/usr/share/dynare/matlab/@dseries/mtimes.m is in dynare-common 4.4.1-1build1.
This file is owned by root:root, with mode 0o644.
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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 | function A = mtimes(B,C) % --*-- Unitary tests --*--
%@info:
%! @deftypefn {Function File} {@var{A} =} mtimes (@var{B},@var{C})
%! @anchor{@dseries/mtimes}
%! @sp 1
%! Overloads the mtimes method for the Dynare time series class (@ref{dseries}).
%! @sp 2
%! @strong{Inputs}
%! @sp 1
%! @table @ @var
%! @item B
%! Dynare time series object instantiated by @ref{dseries}.
%! @item C
%! Dynare time series object instantiated by @ref{dseries}.
%! @end table
%! @sp 1
%! @strong{Outputs}
%! @sp 1
%! @table @ @var
%! @item A
%! Dynare time series object.
%! @end deftypefn
%@eod:
% Copyright (C) 2012-2014 Dynare Team
%
% This file is part of Dynare.
%
% Dynare 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.
%
% Dynare 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 Dynare. If not, see <http://www.gnu.org/licenses/>.
if isnumeric(B) && (isscalar(B) || isvector(B))
if ~isdseries(C)
error('dseries::mtimes: Second input argument must be a dseries object!')
end
A = C;
A.data = bsxfun(@times,C.data,B);
return;
end
if isnumeric(C) && (isscalar(C) || isvector(C))
if ~isdseries(B)
error('dseries::mtimes: First input argument must be a dseries object!')
end
A = B;
A.data = bsxfun(@times,B.data,C);
return
end
if isdseries(B) && isdseries(C)
% Element by element multiplication of two dseries object
if ~isequal(B.vobs,C.vobs) && ~(isequal(B.vobs,1) || isequal(C.vobs,1))
error(['dseries::times: Cannot multiply ' inputname(1) ' and ' inputname(2) ' (wrong number of variables)!'])
else
if B.vobs>C.vobs
idB = 1:B.vobs;
idC = ones(1:B.vobs);
elseif B.vobs<C.vobs
idB = ones(1,C.vobs);
idC = 1:C.vobs;
else
idB = 1:B.vobs;
idC = 1:C.vobs;
end
end
if ~isequal(B.freq,C.freq)
error(['dseries::times: Cannot multiply ' inputname(1) ' and ' inputname(2) ' (frequencies are different)!'])
end
if ~isequal(B.nobs,C.nobs) || ~isequal(B.init,C.init)
[B, C] = align(B, C);
end
A = dseries();
A.freq = B.freq;
A.init = B.init;
A.dates = B.dates;
A.nobs = max(B.nobs,C.nobs);
A.vobs = max(B.vobs,C.vobs);
A.name = cell(A.vobs,1);
A.tex = cell(A.vobs,1);
for i=1:A.vobs
A.name(i) = {['multiply(' B.name{idB(i)} ',' C.name{idC(i)} ')']};
A.tex(i) = {['(' B.tex{idB(i)} '*' C.tex{idC(i)} ')']};
end
A.data = bsxfun(@times,B.data,C.data);
else
error()
end
%@test:1
%$ % Define a datasets.
%$ A = rand(10,2); B = randn(10,1);
%$
%$ % Define names
%$ A_name = {'A1';'A2'}; B_name = {'B1'};
%$
%$
%$ % Instantiate a time series object.
%$ try
%$ ts1 = dseries(A,[],A_name,[]);
%$ ts2 = dseries(B,[],B_name,[]);
%$ ts3 = ts1*ts2;
%$ t = 1;
%$ catch
%$ t = 0;
%$ end
%$
%$ if t(1)
%$ t(2) = dyn_assert(ts3.vobs,2);
%$ t(3) = dyn_assert(ts3.nobs,10);
%$ t(4) = dyn_assert(ts3.data,[A(:,1).*B, A(:,2).*B],1e-15);
%$ t(5) = dyn_assert(ts3.name,{'multiply(A1,B1)';'multiply(A2,B1)'});
%$ end
%$ T = all(t);
%@eof:1
%@test:2
%$ % Define a datasets.
%$ A = rand(10,2); B = pi;
%$
%$ % Define names
%$ A_name = {'A1';'A2'};
%$
%$
%$ % Instantiate a time series object.
%$ try
%$ ts1 = dseries(A,[],A_name,[]);
%$ ts2 = ts1*B;
%$ t = 1;
%$ catch
%$ t = 0;
%$ end
%$
%$ if t(1)
%$ t(2) = dyn_assert(ts2.vobs,2);
%$ t(3) = dyn_assert(ts2.nobs,10);
%$ t(4) = dyn_assert(ts2.data,A*B,1e-15);
%$ end
%$ T = all(t);
%@eof:2
%@test:3
%$ % Define a datasets.
%$ A = rand(10,2); B = pi;
%$
%$ % Define names
%$ A_name = {'A1';'A2'};
%$
%$
%$ % Instantiate a time series object.
%$ try
%$ ts1 = dseries(A,[],A_name,[]);
%$ ts2 = B*ts1;
%$ t = 1;
%$ catch
%$ t = 0;
%$ end
%$
%$ if t(1)
%$ t(2) = dyn_assert(ts2.vobs,2);
%$ t(3) = dyn_assert(ts2.nobs,10);
%$ t(4) = dyn_assert(ts2.data,A*B,1e-15);
%$ end
%$ T = all(t);
%@eof:3
%@test:4
%$ % Define a datasets.
%$ A = rand(10,2); B = A(1,:);
%$
%$ % Define names
%$ A_name = {'A1';'A2'};
%$
%$
%$ % Instantiate a time series object.
%$ try
%$ ts1 = dseries(A,[],A_name,[]);
%$ ts2 = B*ts1;
%$ t = 1;
%$ catch
%$ t = 0;
%$ end
%$
%$ if t(1)
%$ t(2) = dyn_assert(ts2.vobs,2);
%$ t(3) = dyn_assert(ts2.nobs,10);
%$ t(4) = dyn_assert(ts2.data,bsxfun(@times,A,B),1e-15);
%$ end
%$ T = all(t);
%@eof:4
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