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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | ismrmrd/ismrmrd-paper-master | ellipse_motion.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/ellipse_motion.m | 1,286 | utf_8 | 9a86f801010ee6877a65e62ef4d79313 | function es = ellipse_motion(ells, varargin)
%function es = ellipse_motion(ells, varargin)
%
% create strum object for simple ellipse motion
% to pass to ellipse_sino()
%
% in:
% ells [ne,6] [centx centy radx rady angle_degrees amplitude]
%
% options:
% 'type' 'linear' (default)
% 'none' no motion, for testing
%... |
github | ismrmrd/ismrmrd-paper-master | feldkamp-jacket.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/feldkamp-jacket.m | 12,207 | utf_8 | 2bb292dba8f6277fea46b8da554df3e9 | function img = feldkamp(cg, ig, proj, varargin)
%|function img = feldkamp(cg, ig, proj, varargin)
%|
%| version modified by james balter and others to try accelereys "jacket"
%| UNDER DEVELOPMENT!
%|
%| FBP reconstruction of cone-beam tomography data collected with
%| a circular source trajectory.
%| See feldkamp_exa... |
github | ismrmrd/ismrmrd-paper-master | fdk_filter.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fdk_filter.m | 1,605 | utf_8 | 641a4c1e8794369e4b5c403b8e5cd444 | function proj = fdk_filter(proj, window, dsd, dfs, ds)
%function proj = fdk_filter(proj, window, dsd, dfs, ds)
%|
%| step 2 of FDK cone-beam CT reconstruction:
%| filter the (zero padded) projections
%|
%| in
%| proj [ns nt na]
%| window [npad] or 'ramp' 'hann'
%| or see fbp2_window()
%|
%| out
%| proj [ns nt na]
... |
github | ismrmrd/ismrmrd-paper-master | ellipse_im.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/ellipse_im.m | 10,054 | utf_8 | 86875eb20d91b1d13e089e456032e3c0 | function [phantom, params] = ellipse_im(ig, params, varargin)
%|function [phantom, params] = ellipse_im(ig, params, options)
%|
%| generate ellipse phantom image from parameters:
%| [x_center y_center x_radius y_radius angle_degrees amplitude]
%|
%| in
%| ig strum image_geom() object
%| params [ne 6] ellipse parame... |
github | ismrmrd/ismrmrd-paper-master | ellipse_sino.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/ellipse_sino.m | 12,751 | utf_8 | eb1b332116538955688c14f638960970 | function [sino, pos, ang] = ellipse_sino(sg, ells, varargin)
%|function [sino, pos, ang] = ellipse_sino(sg, ells, [options])
%|
%| Create sinogram projection of one or more ellipses.
%| Works for both parallel-beam geometry and for fan-beam geometry.
%|
%| in
%| sg sinogram geometry object from sino_geom()
%| ells ... |
github | ismrmrd/ismrmrd-paper-master | rebin_sino.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/rebin_sino.m | 1,104 | utf_8 | 02899ded58ef42a33d260b97b3f433bf | function sino2 = rebin_sino(sino1, geom1, geom2, varargin)
%|function sino2 = rebin_sino(sino1, geom1, geom2, varargin)
%|
%| Rebin a sinogram from the geomtry in "geom1" into the geometry in "geom2"
%| both of which were created using sino_geom().
%| The typical use is to convert between fan-beam and parallel-beam.
... |
github | ismrmrd/ismrmrd-paper-master | fbp2_sino_filter.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp2_sino_filter.m | 3,437 | utf_8 | f5cbff8e37ae0087b637be52862948de | function [sino, Hk, hn, nn] = fbp2_sino_filter(type, sino, varargin)
%|function [sino, Hk, hn, nn] = fbp2_sino_filter(type, sino, [options])
%|
%| Apply ramp-like filters to sinogram(s) for 2D FBP image reconstruction.
%| Both parallel-beam and fan-beam tomographic geometries are supported.
%| This approach of sampli... |
github | ismrmrd/ismrmrd-paper-master | fbp2.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp2.m | 14,231 | utf_8 | f113a7e827a51a13fced2420da07e499 | function out = fbp2(varargin)
%function geom = fbp2(sg, ig, [setup_options])
%function image = fbp2(sino, geom, [recon_options])
%|
%| FBP 2D tomographic image reconstruction for parallel-beam or fan-beam cases,
%| with either flat or arc detector for fan-beam case.
