Matlab实现LSSVM-ABKDE基于最小二乘支持向量机结合自适应带宽核函数密度估计的多变量回归区间预测（完整源码和数据）

%% 闲鱼号/面包多：深度学习与智能算法 function [model, b, X, Y] = trainlssvm(model, X, Y) %% 训练模型 % >> model = trainlssvm(model) % type can be 'classifier' or 'function estimation' (these strings % can be abbreviated into 'c' or 'f', respectively). X and Y are % matrices holding the training input and output data. The i-th % data point is represented by the i-th row X(i,:) and Y(i,:). gam % is the regularization parameter: for gam low minimizing of the % complexity of the model is emphasized, for gam high, good fitting % of the training data points is stressed. kernel_par is the % parameter of the kernel; in the common case of an RBF kernel, a % large sig2 indicates a stronger smoothing. The kernel_type % indicates the function that is called to compute the kernel value % (by default RBF_kernel). Other kernels can be used for example: % % The kernel parameter(s) are passed as a row vector, in the case % no kernel parameter is needed, pass the empty vector! % % The training can either be proceeded by the preprocessing % function ('preprocess') (by default) or not ('original'). The % training calls the preprocessing (prelssvm, postlssvm) and the % encoder (codelssvm) if appropiate. % % In the remainder of the text, the content of the cell determining % the LS-SVM is given by {X,Y, type, gam, sig2}. However, the % additional arguments in this cell can always be added in the % calls. % % If one uses the object oriented interface (see also A.3.14), the training is done by % % >> model = trainlssvm(model) % >> model = trainlssvm(model, X, Y) % % The status of the model checks whether a retraining is % needed. The extra arguments X, Y allow to re-initialize the model % with this new training data as long as its dimensions are the % same as the old initiation. % % The training implementation: % % * The Matlab implementation: a straightforward implementation % based on the matrix division '\' (lssvmMATLAB.m). % % % This implementation allows to train a multidimensional output % problem. If each output uses the same kernel type, kernel % parameters and regularization parameter, this is % straightforward. If not so, one can specify the different types % and/or parameters as a row vector in the appropriate % argument. Each dimension will be trained with the corresponding % column in this vector. % model = trainlssvm(model) % model : Trained object oriented representation of the LS-SVM model % model : Object oriented representation of the LS-SVM model % 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') % gam(*) : Regularization parameter % sig2(*) : Kernel parameter (bandwidth in the case of the 'RBF_kernel') % kernel(*) : Kernel type (by default 'RBF_kernel') % preprocess(*) : 'preprocess'(*) or 'original' %% 初始化模型 if iscell(model) model = initlssvm(model{:}); end %% 判断模型训练状态 if model.status(1) == 't' if (nargout > 1) X = model.xtrain; Y = model.ytrain; b = model.b; model = model.alpha; end return end %% 控制输入 if ~((strcmp(model.kernel_type, 'RBF_kernel') && length(model.kernel_pars) >= 1) ||... (strcmp(model.kernel_type, 'lin_kernel') && length(model.kernel_pars) >= 0) ||... (strcmp(model.kernel_type, 'MLP_kernel') && length(model.kernel_pars) >= 2) ||... (strcmp(model.kernel_type, 'poly_kernel') && length(model.kernel_pars) >= 1)) elseif (model.steps <= 0) error('steps must be larger then 0'); elseif (model.gam <= 0) error('gamma must be larger then 0'); elseif or(model.x_dim <= 0, model.y_dim <= 0) error('dimension of datapoints must be larger than 0'); end %% 编码（分类才需要） if model.code(1) == 'c' model = codelssvm(model); end %% 赋值 try if model.prestatus(1)=='c' changed = 1; else changed = 0; end catch changed = 0; end if model.preprocess(1) == 'p' && changed model = prelssvm(model); elseif model.preprocess(1) == 'o' && changed model = postlssvm(model); end %% 计时开始 tic; %% 执行方式 if size(model.gam, 1) > 1 model.implementation = 'MATLAB'; end %% 递归调用输出维度预测 if model.y_dim > 1 if (length(model.kernel_pars) == model.y_dim || size(model.gam, ... 2) == model.y_dim || numel(model.kernel_type, 2) == model.y_dim) model = trainmultidimoutput(model); if (nargout > 1) X = model.xtrain; Y = model.ytrain; b = model.b; model = model.alpha; else model.duration = toc; model.status = 'trained'; end return end end %% model = lssvmMATLAB(model); if (nargout > 1) X = model.xtrain; Y = model.ytrain; b = model.b; model = model.alpha; else model.duration = toc; model.status = 'trained'; end %% 训练多输出模型 function model = trainmultidimoutput(model) %% 初始化权重 model.alpha = zeros(model.nb_data, model.y_dim); model.b = zeros(1, model.y_dim); for d = 1 : model.y_dim try gam = model.gam(:, d); catch gam = model.gam(:); end try sig2 = model.kernel_pars(:, d); catch sig2 = model.kernel_pars(:); end try kernel = model.kernel_type{d}; catch kernel=model.kernel_type; end [model.alpha(:, d), model.b(d)] = trainlssvm({model.xtrain, model.ytrain(:, d), ... model.type, gam, sig2, kernel, 'original'}); end %% 输出 if (nargout > 1) model = model.alpha; else model.duration = toc; model.status = 'trained'; end