def test_nonlinearfit(self):
"""
Compare NumericalLeastSquares with LinearLeastSquares to see if errors
are implemented consistently.
"""
from symfit import Variable, Parameter, Fit
t_data = np.array([1.4, 2.1, 2.6, 3.0, 3.3])
y_data = np.array([10, 20, 30, 40, 50])
sigma = 0.2
n = np.array([5, 3, 8, 15, 30])
sigma_t = sigma / np.sqrt(n)
# We now define our model
t, y = variables('t, y')
g = Parameter(9.0)
t_model = {t: (2 * y / g)**0.5}
# Different sigma for every point
fit = NonLinearLeastSquares(t_model, y=y_data, t=t_data, sigma_t=sigma_t)
import time
tick = time.time()
fit_result = fit.execute()
# print(time.time() - tick)
fit = NumericalLeastSquares(t_model, y=y_data, t=t_data, sigma_t=sigma_t)
tick = time.time()
num_result = fit.execute()
# print(time.time() - tick)
self.assertAlmostEqual(num_result.value(g), fit_result.value(g))
for cov1, cov2 in zip(num_result.params.covariance_matrix.flatten(), fit_result.params.covariance_matrix.flatten()):
self.assertAlmostEqual(cov1, cov2)
def test_2D_fitting(self):
np.random.seed(1)
xdata = np.random.randint(-10, 11, size=(2, 100))
zdata = 2.5 * xdata[0]**2 + 7.0 * xdata[1]**2
a = Parameter('a')
b = Parameter(name='b', value=10)
x, y, z = variables('x, y, z')
new = {z: a * x**2 + b * y**2}
fit = NonLinearLeastSquares(new, x=xdata[0], y=xdata[1], z=zdata)
fit_result = fit.execute()
self.assertAlmostEqual(fit_result.value(a), 2.5)
self.assertAlmostEqual(np.abs(fit_result.value(b)), 7.0)
def test_2D_fitting(self):
np.random.seed(1)
xdata = np.random.randint(-10, 11, size=(2, 100))
zdata = 2.5*xdata[0]**2 + 7.0*xdata[1]**2
a = Parameter()
b = Parameter(10)
x = Variable()
y = Variable()
z = Variable()
new = {z: a*x**2 + b*y**2}
fit = NonLinearLeastSquares(new, x=xdata[0], y=xdata[1], z=zdata)
fit_result = fit.execute()
self.assertAlmostEqual(fit_result.value(a), 2.5)
self.assertAlmostEqual(np.abs(fit_result.value(b)), 7.0)
def test_nonlinearfit(self):
"""
Compare NumericalLeastSquares with LinearLeastSquares to see if errors
are implemented consistently.
"""
from symfit import Variable, Parameter, Fit
t_data = np.array([1.4, 2.1, 2.6, 3.0, 3.3])
y_data = np.array([10, 20, 30, 40, 50])
sigma = 0.2
n = np.array([5, 3, 8, 15, 30])
sigma_t = sigma / np.sqrt(n)
# We now define our model
t, y = variables('t, y')
g = Parameter('g', 9.0)
t_model = {t: (2 * y / g)**0.5}
# Different sigma for every point
fit = NonLinearLeastSquares(t_model,
y=y_data,
t=t_data,
sigma_t=sigma_t)
import time
tick = time.time()
fit_result = fit.execute()
# print(time.time() - tick)
fit = Fit(t_model,
y=y_data,
t=t_data,
sigma_t=sigma_t,
minimizer=MINPACK)
tick = time.time()
num_result = fit.execute()
# print(time.time() - tick)
self.assertAlmostEqual(num_result.value(g), fit_result.value(g))
for cov1, cov2 in zip(num_result.covariance_matrix.flatten(),
fit_result.covariance_matrix.flatten()):
self.assertAlmostEqual(cov1, cov2)
