Set Based Implementation#

GlobalSetBasedReflectiveEquilibrium#

class rethon.GlobalSetBasedReflectiveEquilibrium#

Bases: GlobalReflectiveEquilibrium

Methods

account(commitments, theory)	Account of the theory w.r.t.
achievement(commitments, theory, ...)	The achievement function $Z$.
commitment_candidates(**kwargs)	Implements basics.ReflectiveEquilibrium.commitment_candidates()
default_model_parameters()	Default model parameters of the standard model.
dialectical_structure()	Return the dialectical structure on which the model is based.
faithfulness(commitments, initial_commitments)	Faithfulness of the commitments w.r.t.
finished(**kwargs)	Implements ReflectiveEquilibrium.finished().
global_optima(initial_commitments)	Searches for globally optimal theory-commitment pairs (according to the achievement function).
hamming_distance(position1, position2, penalties)	The weighted Hamming distance.
is_dirty()	Checks whether the model demand an update of internal attributes.
model_name()	Model name.
model_parameter(name)	Returns model parameters by names.
model_parameter_names()	Returns names (keys) of the model parameters.
model_parameters()	Getting all model parameters as dict.
model_parameters_set_to_default()	Resets the model parameters to their default values.
next_step([time])	Triggers search for next commitments/theory.
penalty(position1, position2, sentence, ...)	A penalty function.
pick_commitment_candidate(...)	Implements :py:func:ReflectiveEquilibrium.pick_commitment_candidate.
pick_theory_candidate(theory_candidates, ...)	Implements :py:func:ReflectiveEquilibrium.pick_theory_candidate.
re_process([initial_commitments, max_steps])	Process of finding a reflective equilibrium based on given initial commitments.
reset_model_parameters(parameters)	Resetting model parameters.
set_dialectical_structure(dialectical_structure)	Set the dialectical structure on which the model is based.
set_dirty(dirty)	Should be used to indicate whether attributes are reset that demand updating other attributes of the model.
set_initial_state(initial_commitments)	Set the initial state of the model.
set_model_parameters([parameters])	Setting model parameters either by a dictionary or key-value pairs.
set_state(state)	Setting the current state of the model.
state()	Getting the current state of the model as REState.
systematicity(theory)	Systematicity of the theory.
theory_candidates(**kwargs)	Implements basics.ReflectiveEquilibrium.theory_candidates()
update(**kwargs)	Implements basics.ReflectiveEquilibrium.update()

get_id
set_id

penalty(position1, position2, sentence, penalties)#

A penalty function.

The penalty function calculates a penalty value for two sentences of two positions given a list of penalty-value and is defined by

$$\begin{array}{r}{d}_{d_0,d_1,d_2,d_3}(\mathcal{A},\mathcal{B},\{s,\mathrm{\neg }s\}):=\{\begin{array}{l}{d}_3\text{ if }\{s,\mathrm{\neg }s\}\subset (\mathcal{A}\cup \mathcal{B}),\\ d_2\text{ if }\{s,\mathrm{\neg }s\}\cap \mathcal{A}\ne \mathrm{\varnothing }\text{ and }\{s,\mathrm{\neg }s\}\cap \mathcal{B}=\mathrm{\varnothing },\\ d_3\text{ if }\{s,\mathrm{\neg }s\}\cap \mathcal{A}=\mathrm{\varnothing }\text{ and }\{s,\mathrm{\neg }s\}\cap \mathcal{B}\ne \mathrm{\varnothing },\\ d_0\text{ otherwise.}\end{array}\end{array}$$

Parameters:

• position1 (Position) –

• position2 (Position) –

• sentence (int) – The index of the sentences with both positions.

• penalties (List[float]) – A float-list of penalty values.

Return type:

float

Returns: