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Span (#24)

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* Changing chebyshev implementation to remove numpy dependencies
* Update span.py
This commit is contained in:
Dario Coscia
2022-10-13 16:04:01 +02:00
committed by GitHub
parent 06932196a8
commit 28421049b2
2 changed files with 13 additions and 13 deletions
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12
pina/chebyshev.py
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@@ -1,7 +1,9 @@
import numpy as np import torch
def chebyshev_roots(n): def chebyshev_roots(n):
""" Return the roots of *n* Chebyshev polynomials (between [-1, 1]) """ """ Return the roots of *n* Chebyshev polynomials (between [-1, 1]) """
coefficents = np.zeros(n+1) pi = torch.acos(torch.zeros(1)).item() * 2
coefficents[-1] = 1 k = torch.arange(n)
return np.polynomial.chebyshev.chebroots(coefficents) nodes = torch.sort(torch.cos(pi * (k + 0.5) / n))[0]
return nodes

14
pina/span.py
View File

@@ -1,4 +1,3 @@
import numpy as np
from .chebyshev import chebyshev_roots from .chebyshev import chebyshev_roots
import torch import torch
@@ -32,20 +31,20 @@ class Span(Location):
""" """
""" """
if mode == 'random': if mode == 'random':
pts = np.random.uniform(size=(n, 1)) pts = torch.rand(size=(n, 1))
elif mode == 'chebyshev': elif mode == 'chebyshev':
pts = np.array([chebyshev_roots(n) * .5 + .5]).reshape(-1, 1) pts = chebyshev_roots(n).mul(.5).add(.5).reshape(-1, 1)
elif mode == 'grid': elif mode == 'grid':
pts = np.linspace(0, 1, n).reshape(-1, 1) pts = torch.linspace(0, 1, n).reshape(-1, 1)
elif mode == 'lh' or mode == 'latin': elif mode == 'lh' or mode == 'latin':
from scipy.stats import qmc from scipy.stats import qmc
sampler = qmc.LatinHypercube(d=1) sampler = qmc.LatinHypercube(d=1)
pts = sampler.random(n) pts = sampler.random(n)
pts = torch.from_numpy(pts)
pts *= bounds[1] - bounds[0] pts *= bounds[1] - bounds[0]
pts += bounds[0] pts += bounds[0]
pts = pts.astype(np.float32)
return pts return pts
def sample(self, n, mode='random', variables='all'): def sample(self, n, mode='random', variables='all'):
@@ -56,10 +55,9 @@ class Span(Location):
result = None result = None
for variable in variables: for variable in variables:
if variable in self.range_.keys(): if variable in self.range_.keys():
bound = np.asarray(self.range_[variable]) bound = torch.tensor(self.range_[variable])
pts_variable = self._sample_range(n, mode, bound) pts_variable = self._sample_range(n, mode, bound)
pts_variable = LabelTensor( pts_variable = LabelTensor(pts_variable, [variable])
torch.from_numpy(pts_variable), [variable])
elif variable in self.fixed_.keys(): elif variable in self.fixed_.keys():
value = self.fixed_[variable] value = self.fixed_[variable]