base
- class thefittest.base.EphemeralNode(generator: Callable)
Methods
__call__
()Call self as a function.
- class thefittest.base.FunctionalNode(value: Any, sign: str | None = None)
- class thefittest.base.Net(inputs: Set | None = None, hidden_layers: List | None = None, outputs: Set | None = None, connects: ndarray[Any, dtype[int64]] | None = None, weights: ndarray[Any, dtype[float64]] | None = None, activs: Dict[int, int] | None = None)
Methods
copy
forward
get_graph
plot
- forward(X: ndarray[Any, dtype[float64]], weights: ndarray[Any, dtype[float64]] | None = None) ndarray[Any, dtype[float64]]
- get_graph() Dict
- plot(ax=None) None
- class thefittest.base.TerminalNode(value: Any, name: str)
- class thefittest.base.Tree(nodes: List[FunctionalNode | TerminalNode | EphemeralNode], n_args: List[int] | ndarray[Any, dtype[int64]] | None = None)
Methods
__call__
()Call self as a function.
concat
copy
full_growing_method
get_args_id
get_common_region
get_graph
get_levels
get_max_level
growing_method
plot
random_tree
set_terminals
subtree
subtree_id
- classmethod full_growing_method(uniset: UniversalSet, max_level: int) Tree
- get_args_id(index: int) ndarray[Any, dtype[int64]]
- get_common_region(other_trees: List | ndarray[Any, dtype[_ScalarType_co]]) Tuple
- get_graph(keep_id: bool = False) Dict
- get_levels(index: int) ndarray[Any, dtype[int64]]
- get_max_level() int64
- classmethod growing_method(uniset: UniversalSet, max_level: int) Tree
- plot(ax: Any = None) None
- classmethod random_tree(uniset: UniversalSet, max_level: int) Tree
- subtree_id(index: int) Tuple[int, int]
- class thefittest.base.UniversalSet(functional_set: Tuple[FunctionalNode, ...], terminal_set: Tuple[TerminalNode | EphemeralNode, ...])
- thefittest.base.init_symbolic_regression_uniset(X: ndarray[Any, dtype[float64]], functional_set_names: Tuple[str, ...] = ('cos', 'sin', 'add', 'sub', 'mul', 'div'), ephemeral_node_generators: Tuple[Callable, ...] | None = None)