FIX: Correct the docstring for the random_state options

QuantEcon · Oct 1, 2017 · 7fcf49d · 7fcf49d
1 parent 23044a4
commit 7fcf49d
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Showing 5 changed files with 6 additions and 12 deletions.
diff --git a/quantecon/discrete_rv.py b/quantecon/discrete_rv.py
@@ -70,8 +70,7 @@ def draw(self, k=1, random_state=None):
         k : scalar(int), optional
             Number of draws to be returned
 
-        random_state : scalar(int) or np.random.RandomState,
-               optional(default=None)
+        random_state : int or np.random.RandomState, optional
             Random seed (integer) or np.random.RandomState instance to set
             the initial state of the random number generator for
             reproducibility. If None, a randomly initialized RandomState is

diff --git a/quantecon/lqcontrol.py b/quantecon/lqcontrol.py
@@ -256,8 +256,7 @@ def compute_sequence(self, x0, ts_length=None, random_state=None):
         ts_length : scalar(int)
             Length of the simulation -- defaults to T in finite case
 
-        random_state : scalar(int) or np.random.RandomState,
-               optional(default=None)
+        random_state : int or np.random.RandomState, optional
             Random seed (integer) or np.random.RandomState instance to set
             the initial state of the random number generator for
             reproducibility. If None, a randomly initialized RandomState is

diff --git a/quantecon/lqnash.py b/quantecon/lqnash.py
@@ -66,8 +66,7 @@ def nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,
         This is the tolerance level for convergence
     max_iter : scalar(int), optional(default=1000)
         This is the maximum number of iteratiosn allowed
-    random_state : scalar(int) or np.random.RandomState,
-           optional(default=None)
+    random_state : int or np.random.RandomState, optional
         Random seed (integer) or np.random.RandomState instance to set
         the initial state of the random number generator for
         reproducibility. If None, a randomly initialized RandomState is

diff --git a/quantecon/lss.py b/quantecon/lss.py
@@ -163,8 +163,7 @@ def simulate(self, ts_length=100, random_state=None):
         ts_length : scalar(int), optional(default=100)
             The length of the simulation
 
-        random_state : scalar(int) or np.random.RandomState,
-               optional(default=None)
+        random_state : int or np.random.RandomState, optional
             Random seed (integer) or np.random.RandomState instance to set
             the initial state of the random number generator for
             reproducibility. If None, a randomly initialized RandomState is
@@ -205,8 +204,7 @@ def replicate(self, T=10, num_reps=100, random_state=None):
             The period that we want to replicate values for
         num_reps : scalar(int), optional(default=100)
             The number of replications that we want
-        random_state : scalar(int) or np.random.RandomState,
-               optional(default=None)
+        random_state : int or np.random.RandomState, optional
             Random seed (integer) or np.random.RandomState instance to set
             the initial state of the random number generator for
             reproducibility. If None, a randomly initialized RandomState is

diff --git a/quantecon/quad.py b/quantecon/quad.py
@@ -106,8 +106,7 @@ def qnwequi(n, a, b, kind="N", equidist_pp=None, random_state=None):
     equidist_pp : array_like, optional(default=None)
         TODO: I don't know what this does
 
-    random_state : scalar(int) or np.random.RandomState,
-           optional(default=None)
+    random_state : int or np.random.RandomState, optional
         Random seed (integer) or np.random.RandomState instance to set
         the initial state of the random number generator for
         reproducibility. If None, a randomly initialized RandomState is