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github | williammortl/ArtificialPancreasSimulator-master | noiseNone.m | .m | ArtificialPancreasSimulator-master/noiseNone.m | 496 | utf_8 | 48e40280d0303261976c8f365f1c0568 | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: William Michael Mortl, and
%%% Sriram Sankaranaraynan
function [out_val] = noiseNone(in_val)
%%% function: noiseNone
%%% description: simply returns the input value
%%... |
github | williammortl/ArtificialPancreasSimulator-master | mealNHanes.m | .m | ArtificialPancreasSimulator-master/mealNHanes.m | 1,755 | utf_8 | 345d4f641a5f6587e7943e7783fa68f0 | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: NHanes study, William Michael Mortl,
%%% Sriram Sankaranaraynan, and Fraser Cameron
function [mealData] = mealNHanes(genderNum, age, BMI)
%%% function: mealNHanes
%%% desc... |
github | izhengfan/Puma560Simulation-master | main_puma_ct.m | .m | Puma560Simulation-master/main_puma_ct.m | 796 | utf_8 | 2a0a5ccc6490524b6ddb30df3dc57953 | function main_puma_ct
[t,x] = ode45(@puma_ct,0:0.001:20,[0 0 0 0 0 0]);
figure(1),
plot(t,x(:,1),'-',t,x(:,2),'--',t,x(:,3),'-.','linewidth',2.5);
legend('q1','q2','q3');
hold on
plot(t,pi/2+sin(t),'--');
xlabel('t/s'),ylabel('q/rad');
hold off
figure(2),
plot(t,x(:,4),'-',t,x(:,5),'--',t,x(:,6),'-.','lin... |
github | izhengfan/Puma560Simulation-master | main_puma_pd.m | .m | Puma560Simulation-master/main_puma_pd.m | 978 | utf_8 | 584d2733187ef5b34b1ae5e4d4d4ddd1 | function main_puma_pd
[t,x] = ode45(@puma_pd,0:0.001:20,[0 0 0 0 0 0]);
plot(t,x(:,1),'-',t,x(:,2),'--',t,x(:,3),'-.','linewidth',2);
legend('q1','q2','q3');
hold on
plot(t,pi*ones(1,length(t))/2,'--');
xlabel('t/s'),ylabel('q/rad');
hold off
end
function xdot = puma_pd(~,q)
xdot = zeros(6,1);
qd = [pi... |
github | izhengfan/Puma560Simulation-master | main_puma_pid.m | .m | Puma560Simulation-master/main_puma_pid.m | 1,063 | utf_8 | e68cf064d60b473958a4017658e4bfbb | function main_puma_pid
[t,x] = ode45(@puma_pid,0:0.001:20,[0 0 0 0 0 0 0 0 0]);
plot(t,x(:,1),'-',t,x(:,2),'--',t,x(:,3),'-.','linewidth',2);
legend('q1','q2','q3');
hold on
plot(t,pi*ones(1,length(t))/2,'--');
xlabel('t/s'),ylabel('q/rad');
hold off
end
function xdot = puma_pid(~,q)
xdot = zeros(9,1);
... |
github | izhengfan/Puma560Simulation-master | main_puma_pdg.m | .m | Puma560Simulation-master/main_puma_pdg.m | 930 | utf_8 | 043dc6cea953cd29ce1acca6ab406dee | function main_puma_pdg
[t,x] = ode45(@puma_pdg,0:0.001:20,[0 0 0 0 0 0]);
plot(t,x(:,1),'-',t,x(:,2),'--',t,x(:,3),'-.','linewidth',2);
legend('q1','q2','q3');
hold on
plot(t,pi*ones(1,length(t))/2,'--');
xlabel('t/s'),ylabel('q/rad');
hold off
end
function xdot = puma_pdg(~,q)
xdot = zeros(6,1);
qd = ... |
github | craffel/librosa-master | makeTestData.m | .m | librosa-master/tests/makeTestData.m | 11,918 | utf_8 | 0bd02edeb0ace2c023804b3036ffb82f | function testData(source_path, output_path)
% testData(source_path, audio_file, output_path)
% source_path = path to DPWE code
% output_path = directory to store generated files
%
% CREATED:2013-03-08 14:32:21 by Brian McFee <brm2132@columbia.edu>
% Generate the test suite data for librosa routines:
%
% hz_... |
github | craffel/librosa-master | makeCTData.m | .m | librosa-master/tests/makeCTData.m | 1,552 | utf_8 | db4f762a69d4aab5f66c2cfce84262d4 | function makeCTData(source_path, output_path)
% testData(source_path, output_path)
% source_path = path to Chroma Toolbox code
% output_path = directory to store generated files
%
% Generate the test suite data for chroma features used by
% the Chroma Toolbox: https://www.audiolabs-erlangen.de/resources/MIR/chrom... |
github | craffel/librosa-master | makeMETdata.m | .m | librosa-master/tests/makeMETdata.m | 3,533 | utf_8 | 0fe468c95140787a0ce3dbe3169e7eec | function makeMETdata(source_path, output_path)
% testData(source_path, audio_file, output_path)
% source_path = path to METLab code
% output_path = directory to store generated files
%
% CREATED:2015-02-16 12:50:31 by Brian McFee <brian.mcfee@nyu.edu>
% Generate the test suite data for spectral features used by
% ... |
github | haefnerlab/sampling_decision-master | C_Projection.m | .m | sampling_decision-master/C_Projection.m | 7,613 | utf_8 | 6fbab0f7b0d65596c3007b96e46329d1 | function P = C_Projection(fct, varargin)
%C_PROJECTION creates gabor projective fields
%
% P = C_PROJECTION(fct, ...) returns a struct with the following fields:
% P.G projection matrices as (flattened 1D pixels)x(n_matrices)
% P.x x-axis
% P.y y-axis
% P.nx, P.ny size of each projective field... |
github | haefnerlab/sampling_decision-master | experiment_PCA.m | .m | sampling_decision-master/experiment_PCA.m | 4,492 | utf_8 | a06e4187beb5885a3dfce79a782ccbbd | %calculating correlation matrix, eigenvalue and eigenvectors
%created by Shuchen Wu
%06/2015
function experiment_PCA(e)
close all;
%load e_Detection_T0_1_X1024_G256_k10_3_t80_c0_rep1000_time100_b5_s001_001_i20.mat
X = e.X;
X(isnan(X))=0;
%%compute Corr_Matrix
%%each row specify neuron(repetition) and each 100 column ... |
github | haefnerlab/sampling_decision-master | Sampling_Gibbs_InPlace_Fast.m | .m | sampling_decision-master/Sampling_Gibbs_InPlace_Fast.m | 16,339 | utf_8 | 2c0a09ad9e51a35f60c1b8a76a9d54f5 | function [X, G, O, L, Style, Task] = Sampling_Gibbs_InPlace_Fast(Ge, S, I, Image, DBG)
% SAMPLING_GIBBS_INPLACE_FAST performs gibbs sampling on the given
% generative model with G and X 'layers' of variables
%
% [X,G,O,L,Style,Task] = SAMPLING_GIBBS_INPLACE_FAST(Ge,S,I,Image,[debug])
% Uses the generative model Ge,... |
github | haefnerlab/sampling_decision-master | Plot_Misc.m | .m | sampling_decision-master/Plot_Misc.m | 2,362 | utf_8 | e8d1e0605b5127628070dc1ae8a05c5c | function Plot_Misc(varargin)
ax=get(gca);
fct=varargin{1};
switch upper(fct)
case 'ID'
if nargin<2, style='k:'; else style=varargin{2}; end
if nargin<3, opt='single'; else opt=varargin{3}; end
x(1)=max([ax.XLim(1) ax.YLim(1)]);
x(2)=min([ax.XLim(2) ax.YLim(2)]);
plot(x,x,st... |
github | haefnerlab/sampling_decision-master | S_Experiment.m | .m | sampling_decision-master/S_Experiment.m | 5,192 | utf_8 | eadc47d387c2b21d2d6960c5f0fe6ad5 | function out = S_Experiment(params)
%S_EXPERIMENT runs the sampling decision model
%
% out = S_Experiment(params) where params has been created by a call to
% S_Exp_Para, runs the specified experiment and returns information in
% the struct 'out'.
P = params;
Check_Parameter_Sanity(P);
%% local copies of variab... |
github | nicklhy/caffe-dev-master | classification_demo.m | .m | caffe-dev-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | SheffieldML/multigp-master | simMultimodelFixParam.m | .m | multigp-master/matlab/simMultimodelFixParam.m | 922 | utf_8 | 84dba0e1f6c4c66ba8516375a16647fa | function model = simMultimodelFixParam(model, options)
% SIMMULTIMODELFIXPARAM Fix parameters for a sparse multi model with SIM
% FORMAT
% DESC Fix the parameters for a sparse multi model that uses SIM kernel.
% RETURN model : model with fixed parameters included
% ARG model : model before fixing the parameters
% A... |
github | SheffieldML/multigp-master | spmultigpTiePseudoInputs.m | .m | multigp-master/matlab/spmultigpTiePseudoInputs.m | 775 | utf_8 | 98d85c6659cb0a0683416706ff23a5ba | function tieInd = spmultigpTiePseudoInputs(model)
% SPMULTIGPTIEPSEUDOINPUTS Gives the indeces to tie the pseudo inputs
% DESC Gives the indexes to tie the pseudo points
% ARG model : input sparse model.
% ARG tieInd : a cell where each component refers to the elements of the
% pseudo inputs to be tied.
%
% COPYRIGHT ... |
github | SheffieldML/multigp-master | ggMultigpTieParam.m | .m | multigp-master/matlab/ggMultigpTieParam.m | 4,597 | utf_8 | b823a5acb12d7382fbbddb6e018634f2 | function tieInd = ggMultigpTieParam(model, options)
% GGMULTIGPTIEPARAM Tie the parameters for a multigp model with GG kernel
% FORMAT
% DESC Tie the parameters for a multigp model that uses GG kernel.
% RETURN tieInd : cell with elements containing the indexes of parameters
% to tie.
% ARG model : model created
% ARG... |
github | SheffieldML/multigp-master | spmultimodelExpandParam.m | .m | multigp-master/matlab/spmultimodelExpandParam.m | 4,179 | utf_8 | 0aacbf8b07c25356a54008101dfa1ee0 | function model = spmultimodelExpandParam(model, params)
% SPMULTIMODELEXPANDPARAM Expand the parameters into a SPMULTIMODEL struct.
% FORMAT
% DESC expands the model parameters from a structure containing
% the information about a sparse multi model multi-output Gaussian process.
% ARG model : the model structure cont... |
github | SheffieldML/multigp-master | multigpOptions.m | .m | multigp-master/matlab/multigpOptions.m | 2,416 | utf_8 | e03cf477015cc9eff5d12f7b5025e92a | function options = multigpOptions(approx)
% MULTIGPOPTIONS Return default options for the MOCAP examples in the LFM model.
% FORMAT
% DESC returns the default options in a structure for a MULTIGP model.
% ARG approx : approximation type, either 'none' (no approximation),
% 'fitc' (fully
% independent training conditio... |
github | SheffieldML/multigp-master | demJuraBatch.m | .m | multigp-master/matlab/demJuraBatch.m | 4,065 | utf_8 | d762044f27b14614a8d26f7c8c18709d | function [maerror, elapsed_time] = demJuraBatch(file, options, numFolds, iters)
% DEMJURABATCH Demonstrate convolution models on JURA data.
% MULTIGP
testHeterotopic = true;
dataSetName = ['juraData' file];
display = 1;
maerror = zeros(numFolds,1);
elapsed_time = zeros(numFolds,1);
[XTemp, yTemp, XTestTemp, yTestT... |
github | SheffieldML/multigp-master | spmultimodelExtractParam.m | .m | multigp-master/matlab/spmultimodelExtractParam.m | 6,084 | utf_8 | 90f13fb0f797083743666a7c126fbbe6 | function [param, names] = spmultimodelExtractParam(model)
% SPMULTIMODELEXTRACTPARAM Extract the parameters of a SPMULTIMODEL struct.
% FORMAT
% DESC extracts the model parameters from a structure containing
% the information about a multi-output Gaussian process contained inside a
% spmultimodel structure.
% ARG mod... |
github | SheffieldML/multigp-master | balanceModify.m | .m | multigp-master/matlab/balanceModify.m | 1,835 | utf_8 | 1e91992d908c8e786dc940dd470d0b18 | function balanceModify(handle, channels, skel, padding, zeroIndices)
% BALANCEMODIFY Update visualisation of skeleton data for balance motion
% FORMAT
% DESC updates a skeleton representation in a 3-D plot for balance motion.
% ARG handle : a vector of handles to the structure to be updated.
% ARG channels : the chann... |
github | SheffieldML/multigp-master | simMultimodelTieParam.m | .m | multigp-master/matlab/simMultimodelTieParam.m | 2,694 | utf_8 | db6ac8f623a81b56a92ace803058bf05 | function tieInd = simMultimodelTieParam(model)
% SIMMULTIMODELTIEPARAM Tie parameters for a sparse multimodel with SIM
% FORMAT
% DESC Tie the parameters for a sparse multimodel that uses SIM kernel.
% RETURN tieInd : cell with elements containing the indexes of parameters
% to tie.
% ARG model : model created
%
% CO... |
github | SheffieldML/multigp-master | ggwhiteMultigpTieParam.m | .m | multigp-master/matlab/ggwhiteMultigpTieParam.m | 2,471 | utf_8 | a6cc50036029be6365ce599969c41e73 | function tieInd = ggwhiteMultigpTieParam(model, options)
% GGWHITEMULTIGPTIEPARAM Tie parameters for a multigp with GGWHITE kernel
% FORMAT
% DESC Tie the parameters for a multigp model that uses GG kernel.
