Error using load
Unable to read file
'hierarchical/demHighFiveHgplvm1': no such file
or directory.

Hello, I tried to run 'demHsvargplvmClassification.m', however, there is no way to find the file 'oil100'. Where can I download this file?

Many thanks

In line 124 of tutorial.m, there is an error "Undefined function or variable 'errorGP'." How to fix that?

What do I do to make it work? Why do I make mistakes in running tutorial.m files?

From the hsvargplvmModelCreate and related documentation:

Layers are indexed bottom-up. The bottom ones, i.e. layer{1}.comp{:} are the observed data. The top layer is the parent latent space.

This means that layer 0 (1 in MATLAB) contains the observed data, and in case of regression is inpX.

In demToyRegression, when using the inputsOutputs initialization

for i=options.H:-1:floor(options.H/2)+1
  options.initX{i} = inpX;
  Q{i} = size(inpX,2);
end

the initialization with observed outputs happens on top layers, starting from options.H, and bottom ones are initialized with ppca on Ytr (observed inputs).
Probably I misunterstood something, thanks for a feedback.

Hello, I would like to ask if deep GP can be used for path prediction of vehicles? It doesn't feel like a cyclical process.

Trying to run the 'tutorial.m' and I am getting the errors below. I believe that I have installed all the requirements listed in 'README'.

Unrecognized function or variable 'optimiDefaultConstraint'.

Error in vargplvmCreate (line 58)
model.betaTransform = optimiDefaultConstraint('positive');

Error in vargplvmEmbed (line 67)
model = vargplvmCreate(latentDim, d, Y, options);

Error in hsvargplvmModelCreate (line 164)
curX = [curX initFunc(m{i}, Q1, initXOptions{h}{:})];

Error in demToyHsvargplvm1 (line 101)
model = hsvargplvmModelCreate(Ytr, options, globalOpt, initXOptions);

Error in demToyUnsupervised (line 38)
demToyHsvargplvm1; % Run the actual demo

Error in tutorial (line 40)
demToyUnsupervised; % Call to the demo

I get this error when I run demToyUnsupervised. When I tried to debug, I found that the dimensions actually don't agree.

Error using  * 
Inner matrix dimensions must agree.

Error in ppcaEmbed (line 50)
    X = Ycentre*u(:, 1:dims)*diag(1./sqrt(v(1:dims)));

Error in vargplvmCreate (line 109)
    X = initFunc(model.m, q);

Error in vargplvmEmbed (line 67)
model = vargplvmCreate(latentDim, d, Y, options);

Error in hsvargplvmModelCreate (line 164)
                curX = [curX initFunc(m{i}, Q1, initXOptions{h}{:})];

Error in demToyHsvargplvm1 (line 101)
model = hsvargplvmModelCreate(Ytr, options, globalOpt, initXOptions);


Error in demToyUnsupervised (line 38)
demToyHsvargplvm1; % Run the actual demo

Error in tutorial (line 39)
demToyUnsupervised; % Call to the demo

When running tutorial.m, the following error occurs.
未定义函数或变量 'svargplvm_init'。

出错 hsvargplvm_init (line 8)
svargplvm_init

出错 demToyHsvargplvm1 (line 35)
hsvargplvm_init;

出错 demToyUnsupervised (line 38)
demToyHsvargplvm1; % Run the actual demo

出错 tutorial (line 39)
demToyUnsupervised; % Call to the demo
I hope you can tell me how to get this program to work, thank you?

Hi, I have tried to use to toolbox by adding external requirements' path but an error occurred that Undefined function or variable 'util_optCreate'.

Error in svargplvm_init (line 9)
defaults = util_optCreate({'optionsType', 'svargplvm'});
Thank you if you could take a look at it.

Error using *
Incorrect dimensions for matrix multiplication. Check that the number of columns in the first matrix
matches the number of rows in the second matrix. To operate on each element of the matrix
individually, use TIMES (.*) for elementwise multiplication.

Error in ppcaEmbed (line 50)
X = Ycentre*u(:, 1:dims)*diag(1./sqrt(v(1:dims)));

Error in vargplvmCreate (line 109)
X = initFunc(model.m, q);

Error in vargplvmEmbed (line 67)
model = vargplvmCreate(latentDim, d, Y, options);

Error in hsvargplvmModelCreate (line 164)
curX = [curX initFunc(m{i}, Q1, initXOptions{h}{:})];

Error in demToyHsvargplvm1 (line 101)
model = hsvargplvmModelCreate(Ytr, options, globalOpt, initXOptions);

Error in demToyUnsupervised (line 38)
demToyHsvargplvm1; % Run the actual demo

Error in tutorial (line 39)
demToyUnsupervised; % Call to the demo

Related documentation

Can "hsvargplvmPosterriorMeanVarSimple.m" be applied to Toy unsupervised learning demo?If not,What changes do I need to make to "hsvargplvmPosterriorMeanVarSimple.m"?