pg.geno.Float¶

Accessible via pg.geno.Float.

class Float(min_value, max_value, scale=None, hints=None, location=, name=None)[source]¶

Represents the genotype for a float-value genome.

Example:

# Create a float decision point within range [0.1, 1.0].
decision_point = pg.geno.floatv(0.1, 1.0)

Attributes:

`decision_points`	Returns all decision points in their declaration order.
`is_categorical`	Returns True if current node is a categorical choice.
`is_custom_decision_point`	Returns True if current node is a custom decision point.
`is_numerical`	Returns True if current node is numerical decision.
`is_subchoice`	Returns True if current node is a subchoice of a multi-choice.
`space_size`	Returns the size of the search space.

Methods:

`format`([compact, verbose, root_indent, show_id])	Format this object.
`validate`(dna)	Validate whether a DNA value conforms to this spec.

property decision_points: List[DecisionPoint][source]¶: Returns all decision points in their declaration order.

format(compact=True, verbose=True, root_indent=0, show_id=True, **kwargs)[source]¶: Format this object.

property is_categorical: bool[source]¶: Returns True if current node is a categorical choice.

property is_custom_decision_point: bool[source]¶: Returns True if current node is a custom decision point.

property is_numerical: bool[source]¶: Returns True if current node is numerical decision.

property is_subchoice: bool[source]¶: Returns True if current node is a subchoice of a multi-choice.

property space_size: int[source]¶: Returns the size of the search space. Use -1 for infinity.

validate(dna)[source]¶

Validate whether a DNA value conforms to this spec.