Ejemplo n.º 1
0
    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)
Ejemplo n.º 2
0
    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)
Ejemplo n.º 3
0
    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)
Ejemplo n.º 4
0
    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)