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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 |
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github | wanghan0501/convolutional_sparse_coding-master | cbpdn_rank.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdn_rank.m | 11,085 | utf_8 | 3c32aee454510bbb1b9f2d1f4d422844 | function [Y, optinf] = cbpdn_rank(D, S, lambda, opt)
% cbpdn -- Convolutional Basis Pursuit DeNoising
%
% argmin_{x_m} (1/2)||\sum_m d_m * x_m - s||_2^2 +
% lambda \sum_m ||x_m||_1
%
% The solution is computed using an ADMM approach (see
% boyd-2010-distributed) with e... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdn_low_sparse.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdn_low_sparse.m | 11,314 | utf_8 | 676fce8f71b3f64551bb909163b73533 | function [Y, optinf] = cbpdn_low_sparse(D, S, lambda_s, lambda_r,opt)
% cbpdn -- Convolutional Basis Pursuit DeNoising
%
% argmin_{x_m} (1/2)||\sum_m d_m * x_m - s||_2^2 +
% lambda \sum_m ||x_m||_1
%
% The solution is computed using an ADMM approach (see
% boyd-2010-di... |
github | wanghan0501/convolutional_sparse_coding-master | celnet_gpu.m | .m | convolutional_sparse_coding-master/SparseCode/celnet_gpu.m | 12,262 | utf_8 | 3bfe8fecce50ec8ea826ac2b286c605d | function [Y, optinf] = celnet_gpu(D, S, lambda, mu, opt)
% celnet_gpu -- Convolutional Elastic Net (GPU version)
%
% argmin_{x_m} (1/2)||\sum_m d_m * x_m - s||_2^2 +
% lambda \sum_m ||x_m||_1 + (mu/2) \sum_m ||x_m||_2^2
%
% The solution is computed using an ADMM approach (see
% ... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdnms.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdnms.m | 10,773 | utf_8 | 106a9b0dc6d99d787c98deb2b7f52d58 | function [X, optinf] = cbpdnms(D, S, lambda, opt)
% cbpdnms -- Convolutional Basis Pursuit DeNoising (Mask Simulation)
%
% argmin_{x_k} (1/2)||W (\sum_k d_k * x_k - s)||_2^2 +
% lambda \sum_k ||x_k||_1
%
% The solution is computed using an ADMM approach (see
% boyd-201... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdnmd.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdnmd.m | 10,799 | utf_8 | b23bbc7e352dd855e43948e1868c0ff1 | function [X, optinf] = cbpdnmd(D, S, lambda, opt)
% cbpdnmd -- Convolutional Basis Pursuit DeNoising (Mask Decoupling)
%
% argmin_{x_k} (1/2)||W \sum_k d_k * x_k - s||_2^2 +
% lambda \sum_k ||x_k||_1
%
% The solution is computed using an ADMM approach (see
% boyd-2010-... |
github | wanghan0501/convolutional_sparse_coding-master | bpdnjnt.m | .m | convolutional_sparse_coding-master/SparseCode/bpdnjnt.m | 8,880 | utf_8 | c1bc97667469900f3e6bd14cb3ac45fe | function [Y, optinf] = bpdnjnt(D, S, lambda, mu, opt)
% bpdnjnt -- Basis Pursuit DeNoising with l2,1 joint sparsity
%
% argmin_X (1/2)||D*X - s||_F^2 + lambda*||X||_1 +
% mu*||X||_{2,1}
%
% The solution is computed using the ADMM approach (see
% boyd-2010-distributed for detai... |
github | wanghan0501/convolutional_sparse_coding-master | celnet.m | .m | convolutional_sparse_coding-master/SparseCode/celnet.m | 11,532 | utf_8 | bc859bf5ebdd67452a48f16c23415b26 | function [Y, optinf] = celnet(D, S, lambda, mu, opt)
% celnet -- Convolutional Elastic Net
%
% argmin_{x_m} (1/2)||\sum_m d_m * x_m - s||_2^2 +
% lambda \sum_m ||x_m||_1 + (mu/2) \sum_m ||x_m||_2^2
%
% The solution is computed using an ADMM approach (see
% boyd-2010-distrib... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdn.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdn.m | 10,413 | utf_8 | 27738234c72ea3eb346577080e7e8640 | function [Y, optinf] = cbpdn(D, S, lambda, opt)
% cbpdn -- Convolutional Basis Pursuit DeNoising
%
% argmin_{x_m} (1/2)||\sum_m d_m * x_m - s||_2^2 +
% lambda \sum_m ||x_m||_1
%
% The solution is computed using an ADMM approach (see
% boyd-2010-distributed) with effici... |
github | wanghan0501/convolutional_sparse_coding-master | elnet.m | .m | convolutional_sparse_coding-master/SparseCode/elnet.m | 8,513 | utf_8 | ad0ba145ba80323e93f2f1cfabdbfb88 | function [Y, optinf] = elnet(D, S, lambda, mu, opt)
% elnet -- Elastic Net
%
% argmin_x (1/2)||D*x - s||_2^2 + lambda*||x||_1 + (mu/2) ||x||_2^2
%
% The solution is computed using the ADMM approach (see
% boyd-2010-distributed for details).
%
% Usage:
% [Y, optinf] = elnet(D, S, lambda, m... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdn_gpu.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdn_gpu.m | 10,916 | utf_8 | e6b2b039c00e53be0b0eacdf0428f4ea | function [Y, optinf] = cbpdn_gpu(D, S, lambda, opt)
% cbpdn_gpu -- Convolutional Basis Pursuit DeNoising (GPU version)
%
% argmin_{x_m} (1/2)||\sum_m d_m * x_m - s||_2^2 +
% lambda \sum_m ||x_m||_1
%
% The solution is computed using an ADMM approach (see
% boyd-2010-di... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdnjnt.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdnjnt.m | 11,491 | utf_8 | 4159f4569e221507f7cf594135d490d7 | function [Y, optinf] = cbpdnjnt(D, S, lambda, mu, opt)
% cbpdnjnt -- Convolutional Basis Pursuit DeNoising with Joint Sparsity
%
% argmin_{x_k} (1/2)||\sum_k d_k * x_k - s||_2^2 +
% lambda \sum_k ||x_k||_1 +
% mu ||{x_k}||_{2,1}
%
% The solution is co... |
github | wanghan0501/convolutional_sparse_coding-master | bpdngrp.m | .m | convolutional_sparse_coding-master/SparseCode/bpdngrp.m | 9,332 | utf_8 | 7b8f97e92c355ae6a5ab23882c88b226 | function [Y, optinf] = bpdngrp(D, S, lambda, mu, g, opt)
% bpdngrp -- Basis Pursuit DeNoising with l2,1 group sparsity
%
% argmin_x (1/2)||D*x - s||_2^2 + lambda*||x||_1 +
% mu * \sum_l ||G_l(x)||_2
%
% The solution is computed using the ADMM approach (see
% boyd-2010-distribut... |
github | wanghan0501/convolutional_sparse_coding-master | bpdndl.m | .m | convolutional_sparse_coding-master/DictLearn/bpdndl.m | 12,682 | utf_8 | 3d5b1793a1a5f558609c6b25c38299ec | function [G, Y, optinf] = bpdndl(D0, S, lambda, opt)
% bpdndl -- BPDN Dictionary Learning
%
% argmin_{D,X} (1/2)||D X - S||_2^2 + lambda ||X||_1
%
% The solution is computed using Augmented Lagrangian methods
% (see boyd-2010-distributed for details).
%
% Usage:
% [D, X, optinf] = bpdndl(... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdndl_rank.m | .m | convolutional_sparse_coding-master/DictLearn/cbpdndl_rank.m | 16,753 | utf_8 | 33a940c2af3c9d287304d391f84c4fd1 | function [D, Y, optinf] = cbpdndl_rank(D0, S, lambda, opt)
% cbpdndl_rank -- Convolutional BPDN Dictionary Learning
%
% argmin_{x_m,d_m} (1/2) \sum_k ||\sum_m d_m * x_k,m - s_k||_2^2 +
% lambda \sum_k \sum_m ||x_k,m||_1
%
% The solution is computed using Augmented Lagrangian m... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdndl_rank_gpu.m | .m | convolutional_sparse_coding-master/DictLearn/cbpdndl_rank_gpu.m | 17,919 | utf_8 | dc940180f1ad1ba4b8b672ccf6bd04fc | function [D, Y, optinf] = cbpdndl_rank_gpu(D0, S, lambda, opt)
% cbpdndl_rank_gpu -- Convolutional BPDN Dictionary Learning
%
% argmin_{x_m,d_m} (1/2) \sum_k ||\sum_m d_m * x_k,m - s_k||_2^2 +
% lambda \sum_k \sum_m ||x_k,m||_1
%
% The solution is computed using Augmented Lagr... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdndl.m | .m | convolutional_sparse_coding-master/DictLearn/cbpdndl.m | 16,388 | utf_8 | 960792294ced2b7a9f82104bc944bdeb | function [D, Y, optinf] = cbpdndl(D0, S, lambda, opt)
% cbpdndl -- Convolutional BPDN Dictionary Learning
%
% argmin_{x_m,d_m} (1/2) \sum_k ||\sum_m d_m * x_k,m - s_k||_2^2 +
% lambda \sum_k \sum_m ||x_k,m||_1
%
% The solution is computed using Augmented Lagrangian methods
% ... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdndl_low_sparse.m | .m | convolutional_sparse_coding-master/DictLearn/cbpdndl_low_sparse.m | 17,151 | utf_8 | faf23526a0c9e3f8ce9c36ebb102696a | function [D, Y, optinf] = cbpdndl_low_sparse(D0, S, lambda_r,lambda_s, opt)
% cbpdndl -- Convolutional BPDN Dictionary Learning
%
% argmin_{x_m,d_m} (1/2) \sum_k ||\sum_m d_m * x_k,m - s_k||_2^2 +
% lambda \sum_k \sum_m ||x_k,m||_1
%
% The solution is computed using Augmented ... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdndlms.m | .m | convolutional_sparse_coding-master/DictLearn/cbpdndlms.m | 17,080 | utf_8 | e4996dd9887a46268c0d4abd8b6077b5 | function [D, Y, optinf] = cbpdndlms(D0, S, lambda, opt)
% cbpdndlms -- Convolutional BPDN Dictionary Learning (Mask Simulation)
%
% argmin_{x_m,d_m} (1/2) \sum_k ||W \sum_m d_m * x_k,m - s_k||_2^2 +
% lambda \sum_k \sum_m ||x_k,m||_1
%
% The solution is computed using Augmente... |
github | wanghan0501/convolutional_sparse_coding-master | ccmod.m | .m | convolutional_sparse_coding-master/DictLearn/ccmod.m | 10,515 | utf_8 | 5b4a7f8d3e714708070d3067dcb900e0 | function [D, optinf] = ccmod(X, S, dsz, opt)
% ccmod -- Convolutional Constrained Method of Optimal Directions (MOD)
%
% argmin_{d_m} (1/2) \sum_k ||\sum_m x_k,m * d_m - s_k||_2^2
% such that ||d_m||_2 = 1
%
% The solution is computed using the ADMM approach (see
% boyd-201... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdndl_gpu.m | .m | convolutional_sparse_coding-master/DictLearn/cbpdndl_gpu.m | 17,223 | utf_8 | f503b15857b3a6c14895f23722dd20bf | function [D, Y, optinf] = cbpdndl_gpu(D0, S, lambda, opt)
% cbpdndl_gpu -- Convolutional BPDN Dictionary Learning (GPU version)
%
% argmin_{x_m,d_m} (1/2) \sum_k ||\sum_m d_m * x_k,m - s_k||_2^2 +
% lambda \sum_k \sum_m ||x_k,m||_1
%
% The solution is computed using Augmented ... |
github | wanghan0501/convolutional_sparse_coding-master | ccmod_gpu.m | .m | convolutional_sparse_coding-master/DictLearn/ccmod_gpu.m | 11,211 | utf_8 | 017b0a0411e32bb7ab1bde89c394348c | function [D, optinf] = ccmod_gpu(X, S, dsz, opt)
% ccmod_gpu -- Convolutional Constrained Method of Optimal Directions
% (MOD) (GPU version)
%
% argmin_{d_m} (1/2) \sum_k ||\sum_m x_k,m * d_m - s_k||_2^2
% such that ||d_m||_2 = 1
%
% The solution of the Convolutional C... |
github | wanghan0501/convolutional_sparse_coding-master | cmod.m | .m | convolutional_sparse_coding-master/DictLearn/cmod.m | 8,290 | utf_8 | 24954ffeb844d1dccd2b8cbea196190f | function [G, optinf] = cmod(X, S, opt)
% cmod -- Constrained Method of Optimal Directions (MOD)
%
% argmin_D (1/2)||D X - S||_2^2 such that ||d_k||_2 = 1
% where d_k are columns of D
%
% The solution is computed using the ADMM approach (see
% boyd-... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdndlmd.m | .m | convolutional_sparse_coding-master/DictLearn/cbpdndlmd.m | 18,514 | utf_8 | db3b0d7d52f06931c0a6b9f3b65d57af | function [D, Y, optinf] = cbpdndlmd(D0, S, lambda, opt)
% cbpdndlmd -- Convolutional BPDN Dictionary Learning (Mask Decoupling)
%
% argmin_{x_m,d_m} (1/2) \sum_k ||W \sum_m d_m * x_k,m - s_k||_2^2 +
% lambda \sum_k \sum_m ||x_k,m||_1
%
% The solution is computed using Augmente... |
github | changken1/IDH_Prediction-master | readDICOMdir.m | .m | IDH_Prediction-master/MatlabScripts/readDICOMdir.m | 6,680 | utf_8 | f26b5bc8fcfd0af05c2feb487282707e | function [sData] = readDICOMdir(dicomPath,waitB)
% -------------------------------------------------------------------------
% function [sData] = readDICOMdir(dicomPath,waitB)
% -------------------------------------------------------------------------
% DESCRIPTION:
% This function reads the DICOM content of a single ... |
github | changken1/IDH_Prediction-master | load_nii_ext.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_nii_ext.m | 5,337 | utf_8 | fa0e831b0a596c3208b21bddc1c6d812 | % Load NIFTI header extension after its header is loaded using load_nii_hdr.
%
% Usage: ext = load_nii_ext(filename)
%
% filename - NIFTI file name.
%
% Returned values:
%
% ext - Structure of NIFTI header extension, which includes num_ext,
% and all the extended header sections in the header extension.
% ... |
github | changken1/IDH_Prediction-master | rri_orient.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/rri_orient.m | 2,251 | utf_8 | 4253fb96b9189a8a4bad49661d9ecac3 | % Convert image of different orientations to standard Analyze orientation
%
% Usage: nii = rri_orient(nii);
% Jimmy Shen (jimmy@rotman-baycrest.on.ca), 26-APR-04
%___________________________________________________________________
function [nii, orient, pattern] = rri_orient(nii, varargin)
if nargin > 1
... |
github | changken1/IDH_Prediction-master | save_untouch0_nii_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_untouch0_nii_hdr.m | 8,594 | utf_8 | 7e8b1b327e1924837820f75780d52d01 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function save_nii_hdr(hdr, fid)
if ~isequal(hdr.hk.sizeof_hdr,348),
error('hdr.hk.sizeof_hdr must be 348.');
end
write_header(hdr, fid);
return; % save_nii_hdr
%---------------------------------------------------------------... |
github | changken1/IDH_Prediction-master | rri_zoom_menu.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/rri_zoom_menu.m | 737 | utf_8 | d8151523470b0fba970eb1d98ba56030 | % Imbed a zoom menu to any figure.
%
% Usage: rri_zoom_menu(fig);
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
%--------------------------------------------------------------------
function menu_hdl = rri_zoom_menu(fig)
if isnumeric(fig)
menu_hdl = uimenu('Parent',fig, ...
'Label','Zoom on', ..... |
github | changken1/IDH_Prediction-master | rri_select_file.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/rri_select_file.m | 16,599 | utf_8 | e349954ca803370f62ceeabdbab5912e | function [selected_file, selected_path] = rri_select_file(varargin)
%
% USAGE: [selected_file, selected_path] = ...
% rri_select_file(dir_name, fig_title)
%
% Allow user to select a file from a list of Matlab competible
% file format
%
% Example:
%
% [selected_file, selected_path] = ...
% rri_select_... |
github | changken1/IDH_Prediction-master | clip_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/clip_nii.m | 3,306 | utf_8 | a70bdbed5a0813312d4c83f94b99a710 | % CLIP_NII: Clip the NIfTI volume from any of the 6 sides
%
% Usage: nii = clip_nii(nii, [option])
%
% Inputs:
%
% nii - NIfTI volume.
%
% option - struct instructing how many voxel to be cut from which side.
%
% option.cut_from_L = ( number of voxel )
% option.cut_from_R = ( number of voxel )
% option.cut_from_P ... |
github | changken1/IDH_Prediction-master | affine.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/affine.m | 16,110 | utf_8 | 768d2303e551a9584685bdb01abf6f8b | % Using 2D or 3D affine matrix to rotate, translate, scale, reflect and
% shear a 2D image or 3D volume. 2D image is represented by a 2D matrix,
% 3D volume is represented by a 3D matrix, and data type can be real
% integer or floating-point.
%
% You may notice that MATLAB has a function called 'imtransform.m' fo... |
github | changken1/IDH_Prediction-master | load_untouch_nii_img.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_untouch_nii_img.m | 14,756 | utf_8 | 688b2a42f8071c6402a037c7ca923689 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function [img,hdr] = load_untouch_nii_img(hdr,filetype,fileprefix,machine,img_idx,dim5_idx,dim6_idx,dim7_idx,old_RGB,slice_idx)
if ~exist('hdr','var') | ~exist('filetype','var') | ~exist('fileprefix','var') | ~exist('machine','var')
error('U... |
github | changken1/IDH_Prediction-master | load_untouch_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_untouch_nii.m | 6,182 | utf_8 | 93108a725d2e357d773c8aa0acf71328 | % Load NIFTI or ANALYZE dataset, but not applying any appropriate affine
% geometric transform or voxel intensity scaling.
%
% Although according to NIFTI website, all those header information are
% supposed to be applied to the loaded NIFTI image, there are some
% situations that people do want to leave the origi... |
github | changken1/IDH_Prediction-master | collapse_nii_scan.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/collapse_nii_scan.m | 6,778 | utf_8 | 64b1cb0f7cd9e095d3c11ca66453df69 | % Collapse multiple single-scan NIFTI files into a multiple-scan NIFTI file
%
% Usage: collapse_nii_scan(scan_file_pattern, [collapsed_fileprefix], [scan_file_folder])
%
% Here, scan_file_pattern should look like: 'myscan_0*.img'
% If collapsed_fileprefix is omit, 'multi_scan' will be used
% If scan_file_folder is... |
github | changken1/IDH_Prediction-master | rri_orient_ui.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/rri_orient_ui.m | 5,384 | utf_8 | e1196b81940d9f93fbdb43c33799e587 | % Return orientation of the current image:
% orient is orientation 1x3 matrix, in that:
% Three elements represent: [x y z]
% Element value: 1 - Left to Right; 2 - Posterior to Anterior;
% 3 - Inferior to Superior; 4 - Right to Left;
% 5 - Anterior to Posterior; 6 - Superior to Inferior;
% e.g.:
% Standard RAS Or... |
github | changken1/IDH_Prediction-master | load_untouch0_nii_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_untouch0_nii_hdr.m | 8,093 | utf_8 | 3de9ff6a1da47b56ae680e7660eaa041 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function hdr = load_nii_hdr(fileprefix, machine)
fn = sprintf('%s.hdr',fileprefix);
fid = fopen(fn,'r',machine);
if fid < 0,
msg = sprintf('Cannot open file %s.',fn);
error(msg);
else
fseek(fid,0,'bof');
hdr =... |
github | changken1/IDH_Prediction-master | load_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_nii.m | 6,808 | utf_8 | d098a5dbea3cd4ad76cea624ffbef9db | % Load NIFTI or ANALYZE dataset. Support both *.nii and *.hdr/*.img
% file extension. If file extension is not provided, *.hdr/*.img will
% be used as default.
%
% A subset of NIFTI transform is included. For non-orthogonal rotation,
% shearing etc., please use 'reslice_nii.m' to reslice the NIFTI file.
% It will... |
github | changken1/IDH_Prediction-master | unxform_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/unxform_nii.m | 1,181 | utf_8 | a77d113be34b09d588b2eb326a3c65c8 | % Undo the flipping and rotations performed by xform_nii; spit back only
% the raw img data block. Initial cut will only deal with 3D volumes
% strongly assume we have called xform_nii to write down the steps used
% in xform_nii.
%
% Usage: a = load_nii('original_name');
% manipulate a.img to make array... |
github | changken1/IDH_Prediction-master | load_untouch_nii_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_untouch_nii_hdr.m | 8,522 | utf_8 | 2d4bc8c8ffb83b37daf1e8dd87c108e6 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function hdr = load_nii_hdr(fileprefix, machine, filetype)
if filetype == 2
fn = sprintf('%s.nii',fileprefix);
if ~exist(fn)
msg = sprintf('Cannot find file "%s.nii".', fileprefix);
error(msg);
end
else
... |
github | changken1/IDH_Prediction-master | save_nii_ext.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_nii_ext.m | 977 | utf_8 | b60a98ab7537a883dc3ffef3175f19ae | % Save NIFTI header extension.
%
% Usage: save_nii_ext(ext, fid)
%
% ext - struct with NIFTI header extension fields.
%
% NIFTI data format can be found on: http://nifti.nimh.nih.gov
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
function save_nii_ext(ext, fid)
if ~exist('ext','var') | ~exist('fid','var')
... |
github | changken1/IDH_Prediction-master | view_nii_menu.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/view_nii_menu.m | 14,415 | utf_8 | 32dd591fa1070721f0255f47f6e02510 | % Imbed Zoom, Interp, and Info menu to view_nii window.
%
% Usage: view_nii_menu(fig);
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
%--------------------------------------------------------------------
function menu_hdl = view_nii_menu(fig, varargin)
if isnumeric(fig)
menu_hdl = init(fig);
retur... |
github | changken1/IDH_Prediction-master | save_untouch_header_only.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_untouch_header_only.m | 2,132 | utf_8 | 5f0515ef6a35f171bc8371d0f3fd365d | % This function is only used to save Analyze or NIfTI header that is
% ended with .hdr and loaded by load_untouch_header_only.m. If you
% have NIfTI file that is ended with .nii and you want to change its
% header only, you can use load_untouch_nii / save_untouch_nii pair.
%
% Usage: save_untouch_header_only(hd... |
github | changken1/IDH_Prediction-master | pad_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/pad_nii.m | 3,712 | utf_8 | 0b9de8feba6840e2d8ea1ab1752747c7 | % PAD_NII: Pad the NIfTI volume from any of the 6 sides
%
% Usage: nii = pad_nii(nii, [option])
%
% Inputs:
%
% nii - NIfTI volume.
%
% option - struct instructing how many voxel to be padded from which side.
%
% option.pad_from_L = ( number of voxel )
% option.pad_from_R = ( number of voxel )
% option.pad_from_P ... |
github | changken1/IDH_Prediction-master | load_nii_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_nii_hdr.m | 10,031 | utf_8 | e95839e314863f7ee463cc2626dd447c | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function [hdr, filetype, fileprefix, machine] = load_nii_hdr(fileprefix)
if ~exist('fileprefix','var'),
error('Usage: [hdr, filetype, fileprefix, machine] = load_nii_hdr(filename)');
end
machine = 'ieee-le';
new_ext = 0;
if fin... |
github | changken1/IDH_Prediction-master | save_untouch_slice.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_untouch_slice.m | 19,683 | utf_8 | 364468e5dbd3790c1aadf9a768534f1f | % Save back to the original image with a portion of slices that was
% loaded by "load_untouch_nii". You can process those slices matrix
% in any way, as long as their dimension is not altered.
%
% Usage: save_untouch_slice(slice, filename, ...
% slice_idx, [img_idx], [dim5_idx], [dim6_idx], [dim7_idx])
%
% slice ... |
github | changken1/IDH_Prediction-master | load_nii_img.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_nii_img.m | 12,328 | utf_8 | b1b9dd2838a8f217b10fefdc8a931d5e | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function [img,hdr] = load_nii_img(hdr,filetype,fileprefix,machine,img_idx,dim5_idx,dim6_idx,dim7_idx,old_RGB)
if ~exist('hdr','var') | ~exist('filetype','var') | ~exist('fileprefix','var') | ~exist('machine','var')
error('Usage: [img,hdr] = ... |
github | changken1/IDH_Prediction-master | bresenham_line3d.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/bresenham_line3d.m | 4,493 | utf_8 | c19f06df423676afeb59762ac55c0c2f | % Generate X Y Z coordinates of a 3D Bresenham's line between
% two given points.
%
% A very useful application of this algorithm can be found in the
% implementation of Fischer's Bresenham interpolation method in my
% another program that can rotate three dimensional image volume
% with an affine matrix:
% http... |
github | changken1/IDH_Prediction-master | make_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/make_nii.m | 6,849 | utf_8 | 3c7c8b81655c111a9ce4b82086bde4f5 | % Make NIfTI structure specified by an N-D matrix. Usually, N is 3 for
% 3D matrix [x y z], or 4 for 4D matrix with time series [x y z t].
% Optional parameters can also be included, such as: voxel_size,
% origin, datatype, and description.
%
% Once the NIfTI structure is made, it can be saved into NIfTI fil... |
github | changken1/IDH_Prediction-master | verify_nii_ext.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/verify_nii_ext.m | 1,676 | utf_8 | db3d32ecba688905185f5ed01b409fd1 | % Verify NIFTI header extension to make sure that each extension section
% must be an integer multiple of 16 byte long that includes the first 8
% bytes of esize and ecode. If the length of extension section is not the
% above mentioned case, edata should be padded with all 0.
%
% Usage: [ext, esize_total] = verif... |
github | changken1/IDH_Prediction-master | get_nii_frame.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/get_nii_frame.m | 4,333 | utf_8 | 8b0cba9d07733a6f82753b0c40b51107 | % Return time frame of a NIFTI dataset. Support both *.nii and
% *.hdr/*.img file extension. If file extension is not provided,
% *.hdr/*.img will be used as default.
%
% It is a lightweighted "load_nii_hdr", and is equivalent to
% hdr.dime.dim(5)
%
% Usage: [ total_scan ] = get_nii_frame(filename)
%
% filen... |
github | changken1/IDH_Prediction-master | flip_lr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/flip_lr.m | 3,484 | utf_8 | a0b2d0189d90339a841863efeb60681a | % When you load any ANALYZE or NIfTI file with 'load_nii.m', and view
% it with 'view_nii.m', you may find that the image is L-R flipped.
% This is because of the confusion of radiological and neurological
% convention in the medical image before NIfTI format is adopted. You
% can find more details from:
%
% http... |
github | changken1/IDH_Prediction-master | save_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_nii.m | 9,404 | utf_8 | 88aa93174482539fe993ac335fb01541 | % Save NIFTI dataset. Support both *.nii and *.hdr/*.img file extension.
% If file extension is not provided, *.hdr/*.img will be used as default.
%
% Usage: save_nii(nii, filename, [old_RGB])
%
% nii.hdr - struct with NIFTI header fields (from load_nii.m or make_nii.m)
%
% nii.img - 3D (or 4D) matrix of NIFTI... |
github | changken1/IDH_Prediction-master | rri_file_menu.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/rri_file_menu.m | 3,974 | utf_8 | 1ec91620ceb4108dde9a63945380028f | % Imbed a file menu to any figure. If file menu exist, it will append
% to the existing file menu. This file menu includes: Copy to clipboard,
% print, save, close etc.
%
% Usage: rri_file_menu(fig);
%
% rri_file_menu(fig,0) means no 'Close' menu.
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
%---------... |
github | changken1/IDH_Prediction-master | reslice_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/reslice_nii.m | 9,817 | utf_8 | 05783cd4f127a22486db67a9cc89ad2a | % The basic application of the 'reslice_nii.m' program is to perform
% any 3D affine transform defined by a NIfTI format image.
%
% In addition, the 'reslice_nii.m' program can also be applied to
% generate an isotropic image from either a NIfTI format image or
% an ANALYZE format image.
%
% The resliced NIfTI fi... |
github | changken1/IDH_Prediction-master | save_untouch_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_untouch_nii.m | 6,494 | utf_8 | 50fa95cbb847654356241a853328f912 | % Save NIFTI or ANALYZE dataset that is loaded by "load_untouch_nii.m".
% The output image format and file extension will be the same as the
% input one (NIFTI.nii, NIFTI.img or ANALYZE.img). Therefore, any file
% extension that you specified will be ignored.
%
% Usage: save_untouch_nii(nii, filename)
%
% nii -... |
github | changken1/IDH_Prediction-master | view_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/view_nii.m | 139,608 | utf_8 | 74f9dea7539a45a7993beb22becf2fa2 | % VIEW_NII: Create or update a 3-View (Front, Top, Side) of the
% brain data that is specified by nii structure
%
% Usage: status = view_nii([h], nii, [option]) or
% status = view_nii(h, [option])
%
% Where, h is the figure on which the 3-View will be plotted;
% nii is the brain data in NIFTI format;
% option is... |
github | changken1/IDH_Prediction-master | mat_into_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/mat_into_hdr.m | 2,608 | utf_8 | d53006b93ff90a4a5561d16ff2f4e9a6 | %MAT_INTO_HDR The old versions of SPM (any version before SPM5) store
% an affine matrix of the SPM Reoriented image into a matlab file
% (.mat extension). The file name of this SPM matlab file is the
% same as the SPM Reoriented image file (.img/.hdr extension).
%
% This program will convert the ANALYZE 7.5 SPM Reor... |
github | changken1/IDH_Prediction-master | xform_nii.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/xform_nii.m | 18,107 | utf_8 | 29a1cff91c944d6a93e5101946a5da4d | % internal function
% 'xform_nii.m' is an internal function called by "load_nii.m", so
% you do not need run this program by yourself. It does simplified
% NIfTI sform/qform affine transform, and supports some of the
% affine transforms, including translation, reflection, and
% orthogonal rotation (N*90 degree... |
github | changken1/IDH_Prediction-master | make_ana.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/make_ana.m | 5,455 | utf_8 | 2f62999cbcad72129c892135ff492a1e | % Make ANALYZE 7.5 data structure specified by a 3D or 4D matrix.
% Optional parameters can also be included, such as: voxel_size,
% origin, datatype, and description.
%
% Once the ANALYZE structure is made, it can be saved into ANALYZE 7.5
% format data file using "save_untouch_nii" command (for more detail,... |
github | changken1/IDH_Prediction-master | extra_nii_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/extra_nii_hdr.m | 7,830 | utf_8 | 853f39f00cbf133e90d0f2cf08d79488 | % Decode extra NIFTI header information into hdr.extra
%
% Usage: hdr = extra_nii_hdr(hdr)
%
% hdr can be obtained from load_nii_hdr
%
% NIFTI data format can be found on: http://nifti.nimh.nih.gov
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
function hdr = extra_nii_hdr(hdr)
switch hdr.dime.datatype
ca... |
github | changken1/IDH_Prediction-master | rri_xhair.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/rri_xhair.m | 2,208 | utf_8 | b3ae9df90d43e5d9538b6b135fa8af20 | % rri_xhair: create a pair of full_cross_hair at point [x y] in
% axes h_ax, and return xhair struct
%
% Usage: xhair = rri_xhair([x y], xhair, h_ax);
%
% If omit xhair, rri_xhair will create a pair of xhair; otherwise,
% rri_xhair will update the xhair. If omit h_ax, current axes will
% be used.... |
github | changken1/IDH_Prediction-master | save_untouch_nii_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_untouch_nii_hdr.m | 8,514 | utf_8 | 582f82c471a9a8826eda59354f61dd1a | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function save_nii_hdr(hdr, fid)
if ~isequal(hdr.hk.sizeof_hdr,348),
error('hdr.hk.sizeof_hdr must be 348.');
end
write_header(hdr, fid);
return; % save_nii_hdr
%---------------------------------------------------------------... |
github | changken1/IDH_Prediction-master | expand_nii_scan.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/expand_nii_scan.m | 1,333 | utf_8 | 748da05d09c1a005401c67270c4b94ab | % Expand a multiple-scan NIFTI file into multiple single-scan NIFTI files
%
% Usage: expand_nii_scan(multi_scan_filename, [img_idx], [path_to_save])
%
% NIFTI data format can be found on: http://nifti.nimh.nih.gov
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
function expand_nii_scan(filename, img_idx, newpath)
... |
github | changken1/IDH_Prediction-master | load_untouch_header_only.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/load_untouch_header_only.m | 7,068 | utf_8 | 8996c72db42b01029c92a4ecd88f4b21 | % Load NIfTI / Analyze header without applying any appropriate affine
% geometric transform or voxel intensity scaling. It is equivalent to
% hdr field when using load_untouch_nii to load dataset. Support both
% *.nii and *.hdr file extension. If file extension is not provided,
% *.hdr will be used as default.
% ... |
github | changken1/IDH_Prediction-master | bipolar.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/bipolar.m | 2,145 | utf_8 | 295f87ece96ca4c5dff8dce4cd912a34 | %BIPOLAR returns an M-by-3 matrix containing a blue-red colormap, in
% in which red stands for positive, blue stands for negative,
% and white stands for 0.
%
% Usage: cmap = bipolar(M, lo, hi, contrast); or cmap = bipolar;
%
% cmap: output M-by-3 matrix for BIPOLAR colormap.
% M: number of shades in the color... |
github | changken1/IDH_Prediction-master | save_nii_hdr.m | .m | IDH_Prediction-master/MatlabScripts/NIFTI/save_nii_hdr.m | 9,270 | utf_8 | f97c194f5bfc667eb4f96edf12be02a7 | % internal function
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
function save_nii_hdr(hdr, fid)
if ~exist('hdr','var') | ~exist('fid','var')
error('Usage: save_nii_hdr(hdr, fid)');
end
if ~isequal(hdr.hk.sizeof_hdr,348),
error('hdr.hk.sizeof_hdr must be 348.');
end
if hdr.h... |
github | andersfp/XFrFT-master | frfft1gpusp.m | .m | XFrFT-master/frfft1gpusp.m | 6,380 | utf_8 | fac15f6a9321b677486717088b80aa5a | function res = frfft1gpusp(fc,a)
% Calculate the 1D fractional Fourier transform along the first dimension
% of the input (fc). The transform order is given by the second input (a).
% The input (fc) must have an even number of rows.
% Single precision only. Requires a compatible GPU.
%
% Example of usage:
% res... |
github | andersfp/XFrFT-master | frfft1gpu.m | .m | XFrFT-master/frfft1gpu.m | 6,255 | utf_8 | a4578bd00d2773bfabb86d55717ae285 | function res = frfft1gpu(fc,a)
% Calculate the 1D fractional Fourier transform along the first dimension
% of the input (fc). The transform order is given by the second input (a).
% The input (fc) must have an even number of rows.
% Requires a compatible GPU.
%
% Example of usage:
% res = frfft1gpu(fc,a)
%
% ... |
github | andersfp/XFrFT-master | frfft1for.m | .m | XFrFT-master/frfft1for.m | 5,202 | utf_8 | 8eb3e984f45dc5b013b411432b393e76 | function res = frfft1for(fc,a)
% Calculate the 1D fractional Fourier transform along the first dimension
% of the input (fc). The transform order is given by the second input (a).
% The input (fc) must have an even number of rows.
%
% Example of usage:
% res = frfft1for(fc,a)
%
% The function supports double a... |
github | andersfp/XFrFT-master | frfft1par.m | .m | XFrFT-master/frfft1par.m | 5,309 | utf_8 | d55fcfb57c8b274ace60a1b495fe0284 | function res = frfft1par(fc,a)
% Calculate the 1D fractional Fourier transform along the first dimension
% of the input (fc). The transform order is given by the second input (a).
% The input (fc) must have an even number of rows.
% Requires Parallel Toolbox.
%
% Example of usage:
% res = frfft1par(fc,a)
%
% ... |
github | andersfp/XFrFT-master | frfft1vec.m | .m | XFrFT-master/frfft1vec.m | 4,977 | utf_8 | 5d684293c100ed266a413c558f649f2c | function res = frfft1vec(fc,a)
% Calculate the 1D fractional Fourier transform along the first dimension
% of the input (fc). The transform order is given by the second input (a).
% The input (fc) must have an even number of rows.
%
% Example of usage:
% res = frfft1vec(fc,a)
%
% The function supports double a... |
github | Hadisalman/stoec-master | SMC_Update.m | .m | stoec-master/code/Fig_1_comparisons/SMC/SMC_Update.m | 2,618 | utf_8 | 1474d6577f0d7a61f9d8455eb5b86202 | function [pose, Ck] = SMC_Update(pose, Ck, time, opt)
%% parameters needed from options(opt)
Lx = opt.L(1);
Ly = opt.L(2);
xmin = opt.DomainBounds.xmin;
ymin = opt.DomainBounds.ymin;
dt = opt.sim.dt;
Nagents = opt.nagents;
KX = opt.erg.KX;
KY= opt.erg.KY;
LK = opt.erg.LK;
HK= opt.erg.HK;
muk = opt.erg.muk... |
github | Hadisalman/stoec-master | freezeColors.m | .m | stoec-master/code/Include/freezeColors.m | 9,815 | utf_8 | 2068d7a4f7a74d251e2519c4c5c1c171 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | Hadisalman/stoec-master | arrow.m | .m | stoec-master/code/Include/arrow.m | 55,176 | utf_8 | 408035a3cb41890dbada1861c1ec78e7 | function [h,yy,zz] = arrow(varargin)
% ARROW Draw a line with an arrowhead.
%
% ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points
% should be vectors of length 2 or 3, or matrices with 2 or 3
% columns), and returns the graphics handle of the arrow(s).
%
% ARROW uses the mouse (cl... |
github | Hadisalman/stoec-master | TruncatedGaussian.m | .m | stoec-master/code/Include/TruncatedGaussian.m | 6,804 | utf_8 | 125bc65500771dd6664b2327487ba9dd | function [X meaneffective sigmaeffective] = TruncatedGaussian(sigma, range, varargin)
% function X = TruncatedGaussian(sigma, range)
% X = TruncatedGaussian(sigma, range, n)
%
% Purpose: generate a pseudo-random vector X of size n, X are drawn from
% the truncated Gaussian distribution in a RANGE braket; ... |
github | Hadisalman/stoec-master | gridfit.m | .m | stoec-master/code/Include/gridfit.m | 34,995 | utf_8 | e58c0dba921cb156ee39a27dd18a4d1c | function [zgrid,xgrid,ygrid] = gridfit(x,y,z,xnodes,ynodes,varargin)
% gridfit: estimates a surface on a 2d grid, based on scattered data
% Replicates are allowed. All methods extrapolate to the grid
% boundaries. Gridfit uses a modified ridge estimator to
% generate the surface, where the bi... |
github | Hadisalman/stoec-master | RegularizeData3D.m | .m | stoec-master/code/Include/RegularizeData3D.m | 39,576 | utf_8 | 70e5294ed3d4f8726fe2518bd8b0d6cb | function [zgrid,xgrid,ygrid] = RegularizeData3D(x,y,z,xnodes,ynodes,varargin)
% RegularizeData3D: Produces a smooth 3D surface from scattered input data.
%
% RegularizeData3D is a modified version of GridFit from the Matlab File Exchange.
% RegularizeData3D does essentially the same thing, but is an a... |
github | Hadisalman/stoec-master | cem_Elif.m | .m | stoec-master/code/Include/gpas-master/cem_Elif.m | 8,782 | utf_8 | 06bef7b59249a3e3354d8770c6d0e6c5 | function [x, c, mu, C] = cem(fun, x0, opts, varargin)
% The cross-entropy method
% @param fun function to be minimized
% @param x0 initial guess
% options:
% @param opts.N: number of samples
% @param opts.rho: quantile (e.g. 0.1)
% @param opts.C: initial covariance
% @param opts.iter: total iterations
% @param opts.v:... |
github | Hadisalman/stoec-master | gp_opt.m | .m | stoec-master/code/Include/gpas-master/gp_opt.m | 1,040 | utf_8 | 73bfd9ba07253327064d9f410c151b93 | function f = gp_opt(fun, sample, N)
if f < gp.fmin
gp.fmin = f;
gp.xmin = x;
end
gp = gp_add(gp, x, f);
global S
% S.N - number of initial samples
% initial samples
S.xs = feval(sample, S.N0);
%S.xs = [-.2 0 .2 .21 .4];
S.fs = feval(fun, S.xs);
% test points
S.xss = feval(sample, S.Nmax);
S.fss = feval(fun,... |
github | Hadisalman/stoec-master | gp_test2.m | .m | stoec-master/code/Include/gpas-master/gp_test2.m | 4,172 | utf_8 | 5b42ddc0709d4b8772ea78ee0a05c916 | function f = gp_test2
% An example of path planning b/n two given states around an
% obstacle and learning the optimal waypoint the system
% should pass through
clear
N0 = 25;
opt.figs(1) = figure;
opt.figs(2) = figure;
opt.figs(3) = figure;
opt.figs(4) = figure;
opt.dr = .2;
opt.xi = [-2.5; -2.5];
opt.xf = [2.5... |
github | Hadisalman/stoec-master | odom_node.m | .m | stoec-master/code/Include/gpas-master/odom_node.m | 1,232 | utf_8 | cff6ebe13248643b3777d386676740a3 | function S = odom_node(S)
% Simulate odometry data ROS node, by waiting for
% commanded path and taking the next pose along the path
%
%
rosshutdown
if isfield(S, 'ROS_MASTER_URI')
setenv('ROS_MASTER_URI', S.ROS_MASTER_URI)
end
if isfield(S, 'ROS_IP')
setenv('ROS_IP', S.ROS_IP)
end
rosinit
% published odometry... |
github | Hadisalman/stoec-master | srec.m | .m | stoec-master/code/Include/gpas-master/srec.m | 11,940 | utf_8 | ec8b6e621cd73fe47ca200a75dc4ce8e | function f = srec
%Demonstration of Receding Horizon Adaptive Sampling for
%discovering peak concentration in a 2d scalar field
clear
N0 = 25;
if 0
opt.figs(1) = figure;
opt.figs(2) = figure;
opt.figs(3) = figure;
opt.figs(4) = figure;
else
opt.figs = [];
end
opt.dr = 5;
%opt.xi = [-45; -45; pi/4];
opt.xi = [2... |
github | Hadisalman/stoec-master | gp_optparams.m | .m | stoec-master/code/Include/gpas-master/gp_optparams.m | 251 | utf_8 | cca5be5e042c465e7871e16bce99a82b | function gp = gp_optparams(gp);
p = [gp.l, gp.s];
[p,FVAL,EXITFLAG,OaUTPUT] = fminsearch(@(p) gp_minhp(p, gp), p);
gp.l = p(1);
gp.s = p(2);
gp = gp_train(gp);
function f = gp_minhp(p, gp)
gp.l = p(1);
gp.s = p(2);
gp = gp_train(gp);
f = -gp.lp; |
github | Hadisalman/stoec-master | env_node.m | .m | stoec-master/code/Include/gpas-master/env_node.m | 1,439 | utf_8 | 31e37716cd1aa05b64e54bfc13264e42 | function S = env_node(S)
% Simulate environmental data ROS node
% Will send back data after receiving odom
% or could just broadcast when new data is available
%
% @param S.envFile environment image file
% scale
% xlb, xub bounds
% sigma meas noise
%
rosshutdown
if isfield(S, 'ROS_MASTER_UR... |
github | Hadisalman/stoec-master | gp_init.m | .m | stoec-master/code/Include/gpas-master/gp_init.m | 498 | utf_8 | 3db97c58bea8c7ae61ac0dcebc3988cf | function gp = gp_init(xs, fs, opts)
% Initialize a GP over f(x) using an initial dataset (xs, fs)
%
% Required options
% opts.l
% opts.s
gp = [];
gp.l = opts.l;
gp.s = opts.s;
gp.sigma = opts.sigma;
gp.xs = xs;
gp.fs = fs;
gp = gp_train(gp);
% optimize hyperparams
%p = [gp.l, gp.s];
%
%[p,FVAL,EXITFLAG,OaUTPUT] =... |
github | Hadisalman/stoec-master | gpas_node.m | .m | stoec-master/code/Include/gpas-master/gpas_node.m | 14,050 | utf_8 | f85451a29d338c7511a21d52d494c4cd | function f = gpas_node(opt)
% Adaptive Sampling for discovering peak concentration in a 2d scalar field
%
% Author: Marin Kobilarov, marin(at)jhu.edu
% Options:
% workspace lower bound
if ~isfield(opt, 'xlb')
opt.xlb = [-50;-50];
end
% workspace upper bound
if ~isfield(opt, 'xub')
opt.xub = [50;50];
end
% grid... |
github | Hadisalman/stoec-master | cem.m | .m | stoec-master/code/Include/gpas-master/cem.m | 8,782 | utf_8 | 06bef7b59249a3e3354d8770c6d0e6c5 | function [x, c, mu, C] = cem(fun, x0, opts, varargin)
% The cross-entropy method
% @param fun function to be minimized
% @param x0 initial guess
% options:
% @param opts.N: number of samples
% @param opts.rho: quantile (e.g. 0.1)
% @param opts.C: initial covariance
% @param opts.iter: total iterations
% @param opts.v:... |
github | Hadisalman/stoec-master | gp_test3.m | .m | stoec-master/code/Include/gpas-master/gp_test3.m | 3,828 | utf_8 | ed5a82390730af6a1554eb97d60094cf | function f = gp_test3
clear
N0 = 500;
Ns = 5000;
opt.Ns = Ns;
opt.xi = [-2.5; -2.5];
opt.xf = [2.5; 2.5];
opt.xr = -2.5:.1:2.5;
opt.yr = -2.5:.1:2.5;
%%%%%%%%%
% 5 g%
% %
% 12 4 %
% 3 %
%s %
%%%%%%%%%
opt.os = [-1.2, -.8, -.25, 1.3, 0.1;
0, -.3, -1, -.6, 1.8];
opt.r = [... |
github | Hadisalman/stoec-master | likBeta.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likBeta.m | 4,830 | utf_8 | 8e503690924874d07a77dc48bc238db1 | function [varargout] = likBeta(link, hyp, y, mu, s2, inf, i)
% likBeta - Beta likelihood function for interval data y from [0,1].
% The expression for the likelihood is
% likBeta(f) = 1/Z * y^(mu*phi-1) * (1-y)^((1-mu)*phi-1) with
% mean=mu and variance=mu*(1-mu)/(1+phi) where mu = g(f) is the Beta intensity,
% f ... |
github | Hadisalman/stoec-master | likT.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likT.m | 4,776 | utf_8 | 6463e0fed8f6484854dd3dd212db5202 | function [varargout] = likT(hyp, y, mu, s2, inf, i)
% likT - Student's t likelihood function for regression.
% The expression for the likelihood is
% likT(t) = Z * ( 1 + (t-y)^2/(nu*sn^2) ).^(-(nu+1)/2),
% where Z = gamma((nu+1)/2) / (gamma(nu/2)*sqrt(nu*pi)*sn)
% and y is the mean (for nu>1) and nu*sn^2/(nu-2) is ... |
github | Hadisalman/stoec-master | likLaplace.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likLaplace.m | 6,922 | iso_8859_13 | 9673b9c57508bdbfd0dc917f10944f80 | function [varargout] = likLaplace(hyp, y, mu, s2, inf, i)
% likLaplace - Laplacian likelihood function for regression.
% The expression for the likelihood is
% likLaplace(t) = exp(-|t-y|/b)/(2*b) with b = sn/sqrt(2),
% where y is the mean and sn^2 is the variance.
%
% The hyperparameters are:
%
% hyp = [ log(sn) ... |
github | Hadisalman/stoec-master | likGaussWarp.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likGaussWarp.m | 9,118 | utf_8 | baca6bc6eb9f081dff2f85d7a4eb8318 | function [varargout] = likGaussWarp(warp, hyp, y, mu, varargin)
% likGaussWarp - Warped Gaussian likelihood for regression.
% The expression for the likelihood is
% likGaussWarp( y | t ) = likGauss( g(y) | t ) * g'(y),
% where likGauss is the Gaussian likelihood and g is the warping function.
%
% The hyperparamete... |
github | Hadisalman/stoec-master | likWeibull.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likWeibull.m | 4,548 | utf_8 | 5134b34b56b016f15d716469fb93c583 | function [varargout] = likWeibull(link, hyp, y, mu, s2, inf, i)
% likWeibull - Weibull likelihood function for strictly positive data y. The
% expression for the likelihood is
% likWeibull(f) = g1*ka/mu * (g1*y/mu)^(ka-1) * exp(-(g1*y/mu)^ka) with
% gj = gamma(1+j/ka), mean=mu and variance=mu^2*(g2/g1^2-1) where mu... |
github | Hadisalman/stoec-master | likGamma.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likGamma.m | 4,573 | utf_8 | 30195b20deb79baed3429087b58977a8 | function [varargout] = likGamma(link, hyp, y, mu, s2, inf, i)
% likGamma - Gamma likelihood function for strictly positive data y. The
% expression for the likelihood is
% likGamma(f) = al^al*y^(al-1)/gamma(al) * exp(-y*al/mu) / mu^al with
% mean=mu and variance=mu^2/al where mu = g(f) is the Gamma intensity, f is... |
github | Hadisalman/stoec-master | likInvGauss.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likInvGauss.m | 4,679 | utf_8 | 1bffc204bfdee3ee427008906bce81ad | function [varargout] = likInvGauss(link, hyp, y, mu, s2, inf, i)
% likInvGauss - Inverse Gaussian likelihood function for strictly positive data
% y. The expression for the likelihood is
% likInvGauss(f) = sqrt(lam/(2*pi*y^3))*exp(-lam*(mu-y)^2/(2*mu^2*y)) with
% mean=mu and variance=mu^3/lam where mu = g(f) is th... |
github | Hadisalman/stoec-master | likPoisson.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likPoisson.m | 4,178 | utf_8 | 9bdb4f7a4905445839d4697149efc827 | function [varargout] = likPoisson(link, hyp, y, mu, s2, inf, i)
% likPoisson - Poisson likelihood function for count data y. The expression for
% the likelihood is
% likPoisson(f) = mu^y * exp(-mu) / y! with mean=variance=mu
% where mu = g(f) is the Poisson intensity, f is a
% Gaussian process, y is the non-negativ... |
github | Hadisalman/stoec-master | likLogistic.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likLogistic.m | 6,137 | utf_8 | 0227c40f8798f8f47d1f32e9dfd6e946 | function [varargout] = likLogistic(hyp, y, mu, s2, inf, i)
% likLogistic - logistic function for binary classification or logit regression.
% The expression for the likelihood is
% likLogistic(t) = 1./(1+exp(-t)).
%
% Several modes are provided, for computing likelihoods, derivatives and moments
% respectively, see... |
github | Hadisalman/stoec-master | likSech2.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likSech2.m | 8,514 | utf_8 | 25a639e43b4bcdc60d8fd113ded18611 | function [varargout] = likSech2(hyp, y, mu, s2, inf, i)
% likSech2 - sech-square likelihood function for regression. Often, the sech-
% square distribution is also referred to as the logistic distribution not to be
% confused with the logistic function for classification. The expression for the
% likelihood is
% li... |
github | Hadisalman/stoec-master | likGumbel.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/lik/likGumbel.m | 3,976 | utf_8 | e181712e58f8360c4d43c5c354d8431a | function [varargout] = likGumbel(sign, hyp, y, mu, s2, inf, i)
% likGumbel - Gumbel likelihood function for extremal value regression.
% The expression for the likelihood is
% likGumbel(t) = exp(-z-exp(-z))/be, z = ga+s*(y-t)/be, be = sn*sqrt(6)/pi
% where s={+1,-1} is a sign switching between left and right skewed... |
github | Hadisalman/stoec-master | priorSmoothBox1.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/prior/priorSmoothBox1.m | 1,617 | utf_8 | df60218e999e45adf5f4204501f3c42f | function [lp,dlp] = priorSmoothBox1(a,b,eta,x)
% Univariate smoothed box prior distribution with linear decay in the log domain
% and infinite support over the whole real axis.
% Compute log-likelihood and its derivative or draw a random sample.
% The prior distribution is parameterized as:
%
% p(x) = sigmoid(... |
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