move tests into class

theano/tensor/tests/test_basic.py

@@ -1826,7 +1826,8 @@ def test_eye():
         yield check, dtype, 5, 3, -1
 
 
-def test_tri():
+class test_triangle(unittest.TestCase):
+    def test_tri(self):
         def check(dtype, N, M_=None, k=0):
             # Theano does not accept None as a tensor.
             # So we must use a real value.
@@ -1841,8 +1842,9 @@ def test_tri():
             f = function([N_symb, M_symb, k_symb],
                         tri(N_symb, M_symb, k_symb, dtype=dtype))
             result = f(N, M, k)
-        assert numpy.allclose(result, numpy.tri(N, M_, k, dtype=dtype))
-        assert result.dtype == numpy.dtype(dtype)
+            self.assertTrue(
+                numpy.allclose(result, numpy.tri(N, M_, k, dtype=dtype)))
+            self.assertTrue(result.dtype == numpy.dtype(dtype))
         for dtype in ALL_DTYPES:
             yield check, dtype, 3
             # M != N, k = 0
@@ -1859,22 +1861,22 @@ def test_tri():
             yield check, dtype, 5, 3, -1
 
 
-def test_tril_triu():
+    def test_tril_triu(self):
         def check_l(m, k=0):
             m_symb = matrix(dtype=m.dtype)
             k_symb = iscalar()
             f = function([m_symb, k_symb], tril(m_symb, k_symb))
             result = f(m, k)
-        assert numpy.allclose(result, numpy.tril(m, k))
-        assert result.dtype == numpy.dtype(dtype)
+            self.assertTrue(numpy.allclose(result, numpy.tril(m, k)))
+            self.assertTrue(result.dtype == numpy.dtype(dtype))
 
         def check_u(m, k=0):
             m_symb = matrix(dtype=m.dtype)
             k_symb = iscalar()
             f = function([m_symb, k_symb], triu(m_symb, k_symb))
             result = f(m, k)
-        assert numpy.allclose(result, numpy.triu(m, k))
-        assert result.dtype == numpy.dtype(dtype)
+            self.assertTrue(numpy.allclose(result, numpy.triu(m, k)))
+            self.assertTrue(result.dtype == numpy.dtype(dtype))
 
         for dtype in ALL_DTYPES:
             m = rand_of_dtype((10, 10), dtype)
@@ -1896,7 +1898,8 @@ def test_tril_triu():
             yield check_u, m, -1
 
 
-def test_nonzero():
+class test_nonzero(unittest.TestCase):
+    def test_nonzero(self):
         def check(m):
             m_symb = theano.tensor.tensor(dtype=m.dtype,
                                         broadcastable = (False,) * m.ndim)
@@ -1904,15 +1907,12 @@ def test_nonzero():
             f_tuple = function([m_symb], nonzero(m_symb, return_matrix=False))
             f_matrix = function([m_symb], nonzero(m_symb, return_matrix=True))
 
-        assert numpy.allclose(f_matrix(m), numpy.vstack(numpy.nonzero(m)))
+            self.assertTrue(numpy.allclose(f_matrix(m), numpy.vstack(numpy.nonzero(m))))
             for i, j in zip(f_tuple(m), numpy.nonzero(m)):
-            assert numpy.allclose(i, j)
+                self.assertTrue(numpy.allclose(i, j))
 
         rand0d = numpy.array(rand())
-    check(rand0d)
-
-    rand0d_0 = numpy.array(0, dtype = theano.config.floatX)
-    check(rand0d_0)
+        self.assertRaises(ValueError, check, rand0d)
 
         rand1d = rand(8)
         rand1d[:4] = 0
@@ -1930,7 +1930,8 @@ def test_nonzero():
         rand4d[:4] = 0
         check(rand4d)
 
-def test_flatnonzero():
+
+    def test_flatnonzero(self):
         def check(m):
             m_symb = theano.tensor.tensor(dtype=m.dtype,
                                         broadcastable = (False,) * m.ndim)
@@ -1939,10 +1940,7 @@ def test_flatnonzero():
             assert numpy.allclose(result, numpy.flatnonzero(m))
 
         rand0d = numpy.array(rand())
-    check(rand0d)
-
-    rand0d_0 = numpy.array(0, dtype = theano.config.floatX)
-    check(rand0d_0)
+        self.assertRaises(ValueError, check, rand0d)
 
         rand1d = rand(8)
         rand1d[:4] = 0
@@ -1960,7 +1958,7 @@ def test_flatnonzero():
         rand4d[:4] = 0
         check(rand4d)
 
-def test_nonzero_values():
+    def test_nonzero_values(self):
         def check(m):
             m_symb = theano.tensor.tensor(dtype=m.dtype,
                                         broadcastable = (False,) * m.ndim)
@@ -1968,6 +1966,9 @@ def test_nonzero_values():
             result = f(m)
             assert numpy.allclose(result, m[numpy.nonzero(m)])
 
+        rand0d = rand()
+        self.assertRaises(ValueError, check, rand0d)
+
         rand1d = rand(8)
         rand1d[:4] = 0
         check(rand1d)