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支持向量机matlab实现源代码

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edit svmtrain

>>edit svmclassify >>edit svmpredict

function [svm_struct, svIndex] =

svmtrain(training, groupnames, varargin) %SVMTRAIN trains a support vector machine classifier %

% SVMStruct = SVMTRAIN(TRAINING,GROUP) trains a support vector machine

% classifier using data TRAINING taken from two groups given by GROUP.

% SVMStruct contains information about the trained classifier that is

% used by SVMCLASSIFY for classification. GROUP is a column vector of

% values of the same length as TRAINING that defines two groups. Each

% element of GROUP specifies the group the corresponding row of TRAINING

% belongs to. GROUP can be a numeric vector, a string array, or a cell

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% array of strings. SVMTRAIN treats NaNs or empty strings in GROUP as

% missing values and ignores the corresponding rows of TRAINING. %

% SVMTRAIN(...,'KERNEL_FUNCTION',KFUN) allows you to specify the kernel

% function KFUN used to map the training data into kernel space. The

% default kernel function is the dot product. KFUN can be one of the

% following strings or a function handle: %

% 'linear' Linear kernel or dot product % 'quadratic' Quadratic kernel

% 'polynomial' Polynomial kernel (default order 3)

% 'rbf' Gaussian Radial Basis Function kernel % 'mlp' Multilayer Perceptron kernel (default scale 1)

% function A kernel function specified using @, % for example @KFUN, or an anonymous function

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% A kernel function must be of the form %

% function K = KFUN(U, V) %

% The returned value, K, is a matrix of size M-by-N, where U and V have M

% and N rows respectively. If KFUN is parameterized, you can use

% anonymous functions to capture the problem-dependent parameters. For

% example, suppose that your kernel function is %

% function k = kfun(u,v,p1,p2) % k = tanh(p1*(u*v')+p2); %

% You can set values for p1 and p2 and then use an anonymous function: % @(u,v) kfun(u,v,p1,p2). %

% SVMTRAIN(...,'POLYORDER',ORDER) allows you to specify the order of a

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% polynomial kernel. The default order is 3. %

% SVMTRAIN(...,'MLP_PARAMS',[P1 P2]) allows you to specify the

% parameters of the Multilayer Perceptron (mlp) kernel. The mlp kernel

% requires two parameters, P1 and P2, where K = tanh(P1*U*V' + P2) and P1

% > 0 and P2 < 0. Default values are P1 = 1 and P2 = -1. %

% SVMTRAIN(...,'METHOD',METHOD) allows you to specify the method used

% to find the separating hyperplane. Options are %

% 'QP' Use quadratic programming (requires the Optimization Toolbox)

% 'LS' Use least-squares method %

% If you have the Optimization Toolbox, then the QP method is the default

% method. If not, the only available method is LS.

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% SVMTRAIN(...,'QUADPROG_OPTS',OPTIONS) allows you to pass an OPTIONS

% structure created using OPTIMSET to the QUADPROG function when using

% the 'QP' method. See help optimset for more details. %

% SVMTRAIN(...,'SHOWPLOT',true), when used with two-dimensional data,

% creates a plot of the grouped data and plots the separating line for % the classifier. %

% Example:

% % Load the data and select features for classification % load fisheriris

% data = [meas(:,1), meas(:,2)]; % % Extract the Setosa class

% groups = ismember(species,'setosa');

% % Randomly select training and test sets

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% [train, test] = crossvalind('holdOut',groups); % cp = classperf(groups);

% % Use a linear support vector machine classifier % svmStruct =

svmtrain(data(train,:),groups(train),'showplot',true); % classes =

svmclassify(svmStruct,data(test,:),'showplot',true);

% % See how well the classifier performed % classperf(cp,classes,test); % cp.CorrectRate %

% See also CLASSIFY, KNNCLASSIFY, QUADPROG, SVMCLASSIFY.

% $Revision: 1.1.12.1 $ $Date: 2004/12/24 20:43:35 $ % References:

% [1] Kecman, V, Learning and Soft Computing, % MIT Press, Cambridge, MA. 2001.

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% [2] Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B.,

% Vandewalle, J., Least Squares Support Vector Machines,

% World Scientific, Singapore, 2002.

% [3] Scholkopf, B., Smola, A.J., Learning with Kernels,

% MIT Press, Cambridge, MA. 2002. %

% SVMTRAIN(...,'KFUNARGS',ARGS) allows you to pass additional

% arguments to kernel functions. % set defaults plotflag = false; qp_opts = []; kfunargs = {};

setPoly = false; usePoly = false; setMLP = false; useMLP = false; if ~isempty(which('quadprog')) useQuadprog = true; else

useQuadprog = false;

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end

% set default kernel function kfun = @linear_kernel; % check inputs if nargin < 2

error(nargchk(2,Inf,nargin)) end

numoptargs = nargin -2; optargs = varargin;

% grp2idx sorts a numeric grouping var ascending, and a string grouping

% var by order of first occurrence

[g,groupString] = grp2idx(groupnames);

% check group is a vector -- though char input is special...

if ~isvector(groupnames) && ~ischar(groupnames) error('Bioinfo:svmtrain:GroupNotVector',... 'Group must be a vector.'); end

% make sure that the data is correctly oriented. if size(groupnames,1) == 1 groupnames = groupnames';

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end

% make sure data is the right size n = length(groupnames); if size(training,1) ~= n if size(training,2) == n training = training'; else

error('Bioinfo:svmtrain:DataGroupSizeMismatch',...

'GROUP and TRAINING must have the same number of rows.') end end

% NaNs are treated as unknown classes and are removed from the training % data

nans = find(isnan(g)); if length(nans) > 0 training(nans,:) = []; g(nans) = []; end

ngroups = length(groupString);

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if ngroups > 2

error('Bioinfo:svmtrain:TooManyGroups',...

'SVMTRAIN only supports classification into two groups.\\nGROUP contains %d different groups.',ngroups) end

% convert to 1, -1. g = 1 - (2* (g-1));

% handle optional arguments if numoptargs >= 1

if rem(numoptargs,2)== 1

error('Bioinfo:svmtrain:IncorrectNumberOfArguments',...

'Incorrect number of arguments to %s.',mfilename); end

okargs =

{'kernel_function','method','showplot','kfunargs','quadprog_opts','polyorder','mlp_params'}; for j=1:2:numoptargs pname = optargs{j}; pval = optargs{j+1};

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k = strmatch(lower(pname), okargs);%#ok if isempty(k)

error('Bioinfo:svmtrain:UnknownParameterName',...

'Unknown parameter name: %s.',pname); elseif length(k)>1

error('Bioinfo:svmtrain:AmbiguousParameterName',...

'Ambiguous parameter name: %s.',pname); else

switch(k)

case 1 % kernel_function if ischar(pval)

okfuns = {'linear','quadratic',... 'radial','rbf','polynomial','mlp'};

funNum = strmatch(lower(pval), okfuns);%#ok if isempty(funNum) funNum = 0; end

switch funNum %maybe make this less strict in the future case 1

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kfun = @linear_kernel; case 2

kfun = @quadratic_kernel; case {3,4}

kfun = @rbf_kernel; case 5

kfun = @poly_kernel; usePoly = true; case 6

kfun = @mlp_kernel; useMLP = true; otherwise

error('Bioinfo:svmtrain:UnknownKernelFunction',...

'Unknown Kernel Function %s.',kfun); end

elseif isa (pval, 'function_handle') kfun = pval; else

error('Bioinfo:svmtrain:BadKernelFunction',... 'The kernel function input does not appear to be a function handle\\nor valid function name.')

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end

case 2 % method

if strncmpi(pval,'qp',2) useQuadprog = true;

if isempty(which('quadprog'))

warning('Bioinfo:svmtrain:NoOptim',...

'The Optimization Toolbox is required to use the quadratic programming method.') useQuadprog = false; end

elseif strncmpi(pval,'ls',2) useQuadprog = false; else

error('Bioinfo:svmtrain:UnknownMethod',... 'Unknown method option %s. Valid methods are ''QP'' and ''LS''',pval); end

case 3 % display if pval ~= 0

if size(training,2) == 2 plotflag = true; else

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warning('Bioinfo:svmtrain:OnlyPlot2D',...

'The display option can only plot 2D training data.') end end

case 4 % kfunargs if iscell(pval) kfunargs = pval; else

kfunargs = {pval}; end

case 5 % quadprog_opts if isstruct(pval) qp_opts = pval;

elseif iscell(pval)

qp_opts = optimset(pval{:}); else

error('Bioinfo:svmtrain:BadQuadprogOpts',... 'QUADPROG_OPTS must be an opts structure.'); end

case 6 % polyorder

if ~isscalar(pval) || ~isnumeric(pval)

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error('Bioinfo:svmtrain:BadPolyOrder',... 'POLYORDER must be a scalar value.'); end

if pval ~=floor(pval) || pval < 1

error('Bioinfo:svmtrain:PolyOrderNotInt',... 'The order of the polynomial kernel must be a positive integer.') end

kfunargs = {pval}; setPoly = true; case 7 % mlpparams if numel(pval)~=2

error('Bioinfo:svmtrain:BadMLPParams',... 'MLP_PARAMS must be a two element array.'); end

if ~isscalar(pval(1)) || ~isscalar(pval(2)) error('Bioinfo:svmtrain:MLPParamsNotScalar',...

'The parameters of the multi-layer perceptron kernel must be scalar.'); end

kfunargs = {pval(1),pval(2)};

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setMLP = true; end end end end

if setPoly && ~usePoly

warning('Bioinfo:svmtrain:PolyOrderNotPolyKernel',...

'You specified a polynomial order but not a polynomial kernel'); end

if setMLP && ~useMLP

warning('Bioinfo:svmtrain:MLPParamNotMLPKernel',...

'You specified MLP parameters but not an MLP kernel'); end

% plot the data if requested if plotflag

[hAxis,hLines] = svmplotdata(training,g); legend(hLines,cellstr(groupString)); end

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% calculate kernel function try

kx = feval(kfun,training,training,kfunargs{:}); % ensure function is symmetric kx = (kx+kx')/2; catch

error('Bioinfo:svmtrain:UnknownKernelFunction',...

'Error calculating the kernel function:\\n%s\\n', lasterr); end

% create Hessian

% add small constant eye to force stability H =((g*g').*kx) +

sqrt(eps(class(training)))*eye(n); if useQuadprog

% The large scale solver cannot handle this type of problem, so turn it % off.

qp_opts = optimset(qp_opts,'LargeScale','Off'); % X=QUADPROG(H,f,A,b,Aeq,beq,LB,UB,X0,opts) alpha = quadprog(H,-ones(n,1),[],[],...

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g',0,zeros(n,1),inf

*ones(n,1),zeros(n,1),qp_opts);

% The support vectors are the non-zeros of alpha svIndex = find(alpha > sqrt(eps)); sv = training(svIndex,:);

% calculate the parameters of the separating line from the support % vectors.

alphaHat = g(svIndex).*alpha(svIndex);

% Calculate the bias by applying the indicator function to the support

% vector with largest alpha.

[maxAlpha,maxPos] = max(alpha); %#ok bias = g(maxPos) -

sum(alphaHat.*kx(svIndex,maxPos));

% an alternative method is to average the values over all support vectors % bias = mean(g(sv)' -

sum(alphaHat(:,ones(1,numSVs)).*kx(sv,sv))); % An alternative way to calculate support vectors is to look for zeros of

% the Lagrangians (fifth output from QUADPROG).

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% [alpha,fval,output,exitflag,t] = quadprog(H,-ones(n,1),[],[],... % g',0,zeros(n,1),inf *ones(n,1),zeros(n,1),opts); %

% sv = t.lower < sqrt(eps) & t.upper < sqrt(eps); else % Least-Squares

% now build up compound matrix for solver A = [0 g';g,H];

b = [0;ones(size(g))]; x = A\\b;

% calculate the parameters of the separating line from the support % vectors.

sv = training; bias = x(1);

alphaHat = g.*x(2:end); end

svm_struct.SupportVectors = sv; svm_struct.Alpha = alphaHat; svm_struct.Bias = bias;

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svm_struct.KernelFunction = kfun;

svm_struct.KernelFunctionArgs = kfunargs; svm_struct.GroupNames = groupnames; svm_struct.FigureHandles = []; if plotflag

hSV = svmplotsvs(hAxis,svm_struct);

svm_struct.FigureHandles = {hAxis,hLines,hSV}; end

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