demo.m

%initalize the parameters of the algorithm
parameters = struct();

%set the hyperparameters of gamma prior used for sample weights
parameters.alpha_lambda = 1;
parameters.beta_lambda = 1;

%set the hyperparameters of gamma prior used for intermediate noise
parameters.alpha_upsilon = 1;
parameters.beta_upsilon = 1;

%set the hyperparameters of gamma prior used for bias
parameters.alpha_gamma = 1;
parameters.beta_gamma = 1;

%set the hyperparameters of gamma prior used for kernel weights
parameters.alpha_omega = 1;
parameters.beta_omega = 1;

%set the hyperparameters of gamma prior used for output noise
parameters.alpha_epsilon = 1;
parameters.beta_epsilon = 1;

%%% IMPORTANT %%%
%For gamma priors, you can experiment with three different (alpha, beta) values
%(1, 1) => default priors
%(1e-10, 1e+10) => good for obtaining sparsity
%(1e-10, 1e-10) => good for small sample size problems (like in Nature Biotechnology paper)

%set the number of iterations
parameters.iteration = 200;

%determine whether you want to calculate and store the lower bound values
parameters.progress = 0;

%set the seed for random number generator used to initalize random variables
parameters.seed = 1606;

%set the number of tasks (e.g., the number of compounds in Nature Biotechnology paper)
T = ??;
%set the number of kernels (e.g., the number of views in Nature Biotechnology paper)
P = ??;

%initialize the kernels and outputs of each task for training
Ktrain = cell(1, T);
ytrain = cell(1, T);
for t = 1:T
    Ktrain{t} = ??; %should be an Ntra x Ntra x P matrix containing similarity values between training samples of task t
	ytrain{t} = ??; %should be an Ntra x 1 matrix containing target outputs of task t
end

%perform training
state = bayesian_multitask_multiple_kernel_learning_train(Ktrain, ytrain, parameters);

%display the kernel weights
display(state.be.mu((T+1):(T+P)));

%initialize the kernels of each task for testing
Ktest = cell(1, T);
for t = 1:T
    Ktest{t} = ??; %should be an Ntra x Ntest x P matrix containing similarity values between training and test samples of task t
end

%perform prediction
prediction = bayesian_multitask_multiple_kernel_learning_test(Ktest, state);

%display the predictions for each task
for t = 1:T
    display(prediction.y{t}.mu);
end