how to create equation from gaussian trained model from regression learner?

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Nilanshu Mahant 2022-5-7

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https://ww2.mathworks.cn/matlabcentral/answers/1713670-how-to-create-equation-from-gaussian-trained-model-from-regression-learner

编辑： Saarthak Gupta 2023-12-15

But, then it give me larger error while I'm creating equation from this. As I have a different flow chart of equation. Is it possible to make equation using regression learner. I'm creating model gaussian regression matern 5/2 GPR. How can I get the equation from the this trained model. I'm selecting pressure, temperature, humidity as input and trying to get some output. The y estimated values from the trainedmodel is fitting good. But, Now I want to make equation from this model and how can I make that?

I'm also attaching the input trained data file and test data file.

I want to find coefficient for gaussian equation.

Is there any code or function to calculated the gaussian coefficients?

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Nathan Williams 2022-7-28

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Answer 1

Saarthak Gupta 2023-12-7

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https://ww2.mathworks.cn/matlabcentral/answers/1713670-how-to-create-equation-from-gaussian-trained-model-from-regression-learner#answer_1367274

编辑：Saarthak Gupta 2023-12-15

Hi Nilanshu,

Looks like you are trying to obtain a closed-form expression for the Gaussian Process Regression model trained on your dataset.

Unlike other regression models such as Linear and Nonlinear, Gaussian process regression (GPR) models are nonparametric kernel-based probabilistic models. In GPR models, an instance of the response y can be modelled as:

where f(x)~GP(0,k(x,x′)), that is f(x) are from a zero mean GP with covariance function, k(x,x′). h(x) are a set of basis functions that transform the original feature vector x in Rd into a new feature vector h(x) in Rp. β is a p-by-1 vector of basis function coefficients.

Note: The ’fitrgp’ function is used to fit a GPR model. The function estimates the covariance function, noise variance and the coefficient vector of fixed-basis functions. These properties can be accessed from the ‘RegressionGP’ object which is returned by the function.

Since the model is probabilistic, the response cannot be captured in a closed form expression. Instead, we give the probability density of a response

at a new point

, given y, X (training data) as: