More bug fixed

tidy up  pm.timeseries and fixed *arg for `tau`
fixed _repr_latex for Normalmixture

Author: Junpeng Lao

pymc3/distributions/mixture.py

@@ -168,7 +168,8 @@ class NormalMixture(Mixture):
     def __init__(self, w, mu, *args, **kwargs):
         _, sd = get_tau_sd(tau=kwargs.pop('tau', None),
                            sd=kwargs.pop('sd', None))
-
+        self.mu = mu = tt.as_tensor_variable(mu)
+        self.sd = sd = tt.as_tensor_variable(sd)
         super(NormalMixture, self).__init__(w, Normal.dist(mu, sd=sd),
                                             *args, **kwargs)
 
@@ -177,8 +178,8 @@ def _repr_latex_(self, name=None, dist=None):
             dist = self
         mu = dist.mu
         w = dist.w
-        sigma = dist.sigma
+        sd = dist.sd
         return r'${} \sim \text{{NormalMixture}}(\mathit{{w}}={}, \mathit{{mu}}={}, \mathit{{sigma}}={})$'.format(name,
                                                 get_variable_name(w),
                                                 get_variable_name(mu),
-                                                get_variable_name(sigma))
+                                                get_variable_name(sd))

pymc3/distributions/multivariate.py

@@ -696,7 +696,7 @@ def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, testv
     c = tt.sqrt(ChiSquared('c', nu - np.arange(2, 2 + n_diag), shape=n_diag,
                            testval=diag_testval))
     pm._log.info('Added new variable c to model diagonal of Wishart.')
-    z = Normal('z', 0, 1, shape=n_tril, testval=tril_testval)
+    z = Normal('z', 0., 1., shape=n_tril, testval=tril_testval)
     pm._log.info('Added new variable z to model off-diagonals of Wishart.')
     # Construct A matrix
     A = tt.zeros(S.shape, dtype=np.float32)

pymc3/distributions/timeseries.py

@@ -2,8 +2,8 @@
 from theano import scan
 
 from pymc3.util import get_variable_name
+from .continuous import get_tau_sd, Normal, Flat
 from . import multivariate
-from . import continuous
 from . import distribution
 
 __all__ = [
@@ -41,9 +41,9 @@ def logp(self, x):
 
         x_im1 = x[:-1]
         x_i = x[1:]
-        boundary = continuous.Normal.dist(0, tau_e).logp
+        boundary = Normal.dist(0., tau=tau_e).logp
 
-        innov_like = continuous.Normal.dist(k * x_im1, tau_e).logp(x_i)
+        innov_like = Normal.dist(k * x_im1, tau=tau_e).logp(x_i)
         return boundary(x[0]) + tt.sum(innov_like) + boundary(x[-1])
 
     def _repr_latex_(self, name=None, dist=None):
@@ -72,9 +72,10 @@ class GaussianRandomWalk(distribution.Continuous):
         distribution for initial value (Defaults to Flat())
     """
 
-    def __init__(self, tau=None, init=continuous.Flat.dist(), sd=None, mu=0.,
+    def __init__(self, tau=None, init=Flat.dist(), sd=None, mu=0.,
                  *args, **kwargs):
         super(GaussianRandomWalk, self).__init__(*args, **kwargs)
+        tau, sd = get_tau_sd(tau=tau, sd=sd)
         self.tau = tau = tt.as_tensor_variable(tau)
         self.sd = sd = tt.as_tensor_variable(sd)
         self.mu = mu = tt.as_tensor_variable(mu)
@@ -90,7 +91,7 @@ def logp(self, x):
         x_im1 = x[:-1]
         x_i = x[1:]
 
-        innov_like = continuous.Normal.dist(mu=x_im1 + mu, tau=tau, sd=sd).logp(x_i)
+        innov_like = Normal.dist(mu=x_im1 + mu, sd=sd).logp(x_i)
         return init.logp(x[0]) + tt.sum(innov_like)
 
     def _repr_latex_(self, name=None, dist=None):
@@ -125,8 +126,8 @@ class GARCH11(distribution.Continuous):
         initial_vol >= 0, distribution for initial volatility, sigma_0
     """
 
-    def __init__(self, omega=None, alpha_1=None, beta_1=None,
-                 initial_vol=None, *args, **kwargs):
+    def __init__(self, omega, alpha_1, beta_1,
+                 initial_vol, *args, **kwargs):
         super(GARCH11, self).__init__(*args, **kwargs)
 
         self.omega = omega = tt.as_tensor_variable(omega)
@@ -150,7 +151,7 @@ def volatility_update(x, vol, w, a, b):
 
     def logp(self, x):
         vol = self.get_volatility(x)
-        return tt.sum(continuous.Normal.dist(0, sd=vol).logp(x))
+        return tt.sum(Normal.dist(0., sd=vol).logp(x))
 
     def _repr_latex_(self, name=None, dist=None):
         if dist is None:
@@ -188,7 +189,7 @@ def logp(self, x):
         f, g = self.sde_fn(x[:-1], *self.sde_pars)
         mu = xt + self.dt * f
         sd = tt.sqrt(self.dt) * g
-        return tt.sum(continuous.Normal.dist(mu=mu, sd=sd).logp(x[1:]))
+        return tt.sum(Normal.dist(mu=mu, sd=sd).logp(x[1:]))
 
     def _repr_latex_(self, name=None, dist=None):
         if dist is None:
@@ -211,7 +212,7 @@ class MvGaussianRandomWalk(distribution.Continuous):
     init : distribution
         distribution for initial value (Defaults to Flat())
     """
-    def __init__(self, mu=0., cov=None, init=continuous.Flat.dist(),
+    def __init__(self, mu=0., cov=None, init=Flat.dist(),
                  *args, **kwargs):
         super(MvGaussianRandomWalk, self).__init__(*args, **kwargs)
         if cov is None:
@@ -260,7 +261,7 @@ class MvStudentTRandomWalk(distribution.Continuous):
     init : distribution
         distribution for initial value (Defaults to Flat())
     """
-    def __init__(self, nu, mu=0., cov=None, init=continuous.Flat.dist(),
+    def __init__(self, nu, mu=0., cov=None, init=Flat.dist(),
                  *args, **kwargs):
         super(MvStudentTRandomWalk, self).__init__(*args, **kwargs)
         self.mu = mu = tt.as_tensor_variable(mu)