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API
Overview
GP
fvGP
Logging
Configuring logging
Examples
FVGP Single Task Notebook
Import fvgp and relevant libraries
Defining some input data and testing points
Setting up the fvgp single task object
Training our gaussian process regression on given data
Looking the posterior mean at the test points
Training Asynchronously
Updating asynchronously
Killing training
Looking at the posterior mean at the test points
Custom Kernels
Training our gaussian process regression on given data
Looking the posterior mean at the test points
Prior Mean Functions
NOTE: The prior mean function must return a 1d vector, e.g., (100,)
Training our gaussian process regression on given data
Looking the posterior mean at the test points
FVGP MULTI Task Notebook
This first cell has nothing to do with gpCAM, it’s just a function to plot some results later
Import fvgp and relevant libraries
Defining some input data and testing points
Setting up the fvgp multi task object
Training our gaussian process regression on given data
Looking at the posterior mean at the test points (remember that we did not define a particularly good kernel)
fvGP
Index
Edit on GitHub
Index
D
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E
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F
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G
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H
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K
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L
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M
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N
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P
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S
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T
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U
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V
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X
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Y
D
default_kernel() (fvgp.gp.GP method)
default_mean_function() (fvgp.gp.GP method)
dot_product_kernel() (fvgp.gp.GP method)
E
entropy() (fvgp.gp.GP method)
exponential_kernel() (fvgp.gp.GP method)
exponential_kernel_robust() (fvgp.gp.GP method)
F
fvGP (class in fvgp.fvgp)
G
GP (class in fvgp.gp)
gp_entropy() (fvgp.gp.GP method)
gp_entropy_grad() (fvgp.gp.GP method)
gp_kl_div() (fvgp.gp.GP method)
gp_kl_div_grad() (fvgp.gp.GP method)
gp_prior() (fvgp.gp.GP method)
gp_prior_grad() (fvgp.gp.GP method)
H
hyperparameters (fvgp.gp.GP attribute)
K
kill_training() (fvgp.gp.GP method)
kl_div() (fvgp.gp.GP method)
L
linear_kernel() (fvgp.gp.GP method)
log_likelihood() (fvgp.gp.GP method)
M
matern_kernel_diff1() (fvgp.gp.GP method)
matern_kernel_diff1_robust() (fvgp.gp.GP method)
matern_kernel_diff2() (fvgp.gp.GP method)
matern_kernel_diff2_robust() (fvgp.gp.GP method)
N
neg_log_likelihood() (fvgp.gp.GP method)
neg_log_likelihood_gradient() (fvgp.gp.GP method)
neg_log_likelihood_hessian() (fvgp.gp.GP method)
non_stat_kernel() (fvgp.gp.GP method)
P
periodic_kernel() (fvgp.gp.GP method)
polynomial_kernel() (fvgp.gp.GP method)
posterior_covariance() (fvgp.gp.GP method)
posterior_covariance_grad() (fvgp.gp.GP method)
posterior_mean() (fvgp.gp.GP method)
posterior_mean_constraint() (fvgp.gp.GP method)
posterior_mean_grad() (fvgp.gp.GP method)
posterior_probability() (fvgp.gp.GP method)
posterior_probability_grad() (fvgp.gp.GP method)
S
shannon_information_gain() (fvgp.gp.GP method)
shannon_information_gain_grad() (fvgp.gp.GP method)
shannon_information_gain_vec() (fvgp.gp.GP method)
slogdet() (fvgp.gp.GP method)
solve() (fvgp.gp.GP method)
sparse_kernel() (fvgp.gp.GP method)
squared_exponential_kernel() (fvgp.gp.GP method)
squared_exponential_kernel_robust() (fvgp.gp.GP method)
stop_training() (fvgp.gp.GP method)
T
train() (fvgp.gp.GP method)
train_async() (fvgp.gp.GP method)
U
update_fvgp_data() (fvgp.fvgp.fvGP method)
update_gp_data() (fvgp.gp.GP method)
update_hyperparameters() (fvgp.gp.GP method)
V
variances (fvgp.gp.GP attribute)
X
x_data (fvgp.gp.GP attribute)
Y
y_data (fvgp.gp.GP attribute)
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