bates_param ( int, optional) – Each coordinate of a point sampled in a stratum is determined as.full_output ( bool) – If True, also the strata are returned as second argument.Num_points ** (1/dimension) must be integer. Stochastic, but more uniform than a random uniform sample. This algorithm divides the hypercube into num_points subcells andĭraws a random uniform point from each cell. Stratified sampling in the unit hypercube. conventional_stratified_sampling ( num_points, dimension, full_output=False, bates_param=1 ) ¶ 281–297, University of California Press,ĭiversipy.hycusampling. Proceedings of the Fifthīerkeley Symposium on Mathematical Statistics and Probability, Some methods for classification andĪnalysis of multivariate observations. dist_matrix_function ( callable, optional) – The function to compute the distances.In its default setup, this algorithm converges to a centroidal Voronoi random_k_means ( num_points, dimension, num_steps=None, initial_points=None, dist_matrix_function=None, callback=None ) ¶ PhD Thesis, Technische Universität Dortmund.ĭiversipy.hycusampling. callback ( callable, optional) – If provided, it is called in each iteration with the current point.use_reflection_edge_correction ( bool, optional) – If True, selection pressure in boundary regions will be increased byĬonsidering additional distances to virtual points, which areĬreated by mirroring the real points at the boundary.
existing_points ( array_like, optional) – Points that cannot be modified anymore, but should be considered in.initial_points ( array_like, optional) – The point set to improve (if None, a sample is drawn with.num_steps ( int, optional) – The number of iterations to carry out.dimension ( int) – The dimension of the space.num_points ( int) – The number of points to generate.In the set, 2) existing (fixed) points, and 3) the boundary of the Maximize the minimal distance of a point in the set to 1) other points This algorithm carries out a user-specified number of iterations to Maximize the minimal distance in the unit hypercube with extensions. maximin_reconstruction ( num_points, dimension, num_steps=None, initial_points=None, existing_points=None, use_reflection_edge_correction=False, dist_matrix_function=None, callback=None ) ¶ Sampling algorithms ¶ diversipy.hycusampling.
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