improve docstrings of LinearGaussian and LinearStudentT

cde/density_simulation/LinearGaussian.py

@@ -11,14 +11,15 @@ class LinearGaussian(BaseConditionalDensitySimulation):
   y = N(y | mean = mu_slope*x+mu, scale = std_slope*x+std)
 
   Args:
+    ndim_x: number of dimensions of x
     mu: the intercept of the mean line
     mu_slope: the slope of the mean line
     std: the intercept of the std dev. line
     std_slope: the slope of the std. dev. line
     random_seed: seed for the random_number generator
   """
 
-  def __init__(self, mu=0.0, ndim_x=1, mu_slope=0.005, std=0.01, std_slope=0.002, random_seed=None):
+  def __init__(self, ndim_x=1, mu=0.0, mu_slope=0.005, std=0.01, std_slope=0.002, random_seed=None):
     assert std > 0
     self.random_state = np.random.RandomState(seed=random_seed)
     self.random_seed = random_seed

cde/density_simulation/LinearStudentT.py

@@ -5,20 +5,25 @@
 
 class LinearStudentT(BaseConditionalDensitySimulation):
   """
-  A simple, Gaussian conditional distribution where
+  A conditional student-t distribution where
 
-  x = U(-1,1)
-  y = N(y | mean = mu_slope*x+mu, scale = std_slope*x+std)
+  x ~ N(x | 0,I)
+  y ~ Student-t(y | dof(x), loc(x), scale(x))
+  with
+  dof(x) = dof_low + (dof_high - dof_low) * sigmoid(- 2*x)
+  loc(x) = mu_slope*x+mu
+  scale(x) = std_slope*x+std
 
   Args:
+    ndim_x: number of dimensions of x
     mu: the intercept of the mean line
     mu_slope: the slope of the mean line
     std: the intercept of the std dev. line
     std_slope: the slope of the std. dev. line
     random_seed: seed for the random_number generator
   """
 
-  def __init__(self, mu=0.0, ndim_x=10, mu_slope=0.005, std=0.01, std_slope=0.002, dof_low=2, dof_high=10, random_seed=None):
+  def __init__(self, ndim_x=10, mu=0.0, mu_slope=0.005, std=0.01, std_slope=0.002, dof_low=2, dof_high=10, random_seed=None):
     assert std > 0
     self.random_state = np.random.RandomState(seed=random_seed)
     self.random_seed = random_seed