text:
function handles = dtiAdjustDtiXform(handles, refPointName, newCoords, oldCoords)
%  handles = dtiAdjustDtiXform(handles, refPointName, newCoords, [oldCoords])
%
% TO DO:
% Change all coordinate frame transforms so that they are with respect to
% the origin (eg. image center or ac) so that rotations are easier to
% understand!
%
% HISTORY:
%   2004.03.12 RFD (bob@white.stanford.edu) wrote it.

newXform = handles.bg(1).mat;
switch(refPointName)
    case 'ac'
        % FIX THIS! It assumes that the anatomical scale factors are all 1.
        % If we assume that the anatomicals are bg(3), then we can apply
        % that xform to correct this. But maybe there are no anatomicals?
        %handles.bg(3).mat
        %newImgCoords=mrAnatXformCoords(inv(dtiGetStandardXform(handles,handles.bg(1).mat)),newCoords)
        [t,r,s,k] = affineDecompose(newXform);
        if(~exist('oldCoords','var')) oldCoords = t; end
        t = [oldCoords-newCoords];
        newXform = affineBuild(t,r,s,k);
    case 'pc'
        if(~exist('oldCoords','var')) oldCoords = [0 -23 0]; end
        % from the PC, we can compute rotation about Z- and X-axes and the
        % Y-axis scale.
        %[t,r,s,k] = affineDecompose(newXform);
        oldXform = dtiGetStandardXform(handles,handles.bg(1).mat);
        x = [inv(oldXform)*[0,0,0,1; [oldCoords,1]]']'...
            \ [inv(oldXform)*[0,0,0,1; [newCoords,1]]']';
        % set zero scales to 1
        x([(xor(x,eye(4))).*eye(4)]>0) = 1;
        %newXform = newXform*inv(x);
        [t,r,s,k] = affineDecompose(newXform*inv(x));
        % FIX THIS
%         curAcPcDist = sqrt(sum(([0 0 0]-oldCoords).^2));
%         newAcPcDist = sqrt(sum(([0 0 0]-newCoords).^2));
%         scaleDiff = (curAcPcDist-newAcPcDist)./curAcPcDist
%         s(2) = s(2) + scaleDiff;
%         t(2) = t(2) + t(2) .* scaleDiff;
        newXform = affineBuild(t,r,s,k);
    case 'ppc'
        % FIX THIS! We aren't rotating about the proper origin.
        if(~exist('oldCoords','var')) oldCoords = [0 -102 0]; end
        x = [0,0,0,1; [oldCoords,1]] \ [0,0,0,1; [newCoords,1]];
        x([1,11]) = 1;
        [t,r,s,k] = affineDecompose(inv(x)*newXform);
        newXform = affineBuild(t,r,s,k);
    case 'rot'
        [t,r,s,k] = affineDecompose(newXform);
        r = r+newCoords;
        newXform = affineBuild(t,r,s,k);
    case 'scale'
        [t,r,s,k] = affineDecompose(newXform);
        s = s.*newCoords;
        t = t.*newCoords;
        newXform = affineBuild(t,r,s,k);

    otherwise
        error('Unrecognized refpoint name!');
end

% FIX THIS!
handles.bg(1).mat = newXform;
handles.bg(2).mat = newXform;
handles.vec.mat = newXform;

return;


text:
function [ap, prec, recall] = pascal_eval(cls, ds, testset, year, suffix)
% Score detections using the PASCAL development kit.
%   [ap, prec, recall] = pascal_eval(cls, ds, testset, suffix)
%
% Return values
%   ap        Average precision score
%   prec      Precision at each detection sorted from high to low confidence
%   recall    Recall at each detection sorted from high to low confidence
%
% Arguments
%   cls       Object class to evaluate
%   ds        Detection windows returned by pascal_test.m
%   testset   Test set to evaluate against (e.g., 'val', 'test')
%   year      Test set year to use  (e.g., '2007', '2011')
%   suffix    Results are saved to a file named:
%             [cls '_pr_' testset '_' suffix]

% AUTORIGHTS
% -------------------------------------------------------
% Copyright (C) 2011-2012 Ross Girshick
% Copyright (C) 2008, 2009, 2010 Pedro Felzenszwalb, Ross Girshick
%
% This file is part of the voc-releaseX code
% (http://people.cs.uchicago.edu/~rbg/latent/)
% and is available under the terms of an MIT-like license
% provided in COPYING. Please retain this notice and
% COPYING if you use this file (or a portion of it) in
% your project.
% -------------------------------------------------------

conf = voc_config('pascal.year', year, ...
                  'eval.test_set', testset);
cachedir = conf.paths.model_dir;
VOCopts  = conf.pascal.VOCopts;

ids = textread(sprintf(VOCopts.imgsetpath, testset), '%s');

% write out detections in PASCAL format and score
fid = fopen(sprintf(VOCopts.detrespath, 'comp3', cls), 'w');
for i = 1:length(ids);
  bbox = ds{i};
  for j = 1:size(bbox,1)
    fprintf(fid, '%s %f %d %d %d %d\n', ids{i}, bbox(j,end), bbox(j,1:4));
  end
end
fclose(fid);

recall = [];
prec = [];
ap = 0;

do_eval = (str2num(year) <= 2007) | ~strcmp(testset, 'test');
if do_eval
  if str2num(year) == 2006
    [recall, prec, ap] = VOCpr(VOCopts, 'comp3', cls, true);
  else
    % Bug in VOCevaldet requires that tic has been called first
    tic;
    [recall, prec, ap] = VOCevaldet(VOCopts, 'comp3', cls, true);
  end

  % force plot limits
  ylim([0 1]);
  xlim([0 1]);

  print(gcf, '-djpeg', '-r0', [cachedir cls '_pr_' testset '_' suffix '.jpg']);
end

% save results
save([cachedir cls '_pr_' testset '_' suffix], 'recall', 'prec', 'ap');

author: rbgirshick

repo: voc-dpm

save_path: github-repos/MATLAB/rbgirshick-voc-dpm


text:
clc;
clear all;

toolboxpath = '/home/siyuan/Documents/Dataset/SUNRGBD_ALL/SUNRGBDtoolbox/SUNRGBDtoolbox';
addpath(genpath(toolboxpath));
load(fullfile(toolboxpath,'/traintestSUNRGBD/allsplit.mat'));
load(fullfile('SUNRGBDMetaUS.mat'));
new_3d = load('SUNRGBDMeta3DBB_v2.mat');
cls = {'bathtub','bed','bookshelf','box','chair','counter','desk','door','dresser','garbage_bin','lamp','monitor','night_stand','pillow','sink','sofa','table','tv','toilet'};
thresh_iou = [0.01,0.05:0.05:1.6,0.8:0.2:1];
result_path = '/home/siyuan/Documents/cvpr2018/evaluation';
vis = true;

%% evaluation
iou_precision =[];
iou_recall =[];
label_precision =[];
iouHolistic_all = [];
for imageId = 11
    % fake result always use same room and object boxes
    data = SUNRGBDMeta(imageId);
    bdb_3d = load(fullfile(result_path, int2str(imageId), 'bdb_3d.mat'));
    bdb = bdb_3d.bdb;
    layout = load(fullfile(result_path, int2str(imageId), 'layout.mat'));
    predictRoom = layout.layout;
    gt_bdb = new_3d.SUNRGBDMeta(imageId).groundtruth3DBB;
    % evaluate
    for i = 1:size(bdb, 1)
    	predictedBbs(i) = create_bounding_box_3d([bdb(i, 9), bdb(i, 10); bdb(i, 11), bdb(i, 12)], [bdb(i, 3), bdb(i, 4), bdb(i, 5)], [bdb(i, 6), bdb(i, 7), bdb(i, 8)]);
    end
    eval_result = eval_holisticScencePR(predictedBbs',gt_bdb',thresh_iou,cls);
    iouHolistic = eval_holisticScenceIoU(data,data.gtCorner3D,gt_bdb',predictRoom,predictedBbs',vis);

    iou_precision = [iou_precision;eval_result.iou_precision];
    iou_recall = [iou_recall;eval_result.iou_recall];
    label_precision = [label_precision;eval_result.label_precision];
    iouHolistic_all = [iouHolistic_all iouHolistic];
end
%% plot and average
figure;
thresh_iou = [0.01,0.05:0.05:1.6,0.8:0.2:1];
load('iou_precision.mat');
load('iou_recall.mat');
load('iouHolistic_all.mat');
plot(thresh_iou,mean(iou_precision),'r','LineWidth',3);
hold on;
plot(thresh_iou,mean(iou_recall),'g','LineWidth',3);
plot(thresh_iou,mean(label_precision),'b','LineWidth',3);
legend({'geometric precision','geometric recall','recognition recall'},'FontSize',27)
%xlabel('IoU threshold');
axis([0 1 0 1])
iou_precision_average =  mean(iou_precision(:,6));
iou_recall_average =  mean(iou_recall(:,6));
label_precision_average =  100*mean(label_precision(:,6));
iouHolistic_average = mean(iouHolistic_all);
sprintf('Average:\n geometric precision: %.3f\n geometric recall: %.3f\n recognition recall: %.3f\n %.3f\n',...
    iou_precision_average,iou_recall_average,label_precision_average,iouHolistic_average)

