Merge branch 'main' into sptensor_shape

pyttb/cp_als.py

@@ -102,8 +102,9 @@ def cp_als(  # noqa: PLR0912,PLR0913,PLR0915
     factor_matrices[1] =
     [[0.1467... 0.0923...]
      [0.1862... 0.3455...]]
-    >>> print(output)
-    {'params': (0.0001, 1000, 1, [0, 1]), 'iters': 1, 'normresidual': ..., 'fit': ...}
+    >>> print(output["params"]) # doctest: +NORMALIZE_WHITESPACE
+    {'stoptol': 0.0001, 'maxiters': 1000, 'dimorder': [0, 1], 'printitn': 1,\
+     'fixsigns': True}
 
     Example using "nvecs" initialization:
 
@@ -265,7 +266,13 @@ def cp_als(  # noqa: PLR0912,PLR0913,PLR0915
         print(f" Final f = {fit:e}")
 
     output = {
-        "params": (stoptol, maxiters, printitn, dimorder),
+        "params": {
+            "stoptol": stoptol,
+            "maxiters": maxiters,
+            "dimorder": dimorder,
+            "printitn": printitn,
+            "fixsigns": fixsigns,
+        },
         "iters": iteration,
         "normresidual": normresidual,
         "fit": fit,

tests/test_cp_als.py

@@ -208,6 +208,25 @@ def test_cp_als_sptensor_zeros(capsys):
     assert output3["normresidual"] == 0
 
 
+def test_cp_als_tensor_pass_params(capsys, sample_tensor):
+    _, T = sample_tensor
+    KInit = ttb.ktensor.from_function(np.random.random_sample, T.shape, 2)
+
+    _, _, output = ttb.cp_als(T, 2, init=KInit, maxiters=2)
+    capsys.readouterr()
+
+    # passing the same parameters back to the method will yield the exact same results
+    _, _, output1 = ttb.cp_als(T, 2, init=KInit, **output["params"])
+    capsys.readouterr()
+
+    # changing the order should also work
+    _, _, output2 = ttb.cp_als(T, 2, **output["params"], init=KInit)
+    capsys.readouterr()
+
+    assert output["params"] == output1["params"]
+    assert output["params"] == output2["params"]
+
+
 def test_cp_als_tensor_printitn(capsys, sample_tensor):
     _, T = sample_tensor