catlearn.estimator package¶
Submodules¶
catlearn.estimator.general_gp module¶
Function to setup a general GP.
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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.
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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
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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
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catlearn.estimator.general_kernel module¶
Setup a generic kernel.
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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
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catlearn.estimator.general_kernel.
general_kernel
(features, dimension='single')¶ Generate a default kernel.
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catlearn.estimator.general_kernel.
smooth_kernel
(features, dimension='single')¶ Generate a default kernel.
catlearn.estimator.general_preprocess module¶
A default setup for data preprocessing.
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class
catlearn.estimator.general_preprocess.
GeneralPrepreprocess
(clean_type='eliminate')¶ Bases:
object
A general purpose data preprocessing class.
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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.
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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
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