117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
from .chebyshev import chebyshev_roots
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import torch
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from .location import Location
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from .label_tensor import LabelTensor
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class Span(Location):
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def __init__(self, span_dict):
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self.fixed_ = {}
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self.range_ = {}
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for k, v in span_dict.items():
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if isinstance(v, (int, float)):
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self.fixed_[k] = v
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elif isinstance(v, (list, tuple)) and len(v) == 2:
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self.range_[k] = v
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else:
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raise TypeError
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print(span_dict, self.fixed_, self.range_, 'YYYYYYYYYY')
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@property
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def variables(self):
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return list(self.fixed_.keys()) + list(self.range_.keys())
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def update(self, new_span):
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self.fixed_.update(new_span.fixed_)
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self.range_.update(new_span.range_)
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def _sample_range(self, n, mode, bounds):
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"""
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"""
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dim = bounds.shape[0]
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if mode in ['chebyshev', 'grid'] and dim != 1:
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raise RuntimeError('Something wrong in Span...')
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if mode == 'random':
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pts = torch.rand(size=(n, dim))
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elif mode == 'chebyshev':
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pts = chebyshev_roots(n).mul(.5).add(.5).reshape(-1, 1)
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elif mode == 'grid':
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pts = torch.linspace(0, 1, n).reshape(-1, 1)
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elif mode == 'lh' or mode == 'latin':
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from scipy.stats import qmc
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sampler = qmc.LatinHypercube(d=dim)
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pts = sampler.random(n)
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pts = torch.from_numpy(pts)
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pts *= bounds[:, 1] - bounds[:, 0]
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pts += bounds[:, 0]
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return pts
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def sample(self, n, mode='random', variables='all'):
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"""TODO
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"""
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def _1d_sampler(n, mode, variables):
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""" Sample independentely the variables and cross the results"""
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tmp = []
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for variable in variables:
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if variable in self.range_.keys():
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bound = torch.tensor([self.range_[variable]])
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pts_variable = self._sample_range(n, mode, bound)
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pts_variable = pts_variable.as_subclass(LabelTensor)
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pts_variable.labels = [variable]
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tmp.append(pts_variable)
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result = tmp[0]
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for i in tmp[1:]:
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result = result.append(i, mode='cross')
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for variable in variables:
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if variable in self.fixed_.keys():
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value = self.fixed_[variable]
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pts_variable = torch.tensor([[value]]).repeat(
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result.shape[0], 1)
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pts_variable = pts_variable.as_subclass(LabelTensor)
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pts_variable.labels = [variable]
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result = result.append(pts_variable, mode='std')
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return result
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def _Nd_sampler(n, mode, variables):
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""" Sample ll the variables together """
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bounds = torch.tensor(
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[v for k, v in self.range_.items() if k in variables]
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)
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result = self._sample_range(n, mode, bounds)
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result = result.as_subclass(LabelTensor)
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result.labels = list(self.range_.keys())
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for variable in variables:
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if variable in self.fixed_.keys():
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value = self.fixed_[variable]
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pts_variable = torch.tensor([[value]]).repeat(
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result.shape[0], 1)
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pts_variable = pts_variable.as_subclass(LabelTensor)
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pts_variable.labels = [variable]
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result = result.append(pts_variable, mode='std')
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return result
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if variables == 'all':
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variables = list(self.range_.keys()) + list(self.fixed_.keys())
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if mode in ['grid', 'chebyshev']:
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return _1d_sampler(n, mode, variables)
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elif mode in ['random', 'lhs']:
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return _Nd_sampler(n, mode, variables)
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else:
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raise ValueError(f'mode={mode} is not valid.')
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