evolvepy.generator.selection package

Submodules

evolvepy.generator.selection.selection module

evolvepy.generator.selection.selection.choice(a, size=None, replace=True, p=None)

Generates a random sample from a given 1-D array

New in version 1.7.0.

Note

New code should use the choice method of a default_rng() instance instead; please see the random-quick-start.

Parameters:

a (1-D array-like or int) – If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a were np.arange(a)
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.
replace (boolean, optional) – Whether the sample is with or without replacement
p (1-D array-like, optional) – The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a.

Returns:

samples – The generated random samples

Return type:

single item or ndarray

Raises:

ValueError – If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size

See also

randint, shuffle, permutation

Generator.choice: which should be used in new code

Notes

Sampling random rows from a 2-D array is not possible with this function, but is possible with Generator.choice through its axis keyword.

Examples

Generate a uniform random sample from np.arange(5) of size 3:

>>> np.random.choice(5, 3)
array([0, 3, 4]) # random
>>> #This is equivalent to np.random.randint(0,5,3)

Generate a non-uniform random sample from np.arange(5) of size 3:

>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
array([3, 3, 0]) # random

Generate a uniform random sample from np.arange(5) of size 3 without replacement:

>>> np.random.choice(5, 3, replace=False)
array([3,1,0]) # random
>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]

Generate a non-uniform random sample from np.arange(5) of size 3 without replacement:

>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
array([2, 3, 0]) # random

Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance:

>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
>>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
      dtype='<U11')

evolvepy.generator.selection.selection.default_selection()[source]

evolvepy.generator.selection.selection.isin(val, arr)[source]

Select the n best individuals assuming the fitness_array is decreasingly sorted.

Args: fitness_array (np.typing.ArrayLike): array with evaluated fitness from the generation n_selection (int): number of individuals that will be selected

Returns: np.arange(0, n_selection, 1, dtype=np.int32) (np.ndarray): array with indexs of selected individuals

Select the best individuals stocaticaly with the probability of beign chosen equivalent to the individual fitness.

Parameters:

fitness_array (np.typing.ArrayLike) – array with evaluated fitness from the generation
n_selection (int) – number of individuals that will be selected

Returns:

array with indexs of selected individuals

Return type:

selected (np.ndarray)

Select the best individuals in a one vs one test.

Parameters:

fitness_array (np.typing.ArrayLike) – Array with evaluated fitness from the generation
n_selection (int) – Number of individuals that will be selected

Returns:

Array with indexs of selected individuals

Return type:

selected (np.ndarray)

Module contents

EvolvePy’s callbacks. Objects that can be called upon during evolution to change its behavior.