Taweret.mix.gaussian

class Taweret.mix.gaussian.Multivariate(x, models, n_models=0)[source]

Bases: BaseMixer

The multivariate BMM class originally introduced in the BAND SAMBA package. Combines individual models using a Gaussian form.

\[f_{\dagger} = \mathcal{N} \left( \sum_i \frac{f_i/v_i}{1/v_i}, \sum_i \frac{1}{v_i} \right)\]

Example:

m = Multivariate(x=np.linspace(), models=dict(), n_models=0)
m.predict(ci=68)
m.evaluate_weights()

Parameters:

xnumpy.linspace: Input space variable in which mixing is occurring.
modelsdict: Dict of models with BaseModel methods.
n_modelsint: Number of free parameters per model.

Returns:

None.

evaluate()[source]: Evaluate the mixed model at one set of parameters. Not needed for this mixing method.

evaluate_weights()[source]

Calculate the weights for each model in the mixed model over the input space.

Returns:

weightsnumpy.ndarray: Array of model weights calculated in the Multivariate.predict function.

property map: Return the MAP values of the parameters. Not needed for this method.

property posterior: Return the posterior of the parameters. Not needed for this mixing method.

predict(ci=68)[source]

The f_dagger function responsible for mixing the models together in a Gaussian way. Based on the first two moments of the distribution: mean and variance.

Parameters:

ciint, list: The desired credibility interval(s) (1-sigma, 2-sigma)

Returns:

mean, intervals, std_devnumpy.ndarray: The mean, credible intervals, and std_dev of the predicted mixed model

predict_weights()[source]: Predict the weights of the mixed model. Returns mean and intervals from the posterior of the weights. Not needed for this mixing method.

property prior: Return the prior of the parameters in the mixing. Not needed for this method.

prior_predict()[source]: Find the predicted prior distribution. Not needed for this mixing method.

sample_prior()[source]: Returns samples from the prior distributions for the various weight parameters. Not needed for this mixing method.

set_prior()[source]: Set the priors on the parameters. Not needed for this mixing method.

train()[source]: Train the mixed model by optimizing the weights. Not needed in this mixing method.