dora.server.initialise_sampler

dora.server.initialise_sampler()

Initialise the Sampler Model This expects a dict containing: lower : a list of the lower bounds of the region of interest upper : a list of the upper bounds of the region of interest X_train : training data to train the sampler. a list of n elements where is

element is itself a list with d elements where n is the number of training points and d is the number of dimension (parameters of the forward model)

y_train : the observed values outputted by the forward model for each set

of parameters in X_train. another list of lists. n x s where s is the number of outputs of the forward model. s also corresponds to the number of stacks in the sampler

Optional keys in the dict: add_train_data : a boolean operator that tells the sampler to include the

training data to the sampler rather than just using it to train its parameters. Default = True

n_stacks : the number of stacks in the sampler. this must be equal to the

number of outputs of the forward model

mean : the mean of the training data. this is redundant and will be

removed shortly. Default = 0. TODO(SIMON)