SALib.sample.morris.brute module#

class SALib.sample.morris.brute.BruteForce[source]#

Bases: Strategy

Implements the brute force optimisation strategy

Methods

brute_force_most_distant(input_sample, ...)

Use brute force method to find most distant trajectories

check_input_sample(input_sample, num_params, ...)

Check the input_sample is valid

compile_output(input_sample, num_samples, ...)

Picks the trajectories from the input

compute_distance(m, l)

Compute distance between two trajectories

compute_distance_matrix(input_sample, ...[, ...])

Computes the distance between each and every trajectory

find_maximum(scores, N, k_choices)

Finds the k_choices maximum scores from scores

find_most_distant(input_sample, num_samples, ...)

Finds the 'k_choices' most distant choices from the 'num_samples' trajectories contained in 'input_sample'

mappable(combos, pairwise, distance_matrix)

Obtains scores from the distance_matrix for each pairwise combination held in the combos array

nth(iterable, n[, default])

Returns the nth item or a default value

run_checks(number_samples, k_choices)

Runs checks on k_choices

sample(input_sample, num_samples, ...[, ...])

Computes the optimum k_choices of trajectories from the input_sample.

grouper
brute_force_most_distant(input_sample: ndarray, num_samples: int, num_params: int, k_choices: int, num_groups: int = None) → List[source]#

Use brute force method to find most distant trajectories

Parameters:

  • input_sample (numpy.ndarray)

  • num_samples (int) – The number of samples to generate

  • num_params (int) – The number of parameters

  • k_choices (int) – The number of optimal trajectories

  • num_groups (int, default=None) – The number of groups

Return type:

list

find_maximum(scores, N, k_choices)[source]#

Finds the k_choices maximum scores from scores

Parameters:
Return type:

list

find_most_distant(input_sample: ndarray, num_samples: int, num_params: int, k_choices: int, num_groups: int = None) → ndarray[source]#

Finds the ‘k_choices’ most distant choices from the ‘num_samples’ trajectories contained in ‘input_sample’

Parameters:

  • input_sample (numpy.ndarray)

  • num_samples (int) – The number of samples to generate

  • num_params (int) – The number of parameters

  • k_choices (int) – The number of optimal trajectories

  • num_groups (int, default=None) – The number of groups

Return type:

numpy.ndarray

static grouper(n, iterable)[source]#
static mappable(combos, pairwise, distance_matrix)[source]#

Obtains scores from the distance_matrix for each pairwise combination held in the combos array

Parameters:
static nth(iterable, n, default=None)[source]#

Returns the nth item or a default value

Parameters:

  • iterable (iterable)

  • n (int)

  • default (default=None) – The default value to return