% RETURN tieInd : cell with elements containing the indexes of parameters
% to tie.
% ARG model : model create... |
github | SheffieldML/multigp-master | lmcMultigpTieParam.m | .m | multigp-master/matlab/lmcMultigpTieParam.m | 2,146 | utf_8 | 73e58437fe5d60d71420e36c4eb26158 | function tieInd = lmcMultigpTieParam(model, options)
% LMCMULTIGPTIEPARAM Tie the parameters for a multigp model with LMC kernel
% FORMAT
% DESC Tie the parameters for a multigp model that uses LMC kernel.
% RETURN tieInd : cell with elements containing the indexes of parameters
% to tie.
% ARG model : model created
%... |
github | SheffieldML/multigp-master | simglobalMultimodelTieParam.m | .m | multigp-master/matlab/simglobalMultimodelTieParam.m | 822 | utf_8 | 2b5c32c12aad0b3eb4a4321e204c5da4 | function tieInd = simglobalMultimodelTieParam(model)
% SIMGLOBALMULTIMODELTIEPARAM Tie parameters for a sparse multimodel
% FORMAT
% DESC Tie the parameters for a sparse multimodel that uses SIM kernel.
% RETURN tieInd : cell with elements containing the indexes of parameters
% to tie.
% ARG model : model created
%
... |
github | mobeets/mpm-master | mpm.m | .m | mpm-master/mpm.m | 43,323 | utf_8 | 0820f5399fa60b35c67ba53283bc0211 | function mpm(action, varargin)
%MPM Matlab Package Manager
% function mpm(ACTION, varargin)
%
% ACTION can be any of the following:
% 'init' add all installed packages in default install directory to path
% 'search' finds a url for a package by name (searches Github and File Exchange)
% 'install' instal... |
github | canlab/RobustToolbox-master | robseed.m | .m | RobustToolbox-master/robust_toolbox/robseed.m | 15,338 | utf_8 | 5f9cae0a7f918b61d1a5777a52dbc826 | function EXPT = robseed(EXPT,cl,varargin)
% EXPT = robseed(EXPT,cl,[which cons],[dools],[mask],[doglobal])
%
% Tor Wager, last modified 4/12/04 to add OLS and difference maps
% Modified may 21 by tor to force output data type to float
%
% This function does a robust GLM
% that regresses each of the images in EXP... |
github | canlab/RobustToolbox-master | robust_htw_results_plots.m | .m | RobustToolbox-master/robust_toolbox/robust_htw_results_plots.m | 8,366 | utf_8 | 4a747e568f1d623fab5c656d959f4477 | function varargout = robust_htw_results_plots(meth, varargin)
%
% This function is a specialized function for the scnlab htw estimation
% tools
% For use with robust regression directories specifically
% IN this framework, first level models must be run with SPM 5/8, and
% apply_derivative_boost.m must be used to reco... |
github | canlab/RobustToolbox-master | robust_reg_load_files_to_objects.m | .m | RobustToolbox-master/robust_toolbox/robust_reg_load_files_to_objects.m | 3,609 | utf_8 | 85f16496fc769b5b4d1c2fc9ead355f7 | function [trob, names, mask_obj, nsubjects, weights, SETUP] = robust_reg_load_files_to_objects(robust_reg_directory)
%
% Load files from a CANLab robust regression directory into objects
% Objects contain information needed for results display and tables.
%
% trob statistic_image object with one image per contrast... |
github | canlab/RobustToolbox-master | robfit.m | .m | RobustToolbox-master/robust_toolbox/robfit.m | 20,142 | utf_8 | ab7a707a990f45e3867553468ee15c20 | % EXPT = robfit(EXPT, [which cons], [do ols], [mask], [nocenter])
%
% This function does a robust fit on contrasts specified in EXPT.SNPM.connames,
% using images in EXPT.SNPM.P
% The model should be stored (without intercept) in EXPT.cov
% Empty EXPT.cov will result in a one-sample t-test
%
% For each contrast, it sav... |
github | canlab/RobustToolbox-master | robust_nonparam_results.m | .m | RobustToolbox-master/robust_toolbox/Robust_stats_functions/robust_nonparam_results.m | 10,808 | utf_8 | bc3c08abd3d998f10a5f926fbe0ddf1f | function R = robust_nonparam_results(R,myalpha)
% R = robust_nonparam_results(R,[corrected alpha level])
%
% Updated: Jan 2008, by Tor Wager, to add primary uncorrected thresholds
k = size(R.X,2);
v = size(R.correct.t,2);
if nargin < 2, myalpha = .05; end
% banner
fprintf(1,'\n* =====================================... |
github | canlab/RobustToolbox-master | robust_reg_matrix.m | .m | RobustToolbox-master/robust_toolbox/Robust_stats_functions/robust_reg_matrix.m | 3,441 | utf_8 | 9f7ce2ff843585e6295e64a00a776404 | function [b,t,p,sig,f,fp,fsig,stat] = robust_reg_matrix(X,Y,dochk)
% [b,t,p,sig,F,fp,fsig,stat] = robust_reg_matrix(X,dat,[do checks and verbose output (1/0)])
%
% takes a data matrix Y (voxels x obs.) and model matrix X ( and returns the robust reg
% coefficients and other things, and significance
%
% X should already... |
github | canlab/RobustToolbox-master | robust_reg_nonparam.m | .m | RobustToolbox-master/robust_toolbox/Robust_stats_functions/robust_reg_nonparam.m | 22,223 | utf_8 | 7a2e793aa2957bcab5b91d6b4bab9d62 |
function R = robust_reg_nonparam(X,niter,varargin)
%
% Robust regression with nonparametric correction for multiple comparisons
% Correction is returned both mapwise (all voxels) and contiguous
% cluster-by-cluster, for small volume correction (svc)
%
% Overview: takes a data matrix Y (voxels x obs.) and
% model matri... |
github | canlab/RobustToolbox-master | robust_nonparam_displayregions.m | .m | RobustToolbox-master/robust_toolbox/Robust_stats_functions/robust_nonparam_displayregions.m | 7,007 | utf_8 | 51f62894c88ca344bf395d144fed2d72 | function [A,R,cl_extent,dat_extent,cl_extent_pos,cl_extent_neg] = robust_nonparam_displayregions(R,wh_regressor,wh_field,cortype,varargin)
