Source code for nubo.utils.generate_inputs

import torch
from torch import Tensor
from typing import Optional
from .latin_hypercube import LatinHypercubeSampling
from .transform import unnormalise

[docs] def gen_inputs(num_points: int, num_dims: int, bounds: Optional[Tensor]=None, num_lhd: Optional[int]=1000) -> Tensor: """ Generate data inputs from a maximin Latin hypercube design or from a uniform distribution for one data point. Parameters ---------- num_points : ``int`` Number of points. num_dims : ``int`` Number of input dimensions. bounds : ``torch.Tensor``, optional (size 2 x `num_dims`) Bounds of input space, default is none. If none, bounds are a [0, 1]^`num_dims`. num_lhd : ``int``, optional Number of Latin hypercube designs to consider, default is 1000. Returns ------- ``torch.Tensor`` (size `num_points` x `num_dims`) Input data. """ if bounds == None: bounds = torch.Tensor([[0.]*num_dims, [1.]*num_dims]) if num_points == 1: points = torch.rand((1, num_dims)) else: lhs = LatinHypercubeSampling(dims=num_dims) points = lhs.maximin(points=num_points, samples=num_lhd) points = unnormalise(points, bounds=bounds) return points