75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
import numpy as np
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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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@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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if mode == 'random':
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pts = np.random.uniform(size=(n, 1))
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elif mode == 'chebyshev':
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pts = np.array([chebyshev_roots(n) * .5 + .5]).reshape(-1, 1)
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elif mode == 'grid':
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pts = np.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=1)
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pts = sampler.random(n)
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pts *= bounds[1] - bounds[0]
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pts += bounds[0]
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pts = pts.astype(np.float32)
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return pts
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def sample(self, n, mode='random', variables='all'):
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if variables == 'all':
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variables = list(self.range_.keys()) + list(self.fixed_.keys())
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result = None
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for variable in variables:
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if variable in self.range_.keys():
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bound = np.asarray(self.range_[variable])
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pts_variable = self._sample_range(n, mode, bound)
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pts_variable = LabelTensor(
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torch.from_numpy(pts_variable), [variable])
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elif variable in self.fixed_.keys():
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value = self.fixed_[variable]
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pts_variable = LabelTensor(torch.ones(n, 1)*value, [variable])
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if result is None:
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result = pts_variable
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else:
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intersect = 'std' if mode == 'random' else 'cross'
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result = result.append(pts_variable, intersect)
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return result
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