How to train a Gaussian Process Regression Model to produce a single output based on multiple input?
I have a set of test data containing 5 input variables and 1 output. There are 3 sets of such data.
I would like to train a machine learning (Gaussian Process Regression Model) to predict the output when the input variables are changed to other random values. I have tried following the example on https://uk.mathworks.com/help/stats/gaussian-process-regression-models.html where I modified the example code by the following:
In the example code, it was input as:
x_observed = linspace(0,10,21)’;
y_observed1 = x_observed.*sin(x_observed);
y_observed2 = y_observed1 + 0.5*randn(size(x_observed));
How do I modify the x_observed to contain 5 input variables and the y_observed to contain the output variable for each of the 3 sets of test data? This is what I have done so far:
% Sample input matrices
% matrix(design no.) = [var1 var2 var3 var4 var5]
matrix1 = [0.9 120 25 450 12];
matrix2 = [2.25 200 35 920 14];
matrix3 = [0.9 200 35 920 14];
% definition of output variable
% "Y" (output) = membrane + bending stress, in MPa, from ANSYS FEM model
%Sample output matrices
% o(design no.) = [membrane + bending stress in MPa]
o1 = [242.81];
o2 = [237.09];
o3 = [213.18];
Any help would be appreciated, thank you.
Regards,
KTI have a set of test data containing 5 input variables and 1 output. There are 3 sets of such data.
I would like to train a machine learning (Gaussian Process Regression Model) to predict the output when the input variables are changed to other random values. I have tried following the example on https://uk.mathworks.com/help/stats/gaussian-process-regression-models.html where I modified the example code by the following:
In the example code, it was input as:
x_observed = linspace(0,10,21)’;
y_observed1 = x_observed.*sin(x_observed);
y_observed2 = y_observed1 + 0.5*randn(size(x_observed));
How do I modify the x_observed to contain 5 input variables and the y_observed to contain the output variable for each of the 3 sets of test data? This is what I have done so far:
% Sample input matrices
% matrix(design no.) = [var1 var2 var3 var4 var5]
matrix1 = [0.9 120 25 450 12];
matrix2 = [2.25 200 35 920 14];
matrix3 = [0.9 200 35 920 14];
% definition of output variable
% "Y" (output) = membrane + bending stress, in MPa, from ANSYS FEM model
%Sample output matrices
% o(design no.) = [membrane + bending stress in MPa]
o1 = [242.81];
o2 = [237.09];
o3 = [213.18];
Any help would be appreciated, thank you.
Regards,
KT I have a set of test data containing 5 input variables and 1 output. There are 3 sets of such data.
I would like to train a machine learning (Gaussian Process Regression Model) to predict the output when the input variables are changed to other random values. I have tried following the example on https://uk.mathworks.com/help/stats/gaussian-process-regression-models.html where I modified the example code by the following:
In the example code, it was input as:
x_observed = linspace(0,10,21)’;
y_observed1 = x_observed.*sin(x_observed);
y_observed2 = y_observed1 + 0.5*randn(size(x_observed));
How do I modify the x_observed to contain 5 input variables and the y_observed to contain the output variable for each of the 3 sets of test data? This is what I have done so far:
% Sample input matrices
% matrix(design no.) = [var1 var2 var3 var4 var5]
matrix1 = [0.9 120 25 450 12];
matrix2 = [2.25 200 35 920 14];
matrix3 = [0.9 200 35 920 14];
% definition of output variable
% "Y" (output) = membrane + bending stress, in MPa, from ANSYS FEM model
%Sample output matrices
% o(design no.) = [membrane + bending stress in MPa]
o1 = [242.81];
o2 = [237.09];
o3 = [213.18];
Any help would be appreciated, thank you.
Regards,
KT gaussian process regression model, machine learning MATLAB Answers — New Questions