add more distributions to roulette

preliz/distributions/continuous.py

@@ -583,7 +583,8 @@ def _parametrization(self, mu=None, sigma=None, nu=None):
         self.sigma = sigma
         self.param_names = ("mu", "sigma", "nu")
         self.params = (mu, sigma, nu)
-        self.params_support = ((-np.inf, np.inf), (eps, np.inf), (eps, np.inf))
+        #  if nu is too small we get a non-smooth distribution
+        self.params_support = ((-np.inf, np.inf), (eps, np.inf), (1e-4, np.inf))
         if all_not_none(mu, sigma, nu):
             self._update(mu, sigma, nu)
 
@@ -602,7 +603,7 @@ def _update(self, mu, sigma, nu):
 
     def _fit_moments(self, mean, sigma):
         # Just assume this is a approximately Gaussian
-        self._update(mean, sigma, 1e-6)
+        self._update(mean, sigma, 1e-4)
 
     def _fit_mle(self, sample, **kwargs):
         K, mu, sigma = self.dist.fit(sample, **kwargs)

preliz/distributions/discrete.py

@@ -856,9 +856,7 @@ def _update(self, mu, alpha):
         self._update_rv_frozen()
 
     def _fit_moments(self, mean, sigma):
-        mu = mean
-        alpha = mean**2 / (sigma**2 - mean)
-        self._update(mu, alpha)
+        optimize_moments(self, mean, sigma)
 
     def _fit_mle(self, sample):
         optimize_ml(self, sample)
@@ -981,7 +979,7 @@ def __init__(self, psi=None, n=None, p=None):
         self.psi = psi
         self.n = n
         self.p = p
-        self.dist = ZIBinomial
+        self.dist = _ZIBinomial
         self.support = (0, np.inf)
         self._parametrization(psi, n, p)
 
@@ -1092,7 +1090,7 @@ def __init__(self, psi=None, mu=None, alpha=None, p=None, n=None):
         self.p = p
         self.alpha = alpha
         self.mu = mu
-        self.dist = ZINegativeBinomial
+        self.dist = _ZINegativeBinomial
         self.support = (0, np.inf)
         self._parametrization(psi, mu, alpha, p, n)
 
@@ -1204,7 +1202,7 @@ def __init__(self, psi=None, mu=None):
         super().__init__()
         self.psi = psi
         self.mu = mu
-        self.dist = ZIPoisson
+        self.dist = _ZIPoisson
         self.support = (0, np.inf)
         self._parametrization(psi, mu)
 
@@ -1238,7 +1236,7 @@ def _fit_mle(self, sample):
         optimize_ml(self, sample)
 
 
-class ZIBinomial(stats.rv_continuous):
+class _ZIBinomial(stats.rv_continuous):
     def __init__(self, psi=None, n=None, p=None):
         super().__init__()
         self.psi = psi
@@ -1296,7 +1294,7 @@ def rvs(self, size=1):  # pylint: disable=arguments-differ
         return samples
 
 
-class ZINegativeBinomial(stats.rv_continuous):
+class _ZINegativeBinomial(stats.rv_continuous):
     def __init__(self, psi=None, p=None, n=None):
         super().__init__()
         self.psi = psi
@@ -1355,7 +1353,7 @@ def rvs(self, size=1):  # pylint: disable=arguments-differ
         return samples
 
 
-class ZIPoisson(stats.rv_continuous):
+class _ZIPoisson(stats.rv_continuous):
     def __init__(self, psi=None, mu=None):
         super().__init__()
         self.psi = psi

preliz/internal/optimization.py

@@ -84,8 +84,10 @@ def func(params, dist, x_vals, ecdf):
         return loss
 
     init_vals = np.array(dist.params)[none_idx]
+    bounds = np.array(dist.params_support)[none_idx]
+    bounds = list(zip(*bounds))
 
-    opt = least_squares(func, x0=init_vals, args=(dist, x_vals, ecdf))
+    opt = least_squares(func, x0=init_vals, args=(dist, x_vals, ecdf), bounds=bounds)
     dist._update(*opt["x"])
     loss = opt["cost"]
     return loss

preliz/tests/test_distributions.py

@@ -119,6 +119,8 @@ def test_moments(distribution, params):
         "BetaBinomial",
         "Binomial",
         "DiscreteWeibull",
+        "ExGaussian",
+        "NegativeBinomial",
         "Kumaraswamy",
         "LogitNormal",
         "Rice",

preliz/tests/test_maxent.py

@@ -75,7 +75,7 @@
         (Cauchy(), -1, 1, 0.6, (-np.inf, np.inf), (0, 0.726)),
         (Cauchy(alpha=0.5), -1, 1, 0.6, (-np.inf, np.inf), (0.6000)),
         (ChiSquared(), 2, 7, 0.6, (0, np.inf), (4.002)),
-        (ExGaussian(), 9, 10, 0.8, (-np.inf, np.inf), (9.112, 0.133, 0.495)),
+        (ExGaussian(), 9, 10, 0.8, (-np.inf, np.inf), (9.496, 0.390, 0.003)),
         (ExGaussian(sigma=0.2), 9, 10, 0.8, (-np.inf, np.inf), (9.168, 0.423)),
         (Exponential(), 0, 4, 0.9, (0, np.inf), (0.575)),
         (Gamma(), 0, 10, 0.7, (0, np.inf), (0.868, 0.103)),

preliz/unidimensional/roulette.py

@@ -300,16 +300,17 @@ def reset_dist_panel(x_min, x_max, ax, yticks):
 def get_widgets(x_min, x_max, nrows, ncols):
 
     width_entry_text = widgets.Layout(width="150px")
+    width_distribution_text = widgets.Layout(width="150px", height="125px")
 
-    w_x_min = widgets.IntText(
+    w_x_min = widgets.FloatText(
         value=x_min,
         step=1,
         description="x_min:",
         disabled=False,
         layout=width_entry_text,
     )
 
-    w_x_max = widgets.IntText(
+    w_x_max = widgets.FloatText(
         value=x_max,
         step=1,
         description="x_max:",
@@ -343,10 +344,46 @@ def get_widgets(x_min, x_max, nrows, ncols):
         layout=width_entry_text,
     )
 
-    dist_names = ["Normal", "BetaScaled", "Gamma", "LogNormal", "StudentT"]
+    default_dist = ["Normal", "BetaScaled", "Gamma", "LogNormal", "StudentT"]
+
+    dist_names = [
+        "AsymmetricLaplace",
+        "BetaScaled",
+        "ChiSquared",
+        "ExGaussian",
+        "Exponential",
+        "Gamma",
+        "Gumbel",
+        "HalfNormal",
+        "HalfStudentT",
+        "InverseGamma",
+        "Laplace",
+        "LogNormal",
+        "Logistic",
+        # "LogitNormal", # fails if we add chips at x_value= 1
+        "Moyal",
+        "Normal",
+        "Pareto",
+        "Rice",
+        "SkewNormal",
+        "StudentT",
+        "Triangular",
+        "VonMises",
+        "Wald",
+        "Weibull",
+        "BetaBinomial",
+        "DiscreteWeibull",
+        "Geometric",
+        "NegativeBinomial",
+        "Poisson",
+    ]
 
     w_distributions = widgets.SelectMultiple(
-        options=dist_names, value=dist_names, description="", disabled=False
+        options=dist_names,
+        value=default_dist,
+        description="",
+        disabled=False,
+        layout=width_distribution_text,
     )
 
     return w_x_min, w_x_max, w_ncols, w_nrows, w_repr, w_distributions