【SVM预测】基于布谷鸟搜索算法优化SVM预测附matlab代码.rar

function [svm_struct, svIndex] = svmtrain(training, groupnames, varargin) %SVMTRAIN Train a support vector machine classifier % SVMSTRUCT = SVMTRAIN(TRAINING, Y) trains a support vector machine (SVM) % classifier on data taken from two groups. TRAINING is a numeric matrix % of predictor data. Rows of TRAINING correspond to observations; columns % correspond to features. Y is a column vector that contains the known % class labels for TRAINING. Y is a grouping variable, i.e., it can be a % categorical, numeric, or logical vector; a cell vector of strings; or a % character matrix with each row representing a class label (see help for % groupingvariable). Each element of Y specifies the group the % corresponding row of TRAINING belongs to. TRAINING and Y must have the % same number of rows. SVMSTRUCT contains information about the trained % classifier, including the support vectors, that is used by SVMCLASSIFY % for classification. SVMTRAIN treats NaNs, empty strings or 'undefined' % values as missing values and ignores the corresponding rows in % TRAINING and Y. % % SVMSTRUCT = SVMTRAIN(TRAINING, Y, 'PARAM1',val1, 'PARAM2',val2, ...) % specifies one or more of the following name/value pairs: % % Name Value % 'kernel_function' A string or a function handle specifying the % kernel function used to represent the dot % product in a new space. The value can be one of % the following: % 'linear' - Linear kernel or dot product % (default). In this case, SVMTRAIN % finds the optimal separating plane % in the original space. % 'quadratic' - Quadratic kernel % 'polynomial' - Polynomial kernel with default % order 3. To specify another order, % use the 'polyorder' argument. % 'rbf' - Gaussian Radial Basis Function % with default scaling factor 1. To % specify another scaling factor, % use the 'rbf_sigma' argument. % 'mlp' - Multilayer Perceptron kernel (MLP) % with default weight 1 and default % bias -1. To specify another weight % or bias, use the 'mlp_params' % argument. % function - A kernel function specified using % @(for example @KFUN), or an % anonymous function. 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 M and N are % the number of rows in U and V % respectively. % % 'rbf_sigma' A positive number specifying the scaling factor % in the Gaussian radial basis function kernel. % Default is 1. % % 'polyorder' A positive integer specifying the order of the % polynomial kernel. Default is 3. % % 'mlp_params' A vector [P1 P2] specifying the parameters of MLP % kernel. The MLP kernel takes the form: % K = tanh(P1*U*V' + P2), % where P1 > 0 and P2 < 0. Default is [1,-1]. % % 'method' A string specifying the method used to find the % separating hyperplane. Choices are: % 'SMO' - Sequential Minimal Optimization (SMO) % method (default). It implements the L1 % soft-margin SVM classifier. % 'QP' - Quadratic programming (requires an % Optimization Toolbox license). It % implements the L2 soft-margin SVM % classifier. Method 'QP' doesn't scale % well for TRAINING with large number of % observations. % 'LS' - Least-squares method. It implements the % L2 soft-margin SVM classifier. % % 'options' Options structure created using either STATSET or % OPTIMSET. % * When you set 'method' to 'SMO' (default), % create the options structure using STATSET. % Applicable options: % 'Display' Level of display output. Choices % are 'off' (the default), 'iter', and % 'final'. Value 'iter' reports every % 500 iterations. % 'MaxIter' A positive integer specifying the % maximum number of iterations allowed. % Default is 15000 for method 'SMO'. % * When you set method to 'QP', create the % options structure using OPTIMSET. For details % of applicable options choices, see QUADPROG % options. SVM uses a convex quadratic program, % so you can choose the 'interior-point-convex' % algorithm in QUADPROG. % % 'tolkkt' A positive scalar that specifies the tolerance % with which the Karush-Kuhn-Tucker (KKT) conditions % are checked for method 'SMO'. Default is % 1.0000e-003. % % 'kktviolationlevel' A scalar specifying the fraction of observations % that are allowed to violate the KKT conditions for % method 'SMO'. Setting this value to be positive % helps the algorithm to converge faster if it is % fluctuating near a good solution. Default is 0. % % 'kernelcachelimit' A positive scalar S specifying the size of the % kernel matrix cache for method 'SMO'. The % algorithm keeps a matrix with up to S * S % double-precision numbers in memory. Default is % 5000. When the number of points in TRAINING % exceeds S, the SMO method slows down. It's % recommended to set S as large as your system % permits. % % 'boxconstraint' The box constraint C for the soft margin. C can be % a positive numeric scalar or a vector of positive % numbers with the number of elements equal to the % number of rows in TRAINING. % Default is 1. % * If C is a scalar, it is automatically rescaled % by N/(2*N1) for the observations of group one, % and by N/(2*N2) for the observations of group % two, where N1 is the number of observations in % group one, N2 is the number of observations in % group two. The rescaling is done to take into % account unbalanced groups, i