Number_of_samples ( int) – The number of samples to be generated. It is assumed that no range contains no output variable information in this case. When the aim is to generate a set of samples from a data range, the dataset must be a list containing two lists of equal lengths which contain the variable bounds and sampling_type option must be set to “creation”.
The output variable (y) is assumed to be supplied in the last column. When the aim is to select a set of samples from an existing dataset, the dataset must be a NumPy Array or a Pandas Dataframe and sampling_type option must be set to “selection”. The input data set or range to be sampled. Parametersĭata_input ( NumPy Array, Pandas Dataframe or list) – Initialization of LatinHypercubeSampling class. sample_points () _init_ ( data_input, number_of_samples = None, sampling_type = None ) ¶ LatinHypercubeSampling ( data, 10, sampling_type = "selection" ) > samples = b. # To select 10 LHS samples from "data" > b = rbf. To use: call class with inputs, and then run sample_points method. It should be noted that no minimax criterion has been used in this implementation, so the LHS samples selected will not have space-filling properties. The function returns LHS samples which have been selected randomly after sample space stratification. LatinHypercubeSampling ( data_input, number_of_samples = None, sampling_type = None ) ¶Ī class that performs Latin Hypercube Sampling. The samples can be generated from a set of provided bounds.Īvailable Methods ¶ class. The samples can be selected from a user-provided dataset, or The method carries out Latin Hypercube sampling. Into bins of equal probability with the goal of attaining a more even distribution of sample points in the parameter space that would be possible with pure random sampling. LHS is a stratified random sampling method originally developed for efficient uncertainty assessment.
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