geneticengine.representations.stackgggp
This module relies on much of the GE implementation.
The only difference is the genotype to phenotype mapping, which uses stacks.
Package Contents
Classes
Helper class that provides a standard way to create an ABC using |
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Abstract base class for generic types. |
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Abstract base class for generic types. |
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Abstract base class for generic types. |
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Base class for protocol classes. |
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This representation uses a list of integers to guide the generation of |
Functions
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When given Annotated[T, <annotations>] or List[T], this function returns |
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Annotated[T, <annotations>] or List[T], this function returns |
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Returns whether a class is a Protocol or AbstractBaseClass. |
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Returns whether a type is List[T] for any T. |
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Returns if type is a metahandler. AnnotatedType[int, IntRange(3,10)] is |
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- class geneticengine.representations.stackgggp.Grammar(starting_symbol, considered_subtypes=None, expansion_depthing=False)
- Parameters:
starting_symbol (type)
considered_subtypes (list[type] | None)
expansion_depthing (bool)
- starting_symbol: type
- alternatives: dict[type, list[type]]
- distanceToTerminal: dict[Any, int]
- all_nodes: set[type]
- recursive_prods: set[type]
- terminals: set[type]
- non_terminals: set[type]
- abstract_dist_to_t: dict[type, dict[type, int]]
- validate()
- register_alternative(nonterminal, nodetype)
Register a production A->B Call multiple times with same A to register many possible alternatives.
- Parameters:
nonterminal (type)
nodetype (type)
- register_type(ty)
- Parameters:
ty (type)
- __repr__()
Return repr(self).
- get_all_symbols()
All symbols in the current grammar, including terminals.
- Return type:
tuple[set[type], set[type], set[type]]
- get_distance_to_terminal(ty)
Returns the current distance to terminal of a given type.
- Parameters:
ty (type)
- Return type:
int
- get_min_tree_depth()
Returns the minimum depth a tree must have.
- get_max_node_depth()
Returns the maximum minimum depth a node can have.
- preprocess()
Computes distanceToTerminal via a fixpoint algorithm.
- Return type:
None
- get_weights()
- update_weights(learning_rate, extra_weights)
- get_branching_average_proxy(r, get_nodes_depth_specific, n_individuals=100, max_depth=17)
Get a proxy for the average branching factor of a grammar.
This proxy is a dictionary with the number of non-terminals in each depth of the grammar, obtained by generating <n_individuals> random individuals with depth <max_depth> and analyzing those.
- Parameters:
n_individuals (int)
max_depth (int)
- get_grammar_properties_summary()
Returns a summary of grammar properties:
A depth range (minimum depth and maximum depth of the grammar)
The number of Non-Terminal symbols in the grammar
- A summary of production statistics:
Frequency of Productions in the Right Hand side
The number of recursive productions
Per non-terminal, all the alternative productions
The total number of productions
The average number of productions per non-terminal
The average non-terminals per production for each non-terminal
- Return type:
- class geneticengine.representations.stackgggp.RandomSource
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- abstract randint(min, max, prod='')
- Parameters:
min (int)
max (int)
prod (str)
- Return type:
int
- abstract random_float(min, max, prod='')
- Parameters:
min (float)
max (float)
prod (str)
- Return type:
float
- choice(choices, prod='')
- Parameters:
choices (list[T])
prod (str)
- Return type:
T
- choice_weighted(choices, weights, prod='')
- Parameters:
choices (list[T])
weights (list[float])
prod (str)
- Return type:
T
- shuffle(lst)
- Parameters:
lst (list[T])
- pop_random(lst)
- Parameters:
lst (list[T])
- Return type:
T
- random_bool(prod='')
- Parameters:
prod (str)
- Return type:
bool
- normalvariate(mean, sigma, prod)
- Parameters:
mean (float)
sigma (float)
prod (str)
- Return type:
float
- class geneticengine.representations.stackgggp.RepresentationWithCrossover
Bases:
Generic
[g
]Abstract base class for generic types.
A generic type is typically declared by inheriting from this class parameterized with one or more type variables. For example, a generic mapping type might be defined as:
class Mapping(Generic[KT, VT]): def __getitem__(self, key: KT) -> VT: ... # Etc.
This class can then be used as follows:
def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT: try: return mapping[key] except KeyError: return default
- abstract crossover(random, parent1, parent2)
- Parameters:
parent1 (g)
parent2 (g)
- Return type:
tuple[g, g]
- class geneticengine.representations.stackgggp.RepresentationWithMutation
Bases:
Generic
[g
]Abstract base class for generic types.
A generic type is typically declared by inheriting from this class parameterized with one or more type variables. For example, a generic mapping type might be defined as:
class Mapping(Generic[KT, VT]): def __getitem__(self, key: KT) -> VT: ... # Etc.
This class can then be used as follows:
def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT: try: return mapping[key] except KeyError: return default
- abstract mutate(random, internal)
- Parameters:
internal (g)
- Return type:
g
- class geneticengine.representations.stackgggp.Representation
Bases:
Generic
[g
,p
]Abstract base class for generic types.
A generic type is typically declared by inheriting from this class parameterized with one or more type variables. For example, a generic mapping type might be defined as:
class Mapping(Generic[KT, VT]): def __getitem__(self, key: KT) -> VT: ... # Etc.
This class can then be used as follows:
def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT: try: return mapping[key] except KeyError: return default
- abstract create_genotype(random, **kwargs)
- Parameters:
- Return type:
g
- abstract genotype_to_phenotype(internal)
- Parameters:
internal (g)
- Return type:
p
- class geneticengine.representations.stackgggp.TreeNode
Bases:
Protocol
Base class for protocol classes.
Protocol classes are defined as:
class Proto(Protocol): def meth(self) -> int: ...
Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:
class C: def meth(self) -> int: return 0 def func(x: Proto) -> int: return x.meth() func(C()) # Passes static type check
See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as:
class GenProto(Protocol[T]): def meth(self) -> T: ...
- gengy_labeled: bool
- gengy_distance_to_term: int
- gengy_nodes: int
- gengy_weighted_nodes: int
- gengy_types_this_way: dict[type, list[Any]]
- gengy_init_values: tuple[Any]
- geneticengine.representations.stackgggp.get_arguments(n)
- Parameters:
n – production
- Returns:
list((argname, argtype))
- Return type:
list[tuple[str, type]]
- geneticengine.representations.stackgggp.get_generic_parameter(ty)
When given Annotated[T, <annotations>] or List[T], this function returns T.
- Parameters:
ty (type[Any])
- Return type:
type
- geneticengine.representations.stackgggp.get_generic_parameters(ty)
Annotated[T, <annotations>] or List[T], this function returns Dict[T,]
- Parameters:
ty (type[Any])
- Return type:
list[type]
- geneticengine.representations.stackgggp.is_abstract(t)
Returns whether a class is a Protocol or AbstractBaseClass.
- Parameters:
t (type)
- Return type:
bool
- geneticengine.representations.stackgggp.is_generic_list(ty)
Returns whether a type is List[T] for any T.
- Parameters:
ty (type[Any])
- geneticengine.representations.stackgggp.is_metahandler(ty)
Returns if type is a metahandler. AnnotatedType[int, IntRange(3,10)] is an example of a Metahandler.
Verification is done using the __metadata__, which is the first argument of Annotated
- Parameters:
ty (type)
- Return type:
bool
- class geneticengine.representations.stackgggp.ListWrapper
Bases:
geneticengine.random.sources.RandomSource
- dna: list[int]
- index: int = 0
- randint(min, max, prod='')
- Parameters:
min (int)
max (int)
prod (str)
- Return type:
int
- random_float(min, max, prod='')
- Parameters:
min (float)
max (float)
prod (str)
- Return type:
float
- geneticengine.representations.stackgggp.add_to_stacks(stacks, t, v)
- Parameters:
stacks (dict[type, list[Any]])
t (type)
v (Any)
- geneticengine.representations.stackgggp.create_tree_using_stacks(g, r, max_depth=10)
- Parameters:
r (ListWrapper)
max_depth (int)
- class geneticengine.representations.stackgggp.StackBasedGGGPRepresentation(grammar, max_depth, gene_length=256)
Bases:
geneticengine.representations.api.Representation
[Genotype
,geneticengine.solutions.tree.TreeNode
],geneticengine.representations.api.RepresentationWithMutation
[Genotype
],geneticengine.representations.api.RepresentationWithCrossover
[Genotype
]This representation uses a list of integers to guide the generation of trees in the phenotype.
- Parameters:
grammar (geneticengine.grammar.grammar.Grammar)
max_depth (int)
gene_length (int)
- create_genotype(random, **kwargs)
- Parameters:
- Return type: