Moving final GENENET8 version to master

6 files changed, 10 insertions, 201 deletions

diff --git a/sourcecodes/parameter_learning/createJSON.m b/sourcecodes/parameter_learning/createJSON.m
index e6fd7ea2..59a3aec4 100644
--- a/sourcecodes/parameter_learning/createJSON.m
+++ b/sourcecodes/parameter_learning/createJSON.m
@@ -12,10 +12,6 @@ function  [ ] = createJSON( pre )
    %
 
 
-nnodefile=strcat(pre,'nnode.txt');
-fnnode = fopen(nnodefile,'r');
-nnodes = fscanf(fnnode,'%d');
-
 %  open file for input, include error handling
 dfile=strcat(pre,'structure_input.txt');
 
@@ -26,6 +22,7 @@ end
 
 % Read in first line to get the number of nodes and the node labels.
 buffer = strtrim(fgetl(fin));    %get header line as a string
+nnodes = numel(strfind(buffer,"\t"))+1;
 labels = cell(1,nnodes);
 for j=1:nnodes
     [next,buffer] = strtok(buffer);
diff --git a/sourcecodes/parameter_learning/createSVG.m b/sourcecodes/parameter_learning/createSVG.m
index 2d190b68..47894c1b 100644
--- a/sourcecodes/parameter_learning/createSVG.m
+++ b/sourcecodes/parameter_learning/createSVG.m
@@ -16,10 +16,6 @@ function  [ ] = createSVG( pre )
    %
 
 
-nnodefile=strcat(pre,'nnode.txt');
-fnnode = fopen(nnodefile,'r');
-nnodes = fscanf(fnnode,'%d');
-
 %  open file for input, include error handling
 dfile=strcat(pre,'structure_input.txt');
 
@@ -30,20 +26,19 @@ end
 
 % Read in first line to get the number of nodes and the node labels.
 buffer = fgetl(fin);    %get header line as a string
-
+nnodes = numel(strfind(buffer,"\t")) + 1;
 labels = cell(1,nnodes);
 for j=1:nnodes
-    j, buffer
     [next,buffer] = strtok(buffer);
     labels{j} = next;
 end
 
+
 % Read in the edges
 edges = cell(nnodes,nnodes);
 for i = 1:nnodes
     buffer = fgetl(fin);
     for j = 1:nnodes
-	 i, j, buffer
          [next,buffer] = strtok(buffer);
          edges{i,j} = next;
     end
diff --git a/sourcecodes/parameter_learning/kfoldCrossValid.m b/sourcecodes/parameter_learning/kfoldCrossValid.m
index 40b72c61..2af690d7 100644
--- a/sourcecodes/parameter_learning/kfoldCrossValid.m
+++ b/sourcecodes/parameter_learning/kfoldCrossValid.m
@@ -4,7 +4,7 @@ function kfoldCrossValid(pre,predict_label,nfolds)
 %   that you want to predict and the number of folds that the
 %   data should be divided into.
 
-nfolds = uint8(str2num(nfolds));
+nfolds = uint16(str2num(nfolds));
 
 sfile=strcat(pre,'structure_input.txt');
 dfile=strcat(pre,'continuous_input.txt');
diff --git a/sourcecodes/parameter_learning/modifyEdges.m b/sourcecodes/parameter_learning/modifyEdges.m
index 6542d9fb..c435806a 100644
--- a/sourcecodes/parameter_learning/modifyEdges.m
+++ b/sourcecodes/parameter_learning/modifyEdges.m
@@ -58,7 +58,6 @@ for j=1:nedges
     sources{j} = str2num(next);
 end
 
-
 buffer = fgetl(fin2);
 buffer = buffer(2:end-1);
 buffer = strrep(buffer,"\"","");
@@ -76,7 +75,6 @@ for j=1:nedges
     weights{j} = next;
 end
 
-
 %label_map is the index in "labels" that corresponds to each label in "labels2"
 label_map = cell(1,nnodes);
 for i = 1:nnodes
@@ -99,6 +97,8 @@ for i = 1:nedges
   edges_out(source_i,target_i) = "1";
 end
 
+%scores_out
+
 
 tf = cellfun('isempty',edges_out);
 edges_out(tf) = {"0"};
@@ -113,28 +113,25 @@ for i=1:nnodes
   end
 end
 
