ANOTHER ISSUE with submitWithConfiguration not working in MATLAB R2019b

I saw Umang Patel's issue on this dated Sept 22'19. I have a very similar problem, and have failed to rectify by "addpath & REHASH" answer he received. I use an iMac, and am doing the first assignment by Andrew Ng's Stanford online class on Machine Learning, using his standard package of files and inner library folders for this Exercise Assignment.

My "submit" fails the same way, but with slightly diffeent message:

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

>> submit

'parts' requires one of the following:

Navigation Toolbox

Robotics System Toolbox

Sensor Fusion and Tracking Toolbox

Error in submitWithConfiguration (line 4)

parts = parts(conf);

Error in submit (line 45)

submitWithConfiguration(conf);

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

I believe I have the same code for "submit" as Umang did, but here it is again for accuracy:

***************************************************************************************************************

function submit()

addpath('./lib');

conf.assignmentSlug = 'linear-regression';

conf.itemName = 'Linear Regression with Multiple Variables';

conf.partArrays = { ...

{ ...

'1', ...

{ 'warmUpExercise.m' }, ...

'Warm-up Exercise', ...

}, ...

{ ...

'2', ...

{ 'computeCost.m' }, ...

'Computing Cost (for One Variable)', ...

}, ...

{ ...

'3', ...

{ 'gradientDescent.m' }, ...

'Gradient Descent (for One Variable)', ...

}, ...

{ ...

'4', ...

{ 'featureNormalize.m' }, ...

'Feature Normalization', ...

}, ...

{ ...

'5', ...

{ 'computeCostMulti.m' }, ...

'Computing Cost (for Multiple Variables)', ...

}, ...

{ ...

'6', ...

{ 'gradientDescentMulti.m' }, ...

'Gradient Descent (for Multiple Variables)', ...

}, ...

{ ...

'7', ...

{ 'normalEqn.m' }, ...

'Normal Equations', ...

}, ...

};

conf.output = @output;

submitWithConfiguration(conf);

end

function out = output(partId)

% Random Test Cases

X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))'];

Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2));

X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25];

Y2 = Y1.^0.5 + Y1;

if partId == '1'

out = sprintf('%0.5f ', warmUpExercise());

elseif partId == '2'

out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]'));

elseif partId == '3'

out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10));

elseif partId == '4'

out = sprintf('%0.5f ', featureNormalize(X2(:,2:4)));

elseif partId == '5'

out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]'));

elseif partId == '6'

out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10));

elseif partId == '7'

out = sprintf('%0.5f ', normalEqn(X2, Y2));

end

end

***************************************************************************************************************