% [A,R,cl_extent,dat_extent,cl_extent_pos,cl_extent_neg] = robust_nonparam_displayregions(R,wh_regressor,wh_field,cortype,[optional args])
%
% wh_regressor = 1; % number of re... |
github | canlab/RobustToolbox-master | robust_nonparam_get_sig_clusters.m | .m | RobustToolbox-master/robust_toolbox/robust_nonparametric_subfunctions/robust_nonparam_get_sig_clusters.m | 7,335 | utf_8 | 673d272dbb223d7dac753e6cc17434ec | function [A,R] = robust_nonparam_get_sig_clusters(R,wh_regressor,doextended)
% function [A,R] = robust_nonparam_get_sig_clusters(R,wh_regressor,doextended)
%
% A has:
%A.cl_all,A.cl_sig,A.wh_sig,A.p_sig,R,A.cl_pos,A.cl_neg,A.p_pos,A.p_neg,
%A.pfieldname,A.rfieldname
%
% Get cluster significance information: indices of ... |
github | canlab/RobustToolbox-master | robust_max_partial_corr.m | .m | RobustToolbox-master/robust_toolbox/robust_nonparametric_subfunctions/robust_max_partial_corr.m | 2,260 | utf_8 | a6db44ef40a921a84d6fe9fc395f2580 | function [rrob,prob,Ymaxeffect,whY,Xadj,Yadj] = robust_max_partial_corr(X,Y,doplots,doprint)
% function [rrob,prob,Ymaxeffect,whY,Xadj,Yadj] = robust_max_partial_corr(X,Y,doplots,doprint)
%
% tor wager, aug. 06
%
% finds maximum partial corr. for each column of X in a set of data columns
% Y. Plots are optional (defau... |
github | canlab/RobustToolbox-master | robust_pooled_weight_core.m | .m | RobustToolbox-master/robust_toolbox/robust_nonparametric_subfunctions/robust_pooled_weight_core.m | 7,042 | utf_8 | a6a108e149c9c663078908195b1343b4 | function [t,p,maxt,maxt_by_cl, primary_uncor] = robust_pooled_weight_core(X,Y,b,resid,wts,niter,wh_intercept,clindx,varargin)
%
% [t,p,maxt,maxt_by_cl, primary_uncor] = robust_pooled_weight_core(X,Y,[],[],[],2000,3,R.volInfo.clindx,[existing maxt,maxtbycl]);
%
% [R.correct.weightedt,R.correct.weightedp,R.maxt,R.maxt_... |
github | canlab/RobustToolbox-master | robust_nonparam_interactive_scatterplot.m | .m | RobustToolbox-master/robust_toolbox/robust_nonparametric_subfunctions/robust_nonparam_interactive_scatterplot.m | 5,111 | utf_8 | 5058b3796d308a10874173574b031eb3 | function cl = robust_nonparam_interactive_scatterplot(meth,X,cl,wh_interest,image_names)
% cl = robust_nonparam_interactive_scatterplot(meth,X,cl,wh_interest,image_names)
%
% meth: Method:
% 'max' plots max correlation, uses cl.Ymaxdata field (see
% robust_nonparam_get_sigregions.m)
% 'mean' plots me... |
github | canlab/RobustToolbox-master | robust_nonparam_orthviews.m | .m | RobustToolbox-master/robust_toolbox/robust_nonparametric_subfunctions/robust_nonparam_orthviews.m | 8,564 | utf_8 | 88270dfdcc0a1d0271c126bbd8c922c3 | function [dat,dat_pos,dat_neg,A] = robust_nonparam_orthviews(R,wh_regressor,overlay,showrois,A)
% [dat,dat_pos,dat_neg,A] = robust_nonparam_orthviews(R,wh_regressor,overlay,showrois,[A])
%
% p_sig,cl_pos, and cl_neg inputs can be empty or missing, in which case
% robust_nonparam_get_sig_clusters is run to get it.
%
% A... |
github | canlab/RobustToolbox-master | robust_nonpar_cluster_tables.m | .m | RobustToolbox-master/robust_toolbox/robust_nonparametric_subfunctions/robust_nonpar_cluster_tables.m | 4,672 | utf_8 | 43ec634df9369b77dda29af371b4972d | function robust_nonpar_cluster_tables(R,doextended,wh_regressor,cl_all,cl_sig,cl_pos,cl_neg,rfieldname,pfieldname,uthr,dat,cl_extent_pos,cl_extent_neg)
% robust_nonpar_cluster_tables(R,doextended,wh_regressor,cl_all,cl_sig,cl_pos,cl_neg,rfieldname,pfieldname,uthr,dat,cl_extent_pos,cl_extent_neg)
%
%
% tor wager
% used... |
github | canlab/RobustToolbox-master | robust_results_threshold.m | .m | RobustToolbox-master/robust_toolbox/older_functions/robust_results_threshold.m | 8,903 | utf_8 | ccd86b31e3598b83c12044bac85c45b3 | % [clpos, clneg, dat, volInfo] = robust_results_threshold(pthr, kthr, [cmd strings])
%
% Threshold a p-value image and return values in a second image (designed
% for t-images, but can be anything.)
%
% Command strings
% 'mask', followed by mask image name
% 'overlay', followed by overlay image name
% 'p', followed ... |
github | canlab/RobustToolbox-master | robust_results3.m | .m | RobustToolbox-master/robust_toolbox/older_functions/robust_results3.m | 6,490 | utf_8 | f9c0d2cb3d02d3dc5c9d0e60f735b638 | % function robust_results3(EXPT, u, k, ['display thresholds', thresholds], ['overlay', ovl], ['result names', resultnames], ...
% ['write files', 0|1], ['contrast name', contrastname], ['cov names', covariatenames], ['pause for display', 0|1])
%
% Displays the results of a random effects analysis contained in a robus... |
github | canlab/RobustToolbox-master | robust_results_act_plus_corr.m | .m | RobustToolbox-master/robust_toolbox/older_functions/robust_results_act_plus_corr.m | 6,078 | utf_8 | b3fb4c5120e4193050f51095b77c0e57 | % res = robust_results_act_plus_corr(u1, u2, k1, k2, [covfield], [['mask', mask], ['overlay', overlay], ['writeimgs', 0|1]])
%
% gets activation blobs that overlap with at least one covariate blob, and
% vice versa
% generates output orthviews and tables
%
% tor wager
function res = robust_results_act_plus_corr(u1, u2... |
github | NotBrianZach/VideoBliinds-master | zigzag.m | .m | VideoBliinds-master/zigzag.m | 2,010 | utf_8 | 7e8b8ce96c68947aad53daf8d13c63fa | % Zigzag scan of a matrix
% Argument is a two-dimensional matrix of any size,
% not strictly a square one.
% Function returns a 1-by-(m*n) array,
% where m and n are sizes of an input matrix,
% consisting of its items scanned by a zigzag method.