-
 if test_score == 1
 outfile = strcat(pre_new,'structure_input_temp.txt');
 fout = fopen(outfile,'w');
 fprintf(fout,'%s\t',labels{1:end-1});
-fprintf(fout,'%s\t\n',labels{end});
+fprintf(fout,'%s\n',labels{end});
 for i = 1:nnodes
       fprintf(fout,'%s\t',scores_out{i,1:end-1});
-      fprintf(fout,'%s\t\n',scores_out{i,end});
+      fprintf(fout,'%s\n',scores_out{i,end});
 end
 fclose(fout);
 end
 
-
-
 outfile2 = strcat(pre_new,'structure_input.txt');
 fout2 = fopen(outfile2,'w');
 fprintf(fout2,'%s\t',labels{1:end-1});
-fprintf(fout2,'%s\t\n',labels{end});
+fprintf(fout2,'%s\n',labels{end});
 for i = 1:nnodes
       fprintf(fout2,'%s\t',edges_out{i,1:end-1});
-      fprintf(fout2,'%s\t\n',edges_out{i,end});
+      fprintf(fout2,'%s\n',edges_out{i,end});
 end
 fclose(fout2);
 
diff --git a/sourcecodes/parameter_learning/normpdf.m b/sourcecodes/parameter_learning/normpdf.m
deleted file mode 100644
index 2b154f02..00000000
--- a/sourcecodes/parameter_learning/normpdf.m
+++ /dev/null
@@ -1,50 +0,0 @@
-function p = normpdf(x,m,s);
-% Normal probability density function
-%
-% pdf = normpdf(x,m,s);
-%
-% Computes the PDF of a the normal distribution 
-%    with mean m and standard deviation s
-%    default: m=0; s=1;
-% x,m,s must be matrices of same size, or any one can be a scalar. 
-%
-% see also: NORMCDF, NORMINV 
-
-% Reference(s):
-
-%	Version 1.28   Date: 23.Sep.2002
-%	Copyright (c) 2000-2002 by  Alois Schloegl <a.schloegl@ieee.org>	
-
-%    This program is free software; you can redistribute it and/or modify
-%    it under the terms of the GNU General Public License as published by
-%    the Free Software Foundation; either version 2 of the License, or
-%    (at your option) any later version.
-%
-%    This program is distributed in the hope that it will be useful,
-%    but WITHOUT ANY WARRANTY; without even the implied warranty of
-%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-%    GNU General Public License for more details.
-%
-%    You should have received a copy of the GNU General Public License
-%    along with this program; if not, write to the Free Software
-%    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
-
-if nargin==1,
-        m=0;s=1;
-elseif nargin==2,
-        s=1;
-end;        
-
-% allocate output memory and check size of argument
-z = (x-m)./s;		% if this line causes an error, input arguments do not fit. 
-
-%p = ((2*pi)^(-1/2))*exp(-z.^2/2)./s;
-SQ2PI = 2.5066282746310005024157652848110;
-p = exp(-z.^2/2)./(s*SQ2PI);
-
-p((x==m) & (s==0)) = inf;
-
-p(isinf(z)~=0) = 0;
-
-p(isnan(x) | isnan(m) | isnan(s) | (s<0)) = nan;
-
diff --git a/sourcecodes/parameter_learning/normrnd.m b/sourcecodes/parameter_learning/normrnd.m
deleted file mode 100644
index 0267ddf6..00000000
--- a/sourcecodes/parameter_learning/normrnd.m
+++ /dev/null
@@ -1,130 +0,0 @@
-## Copyright (C) 2012 Rik Wehbring
-## Copyright (C) 1995-2012 Kurt Hornik
-##
-## This file is part of Octave.
-##
-## Octave is free software; you can redistribute it and/or modify it
-## under the terms of the GNU General Public License as published by
-## the Free Software Foundation; either version 3 of the License, or (at
-## your option) any later version.
-##
-## Octave is distributed in the hope that it will be useful, but
-## WITHOUT ANY WARRANTY; without even the implied warranty of
-## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-## General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with Octave; see the file COPYING.  If not, see
-## <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn  {Function File} {} normrnd (@var{mu}, @var{sigma})
-## @deftypefnx {Function File} {} normrnd (@var{mu}, @var{sigma}, @var{r})
