BinarySelectionSolution#

class pybrops.breed.prot.sel.soln.BinarySelectionSolution.BinarySelectionSolution(ndecn, decn_space, decn_space_lower, decn_space_upper, nobj, obj_wt, nineqcv, ineqcv_wt, neqcv, eqcv_wt, nsoln, soln_decn, soln_obj, soln_ineqcv, soln_eqcv, **kwargs)[source]#

Bases: BinarySolution, SelectionSolution

Class representing subset selection solutions.

Constructor for BinarySolution.

Parameters:

ndecn (Integral) – Number of decision variables.
decn_space (numpy.ndarray) – A 1d array containing the set of available elements
decn_space_lower (numpy.ndarray) – A 1d array representing the lower bound of the decision variables.
decn_space_upper (numpy.ndarray) – A 1d array representing the upper bound of the decision variables.
nobj (Integral) – Number of objectives for the problem.
obj_wt (numpy.ndarray, Real, None) –
Objective function weights. Weights from this vector are applied to objective function values via the Hadamard product. If values are 1.0 or -1.0, this can be used to specify minimizing and maximizing objectives, respectively.

If obj_wt is numpy.ndarray, then the array must be of shape (nobj,).

If obj_wt is Real, then the value is broadcast to a numpy.ndarray of shape (nobj,).

If obj_wt is None, then the value 1.0 is broadcast to a numpy.ndarray of shape (nobj,). This assumes that all objectives are to be minimized.
nineqcv (Integral, None) –
Number of inequality constraint violation functions. This is equivalent to the vector length returned by the ineqcv_trans function. Must be Integral greater than or equal to zero.

If nineqcv is None, then set to zero.
ineqcv_wt (numpy.ndarray, None) –
Inequality constraint violation function weights. Weights from this vector are applied to inequality constraint violation function values via the Hadamard product. If values are 1.0 or -1.0, this can be used to specify minimizing and maximizing constraints, respectively.

If ineqcv_wt is numpy.ndarray, then the array must be of shape (nineqcv,).

If ineqcv_wt is Real, then the value is broadcast to a numpy.ndarray of shape (nineqcv,).

If ineqcv_wt is None, then the value 1.0 is broadcast to a numpy.ndarray of shape (nineqcv,). This assumes that all constraints are to be minimized.
neqcv (Integral, None) –
Number of equality constraint violations. This is equivalent to the vector length returned by the eqcv_trans function. Must be Integral greater than or equal to zero.

If neqcv is None, then set to zero.
eqcv_wt (numpy.ndarray, None) –
Equality constraint violation function weights. Weights from this vector are applied to equality constraint violation function values via the Hadamard product. If values are 1.0 or -1.0, this can be used to specify minimizing and maximizing constraints, respectively.

If eqcv_wt is numpy.ndarray, then the array must be of shape (neqcv,).

If eqcv_wt is Real, then the value is broadcast to a numpy.ndarray of shape (neqcv,).

If eqcv_wt is None, then the value 1.0 is broadcast to a numpy.ndarray of shape (neqcv,). This assumes that all constraints are to be minimized.
nsoln (Integral) – Number of solutions.
soln_decn (numpy.ndarray) – Solution decision vectors for each solution. This matrix is of shape (nsoln,ndecn).
soln_obj (numpy.ndarray) – Solution objective vectors for each solution. This matrix is of shape (nsoln,nobj).
soln_ineqcv (numpy.ndarray) – Solution inequality constraint violiation vectors for each solution. This matrix is of shape (nsoln,nineqcv)
soln_eqcv (numpy.ndarray) – Solution equality constraint violiation vectors for each solution. This matrix is of shape (nsoln,neqcv)
kwargs (dict) – Additional keyword arguments.

Methods

Attributes

`decn_space`	Decision space boundaries.
`decn_space_lower`	Lower boundary of the decision space.
`decn_space_upper`	Upper boundary of the decision space.
`eqcv_wt`	Equality constraint violation function weights.
`ineqcv_wt`	Inequality constraint violation function weights.
`ndecn`	Number of decision variables.
`neqcv`	Number of equality constraint violations.
`nineqcv`	Number of inequality constraint violation functions.
`nobj`	Number of objectives.
`nsoln`	Number of solutions to the problem.
`obj_wt`	Objective function weights.
`soln_decn`	Matrix of solution vectors in the decision space.
`soln_eqcv`	Solution equality constraint violation function values.
`soln_ineqcv`	Solution inequality constraint violation function values.
`soln_obj`	Solution objective function values.

property decn_space: ndarray | None#: Decision space boundaries.

property decn_space_lower: ndarray | None#: Lower boundary of the decision space.

property decn_space_upper: ndarray | None#: Upper boundary of the decision space.

property eqcv_wt: ndarray#: Equality constraint violation function weights.

property ineqcv_wt: ndarray#: Inequality constraint violation function weights.

property ndecn: Integral#: Number of decision variables.

property neqcv: Integral#: Number of equality constraint violations.

property nineqcv: Integral#: Number of inequality constraint violation functions.

property nobj: Integral#: Number of objectives.

property nsoln: Integral#: Number of solutions to the problem.

property obj_wt: ndarray#: Objective function weights.

property soln_decn: ndarray#: Matrix of solution vectors in the decision space.

property soln_eqcv: ndarray#: Solution equality constraint violation function values.

property soln_ineqcv: ndarray#: Solution inequality constraint violation function values.

property soln_obj: ndarray#: Solution objective function values.