GPy.examples package

Submodules

GPy.examples.classification module

Gaussian Processes classification examples

crescent_data(model_type='Full', num_inducing=10, seed=10000, kernel=None, optimize=True, plot=True)[source]

Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.

Parameters:
  • model_type – type of model to fit [‘Full’, ‘FITC’, ‘DTC’].
  • inducing (int) – number of inducing variables (only used for ‘FITC’ or ‘DTC’).
  • seed (int) – seed value for data generation.
  • kernel (a GPy kernel) – kernel to use in the model
oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True)[source]

Run a Gaussian process classification on the three phase oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.

sparse_toy_linear_1d_classification(num_inducing=10, seed=10000, optimize=True, plot=True)[source]

Sparse 1D classification example

Parameters:seed (int) – seed value for data generation (default is 4).
sparse_toy_linear_1d_classification_uncertain_input(num_inducing=10, seed=10000, optimize=True, plot=True)[source]

Sparse 1D classification example

Parameters:seed (int) – seed value for data generation (default is 4).
toy_heaviside(seed=10000, max_iters=100, optimize=True, plot=True)[source]

Simple 1D classification example using a heavy side gp transformation

Parameters:seed (int) – seed value for data generation (default is 4).
toy_linear_1d_classification(seed=10000, optimize=True, plot=True)[source]

Simple 1D classification example using EP approximation

Parameters:seed (int) – seed value for data generation (default is 4).
toy_linear_1d_classification_laplace(seed=10000, optimize=True, plot=True)[source]

Simple 1D classification example using Laplace approximation

Parameters:seed (int) – seed value for data generation (default is 4).

GPy.examples.dimensionality_reduction module

bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True)[source]
bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True)[source]
bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k)[source]
bgplvm_simulation(optimize=True, verbose=1, plot=True, plot_sim=False, max_iters=20000.0)[source]
bgplvm_simulation_missing_data(optimize=True, verbose=1, plot=True, plot_sim=False, max_iters=20000.0, percent_missing=0.1, d=13)[source]
bgplvm_simulation_missing_data_stochastics(optimize=True, verbose=1, plot=True, plot_sim=False, max_iters=20000.0, percent_missing=0.1, d=13, batchsize=2)[source]
bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan=False)[source]

model for testing purposes. Samples from a GP with rbf kernel and learns the samples with a new kernel. Normally not for optimization, just model cheking

brendan_faces(optimize=True, verbose=True, plot=True)[source]
cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose=True, plot=True)[source]
gplvm_oil_100(optimize=True, verbose=1, plot=True)[source]
gplvm_simulation(optimize=True, verbose=1, plot=True, plot_sim=False, max_iters=20000.0)[source]
mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw)[source]
mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim=True, **kw)[source]
olivetti_faces(optimize=True, verbose=True, plot=True)[source]
robot_wireless(optimize=True, verbose=True, plot=True)[source]
sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_inducing=15, max_iters=50)[source]
ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k)[source]
ssgplvm_simulation(optimize=True, verbose=1, plot=True, plot_sim=False, max_iters=20000.0, useGPU=False)[source]
ssgplvm_simulation_linear()[source]
stick(kernel=None, optimize=True, verbose=True, plot=True)[source]
stick_bgplvm(model=None, optimize=True, verbose=True, plot=True)[source]

Interactive visualisation of the Stick Man data from Ohio State University with the Bayesian GPLVM.

stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=True)[source]
swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=25, Q=4, sigma=0.2)[source]

GPy.examples.non_gaussian module

boston_example(optimize=True, plot=True)[source]
student_t_approx(optimize=True, plot=True)[source]

Example of regressing with a student t likelihood using Laplace

GPy.examples.regression module

Gaussian Processes regression examples

coregionalization_sparse(optimize=True, plot=True)[source]

A simple demonstration of coregionalization on two sinusoidal functions using sparse approximations.

coregionalization_toy(optimize=True, plot=True)[source]

A simple demonstration of coregionalization on two sinusoidal functions.

epomeo_gpx(max_iters=200, optimize=True, plot=True)[source]

Perform Gaussian process regression on the latitude and longitude data from the Mount Epomeo runs. Requires gpxpy to be installed on your system to load in the data.

multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=10000, max_iters=300, optimize=True, plot=True)[source]

Show an example of a multimodal error surface for Gaussian process regression. Gene 939 has bimodal behaviour where the noisy mode is higher.

olympic_100m_men(optimize=True, plot=True)[source]

Run a standard Gaussian process regression on the Rogers and Girolami olympics data.

olympic_marathon_men(optimize=True, plot=True)[source]

Run a standard Gaussian process regression on the Olympic marathon data.

parametric_mean_function(max_iters=100, optimize=True, plot=True)[source]

A linear mean function with parameters that we’ll learn alongside the kernel

robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True)[source]

Predict the location of a robot given wirelss signal strength readings.

silhouette(max_iters=100, optimize=True, plot=True)[source]

Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper.

simple_mean_function(max_iters=100, optimize=True, plot=True)[source]

The simplest possible mean function. No parameters, just a simple Sinusoid.

sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, optimize=True, plot=True, checkgrad=False)[source]

Run a 1D example of a sparse GP regression.

sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, optimize=True, plot=True, nan=False)[source]

Run a 2D example of a sparse GP regression.

toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize=True, plot=True)[source]
toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize=True, plot=True)[source]
toy_poisson_rbf_1d_laplace(optimize=True, plot=True)[source]

Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.

toy_rbf_1d(optimize=True, plot=True)[source]

Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.

toy_rbf_1d_50(optimize=True, plot=True)[source]

Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.

uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True)[source]

Run a 1D example of a sparse GP regression with uncertain inputs.

GPy.examples.state_space module

Module contents

Examples for GPy.

The examples in this package usually depend on pods so make sure you have that installed before running examples.