physbo.gp.predictor module¶
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class
physbo.gp.predictor.predictor(config, model=None)[source]¶ Bases:
physbo.predictor.base_predictor-
delete_stats()[source]¶ Default function to delete status This function must be overwritten in each model.
- Parameters
args –
kwds –
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fit(training, num_basis=None)[source]¶ Fitting model to training dataset
- Parameters
training (physbo.variable) – dataset for training
num_basis (int) – the number of basis (default: self.config.predict.num_basis)
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get_post_fcov(training, test, diag=True)[source]¶ Calculating posterior variance-covariance matrix of model
- Parameters
training (physbo.variable) – training dataset. If already trained, the model does not use this.
test (physbo.variable) – inputs
diag (bool) – Diagonlization flag in physbo.exact.get_post_fcov function.
- Returns
- Return type
numpy.ndarray
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get_post_fmean(training, test)[source]¶ Calculating posterior mean value of model
- Parameters
training (physbo.variable) – training dataset. If already trained, the model does not use this.
test (physbo.variable) – inputs
- Returns
- Return type
numpy.ndarray
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get_post_samples(training, test, alpha=1)[source]¶ Drawing samples of mean values of model
- Parameters
training (physbo.variable) – training dataset. If already trained, the model does not use this.
test (physbo.variable) – inputs (not used)
alpha (float) – tuning parameter of the covariance by multiplying alpha**2 for np.random.multivariate_normal.
- Returns
- Return type
numpy.ndarray
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get_predict_samples(training, test, N=1)[source]¶ Drawing samples of values of model
- Parameters
training (physbo.variable) – training dataset. If already trained, the model does not use this.
test (physbo.variable) – inputs
N (int) – number of samples (default: 1)
- Returns
- Return type
numpy.ndarray
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