some love for the priors class

GPy/core/priors.py

@@ -10,6 +10,7 @@ from ..util.linalg import pdinv
 class prior:
     def pdf(self,x):
         return np.exp(self.lnpdf(x))
+
     def plot(self):
         rvs = self.rvs(1000)
         pb.hist(rvs,100,normed=True)
@@ -17,45 +18,79 @@ class prior:
         xx = np.linspace(xmin,xmax,1000)
         pb.plot(xx,self.pdf(xx),'r',linewidth=2)
 
+
 class Gaussian(prior):
     """
-    Implementation of the univariate Gaussian probability function, coupled with random variables, since scipy.stats sucks.
+    Implementation of the univariate Gaussian probability function, coupled with random variables.
-    Using Bishop 2006 notation"""
+
+    :param mu: mean
+    :param sigma: standard deviation
+
+
+    .. Note:: Bishop 2006 notation is used throughout the code
+
+    """
+
     def __init__(self,mu,sigma):
         self.mu = float(mu)
         self.sigma = float(sigma)
         self.sigma2 = np.square(self.sigma)
         self.constant = -0.5*np.log(2*np.pi*self.sigma2)
+
     def __str__(self):
         return "N("+str(np.round(self.mu))+', '+str(np.round(self.sigma2))+')'
+
     def lnpdf(self,x):
         return self.constant - 0.5*np.square(x-self.mu)/self.sigma2
+
     def lnpdf_grad(self,x):
         return -(x-self.mu)/self.sigma2
+
     def rvs(self,n):
         return np.random.randn(n)*self.sigma + self.mu
 
+
 class log_Gaussian(prior):
     """
+    Implementation of the univariate *log*-Gaussian probability function, coupled with random variables.
+
+    :param mu: mean
+    :param sigma: standard deviation
+
+    .. Note:: Bishop 2006 notation is used throughout the code
+
     """
+
     def __init__(self,mu,sigma):
         self.mu = float(mu)
         self.sigma = float(sigma)
         self.sigma2 = np.square(self.sigma)
         self.constant = -0.5*np.log(2*np.pi*self.sigma2)
+
     def __str__(self):
         return "lnN("+str(np.round(self.mu))+', '+str(np.round(self.sigma2))+')'
+
     def lnpdf(self,x):
         return self.constant - 0.5*np.square(np.log(x)-self.mu)/self.sigma2 -np.log(x)
+
     def lnpdf_grad(self,x):
         return -((np.log(x)-self.mu)/self.sigma2+1.)/x
+
     def rvs(self,n):
         return np.exp(np.random.randn(n)*self.sigma + self.mu)
 
+
 class multivariate_Gaussian:
     """
-    Implementation of the multivariate Gaussian probability function, coupled with random variables, since scipy.stats sucks.
+    Implementation of the multivariate Gaussian probability function, coupled with random variables.
-    Using Bishop 2006 notation"""
+
+    :param mu: mean (N-dimensional array)
+    :param var: covariance matrix (NxN)
+
+    .. Note:: Bishop 2006 notation is used throughout the code
+
+    """
+
     def __init__(self,mu,var):
         self.mu = np.array(mu).flatten()
         self.var = np.array(var)
@@ -67,17 +102,22 @@ class multivariate_Gaussian:
         self.constant = -0.5*self.D*np.log(2*np.pi) - self.hld
 
     def summary(self):
-        pass #TODO
+        raise NotImplementedError
+
     def pdf(self,x):
         return np.exp(self.lnpdf(x))
+
     def lnpdf(self,x):
         d = x-self.mu
         return self.constant - 0.5*np.sum(d*np.dot(d,self.inv),1)
+
     def lnpdf_grad(self,x):
         d = x-self.mu
         return -np.dot(self.inv,d)
+
     def rvs(self,n):
         return np.random.multivariate_normal(self.mu, self.var,n)
+
     def plot(self):
         if self.D==2:
             rvs = self.rvs(200)
@@ -91,7 +131,15 @@ class multivariate_Gaussian:
 
 
 def gamma_from_EV(E,V):
-    """create an instance of a gamma prior  by specifying the Expected value(s) and Variance(s) of the distribution"""
+    """
+    Creates an instance of a gamma prior  by specifying the Expected value(s)
+    and Variance(s) of the distribution.
+
+    :param E: expected value
+    :param V: variance
+
+    """
+
     a = np.square(E)/V
     b = E/V
     return gamma(a,b)
@@ -99,15 +147,23 @@ def gamma_from_EV(E,V):
 
 class gamma(prior):
     """
-    Implementation of the Gamma probability function, coupled with random variables, since scipy.stats sucks.
+    Implementation of the Gamma probability function, coupled with random variables.
-    Using Bishop 2006 notation
+
+    :param a: shape parameter
+    :param b: rate parameter (warning: it's the *inverse* of the scale)
+
+    .. Note:: Bishop 2006 notation is used throughout the code
+
     """
+    
     def __init__(self,a,b):
         self.a = float(a)
         self.b = float(b)
         self.constant = -gammaln(self.a) + a*np.log(b)
+
     def __str__(self):
         return "Ga("+str(np.round(self.a))+', '+str(np.round(self.b))+')'
+
     def summary(self):
         ret = {"E[x]": self.a/self.b,\
             "E[ln x]": digamma(self.a) - np.log(self.b),\
@@ -118,10 +174,12 @@ class gamma(prior):
         else:
             ret['mode'] = np.nan
         return ret
+
     def lnpdf(self,x):
         return self.constant + (self.a-1)*np.log(x) - self.b*x
+
     def lnpdf_grad(self,x):
         return (self.a-1.)/x - self.b
+
     def rvs(self,n):
         return np.random.gamma(scale=1./self.b,shape=self.a,size=n)
-