Bitarray Implementation

GlobalBitarrayReflectiveEquilibrium

class rethon.GlobalBitarrayReflectiveEquilibrium

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(pos1, pos2, sentence, penalties)	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

hamming_distance(position1, position2, penalties)

The weighted Hamming distance.

A weighted (asymmetric) Hamming Distance \(D\) between two positions \(\mathcal{A}\) and \(\mathcal{B}\) which is defined by

\[D_{d_0,d_1,d_2,d_3}(\mathcal{A}, \mathcal{B}):= \sum_{\{s,\neg s\} \subset S} d_{{d_0,d_1,d_2,d_3}}(\mathcal{A}, \mathcal{B}, \{s,\neg s\})\]

and based on the penalty()-function.

Parameters:

• position1 (Position) –

• position2 (Position) –

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

Return type:

float

Returns:

penalty(pos1, pos2, 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

\[\begin{split}d_{{d_0,d_1,d_2,d_3}}(\mathcal{A}, \mathcal{B}, \{s,\neg s\}):= \begin{cases} d_3 \text{ if } \{s,\neg s\}\subset(\mathcal{A}\cup \mathcal{B}), \\ d_2 \text{ if } \{s,\neg s\}\cap \mathcal{A} \neq \emptyset \text{ and } \{s,\neg s\}\cap \mathcal{B} = \emptyset, \\ d_3 \text{ if } \{s,\neg s\}\cap \mathcal{A} = \emptyset \text{ and } \{s,\neg s\}\cap \mathcal{B} \neq \emptyset, \\ d_0 \text{ otherwise.}\end{cases}\end{split}\]

Parameters:

• position1 –

• position2 –

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

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

• pos1 (Position) –

• pos2 (Position) –

Return type:

float

Returns: