MATLAB: Is the variable undefined

if statementundefined variable

Could someone tell me why 'g' is undefined?

Best Answer

You only define g if age >= 8.

Perhaps you want an "end" after you assign a=0 after your age test.

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MATLAB: Fiiting curve column by column for given data matrix in MATLAB R2014a

Exponential decay function is expressed as follows:

y = F(x|u) = 1 - exp(x/u)

Assuming that this is the correct form of the function here is the function that will give you the correct fit:

function [fitresult, gof] = createFit(x, y)
%CREATEFIT(X,Y)
%  Create a fit.
%
%  Input:
%      X Input : x
%      Y Output: y
%  Output:
%      fitresult : a fit object representing the fit.
%      gof : structure with goodness-of fit info.
[xData, yData] = prepareCurveData( x, y );
% Set up fittype and options.
ft = fittype( '1-exp(x/u)', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.StartPoint = 0.795199901137063;
% Fit model to data.
[fitresult, gof] = fit( xData, yData, ft, opts );
% Plot fit with data.
%figure( 'Name', 'Cumulative exponential decay fit' );
%h = plot( fitresult, xData, yData );
%legend( h, 'y vs. x', ''Cumulative exponential decay fit', 'Location', 'NorthEast' );
% Label axes
%xlabel( 'x' );
%ylabel( 'y' );
%grid on

Once you save the above function as M file, you can use it as follows:

>> testData = rand(20,1);
     >> [rows,cols] = size(testData);
     >> x = transpose(1:rows);
     >> coefficients = cell(1,cols);
     >> for cid=1:cols
     >> [fitresults,gof] = createFit(x,testData(:,1));
     >> coefficients(cid) = {fitresults};
     >> end
     >> coefficients{1}.u % Parameters of fit for first column of testData matrix.

If the form of the function is different then you can substitute the correct one in the function at following line:

       ft = fittype( '<place_correct_form_of_the_function_here>', 'independent', 'x', 'dependent', 'y' );

Rest of the code should remain the same and you can access the parameters as illustrated in the last line of the script.

There is another way for fitting custom functions using GUI. In order to accomplish that please follow these steps:

1. Click on APPS.

2. Click on Curve Fitting.

3. Select X data variable from your workspace. This will be the x-variable for the fit.

4. Select Y data variable from your workspace. This will be the y-variable for the fit.

5. Select Custom Equation from the drop down menu above "Method".

6. Input correct form of the function in the text area.

7. Click on "Fit" if "Auto Fit" is unchecked.

8. To save the work as a function, go to "File" and click on "Generate Code". This will create a MATLAB function for you which will have code to fit the custom function using parameters you have mentioned.

9. When you "Fit" the function using GUI you can see values of the parameters and their confidence intervals in the "Results" panel.

10. If you fit the results programmatically using the MATLAB function generated, you will find the parameter values stored in "fitresult" variable.

Note that the function generated takes two argument vectors of same size namely, "x" and "y". In this case, "x" corresponds to row numbers and "y" variable contains individual columns.

MATLAB: How to choose the best kernel for a Gaussian process regression, possibly using “bayesopt” function? How to predict unseen data with the resulting model

There are two ways you could do this. One of them involves "bayesopt" function.

(1) The easiest way is to use the "fitrgp" function. There is an example of how to optimize a fitrgp hyperparameter (Sigma) on the "fitrgp" function Documentation page:

https://www.mathworks.com/help/stats/fitrgp.html?searchHighlight=fitrgp

To optimize ‘KernelFunction’, using default values for all other hyperparameters, you could do the following (using data from the documentation example):

load(fullfile(matlabroot,'examples','stats','gprdata2.mat'))
model1 = fitrgp(x,y,'OptimizeHyperparameters',{'KernelFunction'});

Because fitrgp tends to be sensitive to the Sigma parameter, you will probably get a better result if you simultaneously optimize KernelFunction and Sigma. Also, searching categorical variables inherently requires more function evaluations, so you should probably run it longer:

model2 = fitrgp(x,y,'OptimizeHyperparameters',{'KernelFunction','Sigma'},...
    'HyperparameterOptimizationOptions', struct('MaxObjectiveEvaluations',100));

The "fitrgp" function documentation page lists the hyperparameters that are eligible for optimization, and what values are searched:

https://www.mathworks.com/help/stats/fitrgp.html#input_argument_namevalue_OptimizeHyperparameters.

You can use this model to make predictions on unseen data directly with the "predict" function:

model2 = fitrgp(x,y,'OptimizeHyperparameters',{'KernelFunction','Sigma'},...
    'HyperparameterOptimizationOptions', struct('MaxObjectiveEvaluations',100));
% Predict on unseen data

xNew = 0.2
ypred = predict(model2, xNew)

(2) A second approach would be to use "bayesopt" function directly, defining your own objective function. Here, the objective function is the 5-fold crossvalidation loss of the model.

load(fullfile(matlabroot,'examples','stats','gprdata2.mat')) 
% Define kernel variable
kernel = optimizableVariable('KernelFunction',{'exponential','squaredexponential','matern32','matern52',...
'rationalquadratic','ardexponential','ardsquaredexponential','ardmatern32','ardmatern52','ardrationalquadratic'},...
'Type','categorical') 
% Call bayesopt, capturing x and y in the objective function

bo = bayesopt(@(T)objFcn(T,x,y), kernel)
% Define an objective function
function Loss = objFcn(Vars, x, y)
m = fitrgp(x, y, 'KernelFunction', char(Vars.KernelFunction), 'KFold', 5);Loss = kfoldLoss(m);
end

(2.1) To optimize both KernelFunction and Sigma using "bayesopt" function, you can do the following:

% Define 2 variables
kernel = optimizableVariable('KernelFunction',{'exponential','squaredexponential','matern32','matern52',...
'rationalquadratic','ardexponential','ardsquaredexponential','ardmatern32','ardmatern52','ardrationalquadratic'},...
'Type','categorical')
sigma = optimizableVariable('Sigma',[1e-4,10],'Transform','log')
 
% Call bayesopt, capturing x and y in the objective function
bo = bayesopt(@(T)objFcn(T,x,y), [sigma, kernel], 'MaxObjectiveEvaluations', 100)
 
function Loss = objFcn(Vars, x, y)
m = fitrgp(x, y, 'KernelFunction', char(Vars.KernelFunction), ...
                 'Sigma', Vars.Sigma, 'ConstantSigma', true,...
                 'KFold', 5);
Loss = kfoldLoss(m);
end

To get a model that can make predictions, you need to fit a model without passing the ‘kfold’ argument, but keeping the optimal kernel function obtained from "bayesopt" function.

kernel= bo.XAtMinObjective.KernelFunction
model = fitrgp(x, y, 'KernelFunction', char(kernel));
% Predict on unseen data
xNew = 0.2
ypred = predict(model2, xNew)

Best Answer

Related Solutions

MATLAB: Fiiting curve column by column for given data matrix in MATLAB R2014a

MATLAB: How to choose the best kernel for a Gaussian process regression, possibly using “bayesopt” function? How to predict unseen data with the resulting model

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