[tests] Add some tests for util module

src/tests/test_utils.py

@@ -8,6 +8,8 @@ Created on Sat Aug 24 14:52:17 2013
 import numpy as np
 import scipy as sp
 
+from nose.tools import assert_raises
+
 import mdptoolbox
 
 from .utils import ACTIONS, STATES
@@ -18,14 +20,14 @@ def test_check_square_stochastic_nonnegative_array_1():
     for a in range(ACTIONS):
         P[a, :, :] = np.eye(STATES)
         R[:, a] = np.random.rand(STATES)
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_square_stochastic_nonnegative_array_2():
     P = np.zeros((ACTIONS, STATES, STATES))
     R = np.random.rand(ACTIONS, STATES, STATES)
     for a in range(ACTIONS):
         P[a, :, :] = np.eye(STATES)
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 # check: P - square, stochastic and non-negative object np.arrays
 
@@ -34,21 +36,21 @@ def test_check_P_square_stochastic_nonnegative_object_array():
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P[a] = np.eye(STATES)
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_P_square_stochastic_nonnegative_object_matrix():
     P = np.empty(ACTIONS, dtype=object)
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P[a] = np.matrix(np.eye(STATES))
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_P_square_stochastic_nonnegative_object_sparse():
     P = np.empty(ACTIONS, dtype=object)
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P[a] = sp.sparse.eye(STATES, STATES).tocsr()
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 # check: P - square, stochastic and non-negative lists
 
@@ -57,21 +59,21 @@ def test_check_P_square_stochastic_nonnegative_list_array():
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P.append(np.eye(STATES))
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_P_square_stochastic_nonnegative_list_matrix():
     P = []
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P.append(np.matrix(np.eye(STATES)))
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_P_square_stochastic_nonnegative_list_sparse():
     P = []
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P.append(sp.sparse.eye(STATES, STATES).tocsr())
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 # check: P - square, stochastic and non-negative dicts
 
@@ -80,21 +82,21 @@ def test_check_P_square_stochastic_nonnegative_dict_array():
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P[a] = np.eye(STATES)
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_P_square_stochastic_nonnegative_dict_matrix():
     P = {}
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P[a] = np.matrix(np.eye(STATES))
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_P_square_stochastic_nonnegative_dict_sparse():
     P = {}
     R = np.random.rand(STATES, ACTIONS)
     for a in range(ACTIONS):
         P[a] = sp.sparse.eye(STATES, STATES).tocsr()
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 # check: R - square stochastic and non-negative sparse
 
@@ -103,7 +105,7 @@ def test_check_R_square_stochastic_nonnegative_sparse():
     R = sp.sparse.csr_matrix(np.random.rand(STATES, ACTIONS))
     for a in range(ACTIONS):
         P[a, :, :] = np.eye(STATES)
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 # check: R - square, stochastic and non-negative object np.arrays
 
@@ -113,7 +115,7 @@ def test_check_R_square_stochastic_nonnegative_object_array():
     for a in range(ACTIONS):
         P[a, :, :] = np.eye(STATES)
         R[a] = np.random.rand(STATES, STATES)
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_R_square_stochastic_nonnegative_object_matrix():
     P = np.zeros((ACTIONS, STATES, STATES))
@@ -121,7 +123,7 @@ def test_check_R_square_stochastic_nonnegative_object_matrix():
     for a in range(ACTIONS):
         P[a, :, :] = np.eye(STATES)
         R[a] = np.matrix(np.random.rand(STATES, STATES))
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 def test_check_R_square_stochastic_nonnegative_object_sparse():
     P = np.zeros((ACTIONS, STATES, STATES))
@@ -129,7 +131,7 @@ def test_check_R_square_stochastic_nonnegative_object_sparse():
     for a in range(ACTIONS):
         P[a, :, :] = np.eye(STATES)
         R[a] = sp.sparse.csr_matrix(np.random.rand(STATES, STATES))
-    assert (mdptoolbox.util.check(P, R) == None)
+    assert mdptoolbox.util.check(P, R) is None
 
 # checkSquareStochastic: square, stochastic and non-negative
 
@@ -137,32 +139,54 @@ def test_checkSquareStochastic_square_stochastic_nonnegative_array():
     P = np.random.rand(STATES, STATES)
     for s in range(STATES):
         P[s, :] = P[s, :] / P[s, :].sum()
-    assert mdptoolbox.util.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) is None
 
 def test_checkSquareStochastic_square_stochastic_nonnegative_matrix():
     P = np.random.rand(STATES, STATES)
     for s in range(STATES):
         P[s, :] = P[s, :] / P[s, :].sum()
     P = np.matrix(P)
-    assert mdptoolbox.util.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) is None
 
 def test_checkSquareStochastic_square_stochastic_nonnegative_sparse():
     P = np.random.rand(STATES, STATES)
     for s in range(STATES):
         P[s, :] = P[s, :] / P[s, :].sum()
     P = sp.sparse.csr_matrix(P)
-    assert mdptoolbox.util.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) is None
 
 # checkSquareStochastic: eye
 
 def test_checkSquareStochastic_eye_array():
     P = np.eye(STATES)
-    assert mdptoolbox.util.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) is None
 
 def test_checkSquareStochastic_eye_matrix():
     P = np.matrix(np.eye(STATES))
-    assert mdptoolbox.util.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) is None
 
 def test_checkSquareStochastic_eye_sparse():
     P = sp.sparse.eye(STATES, STATES).tocsr()
-    assert mdptoolbox.util.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) is None
+
+def test_check_vector_R():
+    R = np.random.rand(STATES)
+    P = [np.matrix(np.eye(STATES))] * 3
+    assert mdptoolbox.util.check(P, R) is None
+
+# Exception tests
+def test_check_P_shape_error_1():
+    P = np.eye(STATES)[:STATES - 1, :STATES]
+    assert_raises(mdptoolbox.util.InvalidMDPError, mdptoolbox.util.check,
+                  P=P, R=np.random.rand(10, 3))
+
+def test_check_P_shape_error_2():
+    P = (np.random.rand(9, 9), np.random.rand(9, 9), np.random.rand(9, 5))
+    assert_raises(mdptoolbox.util.InvalidMDPError, mdptoolbox.util.check,
+                  P=P, R=np.random.rand(9))
+
+def test_check_R_shape_error_1():
+    R = (np.random.rand(9, 9), np.random.rand(9, 9), np.random.rand(9, 5))
+    P = np.random.rand(3, 10, 10)
+    assert_raises(mdptoolbox.util.InvalidMDPError, mdptoolbox.util.check,
+                  P=P, R=R)