This file is indexed.

/usr/share/octave/packages/nan-2.5.9/covm.m is in octave-nan 2.5.9-2.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
function [CC,NN] = covm(X,Y,Mode,W)
% COVM generates covariance matrix
% X and Y can contain missing values encoded with NaN.
% NaN's are skipped, NaN do not result in a NaN output. 
% The output gives NaN only if there are insufficient input data
%
% COVM(X,Mode);
%      calculates the (auto-)correlation matrix of X
% COVM(X,Y,Mode);
%      calculates the crosscorrelation between X and Y
% COVM(...,W);
%	weighted crosscorrelation 
%
% Mode = 'M' minimum or standard mode [default]
% 	C = X'*X; or X'*Y correlation matrix
%
% Mode = 'E' extended mode
% 	C = [1 X]'*[1 X]; % l is a matching column of 1's
% 	C is additive, i.e. it can be applied to subsequent blocks and summed up afterwards
% 	the mean (or sum) is stored on the 1st row and column of C
%
% Mode = 'D' or 'D0' detrended mode
%	the mean of X (and Y) is removed. If combined with extended mode (Mode='DE'), 
% 	the mean (or sum) is stored in the 1st row and column of C. 
% 	The default scaling is factor (N-1). 
% Mode = 'D1' is the same as 'D' but uses N for scaling. 
%
% C = covm(...); 
% 	C is the scaled by N in Mode M and by (N-1) in mode D.
% [C,N] = covm(...);
%	C is not scaled, provides the scaling factor N  
%	C./N gives the scaled version. 
%
% see also: DECOVM, XCOVF

%	$Id: covm.m 9032 2011-11-08 20:25:36Z schloegl $
%	Copyright (C) 2000-2005,2009 by Alois Schloegl <alois.schloegl@gmail.com>	
%       This function is part of the NaN-toolbox
%       http://pub.ist.ac.at/~schloegl/matlab/NaN/

%    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 3 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, see <http://www.gnu.org/licenses/>.


global FLAG_NANS_OCCURED;

if nargin<3,
	W = []; 
        if nargin==2,
		if isnumeric(Y),
			Mode='M';
		else
			Mode=Y;
			Y=[];
		end;
        elseif nargin==1,
                Mode = 'M';
                Y = [];
        elseif nargin==0,
                error('Missing argument(s)');
        end;

elseif (nargin==3) && isnumeric(Y) && ~isnumeric(Mode);
	W = [];

elseif (nargin==3) && ~isnumeric(Y) && isnumeric(Mode);
	W = Mode; 
	Mode = Y;
	Y = [];

elseif (nargin==4) && ~isnumeric(Mode) && isnumeric(Y);
	; %% ok 
else 
	error('invalid input arguments');
end;

Mode = upper(Mode);

[r1,c1]=size(X);
if ~isempty(Y)
        [r2,c2]=size(Y);
        if r1~=r2,
                error('X and Y must have the same number of observations (rows).');
        end;
else
        [r2,c2]=size(X);
end;

persistent mexFLAG2; 
persistent mexFLAG; 
if isempty(mexFLAG2) 
	mexFLAG2 = exist('covm_mex','file');	
end; 
if isempty(mexFLAG) 
	mexFLAG = exist('sumskipnan_mex','file');	
end; 


if ~isempty(W)
	W = W(:);
	if (r1~=numel(W))
		error('Error COVM: size of weight vector does not fit number of rows');
	end;
	%w = spdiags(W(:),0,numel(W),numel(W));
	%nn = sum(W(:)); 
	nn = sum(W);
else
	nn = r1;
end; 


if mexFLAG2 && mexFLAG && ~isempty(W),
	%% the mex-functions here are much slower than the m-scripts below 
	%% however, the mex-functions support weighting of samples. 
	if isempty(FLAG_NANS_OCCURED),
		%% mex-files require that FLAG_NANS_OCCURED is not empty, 
		%% otherwise, the status of NAN occurence can not be returned. 
		FLAG_NANS_OCCURED = logical(0);  % default value 
	end;

	if any(Mode=='D') || any(Mode=='E'),
		[S1,N1] = sumskipnan(X,1,W);
		if ~isempty(Y)
	               	[S2,N2] = sumskipnan(Y,1,W);
	        else
	        	S2 = S1; N2 = N1;
		end;
                if any(Mode=='D'), % detrending mode
       			X  = X - ones(r1,1)*(S1./N1);
                        if ~isempty(Y)
                                Y  = Y - ones(r1,1)*(S2./N2);
                        end;
                end;
	end;

	[CC,NN] = covm_mex(real(X), real(Y), FLAG_NANS_OCCURED, W);
	%% complex matrices 
	if ~isreal(X) && ~isreal(Y)
		[iCC,inn] = covm_mex(imag(X), imag(Y), FLAG_NANS_OCCURED, W);
		CC = CC + iCC;
	end; 
	if isempty(Y) Y = X; end;
	if ~isreal(X)
		[iCC,inn] = covm_mex(imag(X), real(Y), FLAG_NANS_OCCURED, W);
		CC = CC - i*iCC;
	end;
	if ~isreal(Y)
		[iCC,inn] = covm_mex(real(X), imag(Y), FLAG_NANS_OCCURED, W);
		CC = CC + i*iCC;
	end;
	
        if any(Mode=='D') && ~any(Mode=='1'),  %  'D1'
                NN = max(NN-1,0);
        end;
        if any(Mode=='E'), % extended mode
                NN = [nn, N2; N1', NN];
                CC = [nn, S2; S1', CC];
        end;


elseif ~isempty(W),

	error('Error COVM: weighted COVM requires sumskipnan_mex and covm_mex but it is not available');

	%% weighted covm without mex-file support
	%% this part is not working.

elseif ~isempty(Y),
	if (~any(Mode=='D') && ~any(Mode=='E')), % if Mode == M
        	NN = real(X==X)'*real(Y==Y);
		FLAG_NANS_OCCURED = any(NN(:)<nn);
	        X(X~=X) = 0; % skip NaN's
	        Y(Y~=Y) = 0; % skip NaN's
        	CC = X'*Y;

        else  % if any(Mode=='D') | any(Mode=='E'), 
        	[S1,N1] = sumskipnan(X,1);
               	[S2,N2] = sumskipnan(Y,1);
               	NN = real(X==X)'*real(Y==Y);

                if any(Mode=='D'), % detrending mode
			X  = X - ones(r1,1)*(S1./N1);
			Y  = Y - ones(r1,1)*(S2./N2);
			if any(Mode=='1'),  %  'D1'
				NN = NN;
			else	%  'D0'       
				NN = max(NN-1,0);
			end;
                end;
		X(X~=X) = 0; % skip NaN's
		Y(Y~=Y) = 0; % skip NaN's
               	CC = X'*Y;

                if any(Mode=='E'), % extended mode
                        NN = [nn, N2; N1', NN];
                        CC = [nn, S2; S1', CC];
                end;
	end;

else
	if (~any(Mode=='D') && ~any(Mode=='E')), % if Mode == M
		tmp = real(X==X);
		NN  = tmp'*tmp;
		X(X~=X) = 0; % skip NaN's
	        CC = X'*X;
		FLAG_NANS_OCCURED = any(NN(:)<nn);

        else  % if any(Mode=='D') | any(Mode=='E'), 
	        [S,N] = sumskipnan(X,1);
       		tmp = real(X==X);
               	NN  = tmp'*tmp;
       	        if any(Mode=='D'), % detrending mode
        	        X  = X - ones(r1,1)*(S./N);
                       	if any(Mode=='1'),  %  'D1'
                               	NN = NN;
                        else  %  'D0'      
       	                        NN = max(NN-1,0);
               	        end;
                end;
                
       	        X(X~=X) = 0; % skip NaN's
		CC = X'*X;
                if any(Mode=='E'), % extended mode
                        NN = [nn, N; N', NN];
                        CC = [nn, S; S', CC];
                end;
	end

end;


if nargout<2
        CC = CC./NN; % unbiased
end;

%!assert(covm([1;NaN;2],'D'),0.5)
%!assert(covm([1;NaN;2],'M'),2.5)
%!assert(covm([1;NaN;2],'E'),[1,1.5;1.5,2.5])