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Questions tagged [gaussian-process]

A Gaussian process is a stochastic process whose implementation is used for regression and classification problems.

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Multiple-output Gaussian Process regression in scikit-learn

I am using scikit learn for Gaussian process regression (GPR) operation to predict data. My training data are as follows: x_train = np.array([[0,0],[2,2],[3,3]]) #2-D cartesian coordinate points y_train = np.array([[200,250,…
santobedi
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How to change max_iter in optimize function used by sklearn gaussian process regression?

I am using sklearn's GPR library, but occasionally run into this annoying warning: ConvergenceWarning: lbfgs failed to converge (status=2): ABNORMAL_TERMINATION_IN_LNSRCH. Increase the number of iterations (max_iter) or scale the data as shown in: …
M-Wi
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Gaussian process with 2D feature array as input - scikit-learn

I need to implement GPR (Gaussian process regression) in Python using the scikit-learn library. My input X has two features. Ex. X=[x1, x2]. And output is one dimension y=[y1] I want to use two Kernels; RBF and Matern, such that RBF uses the 'x1'…
QuantumGirl
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Gaussian-Process Prediction Confidence Interval Oddities

I'm doing some particle physics analysis and was hoping someone out there could give me some insight on a Gaussian-Process fit I'm trying to use to extrapolate some data. I have data with uncertainties that I'm feeding in to the scikit-learn…
danjump
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How to incorporate individual measurement uncertainties into Gaussian Process?

I have a set of observations, f_i=f(x_i), and I want to construct a probabilistic surrogate, f(x) ~ N[mu(x), sigma(x)], where N is a normal distribution. Each observed output, f_i, is associated with a measurement uncertainty, sigma_i. I would like…
kilojoules
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Combining periodic and aperiodic kernels in sklearn GPR

I am using the scikit-learn Python library to perform Bayesian optimization (on top of Gaussian process regression) on a function which takes seven inputs. Four of these inputs represent physical angles, so the Gaussian kernel should be periodic in…
mikefallopian
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sklearn computation of log marginal likelihood of a Gaussian Process is positive?

I was checking sklearn's implementation of log marginal likelihood of a Gaussian Process (GP). The implementation is based on Algorithm 2.1 in Rasmussen's Gaussian Processes for Machine Learning which I also attached a snapshot of it for…
TNg
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How to manually calculate the expected mean (and variance) of a Gaussian Process Regressor?

I am using GPyTorch to make a Gaussian Process (GP) Regressor using a custom mean function (an n-degree polynomial with bias) and a combination of kernels (Gaussian^2*Linear). I was wondering if it is possible to extract the parameters from the…
TimLevert
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normalize_y in GPR

I'm trying to use GaussianProcessRegressor in sklearn to predict values of unknown. The target values are typically between 1000-10000. Since they are not 0-mean prior, I set the model with normalize_y = False, which is a default setup. from…
pjb
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Theano TypeError during model building with celerite2 (pymc3)

I recently switched from celerite to celerite2 for modelling stellar light curves. I have followed the celerite 2 tutorial closely, adapting it to my needs. My model consists of a flat mean, a jitter term, a granulation term and an SHO term to model…
Stefan Janisch
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Gaussian process regression - explain behaviour

I'm looking into GP regression, but I'm getting some behaviour that I do not understand. Basically, I wanted to show convergence for GP on the osciallatory Genz function (basically a period wave), which led me to this picture Gp convergence, sorry…
anyn12
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How to Save/Load Optimized GPy Regression Model

I'm trying to save my optimized Gaussian process model for use in a different script. My current line of thinking is to store the model information in a json file, utilizing GPy's built-in to_dict and from_dict functions. Something along the lines…
Bocephus85
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Gaussian Process regression hyparameter optimisation using python Grid search

I am started learning Gaussian regression using Sklearn library using my own data points as given below. though I got the result it is inaccurate because I did not do hyperparameter optimisation. I did some couple of google search and written…
Ankita
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Gaussian Process Regressor scikit learn does not recognise 'eval_MSE=True'

I am using Gaussian Process Regressor scikit learn to predit data for the models. While using gp, I also need to find uncertainity in each value present in the dataset. The documentation suggests to use "gp.predict(self, X, eval_MSE=True)". I used…
jimi
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Gaussian Process Regression incremental learning

I am using the scikit-learn implementation of Gaussian Process Regression here and I want to fit single points instead of fitting a whole set of points. But the resulting alpha coefficients should remain the same e.g. gpr2 =…
Manh Khôi Duong
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