author: thusiyuan


text:
function vl_printsize(varargin)
% VL_PRINTSIZE  Set the printing size of a figure
%   VL_PRINTSIZE(R) adjusts the PaperPosition property of the current
%   figure to make the figure paper width equal to R times the width
%   of a 'uslsetter' page, while preserving the aspect ratio of the
%   figure. Then, it sets the PaperSize property to match tightly the
%   size of the figure.
%
%   VL_PRINTSIZE(FIG,R) operates on the specified figure FIG. The
%   function accepts the following optional arguments:
%
%   AspectRatio:: []
%     Change the figure aspect ratio (width/height) to the specified
%     value.
%
%   Reference:: 'horizontal'
%     If set to 'horizontal', VL_PRINTSIZE(R) makes the width of the
%     figure equal to a fraction R of the width of the page. If set to
%     'vertical', the height is used instead.
%
%   PaperType:: 'usletter'
%     Set the type of the reference page to the specified type. Any of
%     the paper types supported by MATLAB can be used (see
%     PRINT()).
%
%   Margin:: 0
%     If greater than zero, VL_PRINTSIZE(R) leaves the specified
%     margin around the figure rather than fitting the paper size
%     tightly around it. The margin is expressed as a fraction of the
%     figure paper width (or height if Reference is set to
%     'vertical').
%
%   PRINTING FIGURES IN MATLAB
%
%   The standard procedure to include a MATLAB figure in a publication
%   consists in printing the figure to an EPS or PDF file (by using
%   the PRINT() function) and then rescaling it to the desired size in
%   the publication. PDF printing has the additional inconvenience
%   that large white margins are generated, and requires cropping the
%   figure too.
%
%   Unfortunately, rescalign a figure in this manner has the usually
%   unwanted effect of rescaling elements of the graphics such as font
%   sizes and line thicknesses. For example, a font which is 12pt in
%   the original MATLAB figure may become too large or too small after
%   rescaling.
%
%   The `proper' way of printing a figure is to specify its desired
%   size on paper before calling the PRINT() function. VL_PRINTSIZE()
%   helps doing so.
%
%   Margins are an additional nuances. When printing to certain
%   formats such as PDF, MATLAB prints in fact entire page (e.g. a
%   whole US Letter sheet), resulting in wide white margin all around
%   the figure. VL_PRINTSIZE() alleviates this problem by setting the
%   paper size to match the figure size tightly. This does not
%   eliminate margins completely, especially if the figure has
%   multiple subplots, but it is certainly a large improvement.
%
%   See also: VL_HELP().

% Author: Andrea Vedaldi

% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% Copyright (C) 2013 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).

if length(varargin) >= 2 && isnumeric(varargin{2})
  % called with two numeric arguments
  fig = varargin{1} ;
  varargin(1) = [] ;
else
  fig = gcf ;
end

sizeRatio = varargin{1} ;
varargin(1) = [] ;

opts.aspectRatio = NaN ;
opts.reference = 'horizontal' ;
opts.paperType = 'usletter' ;
opts.margin = 0 ;
opts = vl_argparse(opts, varargin) ;

opts.reference = lower(opts.reference) ;
if ~ismember(opts.reference, {'horizontal', 'vertical'})
  error('Invalid value ''%s'' for the REFERENCE option', opts.reference) ;
end

% set the paper size to the reference type
if ~isempty(opts.paperType)
    set(fig, 'PaperType', opts.paperType) ;
end
paperSize = get(fig, 'PaperSize') ;

% get the current figure position to compute the current aspect ratio
position = get(fig, 'PaperPosition') ;

% if not specified, compute current aspect ratio
if isnan(opts.aspectRatio)
  opts.aspectRatio = position(3) / position(4) ;
end

% resize the figure
switch opts.reference
  case 'horizontal'
    s = paperSize(1) * sizeRatio ;
  case 'vertical'
    s = paperSize(2) * sizeRatio * opts.aspectRatio ;
end
position(3:4) =  s * [1 1/opts.aspectRatio] ;

% add margin
switch opts.reference
  case 'horizontal'
    position(1) = position(3) * opts.margin ;
    position(2) = position(3) * opts.margin ;
  case 'vertical'
    position(1) = position(4) * opts.margin ;
    position(2) = position(4) * opts.margin ;
end

set(fig, ...
  'PaperPosition', position, ...
  'PaperSize', 2 * position(1:2) + position(3:4)) ;

end

author: yihui-he

repo: panorama

save_path: github-repos/MATLAB/yihui-he-panorama


text:
function ds = dsegment ( p, pv )

%*****************************************************************************80
%
%% DSEGMENT computes the distance of points to line segments.
%
%  Discussion:
%
%    This is a pure MATLAB version of the DSEGMENT routine.
%    Normally, the authors of DISTMESH recommend using a faster procedure
%    in which a MATLAB MEX interface is used to call a compiled C code.
%    But if that is not possible, you can fall back on this slower
%    version.
%
%    Note that the line segments are defined by successive pairs of
%    vertices.  In the very common case that the vertices represent
%    a polygon, it is necessary that the list be extended by repeating
%    the first vertex at the end; otherwise the definition of the
%    polygon, and the distance computation, will be incomplete.
%
%  Licensing:
%
%    (C) 2004 Per-Olof Persson.
%    See COPYRIGHT.TXT for details.
%
%  Parameters:
%
%    Input, real P(NP,2), a set of points.
%
%    Input, real PV(NVS,2), a set of vertices.  Sequential
%    pairs define NVS-1 line segments.
%
%    Output, real DS(NP,NVS-1); DS(I,J) is the (unsigned) distance
%    of point P(I,:) from line segment PV(J,:)--PV(J+1,:).
%
  nvs = size ( pv, 1 );
  np = size ( p, 1 );

  ds = zeros ( np,nvs-1);

  for iv = 1 : nvs-1
    for ip = 1 : np

      v(1:2) = [ pv(iv+1,1) - pv(iv,1), pv(iv+1,2) - pv(iv,2) ];

      w(1:2) = [ p(ip,1) - pv(iv,1), p(ip,2) - pv(iv,2) ];

      c1 = v * w';
      c2 = v * v';

      if ( c1 <= 0.0 )
        ds(ip,iv) = sqrt ( sum ( ( p(ip,1:2) - pv(iv,1:2) ).^2 ) );
      elseif ( c2 <= c1 )
        ds(ip,iv) = sqrt ( sum ( ( p(ip,1:2) - pv(iv+1,1:2) ).^2 ) );
      else
        ds(ip,iv) = sqrt ( sum ( ( p(ip,1:2) ...
          - pv(iv,1:2) - c1 / c2 * v(1:2) ).^2 ) );
      end

    end
  end

  return
end

author: johannesgerer

repo: jburkardt-m


text:
function yp = p15_fun ( neqn, t, y )

%*****************************************************************************80
%
%% P15_FUN evaluates the function for problem P15.
%
%  Discussion:
%
%    30 equations.
%
%    This system models the motion of the five outer planets of the
%    solar system.
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    18 February 2013
%
%  Author:
%
%    John Burkardt
%
%  Reference:
%
%    Wayne Enright, John Pryce,
%    Algorithm 648,
%    ACM Transactions on Mathematical Software,
%    Volume 13, Number 1, pages 28-34.
%
%  Parameters:
%
%    Input, integer NEQN, the number of equations.
%
%    Input, real T, Y(NEQN), the arguments of the derivative
%    function.
%
%    Output, real YP(NEQN), the value of the derivative function.
%
  yp = zeros ( neqn, 1 );

  m = zeros ( 5, 1 );
  k2 = p15_param ( 'GET', 'K2', [] );
  m0 = p15_param ( 'GET', 'M0', [] );
  m(1) = p15_param ( 'GET', 'M1', [] );
  m(2) = p15_param ( 'GET', 'M2', [] );
  m(3) = p15_param ( 'GET', 'M3', [] );
  m(4) = p15_param ( 'GET', 'M4', [] );
  m(5) = p15_param ( 'GET', 'M5', [] );
  i = 0;
  for l = 3 : 3 : 15
    i = i + 1;
    p = y(l-2).^2 + y(l-1).^2 + y(l).^2;
    r(i) = 1.0 / ( p * sqrt ( p ) );
    j = 0;
    for ll = 3 : 3 : 15
      j = j + 1;
      if ( ll ~= l )
        p = ( y(l-2) - y(ll-2) ).^2 + ( y(l-1) - y(ll-1) ).^2 ...
          + ( y(l) - y(ll) ).^2;
        q(i,j) = 1.0 / ( p * sqrt ( p ) );
        q(j,i) = q(i,j);
      end
    end
  end
  i3 = 0;
  for i = 1 : 5
    i3 = i3 + 3;
    for ll = i3-2 : i3
      mm = ll - i3;
      yp(ll) = y(ll+15);
      p = 0.0;
      for j = 1 : 5
        mm = mm + 3;
        if ( j ~= i )
          p = p + m(j) ...
             * ( y(mm) * ( q(i,j) - r(j) ) - y(ll) * q(i,j) );
        end
      end
      yp(ll+15) = k2 * ( - ( m0 + m(i) ) * y(ll) * r(i) + p );
    end
  end

  return
end


text:
% input in seconds

TR = 2;     % 1 = leave in seconds; 2, downsample by 2, etc.

len = 200;  % length in s
tp = 5;    % estimates 20 time points

scanspersess = [40 40 10];     % images per session; length is num sessions

[X,delta,delta_hires,hrf] = onsets2delta(ons,TR,len);

[DX,sf] = tor_make_deconv_mtx3(delta,tp,1,0,1,0,scanspersess);

% for phys data, .01 s -> 1s
Y = RESAMPLE(X,1,100);

author: canlab

repo: CanlabCore

save_path: github-repos/MATLAB/canlab-CanlabCore


text: %NOT Mapping overload

author: marianux

repo: ecg-kit

save_path: github-repos/MATLAB/marianux-ecg-kit


text:
% DEMSWISSROLLLLE1 Demonstrate LLE on the oil data.

% MLTOOLS

[Y, lbls] = lvmLoadData('swissRoll');

options = lleOptions(4);
model = lleCreate(2, size(Y, 2), Y, options);
model = lleOptimise(model, 2);

lvmScatterPlotColor(model, model.Y(:, 2));

if exist('printDiagram') & printDiagram
  lvmPrintPlot(model, model.Y(:, 2), 'SwissRoll', 1, true);
end

author: SheffieldML

repo: GPmat

save_path: github-repos/MATLAB/SheffieldML-GPmat


text:
function [Gu,Gn,w, dt] = spm_csd_fmri_gu(P,dt)
% spectra of neuronal fluctuations and noise
% FORMAT [Gu,Gn,Hz,dt] = spm_csd_fmri_gu(P,dt)
%
% P  - model parameters
% dt - sampling interval
%
% This routine returns the spectra of neuronal fluctuations and noise for a
% standard frequency range specified by the sampling interval
%
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_csd_fmri_gu.m 7270 2018-03-04 13:08:10Z karl $

% compute log-spectral density
%==========================================================================

% frequencies of interest
%--------------------------------------------------------------------------
Hz1  = 1/128;
Hz2  = 1/(2*dt);
nw   = 32;
w    = linspace(Hz1,Hz2,nw)';

% number of nodes and endogenous (neuronal) fluctuations
%--------------------------------------------------------------------------
nn   = size(P.A,1);
nu   = nn;
form = '1/f';

% spectrum of neuronal fluctuations (Gu) and observation noise (Gn)
%==========================================================================

% experimental inputs
%--------------------------------------------------------------------------
Gu    = zeros(nw,nu,nu);
Gn    = zeros(nw,nn,nn);

% neuronal fluctuations (Gu) (1/f or AR(1) form)
%--------------------------------------------------------------------------
for i = 1:nu
    if strcmp(form,'1/f')
        G     = w.^(-exp(P.a(2,1)));
    else
        G     = spm_mar2csd(exp(P.a(2,1)),w);
    end
    Gu(:,i,i) = Gu(:,i,i) + exp(P.a(1,1))*G/sum(G);
end

% region specific observation noise (1/f or AR(1) form)
%--------------------------------------------------------------------------
for i = 1:nn
    if strcmp(form,'1/f')
        G     = w.^(-exp(P.b(2,1))/2);
    else
        G     = spm_mar2csd(exp(P.b(2,1))/2,w);
    end
    Gn(:,i,i) = Gn(:,i,i) + exp(P.c(1,i))*G/sum(G);
end

% global components
%--------------------------------------------------------------------------
if strcmp(form,'1/f')
    G = w.^(-exp(P.b(2,1))/2);
else
    G = spm_mar2csd(exp(P.b(2,1))/2,w);
end
for i = 1:nn
    for j = i:nn
        Gn(:,i,j) = Gn(:,i,j) + exp(P.b(1,1))*G/sum(G);
        Gn(:,j,i) = Gn(:,i,j);
    end
end

author: spm


text:
function [ a, b ] = p51_lim ( )

%*****************************************************************************80
%
%% P51_LIM returns the integration limits for problem 51.
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    04 November 2009
%
%  Author:
%
%    John Burkardt
%
%  Parameters:
%
%    Output, real A, B, the limits of integration.
%
  a = 0.0;
  b = 1.0;

  return
end


text:
function act = softRect(x)
%  act = softRect(x)
%-----------------------------------------------------
% Soft rectification activation function
%-----------------------------------------------------

act = log(1 + exp(x));

author: dustinstansbury

repo: medal


text:
function image=sr_bw(t2,handle_wb,offset_wb,scale_wb,ds,max_shift,th_prob1,th_prob2,no_iter)

if ~exist('ds','var')
    ds=4; % four time super resolution
end
if ~exist('max_shift','var')
    max_shift=8;
end
if ~exist('th_prob1','var')
    th_prob1=0.9;
end
if ~exist('th_prob2','var')
    th_prob2=0.85;
end
if ~exist('no_iter','var')
    no_iter=2;
end