%|
%| To use this, you first call it with the sinogr... |
github | ismrmrd/ismrmrd-paper-master | rebin_helix.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/rebin_helix.m | 6,119 | utf_8 | 4f6af2a74f60b269a4037cb5dbbfb11b | function [sino orbits used] = rebin_helix(cg, ig, proj, varargin)
%function [sino orbits used] = rebin_helix(cg, ig, proj, varargin)
%|
%| A single-slice rebinning (SSRB) method for cone-beam tomography data
%| collected with a helical source trajectory.
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt n... |
github | ismrmrd/ismrmrd-paper-master | rebin_par2fan.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/rebin_par2fan.m | 8,175 | utf_8 | 3cd704207729f06c3d194ad863699c53 | function fsino = rebin_par2fan(psino, pgeom, fgeom, varargin)
%function fsino = rebin_par2fan(psino, pgeom, fgeom, varargin)
%|
%| Rebin parallel-beam (or mojette) sinogram into fan-beam sinogram.
%| Also useful for mojette-to-parallel rebinning.
%|
%| in
%| psino [nr nphi] parallel-beam sinogram
%| pgeom sino_geom(... |
github | ismrmrd/ismrmrd-paper-master | fbp2_sino_weight.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp2_sino_weight.m | 1,873 | utf_8 | c49936aa44b968b851e1fb4c730e9f67 | function sino = fbp2_sino_weight(sg, sino, varargin)
%|function sino = fbp2_sino_weight(sg, sino, varargin)
%|
%| Apply sinogram weighting for first step of 2D fan-beam FBP.
%| This matlab version is the backup alternative for users lacking mex routine.
%|
%| in
%| sg sino_geom()
%| ig image_geom()
%| sino [nb,na... |
github | ismrmrd/ismrmrd-paper-master | fbp2_back_fan.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp2_back_fan.m | 4,230 | utf_8 | 4dd62434653b7cdce46fac43b7320668 | function img = fbp2_back_fan(sg, ig, sino, varargin)
%|function img = fbp2_back_fan(sg, ig, sino, varargin)
%|
%| 2D backprojection for fan-beam FBP.
%| This matlab version is for users lacking mex backprojector.
%|
%| in
%| sg sino_geom()
%| ig image_geom()
%| sino [nb na nz] sinogram (line integrals)
%|
%| opti... |
github | ismrmrd/ismrmrd-paper-master | ellipsoid_proj.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/ellipsoid_proj.m | 3,986 | utf_8 | 3da6b034c013b99de802ccab006c9746 | function proj = ellipsoid_proj(cg, params, varargin)
%|function proj = ellipsoid_proj(cg, params, varargin)
%|
%| Compute set of 2d line-integral projection views of ellipsoid(s).
%| Works for both parallel-beam and cone-beam geometry.
%|
%| in
%| cg ct_geom()
%| params [ne 9] ellipsoid parameters:
%| [x_center ... |
github | ismrmrd/ismrmrd-paper-master | cylinder_im.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/cylinder_im.m | 9,943 | utf_8 | 5eb1466873158c59d7949e69063a4863 | function [phantom, params] = cylinder_im(ig, params, varargin)
%|function [phantom, params] = cylinder_im(ig, params, varargin)
%|
%| Generate (elliptical) cylinder phantom image from parameters.
%| in
%| ig image_geom()
%| params [ne 8] cylinder parameters. if empty, use 3d Defrise
%| [x_center y_center z_center... |
github | ismrmrd/ismrmrd-paper-master | cuboid_proj.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/cuboid_proj.m | 6,283 | utf_8 | 1bcb31d06bb57798e2ff7b271fe5c982 | function proj = cuboid_proj(cg, params, varargin)
%function proj = cuboid_proj(cg, params, varargin)
%|
%| Compute set of 2d line-integral projection views of cuboids.
%| Works for both parallel-beam and cone-beam geometry.
%|
%| in
%| cg ct_geom()
%| params [ne 9] cuboid parameters:
%| [x_center y_center z_cente... |
github | ismrmrd/ismrmrd-paper-master | fbp_dsc.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp_dsc.m | 2,648 | utf_8 | b4db5e3a1356d701e63af219a7a40aff | function x = fbp_dsc(sino, kernel, arg, varargin)
%function x = fbp_dsc(sino, kernel, arg, [options])
% Generic parallel-beam FBP reconstruction.
