Merge pull request #236 from QuantEcon/ddp-dev

Drop `num_actions` from DiscreteDP

quantecon/markov/ddp.py

@@ -180,9 +180,6 @@ class DiscreteDP(object):
  num_states : scalar(int)
  Number of states.
 
- num_actions : scalar(int)
- Number of actions.
-
  num_sa_pairs : scalar(int)
  Number of feasible state-action pairs (or those that yield
  finite rewards).
@@ -322,7 +319,6 @@ def __init__(self, R, Q, beta, s_indices=None, a_indices=None):
 
  self.s_indices = np.asarray(s_indices)
  self.a_indices = np.asarray(a_indices)
- self.num_actions = self.a_indices.max() + 1
 
  if _has_sorted_sa_indices(self.s_indices, self.a_indices):
  a_indptr = np.empty(self.num_states+1, dtype=int)
@@ -372,22 +368,21 @@ def s_wise_max(vals, out=None, out_argmax=None):
  else: # Not self._sa_pair
  if self.R.ndim != 2:
  raise ValueError(msg_dimension)
- self.num_states, self.num_actions = self.R.shape
+ n, m = self.R.shape
 
- if self.Q.shape != \
- (self.num_states, self.num_actions, self.num_states):
+ if self.Q.shape != (n, m, n):
  raise ValueError(msg_shape)
 
+ self.num_states = n
  self.s_indices, self.a_indices = None, None
  self.num_sa_pairs = (self.R > -np.inf).sum()
 
  # Define state-wise maximization
  def s_wise_max(vals, out=None, out_argmax=None):
  """
  Return the vector max_a vals(s, a), where vals is represented
- by a 2-dimensional ndarray of shape (self.num_states,
- self.num_actions). Stored in out, which must be of length
- self.num_states.
+ by a 2-dimensional ndarray of shape (n, m). Stored in out,
+ which must be of length self.num_states.
  out and out_argmax must be of length self.num_states; dtype of
  out_argmax must be int.
 
@@ -780,14 +775,15 @@ def midrange(z):
  u = np.empty(self.num_states)
  sigma = np.empty(self.num_states, dtype=int)
 
+ try:
+ tol = epsilon * (1-self.beta) / self.beta
+ except ZeroDivisionError: # Raised if beta = 0
+ tol = np.inf
+
  for i in range(max_iter):
  # Policy improvement
  self.bellman_operator(v, Tv=u, sigma=sigma)
  diff = u - v
- try:
- tol = epsilon * (1-self.beta) / self.beta
- except ZeroDivisionError: # Raised if beta = 0
- tol = np.inf
  if span(diff) < tol:
  v[:] = u + midrange(diff) * self.beta / (1 - self.beta)
  break