better comments

cvx/random/rand_cov.py

@@ -17,5 +17,8 @@
 
 
 def rand_cov(n):
+    """
+    Construct a random covariance matrix of size n x n
+    """
     a = np.random.randn(n, n)
     return np.transpose(a) @ a

cvx/risk/bounds.py

@@ -50,16 +50,13 @@ def __post_init__(self):
         )
 
     def update(self, **kwargs):
-        # lower = kwargs.get("lower", np.zeros(self.m))
         lower = kwargs[self._f("lower")]
         self.parameter[self._f("lower")].value = np.zeros(self.m)
         self.parameter[self._f("lower")].value[: len(lower)] = lower
 
-        upper = kwargs[self._f("upper")]  # .get("upper", np.ones(self.m))
+        upper = kwargs[self._f("upper")]
         self.parameter[self._f("upper")].value = np.zeros(self.m)
-        self.parameter[self._f("upper")].value[
-            : len(upper)
-        ] = upper  # kwargs.get("upper", np.ones(m))
+        self.parameter[self._f("upper")].value[: len(upper)] = upper
 
     def constraints(self, weights, **kwargs):
         return [

cvx/risk/cvar/cvar.py

@@ -27,8 +27,13 @@ class CVar(Model):
     """Conditional value at risk model"""
 
     alpha: float = 0.95
+    """alpha parameter to determine the size of the tail"""
+
     n: int = 0
+    """number of samples"""
+
     m: int = 0
+    """number of assets"""
 
     def __post_init__(self):
         self.k = int(self.n * (1 - self.alpha))