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lawrennd committed May 28, 2015
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44 changes: 44 additions & 0 deletions Contents.m
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% Matlab Wrapper for SVMlight
% Version 0.92, August 2002
%
% Wrapper functions:
% svml - Wrapper for SVMlight
% svmltrain - Wrapper for SVMlight: Training
% svmlfwd - Wrapper for SVMlight: Prediction
% demsvml1 - Demo program for Matlab SVMlight wrapper
%
% Auxiliary functions to generate and read data files
% svmlread - Read a data file generated by SVM light
% svmlwrite - Write matrix into data file for SVM light
% svmlopt - Generate/alter options structure for SVM light
% consist - Check that arguments are consistent.
%
% Calling routines
% svm_classify - Interface to SVM light, classification module
% svm_learn - Interface to SVM light, learning module
%
%
% Copyright (c) by Anton Schwaighofer (2002)
% mailto:anton.schwaighofer@gmx.net
%
% This program is released unter the GNU General Public License.
%

% $Revision: 1.3 $ $Date: 2002/08/09 20:25:26 $
%
% 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.
%

340 changes: 340 additions & 0 deletions License.txt

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87 changes: 87 additions & 0 deletions consist.m
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function errstring = consist(model, type, inputs, outputs)
%CONSIST Check that arguments are consistent.
%
% Description
%
% ERRSTRING = CONSIST(NET, TYPE, INPUTS) takes a network data structure
% NET together with a string TYPE containing the correct network type,
% a matrix INPUTS of input vectors and checks that the data structure
% is consistent with the other arguments. An empty string is returned
% if there is no error, otherwise the string contains the relevant
% error message. If the TYPE string is empty, then any type of network
% is allowed.
%
% ERRSTRING = CONSIST(NET, TYPE) takes a network data structure NET
% together with a string TYPE containing the correct network type, and
% checks that the two types match.
%
% ERRSTRING = CONSIST(NET, TYPE, INPUTS, OUTPUTS) also checks that the
% network has the correct number of outputs, and that the number of
% patterns in the INPUTS and OUTPUTS is the same. The fields in NET
% that are used are
% type
% nin
% nout
%
% See also
% MLPFWD
%

% Copyright (c) Ian T Nabney (1996-2001)

% Assume that all is OK as default
errstring = '';

% If type string is not empty
if ~isempty(type)
% First check that model has type field
if ~isfield(model, 'type')
errstring = 'Data structure does not contain type field';
return
end
% Check that model has the correct type
s = model.type;
if ~strcmp(s, type)
errstring = ['Model type ''', s, ''' does not match expected type ''',...
type, ''''];
return
end
end

% If inputs are present, check that they have correct dimension
if nargin > 2
if ~isfield(model, 'nin')
errstring = 'Data structure does not contain nin field';
return
end

data_nin = size(inputs, 2);
if model.nin ~= data_nin
errstring = ['Dimension of inputs ', num2str(data_nin), ...
' does not match number of model inputs ', num2str(model.nin)];
return
end
end

% If outputs are present, check that they have correct dimension
if nargin > 3
if ~isfield(model, 'nout')
errstring = 'Data structure does not conatin nout field';
return
end
data_nout = size(outputs, 2);
if model.nout ~= data_nout
errstring = ['Dimension of outputs ', num2str(data_nout), ...
' does not match number of model outputs ', num2str(model.nout)];
return
end

% Also check that number of data points in inputs and outputs is the same
num_in = size(inputs, 1);
num_out = size(outputs, 1);
if num_in ~= num_out
errstring = ['Number of input patterns ', num2str(num_in), ...
' does not match number of output patterns ', num2str(num_out)];
return
end
end
86 changes: 86 additions & 0 deletions demsvml1.m
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function demsvml1()
% DEMSVML1 - Demo program for Matlab SVMlight wrapper
%
% This program shows how Thorsten Joachims SVMlight test examples can
% be executed using the Matlab SVMlight wrapper, plus some additional
% features.
% In order to execute this demo, SVMlight must be installed (on the
% execution path or in the current directory) and the two
% subdirectories 'example1' and 'example2' must be present in the
% current directory.
%
% Please check the source code for more info.
%
% See also SVML, SVMLTRAIN, SVMLFWD, SVMLWRITE, SVMLREAD, SVM_LEARN,
% SVM_CLASSIFY
%

%
% Copyright (c) by Anton Schwaighofer (2002)
% $Revision: 1.3 $ $Date: 2002/08/09 20:27:33 $
% mailto:anton.schwaighofer@gmx.net
%
% This program is released unter the GNU General Public License.
%

fprintf('In order to execute this demo, SVMlight must be installed (on the\n');
fprintf('execution path or in the current directory) and the two\n');
fprintf('subdirectories ''example1'' and ''example2'' must be present in the\n');
fprintf('current directory.\n');
fprintf('Have a look at the source code for details...\n');
fprintf('\nPress the Return key to continue, or Control-C to cancel.\n');