fprintf('start SR\n...');
search_range=[-max_shift max_shift -max_shift max_shift];
image=kron(t2{1},ones(ds));

sigma=-1; % use the default sigma to estimate prob (see also subpixel_register.m)
fprintf('start super-resolution 1st phase...\n');
tic
[image,probs,shs,scores]=sr_one_step_wb(image,t2,'average',ds,search_range,sigma,th_prob1,handle_wb,offset_wb,scale_wb/2); %,...
toc

fprintf('start super-resolution 2nd phase...\n');
for iter=1:no_iter
    tic
    [image,probs,shs,scores]=sr_one_step_wb(image,t2,'sort_pocs',ds,search_range,sigma,th_prob2,handle_wb,offset_wb+(iter-1)*scale_wb/2/no_iter+scale_wb/2,scale_wb/2/no_iter);
    toc
    iter
    if length(find(probs>th_prob2))==length(probs)
          break;
    end
end

author: Sable

repo: mcbench-benchmarks


text:
function obj = coef_f_q_sym_pnp_func_new(in1,in2,in3)
coefs_tq1_1 = in2(1);
coefs_tq1_2 = in2(4);
coefs_tq2_1 = in2(2);
coefs_tq1_3 = in2(7);
coefs_tq2_2 = in2(5);
coefs_tq3_1 = in2(3);
coefs_tq1_4 = in2(10);
coefs_tq2_3 = in2(8);
coefs_tq3_2 = in2(6);
coefs_tq1_5 = in2(13);
coefs_tq2_4 = in2(11);
coefs_tq3_3 = in2(9);
coefs_tq1_6 = in2(16);
coefs_tq2_5 = in2(14);
coefs_tq3_4 = in2(12);
coefs_tq1_7 = in2(19);
coefs_tq2_6 = in2(17);
coefs_tq3_5 = in2(15);
coefs_tq1_8 = in2(22);
coefs_tq2_7 = in2(20);
coefs_tq3_6 = in2(18);
coefs_tq1_9 = in2(25);
coefs_tq2_8 = in2(23);
coefs_tq3_7 = in2(21);
coefs_tq2_9 = in2(26);
coefs_tq3_8 = in2(24);
coefs_tq3_9 = in2(27);
coefs_tq1_10 = in2(28);
coefs_tq2_10 = in2(29);
coefs_tq3_10 = in2(30);
coef_Jacob1_qt_syms1 = in3(1);
coef_Jacob1_qt_syms2 = in3(5);
coef_Jacob2_qt_syms1 = in3(2);
coef_Jacob1_qt_syms3 = in3(9);
coef_Jacob2_qt_syms2 = in3(6);
coef_Jacob3_qt_syms1 = in3(3);
coef_Jacob1_qt_syms4 = in3(13);
coef_Jacob2_qt_syms3 = in3(10);
coef_Jacob3_qt_syms2 = in3(7);
coef_Jacob4_qt_syms1 = in3(4);
coef_Jacob1_qt_syms5 = in3(17);
coef_Jacob2_qt_syms4 = in3(14);
coef_Jacob3_qt_syms3 = in3(11);
coef_Jacob4_qt_syms2 = in3(8);
coef_Jacob1_qt_syms6 = in3(21);
coef_Jacob2_qt_syms5 = in3(18);
coef_Jacob3_qt_syms4 = in3(15);
coef_Jacob4_qt_syms3 = in3(12);
coef_Jacob1_qt_syms7 = in3(25);
coef_Jacob2_qt_syms6 = in3(22);
coef_Jacob3_qt_syms5 = in3(19);
coef_Jacob4_qt_syms4 = in3(16);
coef_Jacob1_qt_syms8 = in3(29);
coef_Jacob2_qt_syms7 = in3(26);
coef_Jacob3_qt_syms6 = in3(23);
coef_Jacob4_qt_syms5 = in3(20);
coef_Jacob1_qt_syms9 = in3(33);
coef_Jacob2_qt_syms8 = in3(30);
coef_Jacob3_qt_syms7 = in3(27);
coef_Jacob4_qt_syms6 = in3(24);
coef_Jacob2_qt_syms9 = in3(34);
coef_Jacob3_qt_syms8 = in3(31);
coef_Jacob4_qt_syms7 = in3(28);
coef_Jacob3_qt_syms9 = in3(35);
coef_Jacob4_qt_syms8 = in3(32);
coef_Jacob4_qt_syms9 = in3(36);
coef_Jacob1_qt_syms10 = in3(37);
coef_Jacob1_qt_syms11 = in3(41);
coef_Jacob1_qt_syms20 = in3(77);
coef_Jacob2_qt_syms10 = in3(38);
coef_Jacob1_qt_syms12 = in3(45);
coef_Jacob1_qt_syms21 = in3(81);
coef_Jacob1_qt_syms30 = in3(117);
coef_Jacob2_qt_syms11 = in3(42);
coef_Jacob2_qt_syms20 = in3(78);
coef_Jacob3_qt_syms10 = in3(39);