% Uses Gtomo2_dsc "system 9" backprojector (specific for pixel-driven FBP)
% in:
% sino [nb,na] sinogram
% kernel for filtering
% arg argument array for Gtomo2_dsc, built u... |
github | ismrmrd/ismrmrd-paper-master | rect_sino.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/rect_sino.m | 7,854 | utf_8 | 9127e4aee0dd7cb2decfe9cf88a3b8e3 | function [sino, pos, ang] = rect_sino(sg, rects, varargin)
%|function [sino, pos, ang] = rect_sino(sg, rects, [options])
%|
%| Create sinogram projection of one or more rectangles.
%| Works for both parallel-beam geometry and for fan-beam geometry.
%|
%| in
%| sg sinogram geometry object from sino_geom()
%| rects [... |
github | ismrmrd/ismrmrd-paper-master | jaszczak1.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/jaszczak1.m | 1,109 | utf_8 | 16f63c6585d36a9a368f028a3e8ad258 | function ell = jaszczak1(diam)
%function ell = jaszczak1(diam)
% Generate ellipse parameters for a Jaszczak phantom of diameter diam.
% Copyright 2005-8-26, Jeff Fessler, The University of Michigan
if nargin < 1, help(mfilename), error(mfilename), end
if streq(diam, 'test'), jaszczak1_test, return, end
nrow = [8 6:-... |
github | ismrmrd/ismrmrd-paper-master | ellipses.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/ellipses.m | 3,596 | utf_8 | a4b5d22c0a4abf9ab0a141a1eba99f07 | function [phantom, params] = ellipses(nx, ny, params, dx, dy, varargin)
%function [phantom, params] = ellipses(nx, ny, params, dx, dy, options)
%
% generate ellipse phantom image from parameters:
% [x_center y_center x_radius y_radius angle_degrees amplitude [oversample]]
%
% in
% nx,ny image size
% params ellipse... |
github | ismrmrd/ismrmrd-paper-master | sino_geom.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/sino_geom.m | 14,014 | utf_8 | 25dcba777aeae79b8b40d5abb14a2253 | function st = sino_geom(type, varargin)
%|function st = sino_geom(type, varargin)
%|
%| Create the "sinogram geometry" structure that describes the sampling
%| characteristics of a given sinogram for a 2D parallel or fan-beam system.
%| Using this structure facilitates "object oriented" code.
%| (Use ct_geom() instea... |
github | ismrmrd/ismrmrd-paper-master | fbp_fan_short_wt.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp_fan_short_wt.m | 5,021 | utf_8 | 419df3614155284e49f90ec43ed41a69 | function [wt scale180] = fbp_fan_short_wt(sg, varargin)
%function [wt scale180] = fbp_fan_short_wt(sg, [options])
%|
%| Sinogram weighting for fan-beam short scan, aka 'Parker weighting'
%|
%| in
%| sg strum sino_geom (sg.orbit_start is ignored)
%| or ct_geom
%|
%| option
%| 'type' 'parker' from parker:82:oss (Med... |
github | ismrmrd/ismrmrd-paper-master | image_geom.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/image_geom.m | 15,328 | utf_8 | a482230ae9eb4e3ef123da6eacf07c47 | function st = image_geom(varargin)
%|function st = image_geom(varargin)
%|
%| Create a "image geometry" structure that describes the sampling
%| characteristics of a single 2d image.
%| Using this structure should facilitate "object oriented" code.
%|
%| options for 2d
%| 'nx' image dimension
%| 'ny' image dimensio... |
github | ismrmrd/ismrmrd-paper-master | fbp2_back.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp2_back.m | 3,161 | utf_8 | 6cf03ba3da99101ac1429594a2870908 | function img = fbp2_back(sg, ig, sino, varargin)
%function img = fbp2_back(sg, ig, sino, varargin)
%|
%| 2D backprojection for FBP. This matlab version is the backup alternative
%| for users lacking the mex backprojector.
%|
%| in
%| sg sino_geom()
%| ig image_geom()
%| sino [nb na] sinogram (line integrals), us... |
github | ismrmrd/ismrmrd-paper-master | ir_proj3_compare1.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/ir_proj3_compare1.m | 3,427 | utf_8 | 06baf4de60ee903fb99ec21ea112f2f4 | function ir_proj3_compare1(fun_proj, fun_im, varargin)
%function ir_proj3_compare1(fun_proj, fun_im, varargin)
%|
%| Compute a set of 2d line-integral projection views of one or more ellipsoids.
%| Works for both parallel-beam and cone-beam geometry.
%|
%| in
%| fun_proj @(cg) make projections given ct_geom()
%| fun_i... |
github | ismrmrd/ismrmrd-paper-master | cuboid_im.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/cuboid_im.m | 5,739 | utf_8 | 1031e64e70dc144ae58c64265ed500cc | function [phantom, params] = cuboid_im(ig, params, varargin)
%function [phantom, params] = cuboid_im(ig, params, varargin)
%|
%| generate cuboid phantom image from parameters:
%| [x_center y_center z_center x_diameter y_diameter z_diameter
%| xy_angle_degrees z_angle_degrees amplitude]
%| in
%| ig image_geom()
%| ... |
github | ismrmrd/ismrmrd-paper-master | fbp2_window.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp2_window.m | 1,767 | utf_8 | 96d5871c44234092fdbbcb3aac7558bb | function window = fbp2_window(n, window)
%|function window = fbp2_window(n, window)
%| compute an apodizing window of length n and fft shift it
if nargin == 1 && streq(n, 'test'), fbp2_window_test, return, end
if nargin < 2, help(mfilename), error(mfilename), end
if ischar(window)
if isempty(window) || streq(windo... |
github | ismrmrd/ismrmrd-paper-master | feldkamp.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/feldkamp.m | 4,416 | utf_8 | e436761f7c31865b195d3e7d91d9feb4 | function img = feldkamp(cg, ig, proj, varargin)
%|function img = feldkamp(cg, ig, proj, varargin)
%|
%| FBP reconstruction of cone-beam tomography data collected with
%| a circular source trajectory.
%| See feldkamp_example.m for example.
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt na] cone-beam pr... |
github | ismrmrd/ismrmrd-paper-master | cbct_back.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/cbct_back.m | 11,260 | utf_8 | b60e20102c6a5aa65b4f0d9261508143 | function back = cbct_back(proj, cg, ig, varargin)
%function back = cbct_back(proj, cg, ig, varargin)
%|
%| cone-beam backprojector for feldkamp.m
%|
%| in
%| proj [ns nt na] cone-beam projection views
%| cg strum ct_geom
%| ig strum image_geom
%|
%| option
%| 'use_mex' 0|1|2|3 1 mex with loop in mex (default)
%| ... |
github | ismrmrd/ismrmrd-paper-master | ellipsoid_im.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/ellipsoid_im.m | 16,065 | utf_8 | a3d82fb24c9b4ccd8dc19b25c40a798b | function [phantom, params] = ellipsoid_im(ig, params, varargin)
%|function [phantom, params] = ellipsoid_im(ig, params, varargin)
%|
%| Generate ellipsoids phantom image from parameters:
%| in
%| ig image_geom()
%| params [ne 9] ellipsoid parameters. if empty, use 3d shepp-logan
%| [x_center y_center z_center x_... |
github | ismrmrd/ismrmrd-paper-master | fbp_ramp.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/fbp_ramp.m | 2,017 | utf_8 | 48449b79b81d25a3c3a6fffac5c23519 | function [h, nn] = fbp_ramp(type, n, ds, dsd)
%|function [h, nn] = fbp_ramp(type, n, ds, dsd)
%|
%| 'ramp-like' filters for parallel-beam and fan-beam FBP reconstruction.
%| This sampled band-limited approach avoids the aliasing that would be
%| caused by sampling the ramp directly in the frequency domain.
%|
%| in
%... |
github | ismrmrd/ismrmrd-paper-master | rebin_fan2par.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/rebin_fan2par.m | 9,730 | utf_8 | f82155bcc8182380c694e10e444e1780 | function psino = rebin_fan2par(fsino, sf, sp, varargin)
%|function psino = rebin_fan2par(fsino, sf, sp, varargin)
%|
%| Rebin fan-beam sinogram into parallel-beam (or mojette) sinogram.
%| (Also useful for parallel-to-mojette rebinning.)
