Matlab实现CNN-LSSVM卷积神经网络结合最小二乘支持向量机多变量时间序列预测（完整源码和数据)

function [model,cost,O3] = tunelssvm(model, varargin) % Tune the hyperparameters of the model with respect to the given performance measure % % 1. Using the functional interface: % % % >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess}) % >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess}, StartingValues) % >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess},... % StartingValues, optfun, optargs) % >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess},... % StartingValues, optfun, optargs, costfun, costargs) % % Outputs % gam : Optimal regularization parameter % sig2 : Optimal kernel parameter(s) % cost(*) : Estimated cost of the optimal hyperparameters % Inputs % X : N x d matrix with the inputs of the training data % Y : N x 1 vector with the outputs of the training data % type : 'function estimation' ('f') or 'classifier' ('c') % igam : Starting value of the regularization parameter % isig2 : Starting value of the kernel parameter(s) (bandwidth in the case of the 'RBF_kernel') % kernel(*) : Kernel type (by default 'RBF_kernel') % preprocess(*) : 'preprocess'(*) or 'original' % StartingValues(*) : Starting values of the optimization routine (or '[]') % optfun(*) : Optimization function (by default 'gridsearch') % optargs(*) : Cell with extra optimization function arguments % costfun(*) : Function estimating the cost-criterion (by default 'crossvalidate') % costargs(*) : Cell with extra cost function arguments % % 2. Using the object oriented interface: % % >> [model, cost] = tunelssvm(model) % >> [model, cost] = tunelssvm(model, StartingValues) % >> [model, cost] = tunelssvm(model, StartingValues, optfun, optargs) % >> [model, cost] = tunelssvm(model, StartingValues, optfun, optargs, costfun, costargs) % % Outputs % model : Object oriented representation of the LS-SVM model with optimal hyperparameters % cost(*) : Estimated cost of the optimal hyperparameters % Inputs % model : Object oriented representation of the LS-SVM model with initial hyperparameters % StartingValues(*): Starting values of the optimization routine (or '[]') % optfun(*) : Optimization function (by default 'gridsearch') % optfun(*) : Cell with extra optimization function arguments % costfun(*) : Function estimating the cost-criterion (by default 'crossvalidate') % optfun(*) : Cell with extra cost function arguments % % See also: % trainlssvm, crossvalidate, validate, gridsearch, linesearch % Copyright (c) 2009, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab % % initiate variables % if iscell(model), model = initlssvm(model{:}); func=1; else func=0; end % % defaults % if length(varargin)>=1, optfun = varargin{1}; else optfun='gridsearch';end if length(varargin)>=2, costfun = varargin{2}; else costfun ='crossvalidatelssvm'; end if length(varargin)>=3, costargs = varargin{3}; else costargs ={}; end if strcmp(costfun,'crossvalidatelssvm') || strcmp(costfun,'rcrossvalidatelssvm') || strcmp(costfun,'crossvalidatesparselssvm') if size(costargs,2)==1, error('Specify the number of folds for CV'); end [Y,omega] = helpkernel(model.xtrain,model.ytrain,model.kernel_type,costargs{2},0); costargs = {Y,costargs{1},omega,costargs{2}}; end if strcmp(costfun,'crossvalidate2lp1') fprintf('\n') disp('-->> Cross-Validation for Correlated Errors: Determine optimal ''l'' for leave (2l+1) out CV') % if user specifies 'l' if numel(costargs)==1, luser = NaN; else luser = costargs{2};end [l,index] = cvl(model.xtrain,model.ytrain,luser); % First determine the 'l' for the CV fprintf(['\n -->> Optimal l = ' num2str(l)]); fprintf('\n') [Y,omega] = helpkernel(model.xtrain,model.ytrain,model.kernel_type,[],1); costargs = {Y,index,omega,costargs{1}}; end if strcmp(costfun,'gcrossvalidatelssvm') || strcmp(costfun,'leaveoneoutlssvm') [Y,omega] = helpkernel(model.xtrain,model.ytrain,model.kernel_type,[],0); costargs = {Y,omega,costargs{1}}; end if strcmp(costfun,'rcrossvalidatelssvm') eval('model.weights = varargin{4};','model.weights = ''wmyriad''; ') end if strcmp(costfun,'crossvalidatelssvm_SIM') [Y,omega] = helpkernel(model.xtrain,model.ytrain,model.kernel_type,[],1); costargs = {model.xtrain,Y,costargs{1},omega,costargs{2}}; end % change the coding type for multiclass and set default 'OneVsOne' if no % coding type specified %if length(varargin)>=5 && ~isempty(varargin{5}) if model.type(1) =='c' && ~(sum(unique(model.ytrain))==1 || sum(unique(model.ytrain))==0) eval('coding = varargin{4};','coding = ''code_OneVsOne''; ') varargin{5}= coding; model = changelssvm(model,'codetype',coding); [yc,cb,oldcb] = code(model.ytrain,coding); y_dimold = model.y_dim; model.ytrain = yc; model.y_dim = size(yc,2); varargin{end} = []; clear yc end % % multiple outputs if (model.y_dim>1)% & (size(model.kernel_pars,1)==model.y_dim |size(model.gam,1)==model.y_dim |prod(size(model.kernel_type,1))==model.y_dim)) disp('-->> tune individual outputs'); if model.type(1) == 'c' fprintf('\n') disp(['-->> Encoding scheme: ',coding]); end costs = zeros(model.y_dim,1); gamt = zeros(1,model.y_dim); for d=1:model.y_dim, sel = ~isnan(model.ytrain(:,d)); % fprintf(['\n\n -> dim ' num2str(d) '/' num2str(model.y_dim) ':\n']); try kernel = model.kernel_type{d}; catch, kernel=model.kernel_type;end [g,s,c] = tunelssvm({model.xtrain(sel,:),model.ytrain(sel,d),model.type,[],[],kernel,'original'},varargin{:}); gamt(:,d) = g; try kernel_part(:,d) = s; catch, kernel_part = [];end costs(d) = c; end model.gam = gamt; model.kernel_pars = kernel_part; if func, O3 = costs; cost = model.kernel_pars; model = model.gam; end % decode to the original model.yfull if model.code(1) == 'c', % changed model.ytrain = code(model.ytrain, oldcb, [], cb, 'codedist_hamming'); model.y_dim = y_dimold; end return end %-------------------------------------------------------------------------% if strcmp(model.kernel_type,'lin_kernel'), if ~strcmp(model.weights,'wmyriad') && ~strcmp(model.weights,'whuber') [par,fval] = csa(rand(1,5),@(x)simanncostfun1(x,model,costfun,costargs)); else [par,fval] = csa(rand(2,5),@(x)simanncostfun1(x,model,costfun,costargs)); model.delta = exp(par(2)); end model = changelssvm(changelssvm(model,'gam',exp(par(1))),'kernel_pars',[]); clear par fprintf('\n') disp([' 1. Coupled Simulated Annealing results: [gam] ' num2str(model.gam)]); disp([' F(X)= ' num2str(fval)]); disp(' ') elseif strcmp(model.kernel_type,'RBF_kernel') || strcmp(model.kernel_type,'sinc_kernel') || strcmp(model.kernel_type,'RBF4_kernel') if ~strcmp(model.weights,'wmyriad') && ~strcmp(model.weights,'whuber') [par,fval] = csa(rand(2,5),@(x)simanncostfun2(x,model,costfun,costargs)); else [par,fval] = csa(rand(3,5),@(x)simanncostfun2(x,model,costfun,costargs)); model.delta = exp(par(3)); end model = changelssvm(changelssvm(model,'gam',exp(par(1))),'kernel_pars',exp(par(2))); % fprintf('\n') % disp([' 1. Coupled Simulated Annealing results: [gam] ' num2str(model.gam)]); % disp(['