You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: ConstrainedUKFTest.m
    path of file: ./repos/libDirectional/lib/tests/filters
    
    the code of the file until where you have to start completion: classdef ConstrainedUKFTest < matlab.unittest.TestCase
   
    properties
    end
    
	methods (Test)
        function testConstrainedUKF(testCase)
            filter = ConstrainedUKF();
            g = GaussianDistribution(0.5,0.7);
            rng default
            
            %% sanity check
            filter.setState(g);
            g1 = filter.getEstimate();
            testCase.verifyClass(g1, 'GaussianDistribution');
            testCase.verifyEqual(g.mu, g1.mu);
            %testCase.verifyEqual(g.C, g1.C);
            
            %% predict identity
%             filter.setState(g);
%             filter.predictIdentity(g);
%             g2 = filter.getEstimate();
%             testCase.verifyClass(g2, 'GaussianDistribution');
%             testCase.verifyEqual(g2.mu, g.mu+g.mu);
%             testCase.verifyEqual(g2.C, g.C + g.C);
            
            %% predict nonlinear
            filter.setState(g);
            filter.predictNonlinear(@(x) x, g);
            g3 = filter.getEstimate();
            testCase.verifyClass(g3, 'GaussianDistribution');
            %testCase.verifyEqual(g3.mu, g.mu+g.mu, 'RelTol', 1E-10);
            %testCase.verifyGreaterThan(g3.C, g.C);
            
            %% true nonlinear test
            g4 = GaussianDistribution(0, 0.7);
            filter.setState(g4);
            filter.predictNonlinear(@(x) x^3, g4);
            g5 = filter.getEstimate();
            %testCase.verifyEqual(g5.mu, g4.mu, 'RelTol', 1E-10);
            %testCase.verifyGreaterThan(g5.C, g4.C);
            
            %% update identity
%             filter.setState(g);
%             filter.updateIdentity(GaussianDistribution(0,g.C),g.mu);
%             g6 = filter.getEstimate();
%             testCase.verifyClass(g6, 'GaussianDistribution');
%             testCase.verifyEqual(g6.mu, g.mu);
%             testCase.verifyEqual(g6.C, g.C/2);
            
            %% update nonlinear
            g7 = GaussianDistribution(0, 0.7);
            filter.setState(g7);

    
function testConstrainedUKF(testCase)
            filter = ConstrainedUKF();
            g = GaussianDistribution(0.5,0.7);
            rng default
            
            %% sanity check
            filter.setState(g);
            g1 = filter.getEstimate();
            testCase.verifyClass(g1, 'GaussianDistribution');
            testCase.verifyEqual(g.mu, g1.mu);
            %testCase.verifyEqual(g.C, g1.C);
            
            %% predict identity
%             filter.setState(g);
%             filter.predictIdentity(g);
%             g2 = filter.getEstimate();
%             testCase.verifyClass(g2, 'GaussianDistribution');
%             testCase.verifyEqual(g2.mu, g.mu+g.mu);
%             testCase.verifyEqual(g2.C, g.C + g.C);
            
            %% predict nonlinear
            filter.setState(g);
            filter.predictNonlinear(@(x) x, g);
            g3 = filter.getEstimate();
            testCase.verifyClass(g3, 'GaussianDistribution');
            %testCase.verifyEqual(g3.mu, g.mu+g.mu, 'RelTol', 1E-10);
            %testCase.verifyGreaterThan(g3.C, g.C);
            
            %% true nonlinear test
            g4 = GaussianDistribution(0, 0.7);
            filter.setState(g4);
            filter.predictNonlinear(@(x) x^3, g4);
            g5 = filter.getEstimate();
            %testCase.verifyEqual(g5.mu, g4.mu, 'RelTol', 1E-10);
            %testCase.verifyGreaterThan(g5.C, g4.C);
            
            %% update identity
%             filter.setState(g);
%             filter.updateIdentity(GaussianDistribution(0,g.C),g.mu);
%             g6 = filter.getEstimate();
%             testCase.verifyClass(g6, 'GaussianDistribution');
%             testCase.verifyEqual(g6.mu, g.mu);
%             testCase.verifyEqual(g6.C, g.C/2);
            
            %% update nonlinear
            g7 = GaussianDistribution(0, 0.7);
            filter.setState(g7);
            z = 0.4;
            filter.updateNonlinear(@(x) x, g7, z);
            g8 = filter.getEstimate();
            testCase.verifyGreaterThan(g8.mu, g7.mu);
            testCase.verifyLessThan(g8.mu, z);
            testCase.verifyGreaterThan(g7.C, g8.C);
        end