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methods
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function defaults = preprocessing_defaults(self )
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- defaults = {' IIRFilter ' ,{[0.1 0.5 ],' highpass' },' EpochExtraction' ,[-1.5 1.5 ],' Resampling' ,60 ,' SpectralSelection' ,[0.1 15 ]};
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+ defaults = {' FIRFilter ' ,{[0.1 0.5 ],' highpass' },' EpochExtraction' ,[-1.5 1.5 ],' Resampling' ,60 ,' SpectralSelection' ,[0.1 15 ]};
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end
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function defaults = machine_learning_defaults(self )
@@ -160,7 +160,7 @@ function visualize_model(self,parent,fmodel,pmodel,varargin) %#ok<*INUSD>
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t = title(' Regularization curve' );
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p1 = plot(mean(pmodel .model .losses )); p2= [];
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l1 = xlabel(' Regularization parameter #' );
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- l2 = ylabel(' Prediction loss (MCR) ' );
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+ l2 = ylabel(' Prediction loss' );
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end
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if args .paper
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set([p1 ,p2 ],' LineWidth' ,3 );
@@ -173,8 +173,8 @@ function visualize_model(self,parent,fmodel,pmodel,varargin) %#ok<*INUSD>
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end
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function layout = dialog_layout_defaults(self )
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- layout = {' SignalProcessing.Resampling.SamplingRate' , ' SignalProcessing.IIRFilter .Frequencies' , ...
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- ' SignalProcessing.IIRFilter.Type ' , ' SignalProcessing. EpochExtraction' , ...
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+ layout = {' SignalProcessing.Resampling.SamplingRate' , ' SignalProcessing.FIRFilter .Frequencies' ,...
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+ ' SignalProcessing.EpochExtraction' , ...
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' SignalProcessing.SpectralSelection.FrequencySpecification' , ' ' , ...
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' Prediction.MachineLearning.Learner.Lambdas' ,' Prediction.MachineLearning.Learner.LossFunction' ,...
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' Prediction.MachineLearning.Learner.Regularizer' };
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