SALib.sample.morris.strategy module

Defines a family of algorithms for generating samples

The sample a for use with SALib.analyze.morris.analyze, encapsulate each one, and makes them interchangeable.

Example

>>> localoptimisation = LocalOptimisation()
>>> context = SampleMorris(localoptimisation)
>>> context.sample(input_sample, num_samples, num_params, k_choices, groups)

class SALib.sample.morris.strategy.SampleMorris(strategy)

Bases: object

Computes the optimum k_choices of trajectories from the input_sample.

Parameters:

strategy (Strategy)

Methods

sample(input_sample, num_samples, ...)

Computes the optimum k_choices of trajectories from the input_sample.

sample(input_sample, num_samples, num_params, k_choices, num_groups)

Computes the optimum k_choices of trajectories from the 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) – The number of groups

Returns:

An array of optimal trajectories

Return type:

numpy.ndarray

class SALib.sample.morris.strategy.Strategy

Bases: object

Declare an interface common to all supported algorithms. SampleMorris uses this interface to call the algorithm defined by a ConcreteStrategy.

Methods

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
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.

static check_input_sample(input_sample, num_params, num_samples)

Check the input_sample is valid

Checks input sample is:

• the correct size

• values between 0 and 1

Parameters:

• input_sample (numpy.ndarray)

• num_params (int)

• num_samples (int)

compile_output(input_sample, num_samples, num_params, maximum_combo, num_groups=None)

Picks the trajectories from the input

Parameters:

• input_sample (numpy.ndarray)

• num_samples (int)

• num_params (int)

• maximum_combo (list)

• num_groups (int)

static compute_distance(m, l)

Compute distance between two trajectories

Parameters:

• m (np.ndarray)

• l (np.ndarray)

Return type:

numpy.ndarray

compute_distance_matrix(input_sample, num_samples, num_params, num_groups=None, local_optimization=False)

Computes the distance between each and every trajectory

Each entry in the matrix represents the sum of the geometric distances between all the pairs of points of the two trajectories

If the groups argument is filled, then the distances are still calculated for each trajectory,

Parameters:

• input_sample (numpy.ndarray) – The input sample of trajectories for which to compute the distance matrix

• num_samples (int) – The number of trajectories

• num_params (int) – The number of factors

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

• local_optimization (bool, default=False) – If True, fills the lower triangle of the distance matrix

Returns:

distance_matrix

Return type:

numpy.ndarray

static run_checks(number_samples, k_choices)

Runs checks on k_choices

sample(input_sample, num_samples, num_params, k_choices, num_groups=None)

Computes the optimum k_choices of trajectories from the 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