121 lines
2.8 KiB
Python
121 lines
2.8 KiB
Python
import torch
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from pina import LabelTensor
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from pina.domain import Union, EllipsoidDomain, CartesianDomain
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def test_constructor_two_CartesianDomains():
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Union([
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CartesianDomain({
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'x': [0, 1],
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'y': [0, 1]
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}),
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CartesianDomain({
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'x': [0.5, 2],
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'y': [-1, 0.1]
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})
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])
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def test_constructor_two_EllipsoidDomains():
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Union([
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EllipsoidDomain({
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'x': [-1, 1],
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'y': [-1, 1],
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'z': [-1, 1]
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}),
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EllipsoidDomain({
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'x': [-0.5, 0.5],
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'y': [-0.5, 0.5],
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'z': [-0.5, 0.5]
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})
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])
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def test_constructor_EllipsoidDomain_CartesianDomain():
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Union([
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EllipsoidDomain({
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'x': [-1, 1],
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'y': [-1, 1]
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}),
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CartesianDomain({
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'x': [-0.5, 0.5],
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'y': [-0.5, 0.5]
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})
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])
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def test_is_inside_two_CartesianDomains():
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pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y'])
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pt_2 = LabelTensor(torch.tensor([[-1, -1]]), ['x', 'y'])
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domain = Union([
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CartesianDomain({
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'x': [0, 1],
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'y': [0, 1]
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}),
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CartesianDomain({
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'x': [0.5, 2],
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'y': [-1, 0.1]
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})
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])
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assert domain.is_inside(pt_1) == True
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assert domain.is_inside(pt_2) == False
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def test_is_inside_two_EllipsoidDomains():
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pt_1 = LabelTensor(torch.tensor([[0.5, 0.5, 0.5]]), ['x', 'y', 'z'])
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pt_2 = LabelTensor(torch.tensor([[-1, -1, -1]]), ['x', 'y', 'z'])
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domain = Union([
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EllipsoidDomain({
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'x': [-1, 1],
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'y': [-1, 1],
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'z': [-1, 1]
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}),
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EllipsoidDomain({
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'x': [-0.5, 0.5],
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'y': [-0.5, 0.5],
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'z': [-0.5, 0.5]
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})
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])
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assert domain.is_inside(pt_1) == True
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assert domain.is_inside(pt_2) == False
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def test_is_inside_EllipsoidDomain_CartesianDomain():
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pt_1 = LabelTensor(torch.tensor([[0.5, 0.5]]), ['x', 'y'])
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pt_2 = LabelTensor(torch.tensor([[-1, -1]]), ['x', 'y'])
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domain = Union([
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EllipsoidDomain({
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'x': [-1, 1],
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'y': [-1, 1],
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}),
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CartesianDomain({
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'x': [0.6, 1.5],
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'y': [-2, 0]
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})
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])
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assert domain.is_inside(pt_1) == True
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assert domain.is_inside(pt_2) == False
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def test_sample():
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n = 100
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domain = Union([
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EllipsoidDomain({
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'x': [-1, 1],
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'y': [-1, 1]
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}),
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CartesianDomain({
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'x': [-0.5, 0.5],
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'y': [-0.5, 0.5]
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})
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])
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pts = domain.sample(n)
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assert isinstance(pts, LabelTensor)
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assert pts.shape[0] == n
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n = 105
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pts = domain.sample(n)
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assert isinstance(pts, LabelTensor)
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assert pts.shape[0] == n
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