Another attempt to remove whitespace

pymc3/distributions/continuous.py

@@ -1592,22 +1592,22 @@ class Interpolated(Continuous):
     R"""
     Univariate probability distribution defined as a linear interpolation
     of probability density function evaluated on some lattice of points.
-
+    
     The lattice can be uneven, so the steps between different points can have
     different size and it is possible to vary the precision between regions
     of the support.
-
+    
     The probability density function values don not have to be normalized, as the
     interpolated density is any way normalized to make the total probability
     equal to $1$.
-
+    
     Both parameters `x_points` and values `pdf_points` are not variables, but
     plain array-like objects, so they are constant and cannot be sampled.
-
+    
     ========  ===========================================
     Support   :math:`x \in [x\_points[0], x\_points[-1]]`
     ========  ===========================================
-
+    
     Parameters
     ----------
     x_points : array-like

pymc3/distributions/mixture.py

@@ -20,16 +20,16 @@ def all_discrete(comp_dists):
 class Mixture(Distribution):
     R"""
     Mixture log-likelihood
-
+    
     Often used to model subpopulation heterogeneity
-
+    
     .. math:: f(x \mid w, \theta) = \sum_{i = 1}^n w_i f_i(x \mid \theta_i)
-
+    
     ========  ============================================
     Support   :math:`\cap_{i = 1}^n \textrm{support}(f_i)`
     Mean      :math:`\sum_{i = 1}^n w_i \mu_i`
     ========  ============================================
-
+    
     Parameters
     ----------
     w : array of floats
@@ -40,98 +40,98 @@ class Mixture(Distribution):
     """
     def __init__(self, w, comp_dists, *args, **kwargs):
         shape = kwargs.pop('shape', ())
-
+        
         self.w = w
         self.comp_dists = comp_dists
-
+        
         defaults = kwargs.pop('defaults', [])
-
+        
         if all_discrete(comp_dists):
             dtype = kwargs.pop('dtype', 'int64')
         else:
             dtype = kwargs.pop('dtype', 'float64')
-
+            
             try:
                 self.mean = (w * self._comp_means()).sum(axis=-1)
-
+                
                 if 'mean' not in defaults:
                     defaults.append('mean')
             except AttributeError:
                 pass
-
+        
         try:
             comp_modes = self._comp_modes()
             comp_mode_logps = self.logp(comp_modes)
             self.mode = comp_modes[tt.argmax(w * comp_mode_logps, axis=-1)]
-
+            
             if 'mode' not in defaults:
                 defaults.append('mode')
         except AttributeError:
             pass
-
+        
         super(Mixture, self).__init__(shape, dtype, defaults=defaults,
                                       *args, **kwargs)
-
+    
     def _comp_logp(self, value):
         comp_dists = self.comp_dists
-
+        
         try:
             value_ = value if value.ndim > 1 else tt.shape_padright(value)
-
+            
             return comp_dists.logp(value_)
         except AttributeError:
             return tt.stack([comp_dist.logp(value) for comp_dist in comp_dists],
                             axis=1)
-
+    
     def _comp_means(self):
         try:
             return tt.as_tensor_variable(self.comp_dists.mean)
         except AttributeError:
             return tt.stack([comp_dist.mean for comp_dist in self.comp_dists],
                             axis=1)
-
+    
     def _comp_modes(self):
         try:
             return tt.as_tensor_variable(self.comp_dists.mode)
         except AttributeError:
             return tt.stack([comp_dist.mode for comp_dist in self.comp_dists],
                             axis=1)
-
+    
     def _comp_samples(self, point=None, size=None, repeat=None):
         try:
             samples = self.comp_dists.random(point=point, size=size, repeat=repeat)
         except AttributeError:
             samples = np.column_stack([comp_dist.random(point=point, size=size, repeat=repeat)
                                        for comp_dist in self.comp_dists])
-
+        
         return np.squeeze(samples)
-
+    
     def logp(self, value):
         w = self.w
-
+        
         return bound(logsumexp(tt.log(w) + self._comp_logp(value), axis=-1).sum(),
                      w >= 0, w <= 1, tt.allclose(w.sum(axis=-1), 1))
-
+    
     def random(self, point=None, size=None, repeat=None):
         def random_choice(*args, **kwargs):
             w = kwargs.pop('w')
             w /= w.sum(axis=-1, keepdims=True)
             k = w.shape[-1]
-
+            
             if w.ndim > 1:
                 return np.row_stack([np.random.choice(k, p=w_) for w_ in w])
             else:
                 return np.random.choice(k, p=w, *args, **kwargs)
-
+        
         w = draw_values([self.w], point=point)
-
+        
         w_samples = generate_samples(random_choice,
                                      w=w,
                                      broadcast_shape=w.shape[:-1] or (1,),
                                      dist_shape=self.shape,
                                      size=size).squeeze()
         comp_samples = self._comp_samples(point=point, size=size, repeat=repeat)
-
+        
         if comp_samples.ndim > 1:
             return np.squeeze(comp_samples[np.arange(w_samples.size), w_samples])
         else:
@@ -141,17 +141,17 @@ def random_choice(*args, **kwargs):
 class NormalMixture(Mixture):
     R"""
     Normal mixture log-likelihood
-
+    
     .. math::
-
+        
         f(x \mid w, \mu, \sigma^2) = \sum_{i = 1}^n w_i N(x \mid \mu_i, \sigma^2_i)
-
+    
     ========  =======================================
     Support   :math:`x \in \mathbb{R}`
     Mean      :math:`\sum_{i = 1}^n w_i \mu_i`
     Variance  :math:`\sum_{i = 1}^n w_i^2 \sigma^2_i`
     ========  =======================================
-
+    
     Parameters
     ----------
     w : array of floats
@@ -167,7 +167,7 @@ class NormalMixture(Mixture):
     def __init__(self, w, mu, *args, **kwargs):
         _, sd = get_tau_sd(tau=kwargs.pop('tau', None),
                            sd=kwargs.pop('sd', None))
-
+        
         super(NormalMixture, self).__init__(w, Normal.dist(mu, sd=sd),
                                             *args, **kwargs)
 
@@ -180,4 +180,4 @@ def _repr_latex_(self, name=None, dist=None):
         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(sigma))

pymc3/distributions/multivariate.py

@@ -268,15 +268,15 @@ class MvStudentT(Continuous):
                ({\mathbf x}-{\mu})^T
                {\Sigma}^{-1}({\mathbf x}-{\mu})
              \right]^{(\nu+p)/2}}
-
+    
     
     ========  =============================================
     Support   :math:`x \in \mathbb{R}^k`
     Mean      :math:`\mu` if :math:`\nu > 1` else undefined
     Variance  :math:`\frac{\nu}{\mu-2}\Sigma`
                   if :math:`\nu>2` else undefined
     ========  =============================================
-
+    
     
     Parameters
     ----------
@@ -926,7 +926,7 @@ class LKJCorr(Continuous):
          [- - - 7 8]
          [- - - - 9]
          [- - - - -]]
-
+    
     
     References
     ----------

pymc3/distributions/timeseries.py

@@ -296,4 +296,3 @@ def _repr_latex_(self, name=None, dist=None):
                                                 get_variable_name(nu),
                                                 get_variable_name(mu),
                                                 get_variable_name(cov))
-