mutation

The functions perform utilize Numba decorators for Just-In-Time compilation.

Contains functions that generate sets of mutated clones from continuous or binary vectors, simulating the clonal expansion process in artificial immune systems.

Module: aisp.base.immune
Import: from aisp.base.immune import mutation

Functions

clone_and_mutate_continuous

@njit([(types.float64[:], types.int64, types.float64)], cache=True)
def clone_and_mutate_continuous(
    vector: npt.NDArray[np.float64],
    n: int,
    mutation_rate: float
) -> npt.NDArray[np.float64]:
    ...

Generate a set of mutated clones from a cell represented by a continuous vector.

This function creates n clones of the input vector and applies mutations to each of them, simulating the process of clonal expansion in artificial immune systems.

Parameters

Name	Type	Default	Description
vector	npt.NDArray[np.float64]	-	The original immune cell with continuous values to be cloned and mutated.
n	int	-	The number of mutated clones to be generated.
mutation_rate	float	-	If 0 ≤ mutation_rate < 1, mutation probability per feature. Otherwise, a random number of features is mutated.

Returns

Type	Description
npt.NDArray[np.float64]	An Array(n, len(vector)) containing the n mutated clones of the original vector.

clone_and_mutate_binary

@njit([(types.boolean[:], types.int64, types.float64)], cache=True)
def clone_and_mutate_binary(
    vector: npt.NDArray[np.bool_],
    n: int,
    mutation_rate: float
) -> npt.NDArray[np.bool_]:
    ...

Generate a set of mutated clones from a cell represented by a binary vector.

This function creates n clones of the input binary vector and applies mutations to the bits, simulating clonal expansion in artificial immune systems with discrete representations.

Parameters

Name	Type	Default	Description
vector	npt.NDArray[np.float64]	-	The original immune cell with binary values to be cloned and mutated.
n	int	-	The number of mutated clones to be generated.
mutation_rate	float	-	If 0 ≤ mutation_rate < 1, mutation probability per feature. Otherwise, a random number of features is mutated.

Returns

Type	Description
npt.NDArray[np.bool_]	An Array(n, len(vector)) containing the n mutated clones of the original vector.

clone_and_mutate_ranged

@njit([(types.float64[:], types.int64, types.float64[:, :], types.float64)], cache=True)
def clone_and_mutate_ranged(
    vector: npt.NDArray[np.float64],
    n: int,
    bounds: npt.NDArray[np.float64],
    mutation_rate: float,
) -> npt.NDArray[np.float64]:
    ...

Generate a set of mutated clones from a cell represented by custom ranges per dimension.

This function creates n clones of the input vector and applies mutations to each of them, simulating the process of clonal expansion in artificial immune systems.

Parameters

Name	Type	Default	Description
vector	npt.NDArray[np.float64]	-	The original immune cell with continuous values to be cloned and mutated.
n	int	-	The number of mutated clones to be generated.
bounds	np.ndarray	-	Array (n_features, 2) with min and max per dimension.
mutation_rate	float	-	If 0 ≤ mutation_rate < 1, mutation probability per feature. Otherwise, a random number of features is mutated.

Returns

Type	Description
npt.NDArray[np.float64]	An Array(n, len(vector)) containing the n mutated clones of the original vector.

clone_and_mutate_continuous

@njit([(types.int64[:], types.int64, types.float64)], cache=True)
def clone_and_mutate_permutation(
    vector: npt.NDArray[np.int64],
    n: int,
    mutation_rate: float
) -> npt.NDArray[np.int64]:
    ...

Generate a set of mutated clones by permutation.

Parameters

Name	Type	Default	Description
vector	npt.NDArray[np.int64]	-	The original immune cell with permutation values to be cloned and mutated.
n	int	-	The number of mutated clones to be generated.
mutation_rate	float	-	Probability of mutating each feature 0 ≤ mutation_rate < 1.

Returns

Type	Description
npt.NDArray[np.float64]	An Array(n, len(vector)) containing the n mutated clones of the original vector.