%|
%| in
%| fsino [ns nbeta (L)] fan-beam sinogram (or possibly parallel)
%| sf ... |
github | ismrmrd/ismrmrd-paper-master | ct_geom.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/arch/ct_geom.m | 16,026 | utf_8 | 6cb92a21f4f530e9e29cf6c99ee72174 | function st = ct_geom(type, varargin)
%|function st = ct_geom(type, varargin)
%|
%| Create the "CT geometry" structure that describes the sampling
%| characteristics of a cone-beam CT system (axial or helical).
%| (Use sino_geom() for 2D fan-beam or parallel-beam systems.)
%|
%| in
%| type 'fan' (multi-slice fan-beam... |
github | ismrmrd/ismrmrd-paper-master | feldkamp_old.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/arch/feldkamp_old.m | 6,848 | utf_8 | 03c021cd21f50ef6165b9465dfe0fcca | function img = feldkamp(proj, window, mask, args, varargin)
%function img = feldkamp(proj, window, mask, args, [options])
%
% FBP reconstruction of cone-beam tomography data collected with
% a circular source trajectory.
% See feldkamp_example.m for example.
%
% in:
% proj [nh,nv,na] cone-beam projectons (line integ... |
github | ismrmrd/ismrmrd-paper-master | moj2par_rebin.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/arch/moj2par_rebin.m | 1,405 | utf_8 | 30e5dc14b0339fed90b39dbd7ccca673 | function psino = moj2par_rebin(msino, varargin)
%function psino = moj2par_rebin(msino, varargin)
%
% rebin mojette sinogram into parallel-beam sinogram
%
% in
% psino [nr,na] parallel-beam sinogram
% option
% (many, see arg.* below)
% out
% msino [nm,na] mojette sinogram
%
% Copyright 2005-12-7, Jeff Fessler, The Un... |
github | ismrmrd/ismrmrd-paper-master | fbp_helix_gh.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/arch/fbp_helix_gh.m | 6,329 | utf_8 | d4842420a6b5ecfbebd6606f4d2f8f44 | function [img sino] = fbp_helix_gh(cg, ig, proj, varargin)
%|function [img sino] = fbp_helix_gh(cg, ig, proj, varargin)
%|
%| A single slice rebinning method for cone-beam tomography data
%| collected with a helical source trajectory
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt na] cone-beam project... |
github | ismrmrd/ismrmrd-paper-master | par2moj_rebin.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/arch/par2moj_rebin.m | 2,766 | utf_8 | 9fd3165e88091fe8136b4fb5d5283aa1 | function msino = par2moj_rebin(psino, varargin)
%function msino = par2moj_rebin(psino, varargin)
%
% rebin parallel-beam sinogram into mojette sinogram
%
% in
% psino [nr,na] parallel-beam sinogram
% option
% (many, see arg.* below)
% out
% msino [nm,na] mojette sinogram
%
% Copyright 2005-12-7, Jeff Fessler, The Un... |
github | ismrmrd/ismrmrd-paper-master | fbp_filter.m | .m | ismrmrd-paper-master/code/extern/irt/fbp/arch/fbp_filter.m | 2,080 | utf_8 | 5c5eb4807b76232c9eec5cef1ce0e099 | function [Hk, hn, nn] = fbp_filter(type, n, ds, varargin)
%function [Hk, hn, nn] = fbp_filter(type, n, ds, [options])
%
% 'ramp-like' filters for parallel-beam and fan-beam FBP reconstruction.
% This sampled band-limited approach avoids the aliasing that would be
% caused by sampling the ramp directly in the frequency... |
github | ismrmrd/ismrmrd-paper-master | octave_bug1.m | .m | ismrmrd-paper-master/code/extern/irt/octave/octave_bug1.m | 354 | utf_8 | 4c0c264f51bbf532112a325887373260 | function fun1
a = {1, 2};
b = 'foo';
fun2(a{1}, a{2}, b) % works
fun2(a{:}, b) % fails
function fun2(varargin)
nargin % always is 3
disp(argn) % look at the 'a {:}' here the 2nd time!