% We do basically the same as Joachims example2, just that we load
% the data into Matlab beforehand and use the SVML structure
[Y, X] = svmlread('example1/train.dat');
net = svml('example1/model1', 'Kernel', 0, 'C', 1);
net = svmltrain(net, X, Y);
% Also read out Joachims' test data
[Ytest, Xtest] = svmlread('example1/test.dat');
% Compute prediction on the test data
Ypred = svmlfwd(net, Xtest);
% We can also get SVMlight to print out accuracy and precision/recall, by
% providing the test targets as well:
Ypred = svmlfwd(net, Xtest, Ytest);


% The SVMlight wrapper can also directly access data files in SVMlight's
% format, both during training and testing. This is of course more
% efficient than reading the files into Matlab and writing it back to
% disk again, if the data are already available in SVMlights format.
net = svml('example1/model2', 'Kernel', 0, 'C', 1);
% directly access training data
net = svmltrain(net, 'example1/train.dat');
% Compute prediction on the test data
Ytest = svmlfwd(net, 'example1/test.dat');


% Now for the transductive SVM:
net = svml('example2/model1', 'Kernel', 0, 'C', 1, 'TransLabelFile', ...
'example2/trans_predictions');
% Transductive learner is automatically invoked
% You may just as well load the data into Matlab variables
% [Y, X] = svmlread('example2/train_transduction.dat');
% and go net = svmltrain(net, X, Y);
net = svmltrain(net, 'example2/train_transduction.dat');
% Read out the labels for the unclassified training examples
transLabels = svmlread('example2/trans_predictions');


% We now use only the simple calling routines SVM_LEARN and
% SVM_CLASSIFY. For the options, we only assume that SVMlight is
% installed in the current directory '.'

% Joachims' first example (inductive SVM):
options = svmlopt('ExecPath', '.');
svm_learn(options, 'example1/train.dat', 'example1/model');
svm_classify(options, 'example1/test.dat', 'example1/model', ...
'example1/predictions');
% The file example1/model holds the trained SVM model,
% example1/predictions are the labels for the test points

% Joachims' second example (transductive SVM)
svm_learn(options, 'example2/train_transduction.dat', 'example2/model');
svm_classify(options, 'example2/test.dat', 'example2/model', ...
'example2/predictions');

66 changes: 66 additions & 0 deletions svm_classify.m
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function status = svm_classify(options, data, model, predictions)
% SVM_CLASSIFY - Interface to SVM light, classification module
%
% STATUS = SVM_CLASSIFY(OPTIONS, DATA, MODEL, PREDICTIONS)
% Call the classification program 'svm_classify' of the SVM light
% package.
% OPTIONS must be a structure generated by SVMLOPT. For SVM_CLASSIFY,
% only the options 'ExecPath' and 'Verbosity' are relevant.
% DATA is the name of the file containing the data to be classified
% (the test data). Use SVMLWRITE to convert a Matlab matrix to the
% appropriate format.
% MODEL is the name of the file holding the trained Support Vector
% Machine, generated by SVM_LEARN.
% PREDICTIONS is the file that will store the predicted classes. There
% is one line per test example in output_file containing the value of
% the decision function on that example. The sign of this value
% determines the predicted class. This file can be read into Matlab
% using SVMLREAD.
% If 'svm_learn' is not on the path, OPTIONS must contain a field
% 'ExecPath' with the path of the executable.
% STATUS is the error code returned by SVM light (0 if everything went
% fine)
%
% See also SVML, SVMLOPT, SVMLWRITE, SVMLREAD, SVM_LEARN
%

%
% Copyright (c) by Anton Schwaighofer (2001)
% $Revision: 1.6 $ $Date: 2002/08/09 20:24:12 $
% mailto:anton.schwaighofer@gmx.net
%
% This program is released unter the GNU General Public License.
%

error(nargchk(4, 4, nargin));

Names = fieldnames(options);
[m,n] = size(Names);

s = '';
for i = 1:m,
field = Names{i,:};
value = getfield(options, field);
switch field,
case 'Verbosity'
s = stroption(s, '-v %i', value);
end
end

evalstr = [fullfile(options.ExecPath, 'svm_classify') s ' ' ...
data ' ' model ' ' predictions];
fprintf('\nCalling SVMlight:\n%s\n\n', evalstr);
if isunix,
status = unix(evalstr);
else
status = dos(evalstr);
end


function s = stroption(s, formatstr, value, varargin)
% STROPTION - Add a new option to string
%

if ~isempty(value),
s = [s ' ' sprintf(formatstr, value, varargin{:})];
end
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