70 lines
2.2 KiB
Python
70 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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def sample(self, n, mode='random'):
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bounds = np.array(list(self.range_.values()))
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if mode == 'random':
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pts = np.random.uniform(size=(n, bounds.shape[0]))
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elif mode == 'chebyshev':
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pts = np.array([
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chebyshev_roots(n) * .5 + .5
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for _ in range(bounds.shape[0])])
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grids = np.meshgrid(*pts)
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pts = np.hstack([grid.reshape(-1, 1) for grid in grids])
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elif mode == 'grid':
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pts = np.array([
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np.linspace(0, 1, n)
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for _ in range(bounds.shape[0])])
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grids = np.meshgrid(*pts)
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pts = np.hstack([grid.reshape(-1, 1) for grid in grids])
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print(pts)
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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=bounds.shape[0])
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pts = sampler.random(n)
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# Scale pts
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pts *= bounds[:, 1] - bounds[:, 0]
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pts += bounds[:, 0]
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pts = torch.from_numpy(pts)
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pts_range_ = LabelTensor(pts, list(self.range_.keys()))
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fixed = torch.Tensor(list(self.fixed_.values()))
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pts_fixed_ = torch.ones(pts_range_.tensor.shape[0], len(self.fixed_)) * fixed
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pts_fixed_ = LabelTensor(pts_fixed_, list(self.fixed_.keys()))
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if self.fixed_:
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return LabelTensor.hstack([pts_range_, pts_fixed_])
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else:
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return pts_range_
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def meshgrid(self, n):
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pts = np.array([
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np.linspace(0, 1, n)
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for _ in range(self.bound.shape[0])])
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pts *= self.bound[:, 1] - self.bound[:, 0]
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pts += self.bound[:, 0]
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return np.meshgrid(*pts)
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