-## @deftypefnx {Function File} {} normrnd (@var{mu}, @var{sigma}, @var{r}, @var{c}, @dots{})
-## @deftypefnx {Function File} {} normrnd (@var{mu}, @var{sigma}, [@var{sz}])
-## Return a matrix of random samples from the normal distribution with
-## parameters mean @var{mu} and standard deviation @var{sigma}.
-##
-## When called with a single size argument, return a square matrix with
-## the dimension specified.  When called with more than one scalar argument the
-## first two arguments are taken as the number of rows and columns and any
-## further arguments specify additional matrix dimensions.  The size may also
-## be specified with a vector of dimensions @var{sz}.
-## 
-## If no size arguments are given then the result matrix is the common size of
-## @var{mu} and @var{sigma}.
-## @end deftypefn
-
-## Author: KH <Kurt.Hornik@wu-wien.ac.at>
-## Description: Random deviates from the normal distribution
-
-function rnd = normrnd (mu, sigma, varargin)
-
-  if (nargin < 2)
-    print_usage ();
-  endif
-
-  if (!isscalar (mu) || !isscalar (sigma))
-    [retval, mu, sigma] = common_size (mu, sigma);
-    if (retval > 0)
-      error ("normrnd: mu and sigma must be of common size or scalars");
-    endif
-  endif
-
-  if (iscomplex (mu) || iscomplex (sigma))
-    error ("normrnd: MU and SIGMA must not be complex");
-  endif
-
-  if (nargin == 2)
-    sz = size (mu);
-  elseif (nargin == 3)
-    if (isscalar (varargin{1}) && varargin{1} >= 0)
-      sz = [varargin{1}, varargin{1}];
-    elseif (isrow (varargin{1}) && all (varargin{1} >= 0))
-      sz = varargin{1};
-    else
-      error ("normrnd: dimension vector must be row vector of non-negative integers");
-    endif
-  elseif (nargin > 3)
-    if (any (cellfun (@(x) (!isscalar (x) || x < 0), varargin)))
-      error ("normrnd: dimensions must be non-negative integers");
-    endif
-    sz = [varargin{:}];
-  endif
-
-  if (!isscalar (mu) && !isequal (size (mu), sz))
-    error ("normrnd: mu and sigma must be scalar or of size SZ");
-  endif
-
-  if (isa (mu, "single") || isa (sigma, "single"))
-    cls = "single";
-  else
-    cls = "double";
-  endif
-
-  if (isscalar (mu) && isscalar (sigma))
-    if (!isnan (mu) && !isinf (mu) && (sigma > 0) && (sigma < Inf))
-      rnd =  mu + sigma * randn (sz);
-    else
-      rnd = NaN (sz, cls);
-    endif
-  else
-    rnd = mu + sigma .* randn (sz);
-    k = isnan (mu) | isinf (mu) | !(sigma > 0) | !(sigma < Inf);
-    rnd(k) = NaN;
-  endif
-
-endfunction
-
-
-%!assert(size (normrnd (1,2)), [1, 1]);
-%!assert(size (normrnd (ones(2,1), 2)), [2, 1]);
-%!assert(size (normrnd (ones(2,2), 2)), [2, 2]);
-%!assert(size (normrnd (1, 2*ones(2,1))), [2, 1]);
-%!assert(size (normrnd (1, 2*ones(2,2))), [2, 2]);
-%!assert(size (normrnd (1, 2, 3)), [3, 3]);
-%!assert(size (normrnd (1, 2, [4 1])), [4, 1]);
-%!assert(size (normrnd (1, 2, 4, 1)), [4, 1]);
-
-%% Test class of input preserved
-%!assert(class (normrnd (1, 2)), "double");
-%!assert(class (normrnd (single(1), 2)), "single");
-%!assert(class (normrnd (single([1 1]), 2)), "single");
-%!assert(class (normrnd (1, single(2))), "single");
-%!assert(class (normrnd (1, single([2 2]))), "single");
-
-%% Test input validation
-%!error normrnd ()
-%!error normrnd (1)
-%!error normrnd (ones(3),ones(2))
-%!error normrnd (ones(2),ones(3))
-%!error normrnd (i, 2)
-%!error normrnd (2, i)
-%!error normrnd (1,2, -1)
-%!error normrnd (1,2, ones(2))
-%!error normrnd (1, 2, [2 -1 2])
-%!error normrnd (1,2, 1, ones(2))
-%!error normrnd (1,2, 1, -1)
-%!error normrnd (ones(2,2), 2, 3)
-%!error normrnd (ones(2,2), 2, [3, 2])
-%!error normrnd (ones(2,2), 2, 2, 3)