) s.t. x in C
%
% The projected quasi-Newton sub-problems are solved the spectra... |
github | lijunzh/fd_elastic-master | L1groupGraft.m | .m | fd_elastic-master/src/PQN/groupL1/L1groupGraft.m | 2,228 | utf_8 | 8ea295e38bf112e79fe77edecfb68dac | function [w] = L1groupGraft(funObj,w,groups,lambda,options)
if nargin < 5
options = [];
end
[maxIter,optTol] = myProcessOptions(options,'maxIter',500,'optTol',1e-6);
nVars = length(w);
nGroups = max(groups);
reg = sqrt(accumarray(groups(groups~=0),w(groups~=0).^2));
% Compute Initial Free Variable... |
github | lijunzh/fd_elastic-master | L1groupMinConF.m | .m | fd_elastic-master/src/PQN/groupL1/L1groupMinConF.m | 4,352 | utf_8 | 6a8c330fabfaf0dae63c047818b43b5c | function [w,f] = L1groupMinConF(funObj,w,groups,lambda,options)
% [w] = L1groupMinConF(funObj,w,groups,lambda,options)
if nargin < 5
options = [];
end
[normType,mode,optTol] = myProcessOptions(options,'normType',2,'mode','spg','optTol',1e-6);
nVars = length(w);
nGroups = max(groups);
% Make initial ... |
github | lijunzh/fd_elastic-master | auxGroupL2Project.m | .m | fd_elastic-master/src/PQN/groupL1/auxGroupL2Project.m | 605 | utf_8 | 9c39c0d039de49b67d1d078c6467f3e3 | function w = groupL2Proj(w,p,groupStart,groupPtr)
alpha = w(p+1:end);
w = w(1:p);
for i = 1:length(groupStart)-1
groupInd = groupPtr(groupStart(i):groupStart(i+1)-1);
[w(groupInd) alpha(i)] = projectAux(w(groupInd),alpha(i));
end
w = [w;alpha];
end
%% Function to solve the projection for a sing... |
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