pyhrf.sandbox.stats.
GSVariable
(name, initialization, do_sampling=True, axes_names=None, axes_domains=None)¶check_against_truth
(atol, rtol, inaccuracy_handling='print')¶check_initialization_arg
(ia)¶enable_sampling
(flag=True)¶get_accuracy_against_truth
(abs_error, rel_error, fv, tv, atol, rtol)¶Return the accuray of the estimate fv, compared to the true value tv
get_custom_init
()¶Must return a numpy.ndarray. Consider initializing with a good guess so that sampling converges more quickly.
get_estim_value_for_check
()¶get_random_init
()¶Must return a random numpy.ndarray that will then be used as init value for sampling. For example, it can be a sample from the prior distribution. This function will also be used to test for the sensitivity to initialization.
get_true_value_for_check
()¶get_variable
(vname)¶get_variable_value
(vname)¶Short-hand to get variable among all those defined in the parent sampler
init_observables
()¶init_sampling
()¶reset
()¶sample
()¶Draw a sample conditionally to the current Gibbs Sampler state. Must return a numpy.ndarray.
Variables which have been registered in the parent GibbsSampler object can be retrieved via methods self.get_variable(var_name) and self.get_variable_value(var_name)
set_init_value
()¶Set the initial value of self.current_value, depending on the initialization scenario (random, custom, truth).
set_initialization
(init)¶set_outputs
(outputs, output_type='ndarray')¶Parameters: |
|
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Return: None
set_true_value
(true_value)¶track_obs_quantity
(name, quantity, history_pace=None, axes_names=None, axes_domains=None)¶track_sampled_quantity
(name, quantity, history_pace=None, axes_names=None, axes_domains=None)¶update_observables
()¶Update quantities after the burnin period
pyhrf.sandbox.stats.
GibbsSampler
(sampled_variables, nb_its_max, obs_pace=1, burnin=0.3, sample_hist_pace=-1, obs_hist_pace=-1)¶check_against_truth
(default_atol=0.1, default_rtol=0.1, var_specific_atol=None, var_specific_rtol=None, inaccuracy_handling='print')¶get_outputs
(output_type='ndarray')¶output_type : ‘ndarray’ or ‘cuboid’
get_variable
(vname)¶get_variable_value
(vname)¶iterate_sampling
()¶reset
()¶run
()¶set_initialization
(vname, init)¶set_true_value
(vname, true_value)¶set_true_values
(true_values)¶set_variable
(name, var)¶set_variables
(var_dict)¶stop_criterion
(iteration)¶track_obs_quantity
(name, q, history_pace=None, axes_names=None, axes_domains=None)¶track_sampled_quantity
(name, q, history_pace=None, axes_names=None, axes_domains=None)¶pyhrf.sandbox.stats.
Trajectory
(variable, history_pace, history_start, max_iterations, init_iteration=None, axes_names=None, axes_domains=None)¶Keep track of a numpy array that is modified _inplace_ iteratively TODO: when mature, should be moved to pyhrf.ndarray should replace pyhrf.jde.samplerbase.Trajectory
get_last
()¶Return the last saved element
to_cuboid
()¶Pack the current trajectory in a xndarray
update
(iteration)¶Record the current variable value