coef_Jacob1_qt_syms13 = in3(49);
coef_Jacob1_qt_syms22 = in3(85);
coef_Jacob1_qt_syms31 = in3(121);
coef_Jacob2_qt_syms12 = in3(46);
coef_Jacob2_qt_syms21 = in3(82);
coef_Jacob2_qt_syms30 = in3(118);
coef_Jacob3_qt_syms11 = in3(43);
coef_Jacob3_qt_syms20 = in3(79);
coef_Jacob4_qt_syms10 = in3(40);
coef_Jacob1_qt_syms14 = in3(53);
coef_Jacob1_qt_syms23 = in3(89);
coef_Jacob1_qt_syms32 = in3(125);
coef_Jacob2_qt_syms13 = in3(50);
coef_Jacob2_qt_syms22 = in3(86);
coef_Jacob2_qt_syms31 = in3(122);
coef_Jacob3_qt_syms12 = in3(47);
coef_Jacob3_qt_syms21 = in3(83);
coef_Jacob3_qt_syms30 = in3(119);
coef_Jacob4_qt_syms11 = in3(44);
coef_Jacob4_qt_syms20 = in3(80);
coef_Jacob1_qt_syms15 = in3(57);
coef_Jacob1_qt_syms24 = in3(93);
coef_Jacob2_qt_syms14 = in3(54);
coef_Jacob2_qt_syms23 = in3(90);
coef_Jacob2_qt_syms32 = in3(126);
coef_Jacob3_qt_syms13 = in3(51);
coef_Jacob3_qt_syms22 = in3(87);
coef_Jacob3_qt_syms31 = in3(123);
coef_Jacob4_qt_syms12 = in3(48);
coef_Jacob4_qt_syms21 = in3(84);
coef_Jacob4_qt_syms30 = in3(120);
coef_Jacob1_qt_syms16 = in3(61);
coef_Jacob1_qt_syms25 = in3(97);
coef_Jacob2_qt_syms15 = in3(58);
coef_Jacob2_qt_syms24 = in3(94);
coef_Jacob3_qt_syms14 = in3(55);
coef_Jacob3_qt_syms23 = in3(91);
coef_Jacob3_qt_syms32 = in3(127);
coef_Jacob4_qt_syms13 = in3(52);
coef_Jacob4_qt_syms22 = in3(88);
coef_Jacob4_qt_syms31 = in3(124);
coef_Jacob1_qt_syms17 = in3(65);
coef_Jacob1_qt_syms26 = in3(101);
coef_Jacob2_qt_syms16 = in3(62);
coef_Jacob2_qt_syms25 = in3(98);
coef_Jacob3_qt_syms15 = in3(59);
coef_Jacob3_qt_syms24 = in3(95);
coef_Jacob4_qt_syms14 = in3(56);
coef_Jacob4_qt_syms23 = in3(92);
coef_Jacob4_qt_syms32 = in3(128);
coef_Jacob1_qt_syms18 = in3(69);
coef_Jacob1_qt_syms27 = in3(105);
coef_Jacob2_qt_syms17 = in3(66);
coef_Jacob2_qt_syms26 = in3(102);
coef_Jacob3_qt_syms16 = in3(63);
coef_Jacob3_qt_syms25 = in3(99);
coef_Jacob4_qt_syms15 = in3(60);
coef_Jacob4_qt_syms24 = in3(96);
coef_Jacob1_qt_syms19 = in3(73);
coef_Jacob1_qt_syms28 = in3(109);
coef_Jacob2_qt_syms18 = in3(70);
coef_Jacob2_qt_syms27 = in3(106);
coef_Jacob3_qt_syms17 = in3(67);
coef_Jacob3_qt_syms26 = in3(103);
coef_Jacob4_qt_syms16 = in3(64);
coef_Jacob4_qt_syms25 = in3(100);
coef_Jacob1_qt_syms29 = in3(113);
coef_Jacob2_qt_syms19 = in3(74);
coef_Jacob2_qt_syms28 = in3(110);
coef_Jacob3_qt_syms18 = in3(71);
coef_Jacob3_qt_syms27 = in3(107);
coef_Jacob4_qt_syms17 = in3(68);
coef_Jacob4_qt_syms26 = in3(104);
coef_Jacob2_qt_syms29 = in3(114);
coef_Jacob3_qt_syms19 = in3(75);
coef_Jacob3_qt_syms28 = in3(111);
coef_Jacob4_qt_syms18 = in3(72);
coef_Jacob4_qt_syms27 = in3(108);
coef_Jacob3_qt_syms29 = in3(115);
coef_Jacob4_qt_syms19 = in3(76);
coef_Jacob4_qt_syms28 = in3(112);
coef_Jacob4_qt_syms29 = in3(116);
pinvG1_1 = in1(1);
pinvG1_2 = in1(4);
pinvG2_1 = in1(2);
pinvG1_3 = in1(7);
pinvG2_2 = in1(5);
pinvG3_1 = in1(3);
pinvG2_3 = in1(8);
pinvG3_2 = in1(6);
pinvG3_3 = in1(9);
obj = 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24]);

