Allowing rdims or cdims to be empty array. (#43)

Closes #42

pyttb/tenmat.py

@@ -113,12 +113,19 @@ def from_tensor_type(cls, source, rdims=None, cdims=None, cdims_cyclic=None):
             elif rdims is None and cdims is not None:
                 rdims = np.setdiff1d(alldims, cdims)
 
-
-            dims = np.hstack([rdims, cdims])
+            # if rdims or cdims is empty, hstack will output an array of float not int
+            if rdims.size == 0:
+                dims = cdims.copy()
+            elif cdims.size == 0:
+                dims = rdims.copy()
+            else:
+                dims = np.hstack([rdims, cdims])
             if not len(dims) == n or not (alldims == np.sort(dims)).all():
                 assert False, 'Incorrect specification of dimensions, the sorted concatenation of rdims and cdims must be range(source.ndims).'
 
-            data = np.reshape(source.permute(dims).data, (np.prod(np.array(tshape)[rdims]), np.prod(np.array(tshape)[cdims])), order='F')
+            rprod = 1 if rdims.size == 0 else np.prod(np.array(tshape)[rdims])
+            cprod = 1 if cdims.size == 0 else np.prod(np.array(tshape)[cdims])
+            data = np.reshape(source.permute(dims).data, (rprod, cprod), order='F')
 
             # Create tenmat
             tenmatInstance = cls()

tests/test_tenmat.py

@@ -22,6 +22,14 @@ def sample_ndarray_2way():
     params = {'data':ndarrayInstance, 'shape':shape}
     return params, ndarrayInstance
 
+@pytest.fixture()
+def sample_tensor_3way():
+    data = np.array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.])
+    shape = (2, 3, 2)
+    params = {'data':np.reshape(data, np.array(shape), order='F'), 'shape': shape}
+    tensorInstance = ttb.tensor().from_data(data, shape)
+    return params, tensorInstance
+
 @pytest.fixture()
 def sample_ndarray_4way():
     shape = (2, 2, 2, 2)
@@ -184,8 +192,9 @@ def test_tenmat_initialization_from_data(sample_ndarray_1way, sample_ndarray_2wa
         assert exc in str(excinfo)
 
 @pytest.mark.indevelopment
-def test_tenmat_initialization_from_tensor_type(sample_tenmat_4way, sample_tensor_4way):
+def test_tenmat_initialization_from_tensor_type(sample_tenmat_4way, sample_tensor_3way, sample_tensor_4way):
     (_, tensorInstance) = sample_tensor_4way
+    (_, tensorInstance3) = sample_tensor_3way
     (params, tenmatInstance) = sample_tenmat_4way
     tshape = params['tshape']
     rdims = params['rdims']
@@ -208,6 +217,11 @@ def test_tenmat_initialization_from_tensor_type(sample_tenmat_4way, sample_tenso
     assert tenmatInstance.shape == tenmatTensorRdims.shape
     assert tenmatInstance.tshape == tenmatTensorRdims.tshape
 
+    # Constructor from tensor using empty rdims
+    tenmatTensorRdims = ttb.tenmat.from_tensor_type(tensorInstance3, rdims=np.array([]))
+    data = np.reshape(np.arange(1,13),(1,12))
+    assert (tenmatTensorRdims.data == data).all()
+
     # Constructor from tensor using cdims only
     tenmatTensorCdims = ttb.tenmat.from_tensor_type(tensorInstance, cdims=cdims)
     assert (tenmatInstance.data == tenmatTensorCdims.data).all()
@@ -216,6 +230,11 @@ def test_tenmat_initialization_from_tensor_type(sample_tenmat_4way, sample_tenso
     assert tenmatInstance.shape == tenmatTensorCdims.shape
     assert tenmatInstance.tshape == tenmatTensorCdims.tshape
 
+    # Constructor from tensor using empty cdims
+    tenmatTensorCdims = ttb.tenmat.from_tensor_type(tensorInstance3, cdims=np.array([]))
+    data = np.reshape(np.arange(1,13),(12,1))
+    assert (tenmatTensorCdims.data == data).all()
+
     # Constructor from tensor using rdims and cdims
     tenmatTensorRdimsCdims = ttb.tenmat.from_tensor_type(tensorInstance, rdims=rdims, cdims=cdims)
     assert (tenmatInstance.data == tenmatTensorRdimsCdims.data).all()