don't delete self.sd

Author: MarcoGorelli

pymc3/distributions/continuous.py

@@ -479,7 +479,7 @@ def __init__(self, mu=0, sigma=None, tau=None, sd=None, **kwargs):
         if sd is not None:
             sigma = sd
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
-        self.sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.tau = tt.as_tensor_variable(tau)
 
         self.mean = self.median = self.mode = self.mu = mu = tt.as_tensor_variable(floatX(mu))
@@ -640,7 +640,7 @@ def __init__(
         if sd is not None:
             sigma = sd
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
-        self.sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.tau = tt.as_tensor_variable(tau)
         self.lower_check = tt.as_tensor_variable(floatX(lower)) if lower is not None else lower
         self.upper_check = tt.as_tensor_variable(floatX(upper)) if upper is not None else upper
@@ -836,7 +836,7 @@ def __init__(self, sigma=None, tau=None, sd=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
-        self.sigma = sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
         self.tau = tau = tt.as_tensor_variable(tau)
 
         self.mean = tt.sqrt(2 / (np.pi * self.tau))
@@ -1725,7 +1725,7 @@ def __init__(self, mu=0, sigma=None, tau=None, sd=None, *args, **kwargs):
 
         self.mu = mu = tt.as_tensor_variable(floatX(mu))
         self.tau = tau = tt.as_tensor_variable(tau)
-        self.sigma = sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
 
         self.mean = tt.exp(self.mu + 1.0 / (2 * self.tau))
         self.median = tt.exp(self.mu)
@@ -1885,7 +1885,7 @@ def __init__(self, nu, mu=0, lam=None, sigma=None, sd=None, *args, **kwargs):
         self.nu = nu = tt.as_tensor_variable(floatX(nu))
         lam, sigma = get_tau_sigma(tau=lam, sigma=sigma)
         self.lam = lam = tt.as_tensor_variable(lam)
-        self.sigma = sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
         self.mean = self.median = self.mode = self.mu = mu = tt.as_tensor_variable(mu)
 
         self.variance = tt.switch((nu > 2) * 1, (1 / self.lam) * (nu / (nu - 2)), np.inf)
@@ -2897,7 +2897,7 @@ def __init__(self, nu=1, sigma=None, lam=None, sd=None, *args, **kwargs):
         self.mode = tt.as_tensor_variable(0)
         lam, sigma = get_tau_sigma(lam, sigma)
         self.median = tt.as_tensor_variable(sigma)
-        self.sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.lam = tt.as_tensor_variable(lam)
         self.nu = nu = tt.as_tensor_variable(floatX(nu))
 
@@ -3033,7 +3033,7 @@ def __init__(self, mu=0.0, sigma=None, nu=None, sd=None, *args, **kwargs):
             sigma = sd
 
         self.mu = mu = tt.as_tensor_variable(floatX(mu))
-        self.sigma = sigma = tt.as_tensor_variable(floatX(sigma))
+        self.sigma = self.sd = sigma = tt.as_tensor_variable(floatX(sigma))
         self.nu = nu = tt.as_tensor_variable(floatX(nu))
         self.mean = mu + nu
         self.variance = (sigma ** 2) + (nu ** 2)
@@ -3310,7 +3310,7 @@ def __init__(self, mu=0.0, sigma=None, tau=None, alpha=1, sd=None, *args, **kwar
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.mu = mu = tt.as_tensor_variable(floatX(mu))
         self.tau = tt.as_tensor_variable(tau)
-        self.sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = tt.as_tensor_variable(sigma)
 
         self.alpha = alpha = tt.as_tensor_variable(floatX(alpha))
 
@@ -3712,7 +3712,7 @@ def __init__(self, nu=None, sigma=None, b=None, sd=None, *args, **kwargs):
 
         nu, b, sigma = self.get_nu_b(nu, b, sigma)
         self.nu = nu = tt.as_tensor_variable(floatX(nu))
-        self.sigma = sigma = tt.as_tensor_variable(floatX(sigma))
+        self.sigma = self.sd = sigma = tt.as_tensor_variable(floatX(sigma))
         self.b = b = tt.as_tensor_variable(floatX(b))
 
         nu_sigma_ratio = -(nu ** 2) / (2 * sigma ** 2)
@@ -3983,7 +3983,7 @@ def __init__(self, mu=0, sigma=None, tau=None, sd=None, **kwargs):
             sigma = sd
         self.mu = mu = tt.as_tensor_variable(floatX(mu))
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
-        self.sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.tau = tau = tt.as_tensor_variable(tau)
 
         self.median = invlogit(mu)

pymc3/distributions/mixture.py

@@ -633,7 +633,7 @@ def __init__(self, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), *args, *
         _, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
         self.mu = mu = tt.as_tensor_variable(mu)
-        self.sigma = sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
 
         super().__init__(w, Normal.dist(mu, sigma=sigma, shape=comp_shape), *args, **kwargs)
 

pymc3/distributions/timeseries.py

@@ -116,7 +116,7 @@ def __init__(
             sigma = sd
 
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
-        self.sigma = tt.as_tensor_variable(sigma)
+        self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.tau = tt.as_tensor_variable(tau)
 
         self.mean = tt.as_tensor_variable(0.0)
@@ -211,7 +211,7 @@ def __init__(self, tau=None, init=Flat.dist(), sigma=None, mu=0.0, sd=None, *arg
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.tau = tt.as_tensor_variable(tau)
         sigma = tt.as_tensor_variable(sigma)
-        self.sigma = sigma
+        self.sigma = self.sd = sigma
         self.mu = tt.as_tensor_variable(mu)
         self.init = init
         self.mean = tt.as_tensor_variable(0.0)