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Questions tagged [gpy]

Questions about GPy. GPy is a Gaussian Process (GP) framework written in Python. It includes support for basic GP regression, multiple output GPs (using coregionalization), various noise models, sparse GPs, non-parametric regression and latent variables. Use with the [python] tag

32 questions
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Using GPy Multiple-output coregionalized prediction

I have been facing a problem recently where I believe that a multiple-output GP might be a good candidate. I am at the moment applying a single-output GP to my data and as dimensionality increases, my results keep getting worse. I have tried…
fnaos
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Cannot find plot function in GPy library (python)

I am using the GPy library in Python 2.7 to perform Gaussian Process regressions. I started by following the tutorial notebooks provided in the GitHub page. Sample code : import numpy as np import matplotlib.pyplot as plt f = lambda x :…
saleml
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Most significant input dimensions for GPy.GPCoregionalizedRegression?

I have trained successfully a multi-output Gaussian Process model using an GPy.models.GPCoregionalizedRegression model of the GPy package. The model has ~25 inputs and 6 outputs. The underlying kernel is an GPy.util.multioutput.ICM kernel consisting…
gehbiszumeis
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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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1 answer

R6034 An application has made an attempt to load the C runtime library incorrectly

I am running a python 2.7 code (containing GPy and GPyOpt, python implementation of gaussian process and Bayesian optimization) from Matlab on Anaconda 64bit on Windows 10 and I am facing with the following error: warning in stationary: failed to…
Rayan
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Get confidence limits from Gaussian Process model in Python GPy

I calcualted a Gaussian Process model in Python using GPy: ker0 = GPy.kern.Bias(input_dim=1,variance=1e-2) ... m = GPy.models.GPRegression(x, y, ker0+ker2) I can plot it with m.plot() plt.show and it visualizes the points, the spline and the…
horseshoe
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LinAlgError: not positive definite, even with jitter. When using a conda environment instead of pip

I am trying to fit some random data to a GP with the RBF kernel, using the GPy package. When I change the active dimensions, I get the LinAlgError: not positive definite, even with jitter error. This error is generated only with a conda environment.…
Katerina
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How to combine plot of two GPy models?

I have calculated two GP regression models and would like to have them plotted in the same figure. Model 1 kernel = GPy.kern.RBF(input_dim=1, variance=.1, lengthscale=1.) m1 = GPy.models.GPRegression(xa, ya,kernel) m1.optimize_restarts(num_restarts…
hH1sG0n3
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How to find and uninstall the numpy duplicate versions

I am trying to install the library GPy. Although the installation is successful, I have a question on my numpy version. GPy library can be found here https://github.com/SheffieldML/GPy The current version of my numpy is 1.9.3 >>> import numpy >>>…
Vinod Prime
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gpytorch, regression on targets and classification of gradients to negative or positive

i would like to set up the following model in GPYtorch: i have 4 inputs and i want to predict an output (regression) at the same time, i want to constrain the gradients of 3 inputs to be positive and of 1 input to be negative (with respect to the…
john
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Prediction with GPytorch DeepGP was poor

[Link to DeepGP definition:] (https://drive.google.com/file/d/1UwU9fz6vqxOQ3F0NSKKmRSgiP6pNnbwo/view?usp=sharing) I have defined a DeepGP model using GPytorch, trained it, and the predictions are made as: test_dataset = TensorDataset(test_x,…
Bikal
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GP regression using Poisson likelihood

I am trying to implement GP regression using Poisson likelihood. I followed the example in GPy by doing poisson_likelihood = GPy.likelihoods.Poisson() laplace_inf = GPy.inference.latent_function_inference.Laplace() m = GPy.core.GP(X=X, Y=Y,…
Abhijith
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Multitask/multioutput GPy Coregionalized Regression with non-Gaussian Likelihood and Laplace inference function

I want to perform coregionalized regression in GPy, however I am using a Bernoulli likelihood and then to estimate that as a Gaussian, I use Laplace inference. The code below shows how I would usually run a single-output GP with this set up (with my…
gregory
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How to reproduce results of GPy GPRegression using scikit-learn GaussianProcessRegressor?

Both GPRegression (GPy) and GaussianProcessRegressor (scikit-learn) uses similar initial values and the same optimizer (lbfgs). Why results vary significantly? #!pip -qq install pods #!pip -qq install GPy from sklearn.gaussian_process import…
Zeel B Patel
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Making sense of the standard deviation in GPy for different scaling schemes of training data?

I am trying to make predictions for two different outputs (independent) using two separate models. I am normalising the input and output data using relevant factors from the physics of my problem. But the uncertainty (standard deviation) in the…
cacophony94
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