Explicit layout and fix dense tensor printing (#354)

* Push through explicit "F" order through a variety of places we had hard coded "F".

* Update tensor printing to match MATLAB

* Fix some rebase errors

* Support updated typing in numpy 2.0

Author: ntjohnson1

pyttb/cp_als.py

@@ -92,7 +92,7 @@ def cp_als(  # noqa: PLR0912,PLR0913,PLR0915
      Iter 1: f = ... f-delta = ...
      Final f = ...
     >>> print(M) # doctest: +ELLIPSIS
-    ktensor of shape (2, 2)
+    ktensor of shape (2, 2) with order F
     weights=[108.4715... 8.6114...]
     factor_matrices[0] =
     [[0.4187... 0.3989...]
@@ -101,7 +101,7 @@ def cp_als(  # noqa: PLR0912,PLR0913,PLR0915
     [[0.6188... 0.2581...]
      [0.7854... 0.9661...]]
     >>> print(Minit) # doctest: +ELLIPSIS
-    ktensor of shape (2, 2)
+    ktensor of shape (2, 2) with order F
     weights=[1. 1.]
     factor_matrices[0] =
     [[4.1702...e-01 7.2032...e-01]
@@ -185,7 +185,7 @@ def cp_als(  # noqa: PLR0912,PLR0913,PLR0915
 
     # Reduce dimorder to only those modes we will optimize
     dimorder_in = dimorder  # save for output
-    dimorder = [d for d in dimorder if d in optdims]
+    dimorder = [int(d) for d in dimorder if d in optdims]
 
     # Store the last MTTKRP result to accelerate fitness computation
     U_mttkrp = np.zeros((input_tensor.shape[dimorder[-1]], rank))

pyttb/cp_apr.py

@@ -917,8 +917,8 @@ def tt_cp_apr_pqnr(  # noqa: PLR0912,PLR0913,PLR0915
                 delg = np.zeros((rank, lbfgsMem))
                 rho = np.zeros((lbfgsMem,))
                 lbfgsPos = 0
-                m_rowOLD = np.empty(())
-                gradOLD = np.empty(())
+                m_rowOLD = np.empty((), dtype=m_row.dtype)
+                gradOLD = np.empty((), dtype=m_row.dtype)
 
                 # Iteratively solve the row subproblem with projected quasi-Newton steps
                 for i in range(maxinneriters):
@@ -983,8 +983,8 @@ def tt_cp_apr_pqnr(  # noqa: PLR0912,PLR0913,PLR0915
                     isRowNOTconverged[jj] = 1
 
                     # Update the L-BFGS approximation.
-                    tmp_delm = m_row - m_rowOLD
-                    tmp_delg = gradM - gradOLD
+                    tmp_delm: np.ndarray = m_row - m_rowOLD
+                    tmp_delg: np.ndarray = gradM - gradOLD
                     tmp_delm_dot = tmp_delm.dot(tmp_delg.transpose())
                     if not np.any(tmp_delm_dot == 0):
                         tmp_rho = 1 / tmp_delm_dot

pyttb/gcp/fg_est.py

@@ -162,7 +162,7 @@ def estimate_helper(
     ndim = subs.shape[1]
 
     # Create exploded U's from the model factor matrices
-    Uexp = [np.empty(())] * ndim
+    Uexp = [np.empty((), dtype=factors[0].dtype)] * ndim
     for k in range(ndim):
         Uexp[k] = factors[k][subs[:, k], :]
 

pyttb/gcp/samplers.py

@@ -272,7 +272,7 @@ def nonzeros(
 
     # Select nonzeros
     if samples == nnz:
-        nidx = np.arange(0, nnz)
+        nidx: np.ndarray = np.arange(0, nnz, dtype=int)
     elif with_replacement or samples < nnz:
         nidx = np.random.choice(nnz, size=samples, replace=with_replacement)
     else:

pyttb/hosvd.py

@@ -129,7 +129,7 @@ def hosvd(  # noqa: PLR0912,PLR0913,PLR0915
 
         # Shrink!
         if sequential:
-            Y = Y.ttm(factor_matrices[k].transpose(), k)
+            Y = Y.ttm(factor_matrices[k].transpose(), int(k))
     # Extract final core
     if sequential:
         G = Y