TENMAT: Remove tt_from_dense_matrix since tenmat exists.

pyttb/pyttb_utils.py

@@ -13,41 +13,6 @@
 import pyttb as ttb
 
 
-def tt_from_dense_matrix(
- matrix: np.ndarray,
- shape: Tuple[int, ...],
- mode: int,
- idx: int,
-) -> ttb.tensor:
- """
- Helper function to wrap dense matrix into tensor.
- Inverse of :class:`pyttb.tt_to_dense_matrix`
-
- Parameters
- ----------
- matrix:
- Matrix to (re-)create tensor from.
- mode:
- Mode around which tensor was unwrapped
- idx:
- In {0,1}, idx of mode in matrix, s.b. 0 for tranpose=True
-
- Returns
- -------
- Dense tensor.
- """
- tensorInstance = ttb.tensor(matrix)
- if idx == 0:
- tensorInstance = tensorInstance.permute(np.array([1, 0]))
- tensorInstance = tensorInstance.reshape(shape)
- tensorInstance = tensorInstance.permute(
- np.concatenate(
- (np.arange(1, mode + 1), np.array([0]), np.arange(mode + 1, len(shape)))
- )
- )
- return tensorInstance
-
-
 def tt_union_rows(MatrixA: np.ndarray, MatrixB: np.ndarray) -> np.ndarray:
  """
  Helper function to reproduce functionality of MATLABS intersect(a,b,'rows')

tests/test_pyttb_utils.py

@@ -11,21 +11,6 @@
 import pyttb.pyttb_utils as ttb_utils
 
 
-def test_sptensor_from_dense_matrix():
- tensorInstance = ttb.tensor(np.random.normal(size=(4, 4, 4)))
- for mode in range(tensorInstance.ndims):
- tensorCopy = tensorInstance.copy()
- Xnt = tensorCopy.to_tenmat(np.array([mode]), copy=False).data.transpose()
- Ynt = ttb_utils.tt_from_dense_matrix(Xnt, tensorCopy.shape, mode, 0)
- assert tensorCopy.isequal(Ynt)
-
- for mode in range(tensorInstance.ndims):
- tensorCopy = tensorInstance.copy()
- Xnt = tensorCopy.to_tenmat(np.array([mode]), copy=False).data
- Ynt = ttb_utils.tt_from_dense_matrix(Xnt, tensorCopy.shape, mode, 1)
- assert tensorCopy.isequal(Ynt)
-
-
 def test_tt_union_rows():
  a = np.array([[4, 6], [1, 9], [2, 6], [2, 6], [99, 0]])
  b = np.array([[1, 7], [1, 8], [2, 6]])