%
% Alexey S. Sokolov a.k.a. nICKEL, Moscow, Russia
% June 2007
% alex.nic... |
github | NotBrianZach/VideoBliinds-master | temporal_dc_variation_feature_extraction.m | .m | VideoBliinds-master/temporal_dc_variation_feature_extraction.m | 2,334 | utf_8 | 30cf60eb353f14689113893b434dddd6 | %% Computing the DC temporal variation
%% feature a.k.a. the DC feature
function dt_dc_measure1 = temporal_dc_variation_feature_extraction(frames)
% mblock = 5;
%
% row = size(frames,1);
% col = size(frames,2);
% nFrames = size(frames,3);
%
% dct_diff5x5 = zeros(mblock^2,floor(row/mblock)*floor(col/mblock),nFrame... |
github | NotBrianZach/VideoBliinds-master | motion_feature_extraction.m | .m | VideoBliinds-master/motion_feature_extraction.m | 2,795 | utf_8 | 4f8cc1f29f8582f57eaf824317f945c6 | %% PART B of Video-BLIINDS: Computing the Motion Model
function [mean_Coh10x10 G] = motion_feature_extraction(frames)
%% Step 1: On the Luminance planes: Motion Vector Computation
mblock = 10;
for x=1:size(frames,3)-1
x
tic
imgP = double(frames(:,:,x+1));
imgI = double(frames(:,:,x));
... |
github | NotBrianZach/VideoBliinds-master | minCost.m | .m | VideoBliinds-master/minCost.m | 700 | utf_8 | c34cdd4f124503fc77dd1e669fecc7e8 | % Finds the indices of the cell that holds the minimum cost
%
% Input
% costs : The matrix that contains the estimation costs for a macroblock
%
% Output
% dx : the motion vector component in columns
% dy : the motion vector component in rows
%
% Written by Aroh Barjatya
function [dx, dy, min] = minCost(costs)
... |
github | NotBrianZach/VideoBliinds-master | costFuncMAD.m | .m | VideoBliinds-master/costFuncMAD.m | 519 | utf_8 | fd9dc3e5681ccb90265b57b017c8e5f2 | % Computes the Mean Absolute Difference (MAD) for the given two blocks
% Input
% currentBlk : The block for which we are finding the MAD
% refBlk : the block w.r.t. which the MAD is being computed
% n : the side of the two square blocks
%
% Output
% cost : The MAD for the two blocks
%
% Written ... |
github | NotBrianZach/VideoBliinds-master | motionEstNTSS.m | .m | VideoBliinds-master/motionEstNTSS.m | 10,477 | utf_8 | a4aaa8337c8e23cfa3e5f3adcb8aa80b | % Computes motion vectors using *NEW* Three Step Search method
%
% Based on the paper by R. Li, b. Zeng, and M. L. Liou
% IEEE Trans. on Circuits and Systems for Video Technology
% Volume 4, Number 4, August 1994 : Pages 438:442
%
% Input
% imgP : The image for which we want to find motion vectors
% imgI : The ref... |
github | NotBrianZach/VideoBliinds-master | motionEstTSS.m | .m | VideoBliinds-master/motionEstTSS.m | 4,532 | utf_8 | 3cece2184de8d9ee8d5832369e8eaf15 | % Computes motion vectors using Three Step Search method
%
% Input
% imgP : The image for which we want to find motion vectors
% imgI : The reference image
% mbSize : Size of the macroblock
% p : Search parameter (read literature to find what this means)
%
% Ouput
% motionVect : the motion vectors for each i... |
github | NotBrianZach/VideoBliinds-master | compute_niqe_features.m | .m | VideoBliinds-master/compute_niqe_features.m | 482 | utf_8 | 33d6647a79581a0a5b57dee61fc09b5e |
function niqe_features = compute_niqe_features(frames)
load('frames_modelparameters.mat')
blocksizerow = 96;
blocksizecol = 96;
blockrowoverlap = 0;
blockcoloverlap = 0;
%size(frames,3)
for fr = 6 : size(frames,3)-5
%fr
[mu qq] = computequality(frames(:,:,fr), blocksizerow,blocksizecol,blockrowoverla... |
github | NotBrianZach/VideoBliinds-master | NSS_spectral_ratios_feature_extraction.m | .m | VideoBliinds-master/NSS_spectral_ratios_feature_extraction.m | 3,575 | utf_8 | 85057751d1373e538c021144cb390755 | %% Michele A. Saad, Blind Prediction of Natural Video Quality
%% The IEEE Transactions on Image Processing, January 2014
%% Video BLIINDS Algorithm Code
function [dt_dc_measure2 geo_ratio_features] = NSS_spectral_ratios_feature_extraction(frames)
%% PART A of Video-BLIINDS: Computing the NSS DCT features:
%% Step 1:... |
github | elsamuko/copymove2-master | compare.m | .m | copymove2-master/testing/octave/compare.m | 2,810 | utf_8 | f2a8078b58d60ee48b272247353a1d18 |
clear;
function retval = dct_beautified(v)
sz = max(size(v));
retval = dct2(v(:,:,2)-128)(1:sz,1:sz);
endfunction
function retval = cap_big_one(v)
if(abs(v) > 99)
retval = 0;
else
retval = v;
endif
endfunction
function retval = cap_big(v)
retval = arrayfun(@cap_big_one, v... |
github | elsamuko/copymove2-master | dct_demo.m | .m | copymove2-master/testing/octave/dct_demo.m | 291 | utf_8 | 4f93226cb9e9bbdd91c2295cd10b234c | width = 4;
height = 4;
function retval = mat16x16(x,y,val)
retval = zeros(256,256);
retval(x,y) = 1;
retval = idct2(retval);
endfunction
for y = 1:4
for x = 1:4
pos = 4*(y-1)+x;
subplot(height,width,pos)
imagesc(mat16x16(x,y,1))
endfor
endfor
|
github | elsamuko/copymove2-master | compare2.m | .m | copymove2-master/testing/octave/compare2.m | 1,137 | utf_8 | cfb5aaf31ec0e08b010458610de90e1b | clear;
function retval = selection(block)
retval = zeros(1,9);
retval(1) = block(2,2);
retval(2) = block(2,3);
retval(3) = block(3,2);
retval(4) = block(3,3);
retval(5) = block(3,4);
retval(6) = block(4,3);
retval(7) = block(4,4);
retval(8) = block(2,4);
retval(9) = block(4,2);
... |
github | trgao10/PuenteAlignment-master | assignmentallpossible.m | .m | PuenteAlignment-master/software/RectangularAssignment/assignmentallpossible.m | 4,426 | utf_8 | 6b73f8a3fbe061fcf88b845eaf85fb35 | function [assignment, cost] = assignmentallpossible(distMatrix)
%ASSIGNMENTALLPOSSIBLE Compute solution of assignment problem
% ASSIGNMENTALLPOSSIBLE(DISTMATRIX) computes the optimal assignment
% (minimum overall costs) for the given rectangular distance or cost
% matrix, for example the assignment of tracks ... |
github | trgao10/PuenteAlignment-master | assignmentoptimal.m | .m | PuenteAlignment-master/software/RectangularAssignment/assignmentoptimal.m | 8,280 | utf_8 | 5fe8d4352b1b7cb65dd237e740e9a313 | function [assignment, cost] = assignmentoptimal(distMatrix)
%ASSIGNMENTOPTIMAL Compute optimal assignment by Munkres algorithm
% ASSIGNMENTOPTIMAL(DISTMATRIX) computes the optimal assignment (minimum
% overall costs) for the given rectangular distance or cost matrix, for
% example the assignment of tracks (in... |
github | trgao10/PuenteAlignment-master | qslim.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/qslim.m | 13,152 | utf_8 | 2ab54ac7408e720642cbe21d8b6927bd | function [NFV,smf_fname] = qslim(FV,varargin);
%QSLIM - Mesh simplification, wrapper function for Garland's QSLIM executable program
% function [NFV,smf_fname] = qslim(FV,varargin);
% varargin should be entered in pairs '<option>','<arg>'.
% Valid pairs used in this wrapper are:
%
% '-t', <n>
%
% Specify the desired... |
github | trgao10/PuenteAlignment-master | perform_triangle_flipping.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/perform_triangle_flipping.m | 1,848 | utf_8 | b6a78203795416882735031895a76703 | function face = perform_triangle_flipping(face, flips, options)
% perform_triangle_flipping - apply a sequence of flips to a triangulation
%
% face = perform_triangle_flipping(face, flips, options);
%
% Flip that are not topologically valid (either on a boundary edge or a
% flip that creates a double face) are s... |
github | trgao10/PuenteAlignment-master | perform_point_picking.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/perform_point_picking.m | 4,121 | utf_8 | 9c43a474845c0bc92e5d215f6eda9a88 | function perform_point_picking( PointCloud, face )
% function perform_point_picking( PointCloud, face );
%
% This function shows a 3D point cloud or a mesh and lets the user click select one
% of the points by clicking on it. The selected point will be highlighted
% and its index in the point cloud will be... |
github | trgao10/PuenteAlignment-master | select3d.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/select3d.m | 10,864 | utf_8 | 64f6b58cf8db75c3508f68035be9892e | function [pout, vout, viout, facevout, faceiout] = select3d(obj)
%SELECT3D(H) Determines the selected point in 3-D data space.
% P = SELECT3D determines the point, P, in data space corresponding
% to the current selection position. P is a point on the first
% patch or surface face intersected along the selection ... |
github | trgao10/PuenteAlignment-master | compute_parameterization.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/compute_parameterization.m | 7,948 | utf_8 | ccbb3294b8723d824f8bfa35c4ea64dc | function vertex1 = compute_parameterization(vertex,face, options)
% compute_parameterization - compute a planar parameterization
%
% vertex1 = compute_parameterization(vertex,face, options);
%
% options.method can be:
% 'parameterization': solve classical parameterization, the boundary
% is... |
github | trgao10/PuenteAlignment-master | perform_dijkstra_propagation_old.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/perform_dijkstra_propagation_old.m | 5,116 | utf_8 | 28fc1b6f28ff90c69f6e7c7330aaa33f | function D = perform_dijkstra_propagation_old( G , S )
% dijkstra - Find shortest paths in graphs
%
% D = dijkstra_fast( G , S );
%
% use the full or sparse matrix G in which
% an entry (i,j) represents the arc length between nodes i and j in a
% graph. In a full matrix, the value INF represents the abse... |
github | trgao10/PuenteAlignment-master | compute_boundary.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/compute_boundary.m | 1,951 | utf_8 | a1d4f16ccb164b5d4d3dded2507bbee2 | function boundary=compute_boundary(face, options)
% compute_boundary - compute the vertices on the boundary of a 3D mesh
%
% boundary=compute_boundary(face);
%
% Copyright (c) 2007 Gabriel Peyre
options.null = 0;
verb = getoptions(options, 'verb', 1);
if size(face,1)<size(face,2)
face=face';
end... |
github | trgao10/PuenteAlignment-master | compute_geometric_laplacian.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/compute_geometric_laplacian.m | 5,760 | utf_8 | b6c0f7f8acdbb0c203c5f1a297957760 | function L = compute_geometric_laplacian(vertex,face,type)
% compute_geometric_laplacian - return a laplacian
% of a given triangulation (can be combinatorial or geometric).
%
% L = compute_geometric_laplacian(vertex,face,type);
%
% Type is either :
% - 'combinatorial' : combinatorial laplacian, d... |
github | trgao10/PuenteAlignment-master | check_face_vertex.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/check_face_vertex.m | 669 | utf_8 | c940a837f5afef7c3a7f7aed3aff9f7a | function [vertex,face] = check_face_vertex(vertex,face, options)
% check_face_vertex - check that vertices and faces have the correct size
%
% [vertex,face] = check_face_vertex(vertex,face);
%
% Copyright (c) 2007 Gabriel Peyre
vertex = check_size(vertex,2,4);
face = check_size(face,3,4);
%%%%%%%%%%%%%%%%%%%%%%%... |
github | trgao10/PuenteAlignment-master | plot_graph.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/plot_graph.m | 2,140 | utf_8 | 337033b66fe405903639bcac59c2b34d | function h = plot_graph(A,xy, options)
% plot_graph - display a 2D or 3D graph.
%
% plot_graph(A,xy, options);
%
% options.col set the display (e.g. 'k.-')
%
% Copyright (c) 2006 Gabriel Peyre
if size(xy,1)>size(xy,2)
xy = xy';
end
if nargin<3
options.null = 0;
end
if not(isstruct(options))
col = op... |
github | trgao10/PuenteAlignment-master | perform_delaunay_flipping.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/perform_delaunay_flipping.m | 2,420 | utf_8 | 7c5e250e94e9e69d99c556c805b4d2fe | function [face, flips, flipsinv] = perform_delaunay_flipping(vertex,face,options)
% perform_delaunay_flipping - compute Dalaunay triangulation via flipping
%
% [face1, flips, flipsinv] = perform_delaunay_flipping(vertex,face,options);
%
% Set options.display_flips = 1 for graphical display.
%
% face is turned in... |
github | trgao10/PuenteAlignment-master | check_incircle_edge.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/check_incircle_edge.m | 1,633 | utf_8 | dca2fc799d9cf120df9156a15c47b5fa | function ic = check_incircle_edge(vertex, face, edge)
% check_incicle_edge - compute "empty circle" property for a set of edges
%
% ic = check_incicle_edge(vertex,face, edge);
%
% ic(i)==1 if edge(:,i) is delaunay valid (boundary or empty circles or non convex).
% It thus should be flipped if ic(i)==0.
%
% Cop... |
github | trgao10/PuenteAlignment-master | perform_faces_reorientation.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/perform_faces_reorientation.m | 2,816 | utf_8 | 2cf3d5c1ad6ea271b524352db5492c2c | function faces = perform_faces_reorientation(vertex,faces, options)
% perform_faces_reorientation - reorient the faces with respect to the center of the mesh
%
% faces = perform_faces_reorientation(vertex,faces, options);
%
% try to find a consistant reorientation for faces of a mesh.
%
% if options.method = 'fast... |
github | trgao10/PuenteAlignment-master | plot_spherical_triangulation.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/plot_spherical_triangulation.m | 1,397 | utf_8 | fde5d8ffb7898bb232257895ced3d53d | function plot_spherical_triangulation(svertex,face, options)
% plot_spherical_triangulation - display a nice spherical triangulation
%
% plot_spherical_triangulation(svertex,face,options);
%
% Copyright (c) 2008 Gabriel Peyre
options.null = 0;
hold on;
% draw a background sphere
[X,Y,Z] = sphere(30);
surf(X,Y... |
github | trgao10/PuenteAlignment-master | compute_mesh_weight.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/compute_mesh_weight.m | 2,783 | utf_8 | 435dabe64ab50ca5bf2c467bcbad6ab8 | function W = compute_mesh_weight(vertex,face,type,options)
% compute_mesh_weight - compute a weight matrix
%
% W = compute_mesh_weight(vertex,face,type,options);
%
% W is sparse weight matrix and W(i,j)=0 is vertex i and vertex j are not
% connected in the mesh.
%
% type is either
% 'combinatorial': W(i... |
github | trgao10/PuenteAlignment-master | perform_analysis_regularization.m | .m | PuenteAlignment-master/software/ToolboxGraph/toolbox_graph/toolbox_graph/toolbox/perform_analysis_regularization.m | 2,585 | utf_8 | c15e65a8b4b77823cd42e992ac708563 | function [g,g_list,E] = perform_analysis_regularization(f, G, options)
% perform_analysis_regularization - perform a sparse regularization
%
% [g,g_list,E] = perform_analysis_regularization(f, A, options);
%
% Method solves, given f of length n, for
% min_g E(g) = 1/2*|f-g|^2 + lambda * |A*g|_1
% where A is a... |
github | trgao10/PuenteAlignment-master | markowitz1.m | .m | PuenteAlignment-master/software/mosek/7/tools/examples/fusion/matlab/markowitz1.m | 1,573 | utf_8 | 864cc9542ef27da202d66a654f103cc4 | %
% Copyright: Copyright (c) MOSEK ApS, Denmark. All rights reserved.
%
% File: markowitz1.m
%
% Solves the Markowitz portfolio optimization problem
%
% minimize x'*Sigma*x
% subject to mu'*x >= delta
% e'*x = 1
% x >= 0
%
% or equivalently
%
% minimize t
% sub... |
github | trgao10/PuenteAlignment-master | sdo1.m | .m | PuenteAlignment-master/software/mosek/7/tools/examples/fusion/matlab/sdo1.m | 1,169 | utf_8 | cb68041692bc6ac0a514f93ac6b379ba | function [Xres] = sdo1()
% Solves the semidefinite optimization problem
%
% [2, 1, 0]
% minimize Tr [1, 2, 1] * X
% [0, 1, 2]
%
% [1, 0, 0]
% subject to Tr [0, 1, 0] * X = 1
% [0, 0, 1]
%
% [1, 1, 1]
% ... |
github | trgao10/PuenteAlignment-master | nearestcorr.m | .m | PuenteAlignment-master/software/mosek/7/tools/examples/fusion/matlab/nearestcorr.m | 1,972 | utf_8 | 39fd8cc05b6d2c7a8f981e8e83c2baaa | %%
% Copyright: Copyright (c) MOSEK ApS, Denmark. All rights reserved.
%
% File: nearestcorr.m
%
% Purpose:
% Solves the nearest correlation matrix problem
%
% minimize || A - X ||_F s.t. diag(X) = e, X is PSD
%
% as the equivalent conic program
%
% minimize t
%
% subject to (t, vec(A... |
github | trgao10/PuenteAlignment-master | portfolio.m | .m | PuenteAlignment-master/software/mosek/7/tools/examples/fusion/matlab/portfolio.m | 9,035 | utf_8 | dd7c0bee075343ce1344542144ccbe32 | %%
% File : portfolio.m
%
% Copyright : Copyright (c) MOSEK ApS, Denmark. All rights reserved.
%
% Description :
% Presents several portfolio optimization models.
%
%%
function portfolio(name)
%
% The example
%
% portfolio(name)
%
% reads in data and solves the portfolio models.
%
[n,mu,GT,gamm... |
github | trgao10/PuenteAlignment-master | cvx_version.m | .m | PuenteAlignment-master/software/cvx/cvx_version.m | 14,459 | utf_8 | 7953f4017783e922b27023ad0895060f | function varargout = cvx_version( varargin )
% CVX_VERSION Returns version and environment information for CVX.
%
% When called with no arguments, CVX_VERSION prints out version and
% platform information that is needed when submitting CVX bug reports.
%
% This function is also used internally to return use... |
github | trgao10/PuenteAlignment-master | cvx_grbgetkey.m | .m | PuenteAlignment-master/software/cvx/cvx_grbgetkey.m | 19,096 | utf_8 | 080162e4fd27b14ea8387362148db7d1 | function success = cvx_grbgetkey( kcode, overwrite )
% CVX_GRBGETKEY Retrieves and saves a Gurobi/CVX license.
%
% This function is used to install Gurobi license keys for use in CVX. It
% is called with your Gurobi license code as a string argument; e.g.
%
% cvx_grbgetkey xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
% ... |
github | trgao10/PuenteAlignment-master | HSDNTcorr.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDNTcorr.m | 1,001 | utf_8 | c42eba1c6bae660b88921b7c8747490e | %%************************************************************************
%% HSDNTcorr: corrector step for the NT direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%************************************************************************
f... |
github | trgao10/PuenteAlignment-master | HSDHKMdirfun.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDHKMdirfun.m | 1,551 | utf_8 | 1034e25e48a42d2fa143f93f47961fe9 | %%*******************************************************************
%% HSDHKMdirfun: compute (dX,dZ), given dy, for the HKM direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%****************************************************************... |
github | trgao10/PuenteAlignment-master | HSDsqlp.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDsqlp.m | 11,860 | utf_8 | 00b8311a8efbee36662ca9288870a1cd | %%*****************************************************************************
%% HSDsqlp: solve an semidefinite-quadratic-linear program
%% by infeasible path-following method on the homogeneous self-dual model.
%%
%% [obj,X,y,Z,info,runhist] =
%% HSDsqlp(blk,At,C,b,OPTIONS,X0,y0,Z0);
%%
%% Input: blk: a cel... |
github | trgao10/PuenteAlignment-master | HSDsortA.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDsortA.m | 2,577 | utf_8 | 0a74ddbb8a0c79bf22592d780d865e06 | %%*********************************************************************
%% sortA: sort columns of At{p} in ascending order according to the
%% number of nonzero elements.
