Hi Katy,
I understand you want to train a gpr model using multiple input sequences.When training a gpr model with multiple input sequences, we can directly use a matrix or a table to represent our input data,where each row of the matrix or table corresponds to a different input sequence.
If the input sequences have varying sizes, please follow the given workflow to combine the matrices into a table:
% Sample input matrices
matrix1 = rand(5, 3);
matrix2 = rand(4, 4);
matrix3 = rand(6, 2);
%Sample output matrices
o1 = rand(5,1);
o2 = rand(4,1);
o3 = rand(6,1);
% Combine all input matrices into a cell array
allMatrices = {matrix1, matrix2, matrix3};
% Combine all output matrices into a cell array
outputMatrices = {o1,o2,o3};
% Find the maximum number of columns among all matrices
maxCols = max(cellfun(@(x) size(x, 2), allMatrices));
% Please note cellfun applies function to each content(matrix) in a cell
% array
% Pad each matrix with NaN values to make them all have the same number of columns
paddedMatrices = cellfun(@(x) [x, nan(size(x, 1), maxCols - size(x, 2))], allMatrices, 'UniformOutput', false);
% Observe the sizes of matrices now after operation.
disp(paddedMatrices)
% Convert the padded matrices into a table
combinedTable = vertcat(paddedMatrices{:});
% Assign variable names to the columns
tableVariableNames = strcat("Col_", arrayfun(@num2str, 1:maxCols, 'UniformOutput', false));
% Conversion into table
combinedTable = array2table(combinedTable, 'VariableNames', tableVariableNames);
% Finally apply 'fitrgp' function
model = fitrgp(combinedTable,vertcat(outputMatrices{:}))
For more information please refer to the following:
Hope this helps.
