source: prextra/lassoc.m @ 129

Last change on this file since 129 was 114, checked in by dtax, 8 years ago

STUPID BUG!!!!!

File size: 1.6 KB
Line 
1%LASSOC
2%
3%      W = LASSOC(X, LAMBDA)
4%
5% Train the LASSO classifier on dataset X. LAMBDA is the regularization
6% parameter.
7
8% w = lassoc(x, lambda)
9function w = lassoc(varargin)
10
11argin = shiftargin(varargin,'scalar');
12argin = setdefaults(argin,[],1);
13
14if mapping_task(argin,'definition')
15
16   w = define_mapping(argin,'untrained','LASSO classifier');
17
18elseif mapping_task(argin,'training')
19
20   [x,lambda] = deal(argin{:});
21    % Unpack the dataset.
22    islabtype(x,'crisp');
23    %isvaldset(x,1,2); % at least 1 object per class, 2 classes
24    [n,k,c] = getsize(x);
25
26    % make sure a bias is added:
27    x = [x ones(n,1)];
28
29    if c ~= 2  % two-class classifier:
30        error('Only a two-class classifier is implemented');
31    end
32
33    if exist('lasso')==3 % we have a own compiled mex code
34       beta=-lasso(+x,3-2*getnlab(x),lambda);
35    else % hope that we have a modern Matlab with stats. toolbox:
36       if ~exist('lasso')
37          error('Cannot find the function lasso.m.');
38       end
39       beta=-lasso(+x,3-2*getnlab(x),'Lambda',lambda);
40    end
41
42    % now find out how sparse the result is:
43    nr = sum(abs(beta)>1.0e-8);
44
45    % and store the results:
46    W.beta = beta; % the ultimate weights
47    W.nr = nr;
48    w = prmapping(mfilename,'trained',W,getlablist(x),k,c);
49    w = setname(w,'LASSO classifier (l=%f)',lambda);
50
51else
52    [x,lambda] = deal(argin{1:2});
53    % Evaluate the classifier on new data:
54    W = getdata(lambda);
55    n = size(x,1);
56
57    % make sure a bias is added:
58    x = [x ones(n,1)];
59    %go:
60    out = x*W.beta;
61
62    % and put it nicely in a prtools dataset:
63    w = setdat(x,sigm([-out out]),lambda);
64
65end
66
67return
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