catlearn.estimator package

Submodules

catlearn.estimator.general_gp module

Function to setup a general GP.

class catlearn.estimator.general_gp.GeneralGaussianProcess(clean_type='eliminate', dimension='single', kernel='general')

Bases: object

Define a general setup for the Gaussin process.

This should not be used to try and obtain highly accurate solutions. Though it should give a reasonable model.

gaussian_process_predict(test_features)

Function to make GP predictions on tests data.

Parameters:test_features (array) – The array of test features.
Returns:prediction – The prediction data generated by the Gaussian process.
Return type:dict
train_gaussian_process(train_features, train_targets)

Generate a general gaussian process model.

Parameters:
  • train_features (array) – The array of training features.
  • train_targets (array) – A list of training target values.
Returns:

gp – The trained Gaussian process.

Return type:

object

catlearn.estimator.general_kernel module

Setup a generic kernel.

catlearn.estimator.general_kernel.default_lengthscale(features, dimension='single')

Generate defaults for the kernel lengthscale.

Parameters:
  • features (array) – The feature matrix for the training data.
  • dimension (str) – The number of parameters to return. Can be ‘single’, or ‘features’.
Returns:

std – The standard deviation of the features.

Return type:

array

catlearn.estimator.general_kernel.general_kernel(features, dimension='single')

Generate a default kernel.

catlearn.estimator.general_kernel.smooth_kernel(features, dimension='single')

Generate a default kernel.

catlearn.estimator.general_preprocess module

A default setup for data preprocessing.

class catlearn.estimator.general_preprocess.GeneralPrepreprocess(clean_type='eliminate')

Bases: object

A general purpose data preprocessing class.

process(train_features, train_targets, test_features=None)

Processing function.

Parameters:
  • train_features (array) – The array of training features.
  • train_targets (array) – A list of training target values.
  • test_features (array) – The array of test features.
transform(features)

Function to transform a new set of features.

Parameters:features (array) – A new array of features to clean. This will most likely be the new test features.
Returns:processed – A cleaned and scaled feature set.
Return type:array

Module contents