%%
%% [At,C,b,X0,Z0,permA,permZ] = sortA(blk,At,C,b,X0,Z0);
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tut... |
github | trgao10/PuenteAlignment-master | HSDHKMrhsfun.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDHKMrhsfun.m | 2,666 | utf_8 | 16409ae4672f80ef54a33c31ef30000f | %%*******************************************************************
%% HSDHKMrhsfun: compute the right-hand side vector of the
%% Schur complement equation for the HKM direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*****... |
github | trgao10/PuenteAlignment-master | HSDsqlpcheckconvg.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDsqlpcheckconvg.m | 6,249 | utf_8 | a579e4972fd77d5cc3e11b72bf56d3a9 | %%*****************************************************************************
%% HSDsqlpcheckconvg: check convergence.
%%
%% ZpATynorm, AX, normX, normZ are with respect to the
%% original variables, not the HSD variables.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last ... |
github | trgao10/PuenteAlignment-master | HSDNTdirfun.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDNTdirfun.m | 1,459 | utf_8 | a045827a3ca1adcf8806cfd8234ad5e4 | %%*******************************************************************
%% HSDNTdirfun: compute (dX,dZ), given dy, for the NT direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%******************************************************************... |
github | trgao10/PuenteAlignment-master | HSDNTrhsfun.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDNTrhsfun.m | 3,424 | utf_8 | 02348c55d691a53b023639b8103757be | %%*******************************************************************
%% HSDNTrhsfun: compute the right-hand side vector of the
%% Schur complement equation for the NT direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*********... |
github | trgao10/PuenteAlignment-master | HSDHKMpred.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDHKMpred.m | 2,644 | utf_8 | 81b89c36e0d0bad30836c551264a6a05 | %%*******************************************************************
%% HSDHKMpred: Compute (dX,dy,dZ) for the H..K..M direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*******************************************************************
f... |
github | trgao10/PuenteAlignment-master | HSDsqlpmain.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDsqlpmain.m | 28,301 | utf_8 | df880652075e1e6d94eefd6ddfe38c64 | %%*****************************************************************************
%% HSDsqlp: solve an semidefinite-quadratic-linear program
%% by infeasible path-following method on the homogeneous self-dual model.
%%
%% [obj,X,y,Z,info,runhist] =
%% HSDsqlp(blk,At,C,b,OPTIONS,X0,y0,Z0,kap0,tau0,theta0);
%%
%% ... |
github | trgao10/PuenteAlignment-master | HSDlinsysolve.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDlinsysolve.m | 6,495 | utf_8 | 0644ae75443d221edcf585f5a5736fe5 | %%***************************************************************
%% linsysolve: solve linear system to get dy, and direction
%% corresponding to unrestricted variables.
%%
%% [xx,coeff,L,resnrm] = linsysolve(schur,UU,EE,Bmat,rhs);
%%
%% child functions: mybicgstable.m
%%
%% SDPT3: version 3.1
%% Copyright ... |
github | trgao10/PuenteAlignment-master | HSDsqlpmisc.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDsqlpmisc.m | 3,299 | utf_8 | f36316ddff8099a74241fa1590fc584a | %%*****************************************************************************
%% HSDsqlpmisc:
%% produce infeasibility certificates if appropriate
%%
%% Input: X,y,Z are the original variables, not the HSD variables.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modifi... |
github | trgao10/PuenteAlignment-master | HSDbicgstab.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDbicgstab.m | 3,084 | utf_8 | 96ee9f939e0b2527113539aa0b633ffc | %%*************************************************************************
%% HSDbicgstab
%%
%% [xx,resnrm,flag] = HSDbicgstab(A,b,M1,tol,maxit)
%%
%% iterate on bb - (M1)*AA*x
%%
%% r = b-A*xtrue;
%%
%%*************************************************************************
function [xx,resnrm,flag] = HSDbicgstab(... |
github | trgao10/PuenteAlignment-master | HSDHKMcorr.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDHKMcorr.m | 985 | utf_8 | 1e1983a66956f1d3e4e8e279b1dbe2d0 | %%*****************************************************************
%% HSDHKMcorr: corrector step for the HKM direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*****************************************************************
function [par... |
github | trgao10/PuenteAlignment-master | HSDNTpred.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDNTpred.m | 2,034 | utf_8 | e194daf53d375b24154b1caf94b7646d | %%**********************************************************************
%% HSDNTpred: Compute (dX,dy,dZ) for NT direction.
%%
%% compute SVD of Xchol*Zchol via eigenvalue decompostion of
%% Zchol * X * Zchol' = V * diag(sv2) * V'.
%% compute W satisfying W*Z*W = X.
%% W = G'*G, where G = diag(sqrt(sv)) * (inv... |
github | trgao10/PuenteAlignment-master | HSDsqlpCpert.m | .m | PuenteAlignment-master/software/cvx/sdpt3/HSDSolver/HSDsqlpCpert.m | 2,263 | utf_8 | 326ec0065ebb155fab5ee8b4670ec0db | %%*****************************************************************************
%% HSDsqlpCpert: perturb C.
%%
%%*****************************************************************************
function [At,Cpert] = HSDsqlpCpert(blk,At,par,C,X,Cpert,runhist)
iter = length(runhist.pinfeas);
prim_infeas = runhist.pinfeas(... |
github | trgao10/PuenteAlignment-master | cheby0.m | .m | PuenteAlignment-master/software/cvx/sdpt3/Examples/cheby0.m | 2,576 | utf_8 | a31e95ee5e80694cd1c3f2ceb594d369 | %%**********************************************************
%% cheby0:
%%
%% minimize || p(d) ||_infty
%% p = polynomial of degree <= m such that p(0) = 1.
%%
%% Here d = n-vector
%%----------------------------------------------------------
%% [blk,Avec,C,b,X0,y0,Z0,objval,p] = cheby0(d,m,solve);
%%
%% d ... |
github | trgao10/PuenteAlignment-master | randmat.m | .m | PuenteAlignment-master/software/cvx/sdpt3/Solver/randmat.m | 811 | utf_8 | 483225eceeb882378d1a6fc61914a4be | %%******************************************************
%% randmat: generate an mxn matrix using matlab's
%% rand or randn functions using state = k.
%%
%%******************************************************
function v = randmat(m,n,k,randtype)
try
s = rng;
rng(k);
if strcmp(randtype,... |
github | trgao10/PuenteAlignment-master | skron.m | .m | PuenteAlignment-master/software/cvx/sdpt3/Solver/skron.m | 1,389 | utf_8 | 3aba6bed9dc50b45f766b4a8620c4ac3 | %%***********************************************************************
%% skron: Find the matrix presentation of
%% symmetric kronecker product skron(A,B), where
%% A,B are symmetric.
%%
%% Important: A,B are assumed to be symmetric.
%%
%% K = skron(blk,A,B);
%%
%% blk: a cell array specifying the b... |
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