Products), (preconditioned) conjugate gradient (uses only previous step and a vector beta),īarzilai and Borwein (uses only previous step), or (cyclic) steepest descent. (preconditioned) Hessian-free Newton (uses Hessian-vector Limited-memory BFGS (uses a low-rank Hessian approximation - default), User-supplied Hessian), full quasi-Newton approximation (uses a dense Hessian approximation), Step directions can be computed based on: Exact Newton (requires.Of the non-default features present in minFunc: Parameters do not produce a real valued output (i.e. Interpolation is used to generate trial values, and the method switches to anĪrmijo back-tracking line search on iterations where the objective function Satisfying the strong Wolfe conditions is used to compute the step direction. Restricted to several thousand variables), and usesĪ line search that is robust to several common function pathologies.Ĭall a quasi-Newton strategy, where limited-memory BFGS updates with Shanno-Phua scaling are used inĬomputing the step direction, and a bracketing line-search for a point On many problems, minFunc requires fewer function evaluations to converge thanĬan optimize problems with a much larger number of variables ( fminunc is Interface very similar to the Matlab Optimization Toolbox function fminunc,Īnd can be called as a replacement for this function. Real-valued multivariate functions using line-search methods. MinFunc is a Matlab function for unconstrained optimization of differentiable See the lregdademo.m function for command line usage example.MinFunc - unconstrained differentiable multivariate optimization in Matlab cvi(i) = -1 the sample is always in the calibration set, cvi(i) = 0 the sample is always never used, and cvi(i) = 1,2,3. Each cvi(i) is defined as:Ĭvi(i) = -2 the sample is always in the test set. (cvi) is a vector with the same number of elements as x has rows i.e., length(cvi) = size(x,1). cvi : M element vector with integer elements allowing user defined subsets.display : CV method, OR for Kennard-Stone single split.Options = a structure array with the following fields: From the Analysis window specify the cross-validation method in the usual way (clicking on the model icon's red check-mark, or the "Choose Cross-Validation" link in the flowchart). L2 regularization ('ridge'), L1 regularization ('lasso'), or equally weighted L1 and L2 regularization ('elasticnet').Ĭross-validation can be applied to LREGDA when using either the LREGDA Analysis window or the command line. The 'algorithm' option allows selection of Logistic Regression wit no regularization ('none'), pred a structure, similar to model for the new data.: Structure containing 'lreg' matrix of model coefficients.detail: sub-structure with additional model details and results, including:.model predictions for each input block (when options.blockdetail='normal' x-block predictions are not saved and this will be an empty array).datasource: structure array with information about input data,.model = a standard model structure model with the following fields (see Standard Model Structure):.model = previously generated model (when applying model to new data).y = Y-block (optional) class "double" sample class values,.x = X-block (predictor block) class "double" or "dataset", containing numeric values,.minFunc: unconstrained differentiable multivariate optimization in Matlab. LREGDA solves for the logistic regression model parameters using the minFunc software: To LREGDA then these weights do not need to be re-calculated. The LREGDA modelĬontains quantities (hypothesis coefficients) calculated from the calibration data. Please see this wiki page on building and applying models using the Model Object.īuild an LREGDA model from input dataset X, or input X and Y if classes are in Y, using the specified algorithm and regularization parameter.Īlternatively, if a model is input then LREGDA makes a prediction for an input test X block. Please note that the recommended way to build and apply an LREGDA model from the command line is to use the Model Object. = lregda(x,options) = lregda(x,y,options) = lregda(x,model,options) = lregda(x,y,model,options) = lregda('options') Synopsis lregda - Launches an Analysis window with LREGDA as the selected method. LREGDA Logistic Regression for classification. Predictions based on Logistic Regression (LREGDA) classification models.
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