numel(argn) % always 3
inputname(3)
% fails the second time it is called above with this error
% error: fun2: A(I): index out o... |
github | ismrmrd/ismrmrd-paper-master | pl_pcg_qs_ls.m | .m | ismrmrd-paper-master/code/extern/irt/general/pl_pcg_qs_ls.m | 4,558 | utf_8 | 0fe55de84a5dcfaa9084c76f09cdf9e0 | function [xs, info] = pl_pcg_qs_ls(x, A, data, dercurv, R, varargin)
%|function [xs, info] = pl_pcg_qs_ls(x, A, data, dercurv, R, varargin)
%|
%| Unconstrained generic penalized-likelihood minimization,
%| for arbitrary negative log-likelihood with convex non-quadratic penalty,
%| via preconditioned conjugate gradien... |
github | ismrmrd/ismrmrd-paper-master | pl_iot.m | .m | ismrmrd-paper-master/code/extern/irt/general/pl_iot.m | 6,600 | utf_8 | d14dd429816b03ea810b960e15d62e38 | function [xs, info] = pl_iot(x, Ab, data, R, varargin)
%|function [xs, info] = pl_iot(x, Ab, data, R, [options])
%|
%| Generic penalized-likelihood minimization,
%| for arbitrary negative log-likelihood with convex non-quadratic penalty,
%| via incremental optimization transfer using separable quadratic surrogates.
%... |
github | ismrmrd/ismrmrd-paper-master | pgd_step_test.m | .m | ismrmrd-paper-master/code/extern/irt/general/pgd_step_test.m | 1,847 | utf_8 | 7185a010f86266bb17522a4a0ff9a14f | function pgd_step_test
%function pgd_step_test
% test the PGD algorithm using 2D LS cost function
% cost function terms
kap = 4;
A = [1 0; 0 sqrt(kap)];
W = eye(2);
M = eye(2);
yy = 0;
f.niter = 10;
x = [-kap; 1];
% run PSD
xpsd = qpwls_psd(x, A, W, yy, 0, 'precon', 1, 'niter', f.niter, 'isave', 'all');
% run PGD
... |
github | ismrmrd/ismrmrd-paper-master | subset_start.m | .m | ismrmrd-paper-master/code/extern/irt/general/subset_start.m | 1,272 | utf_8 | 45b77575a8ba7d27613d9ba593a0c2c2 | function [starts, nsubset] = subset_start(nsubset)
%function [starts, nsubset] = subset_start(nsubset)
%|
%| Compute array of subset starting indices "starts" for OS algorithms.
%| If input is an empty matrix, then 1 subset is used.
%| If input is a scalar power of 2 != 1,
%| then the "bit-reversal ordering" is used.
... |
github | ismrmrd/ismrmrd-paper-master | costgrad_check.m | .m | ismrmrd-paper-master/code/extern/irt/general/costgrad_check.m | 1,127 | utf_8 | c815e16fc609ce9ef595173a38630fb7 | function costgrad_check(x, data, costgrad, varargin)
%|function costgrad_check(x, data, costgrad, varargin)
%|
%| Check for consistency between a cost function and its gradient.
%|
%| in
%| x [np 1] point at which to evaluate cost and gradient
%| data {cell} whatever data is needed for the cost function
%| costgrad... |
github | rlajugie/multilabel-master | graph_cut_k.m | .m | multilabel-master/graph_cut_k.m | 1,940 | utf_8 | a51e3c4d8dd113410adecf07688c21c6 | function [ us, Us, obj ] = graph_cut_k( Hi, A, k, V )
%LAGRANGE_INNER_LOOP Summary of this function goes here
% Detailed explanation goes here
STEP = 2;
mup = 1;
[~, ~, fmu0] = f(0, Hi, A, k, V);
[usp, Usp, fmup] = f(mup, Hi, A, k, V);
ca_monte = false;
while ~ca_monte
if (fmup > fmu0)
ca_monte = true;
... |
github | BlueBrain/NEST-master | stdp.m | .m | NEST-master/testsuite/manualtests/stdp.m | 1,351 | utf_8 | 6377d9cfbe952e827ba6200becfca64e |
%% Synaptic dynamics for STDP synapses according to Abigail Morrison's
%% STDP model (see stdp_rec.pdf).
%% author: Moritz Helias, april 2006
%%
function [w]=stdp(w_init, N, T, alpha, mu, lambda, tau, delay, delta_t)
w = w_init;
K_plus=0.0;
K_minus=0.0;
% take into accout dendritic delay
... |
github | mukhtar89/Fast-Fractal-Compression-master | AffineTrnasformation.m | .m | Fast-Fractal-Compression-master/AffineTrnasformation.m | 18,751 | utf_8 | 08934e746ec588fd1ab4de5f8abe997c | function varargout = AffineTrnasformation(varargin)
% AFFINETRNASFORMATION M-file for AffineTrnasformation.fig
% Affine_Transf computes and applies the geometric affine transformation to a 2-D image.
