[tests] Replace mdptoolbox.utils wth .util

The mdptoolbox.utils module was renamed to mdptoolbox.util and the tests
were still using the old name, Replace all occurances of utils with util

src/mdptoolbox/util.py

@@ -111,11 +111,11 @@ def check(P, R):
     --------
     >>> import mdptoolbox, mdptoolbox.example
     >>> P_valid, R_valid = mdptoolbox.example.rand(100, 5)
-    >>> mdptoolbox.utils.check(P_valid, R_valid) # Nothing should happen
+    >>> mdptoolbox.util.check(P_valid, R_valid) # Nothing should happen
     >>> 
     >>> import numpy as np
     >>> P_invalid = np.random.rand(5, 100, 100)
-    >>> mdptoolbox.utils.check(P_invalid, R_valid) # Raises an exception
+    >>> mdptoolbox.util.check(P_invalid, R_valid) # Raises an exception
 
     """
     # Checking P

src/tests/test_examples.py

@@ -27,7 +27,7 @@ def test_example_forest_R_shape():
 
 def test_example_forest_check():
     P, R = mdptoolbox.example.forest(10, 5, 3, 0.2)
-    assert mdptoolbox.utils.check(P, R) == None
+    assert mdptoolbox.util.check(P, R) == None
 
 # exampleRand
 
@@ -38,7 +38,7 @@ def test_example_rand_dense_R_shape():
     assert (R_rand.shape == (ACTIONS, STATES, STATES))
 
 def test_example_rand_dense_check():
-    assert mdptoolbox.utils.check(P_rand, R_rand) == None
+    assert mdptoolbox.util.check(P_rand, R_rand) == None
 
 def test_example_rand_sparse_P_shape():
     assert (len(P_rand_sparse) == ACTIONS)
@@ -51,4 +51,4 @@ def test_example_rand_sparse_R_shape():
         assert (R_rand_sparse[a].shape == (STATES, STATES))
 
 def test_example_rand_sparse_check():
-    assert mdptoolbox.utils.check(P_rand_sparse, R_rand_sparse) == None
+    assert mdptoolbox.util.check(P_rand_sparse, R_rand_sparse) == None

src/tests/test_utils.py

@@ -18,14 +18,14 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 # check: P - square, stochastic and non-negative object np.arrays
 
@@ -34,21 +34,21 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 # check: P - square, stochastic and non-negative lists
 
@@ -57,21 +57,21 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 # check: P - square, stochastic and non-negative dicts
 
@@ -80,21 +80,21 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 # check: R - square stochastic and non-negative sparse
 
@@ -103,7 +103,7 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 # check: R - square, stochastic and non-negative object np.arrays
 
@@ -113,7 +113,7 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 def test_check_R_square_stochastic_nonnegative_object_matrix():
     P = np.zeros((ACTIONS, STATES, STATES))
@@ -121,7 +121,7 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 def test_check_R_square_stochastic_nonnegative_object_sparse():
     P = np.zeros((ACTIONS, STATES, STATES))
@@ -129,7 +129,7 @@ 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.utils.check(P, R) == None)
+    assert (mdptoolbox.util.check(P, R) == None)
 
 # checkSquareStochastic: square, stochastic and non-negative
 
@@ -137,32 +137,32 @@ 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.utils.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) == 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.utils.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) == 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.utils.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) == None
 
 # checkSquareStochastic: eye
 
 def test_checkSquareStochastic_eye_array():
     P = np.eye(STATES)
-    assert mdptoolbox.utils.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) == None
 
 def test_checkSquareStochastic_eye_matrix():
     P = np.matrix(np.eye(STATES))
-    assert mdptoolbox.utils.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) == None
 
 def test_checkSquareStochastic_eye_sparse():
     P = sp.sparse.eye(STATES, STATES).tocsr()
-    assert mdptoolbox.utils.checkSquareStochastic(P) == None
+    assert mdptoolbox.util.checkSquareStochastic(P) == None