author: zarathustr

repo: LibQPEP

save_path: github-repos/MATLAB/zarathustr-LibQPEP


text:
classdef InstanceSegRegLoss < dagnn.Loss
    properties (Transient)
        curSimMat
        weightMat
        mass_
        size_
    end

    methods
        function outputs = forward(obj, inputs, params)
            [h, w, ch, bs] = size(inputs{1});
            sz = [h, w, ch, bs];
            mass = sz(1) * sz(2) + 1;

            obj.size_ = sz;
            obj.mass_ = mass;

            gpuMode = isa(inputs{1}, 'gpuArray');
            if gpuMode
                obj.curSimMat = gpuArray(zeros(sz, 'single'));
                obj.weightMat = gpuArray(zeros(sz, 'single'));
            else
                obj.curSimMat = zeros(sz, 'single');
                obj.weightMat = zeros(sz, 'single');
            end

            for j = 1:sz(4)
                C = inputs{2}(:,:,:,j);
                W = C;
                for ii = 0:4
                    W(W==ii) = sum(C(:)==ii);
                end
                W = W(:)*W(:)';
                W = 1 ./ W;
                W = W ./ sum(W(:));

                C = reshape(C, [sz(1), 1]);
                C = repmat(C, 1, sz(2));
                C = (C==C');
                obj.curSimMat(:,:,:,j) = C;
                obj.weightMat(:,:,:,j) = W;
            end

            outputs{1} = vl_nnloss(inputs{1}, obj.curSimMat, [], ...
                'loss', obj.loss, ...
                'instanceWeights', obj.weightMat) ;% 1./mass
            n = obj.numAveraged ;
            m = n + size(inputs{1},4) ;
            obj.average = (n * obj.average + double(gather(outputs{1}))) / m ;
            obj.numAveraged = m ;
        end

        function [derInputs, derParams] = backward(obj, inputs, params, derOutputs)
            sz = obj.size_;
            mass = obj.mass_;

            gpuMode = isa(inputs{1}, 'gpuArray');
            if gpuMode
                grndLabel = gpuArray(zeros(sz, 'single'));
            else
                grndLabel = zeros(sz, 'single');
            end
            grndLabel = obj.curSimMat;

            derInputs{1} = vl_nnloss(inputs{1}, obj.curSimMat, derOutputs{1}, ...
                'loss', obj.loss, ...
                'instanceWeights', obj.weightMat) ;% 1./mass
            derInputs{2} = [] ;
            derParams = {} ;
        end

        function obj = InstanceSegRegLoss(varargin)
            obj.load(varargin) ;
        end
    end
end

author: aimerykong


text:
function scrollWheel(fig,stats)
% function scrollWheel(fig,stats)
%
% Callback function for the mouse scrollWheel.
%
% APA, 2/29/2016

global stateS

if ~stateS.planLoaded
    return;
end

if stats.VerticalScrollCount > 0
    sliceCallBack('ChangeSlc','prevslice')
else
    sliceCallBack('ChangeSlc','nextslice')
end

author: cerr

repo: CERR

save_path: github-repos/MATLAB/cerr-CERR


text:
%--- help for ts/aggregate ---
%
%  Convert time series to a lower frequency
%
%  ::
%
%     this=aggregate(this,newfreq);
%     this=aggregate(this,newfreq,method);
%
%  Args:
%     this (ts object): time series object
%     newfreq (char): frequency to convert the data to. It must be a lower than the frequency of the data in **this**. Available frequencies are
%
%        - 'Q': quarterly
%        - 'M': monthly
%        - 'H': semiannual
%        - 'W': weekly
%        - 'D': daily
%
%     method ('interpolation' , 'distribution'): method of conversion (default: 'distribution')
%
%        - 'interpolation': Use the value corresponding to the first date of the coarse frequency
%        - 'distribution': Use the average of all observations in the sampling frequency
%
%  Returns:
%     :
%
%     - **this** (ts object): time series object
%
%

author: jmaih