def test_likelihood_fitting_exponential(self):
"""
Fit using the likelihood method.
"""
b = Parameter(4, min=3.0)
x = Variable()
pdf = Exp(x, 1 / b)
# Draw points from an Exp(5) exponential distribution.
np.random.seed(100)
xdata = np.random.exponential(5, 100000)
fit = Likelihood(pdf, xdata)
fit_result = fit.execute()
self.assertAlmostEqual(fit_result.value(b), np.mean(xdata), 3)
def test_likelihood_fitting_exponential(self):
"""
Fit using the likelihood method.
"""
b = Parameter(4, min=3.0)
x = Variable()
pdf = Exp(x, 1/b)
# Draw points from an Exp(5) exponential distribution.
np.random.seed(100)
xdata = np.random.exponential(5, 100000)
fit = Likelihood(pdf, xdata)
fit_result = fit.execute()
self.assertAlmostEqual(fit_result.value(b), np.mean(xdata), 3)
def test_likelihood_fitting_gaussian(self):
"""
Fit using the likelihood method.
"""
mu, sig = parameters('mu, sig')
sig.min = 0.01
sig.value = 3.0
mu.value = 50.
x = Variable()
pdf = Gaussian(x, mu, sig)
# pdf = sympy.exp(-(x - mu)**2/(2*sig**2))/sympy.sqrt(2*sympy.pi*sig**2)
np.random.seed(10)
xdata = np.random.normal(51., 3.5, 100000)
fit = Likelihood(pdf, xdata)
fit_result = fit.execute()
self.assertAlmostEqual(fit_result.value(mu), np.mean(xdata), 1)
self.assertAlmostEqual(fit_result.value(sig), np.std(xdata), 3)
def test_likelihood_fitting_gaussian(self):
"""
Fit using the likelihood method.
"""
mu, sig = parameters('mu, sig')
sig.min = 0.01
sig.value = 3.0
mu.value = 50.
x = Variable()
pdf = Gaussian(x, mu, sig)
# pdf = sympy.exp(-(x - mu)**2/(2*sig**2))/sympy.sqrt(2*sympy.pi*sig**2)
np.random.seed(10)
xdata = np.random.normal(51., 3.5, 100000)
fit = Likelihood(pdf, xdata)
fit_result = fit.execute()
self.assertAlmostEqual(fit_result.value(mu), np.mean(xdata), 1)
self.assertAlmostEqual(fit_result.value(sig), np.std(xdata), 3)
