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: