www.gusucode.com > stats 源码程序 matlab案例代码 > stats/SpecifyCustomRegressionLoss2Example.m
%% Specify Custom Regression Loss %% % Simulate 10000 observations from this model % % $$y = x_{100} + 2x_{200} + e.$$ % % % * $X = {x_1,...,x_{1000}}$ is a 10000-by-1000 sparse matrix with 10% % nonzero standard normal elements. % * _e_ is random normal error with mean 0 and standard deviation % 0.3. % rng(1) % For reproducibility n = 1e4; d = 1e3; nz = 0.1; X = sprandn(n,d,nz); Y = X(:,100) + 2*X(:,200) + 0.3*randn(n,1); X = X'; % Put observations in columns for faster training %% % Train a linear regression model. Reserve 30% of the observations as a % holdout sample. CVMdl = fitrlinear(X,Y,'Holdout',0.3,'ObservationsIn','columns'); Mdl = CVMdl.Trained{1} %% % |CVMdl| is a |RegressionPartitionedLinear| model. It contains % the property |Trained|, which is a 1-by-1 cell array holding a % |RegressionLinear| model that the software trained using the % training set. %% % Extract the training and test data from the partition definition. trainIdx = training(CVMdl.Partition); testIdx = test(CVMdl.Partition); %% % Create an anonymous function that measures Huber loss ($\delta$ = 1), % that is, % % $$\begin{array}{*{20}{c}} % {L = \frac{1}{{\sum {{w_j}} }}\sum\limits_{j = 1}^n {{w_j}{\ell _j}} ,\;\;{\rm{where}}}\\ % {{\ell _j} = \left\{ {\begin{array}{*{20}{c}} % {0.5{{\hat e}^2}}\\ % {\left| {\hat e} \right| - 0.5\;\;} % \end{array}\begin{array}{*{20}{c}} % {{\rm{for}}\;\;{\left| {\hat e} \right|} \le 1}\\ % {{\rm{otherwise}}} % \end{array}} \right..} % \end{array}$$ % % $\hat e_j$ is the residual for observation _j_. Custom loss functions must % be written in a particular form. For rules on writing a custom loss % function, see the |LossFun| name-value pair argument. huberloss = @(Y,Yhat,W)sum(W.*((0.5*(abs(Y-Yhat)<=1).*(Y-Yhat).^2) + ... ((abs(Y-Yhat)>1).*abs(Y-Yhat)-0.5)))/sum(W); %% % Estimate the training- and test-sample regression loss using the % Huber loss function. eTrain = loss(Mdl,X(:,trainIdx),Y(trainIdx),'LossFun',huberloss,... 'ObservationsIn','columns') eTest = loss(Mdl,X(:,testIdx),Y(testIdx),'LossFun',huberloss,... 'ObservationsIn','columns')