def test_taylor_model(self):
a, b = parameters('a, b')
x, y, z = variables('x, y, z')
model = Model({y: a * x + b})
appr = TaylorModel(model)
self.assertEqual(set([a, b]), set(appr.params))
appr.p0 = {a: 2.0, b: 5.0}
self.assertEqual(set(appr.p0.keys()),
set(appr.params_0[p] for p in appr.params))
self.assertTrue(LinearLeastSquares.is_linear(appr))
model = Model({z: a * x**2 + b * y**2})
appr = TaylorModel(model)
appr.p0 = {a: 2, b: 5}
model = Model({z: a * x**2 + b * y**2})
appr_2 = TaylorModel(model)
appr_2.p0 = {a: 1, b: 1}
self.assertTrue(appr == appr_2)
model = Model({y: a * sympy.exp(x * b)})
appr = TaylorModel(model)
appr.p0 = {a: 2.0, b: 5.0}
self.assertTrue(LinearLeastSquares.is_linear(appr))
model = Model({y: sympy.sin(a * x)})
appr = TaylorModel(model)
appr.p0 = {a: 0.0}
self.assertTrue(LinearLeastSquares.is_linear(appr))
def test_taylor_model(self):
a, b = parameters('a, b')
x, y, z = variables('x, y, z')
model = Model({y: a * x + b})
appr = TaylorModel(model)
self.assertEqual(set([a, b]), set(appr.params))
appr.p0 = {a: 2.0, b: 5.0}
self.assertEqual(set(appr.p0.keys()), set(appr.params_0[p] for p in appr.params))
self.assertTrue(LinearLeastSquares.is_linear(appr))
model = Model({z: a * x**2 + b * y**2})
appr = TaylorModel(model)
appr.p0 = {a: 2, b: 5}
model = Model({z: a * x**2 + b * y**2})
appr_2 = TaylorModel(model)
appr_2.p0 = {a: 1, b: 1}
self.assertTrue(appr == appr_2)
model = Model({y: a * sympy.exp(x * b)})
appr = TaylorModel(model)
appr.p0 = {a: 2.0, b: 5.0}
self.assertTrue(LinearLeastSquares.is_linear(appr))
model = Model({y: sympy.sin(a * x)})
appr = TaylorModel(model)
appr.p0 = {a: 0.0}
self.assertTrue(LinearLeastSquares.is_linear(appr))
def test_is_linear(self):
a, b, c, d = parameters('a, b, c, d')
x, y = variables('x, y')
model = Model({y: (a * x + c * x**2) + b})
self.assertTrue(LinearLeastSquares.is_linear(model))
model = Model({y: (a * x + c * x**2) + b + 2})
self.assertTrue(LinearLeastSquares.is_linear(model))
# This test should be made to work in a future version.
# model = Model({y: a * x**2 + sympy.exp(x * b)})
# t_model = (2 * y / g)**0.5
# self.assertTrue(LinearLeastSquares.is_linear(model))
model = Model({y: a * sympy.exp(x * b)})
self.assertFalse(LinearLeastSquares.is_linear(model))
model = Model({y: a * x**3 + b * c})
self.assertFalse(LinearLeastSquares.is_linear(model))
model = Model({y: a * x**3 + b * x + c})
self.assertTrue(LinearLeastSquares.is_linear(model))
def test_is_linear(self):
a, b, c, d = parameters('a, b, c, d')
x, y = variables('x, y')
model = Model({y: (a * x + c*x**2) + b})
self.assertTrue(LinearLeastSquares.is_linear(model))
model = Model({y: (a * x + c*x**2) + b + 2})
self.assertTrue(LinearLeastSquares.is_linear(model))
# This test should be made to work in a future version.
# model = Model({y: a * x**2 + sympy.exp(x * b)})
# t_model = (2 * y / g)**0.5
# self.assertTrue(LinearLeastSquares.is_linear(model))
model = Model({y: a * sympy.exp(x * b)})
self.assertFalse(LinearLeastSquares.is_linear(model))
model = Model({y: a * x**3 + b * c})
self.assertFalse(LinearLeastSquares.is_linear(model))
model = Model({y: a * x**3 + b * x + c})
self.assertTrue(LinearLeastSquares.is_linear(model))