%
% The program main functions are:
% - Load Image: Load the image to be transformed.
% - Transform Image: Computes the ... |
github | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-master/machine-learning-ex2/ex2/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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-master/machine-learning-ex2/ex2/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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-master/machine-learning-ex2/ex2/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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-master/machine-learning-ex2/ex2/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 | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-master/machine-learning-ex4/ex4/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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-master/machine-learning-ex4/ex4/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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-master/machine-learning-ex4/ex4/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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-master/machine-learning-ex4/ex4/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 | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-master/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | ganesshkumar/cs229-ml-assignment-master | porterStemmer.m | .m | cs229-ml-assignment-master/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-master/machine-learning-ex6/ex6/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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-master/machine-learning-ex6/ex6/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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-master/machine-learning-ex6/ex6/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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-master/machine-learning-ex6/ex6/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 | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-master/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-master/machine-learning-ex7/ex7/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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-master/machine-learning-ex7/ex7/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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-master/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | 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 | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | submit.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | submitWithConfiguration.m | .m | cs229-ml-assignment-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | ganesshkumar/cs229-ml-assignment-master | savejson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | loadubjson.m | .m | cs229-ml-assignment-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 | ganesshkumar/cs229-ml-assignment-master | saveubjson.m | .m | cs229-ml-assignment-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 | primme/primme-master | primme_eigs.m | .m | primme-master/Matlab/primme_eigs.m | 36,660 | utf_8 | 25fedc81009d493df762ea2863e67539 | function [varargout] = primme_eigs(varargin)
%PRIMME_EIGS Find a few eigenvalues/vectors of large, sparse Hermitian matrices
%
% D = PRIMME_EIGS(A) returns a vector of A's 6 largest magnitude eigenvalues.
%
% D = PRIMME_EIGS(A,B) returns a vector of the 6 largest magnitude eigenvalues
% of the generalized eigenp... |
github | primme/primme-master | primme_svds.m | .m | primme-master/Matlab/primme_svds.m | 39,671 | utf_8 | 44a3477ca3ea212642daedeb973b2258 | function [varargout] = primme_svds(varargin)
%PRIMME_SVDS Find a few singular values and vectors of large, sparse matrices
%
% S = PRIMME_SVDS(A) returns a vector with the 6 largest singular values of A.
%
% S = PRIMME_SVDS(AFUN,M,N) accepts the function handle AFUN to perform
% the matrix vector products with a... |
github | smahanthi/Video-Stabilization-in-Matlab-master | gradients_xyt.m | .m | Video-Stabilization-in-Matlab-master/gradients_xyt.m | 5,609 | utf_8 | 475e6ef3f4a3abf4145f3af887b0b4ec | function [xg, yg, tg, region] = gradients_xyt(image1, image2, varargin)
%GRADIENTS_XYT Estimate spatial and temporal grey-level gradients
% [XG, YG, TG, REGION] = GRADIENTS_XYT(IMAGE1, IMAGE2, SIGMAS) carries
% out Gaussian smoothing and differencing to estimate the spatial and
% temporal grey-level gradients for... |
github | smahanthi/Video-Stabilization-in-Matlab-master | logtform.m | .m | Video-Stabilization-in-Matlab-master/logtform.m | 1,753 | utf_8 | a4bf458ab2e0928e34ea2bbce1b5ff08 | function t = logtform(rmin, rmax, nr, nw)
% LOGTFORM makes a log-polar transform structure for imtransform
% T = LOGTFORM(RMIN, RMAX, NR, NW) returns the transform structure for
% a system with minimum ring radius RMIN, maximum ring radius RMAX, NR
% rings and NR wedges. The empty matrix may be given for an... |
github | smahanthi/Video-Stabilization-in-Matlab-master | gsmooth2.m | .m | Video-Stabilization-in-Matlab-master/gsmooth2.m | 5,998 | utf_8 | 457373aead98f754c6c218899b7c3115 | function [im, reg] = gsmooth2(im, sigmas, varargin)
%GSMOOTH2 Gaussian image smoothing
% [SMOOTH, REGION] = GSMOOTH2(IMAGE, SIGMAS) carries out Gaussian
% smoothing on an image.
%
% IMAGE must be a 2-D array of class double.
%
% SIGMAS specifies the smoothing constants. This may be a matrix of the
% form [SI... |
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