20 tensor complete documentation for all methods

docs/source/ktensor.rst

@@ -4,5 +4,5 @@ pyttb.ktensor
 .. autoclass:: pyttb.ktensor
     :members:
     :special-members:
-    :exclude-members: __dict__,__weakref__, __slots__, __init__
+    :exclude-members: __dict__, __weakref__, __slots__, __init__
     :show-inheritance:

docs/source/sptensor.rst

@@ -1,8 +1,8 @@
 pyttb.sptensor
 ----------------------
 
-.. automodule:: pyttb.sptensor
+.. autoclass:: pyttb.sptensor
     :members:
     :special-members:
-    :exclude-members: __dict__,__weakref__, __slots__, __init__
+    :exclude-members: __dict__, __weakref__, __slots__, __init__
     :show-inheritance:

docs/source/tenmat.rst

@@ -1,7 +1,7 @@
 pyttb.tenmat
 ====================
 
-.. automodule:: pyttb.tenmat
+.. autoclass:: pyttb.tenmat
     :members:
     :special-members:
     :exclude-members: __dict__, __weakref__, __slots__, __init__

docs/source/tensor.rst

@@ -1,7 +1,7 @@
 pyttb.tensor
 --------------------
 
-.. automodule:: pyttb.tensor
+.. autoclass:: pyttb.tensor
     :members:
     :special-members:
     :exclude-members: __dict__, __weakref__, __slots__, __init__

docs/source/ttensor.rst

@@ -1,7 +1,7 @@
 pyttb.ttensor
 --------------------
 
-.. automodule:: pyttb.ttensor
+.. autoclass:: pyttb.ttensor
     :members:
     :special-members:
     :exclude-members: __dict__, __weakref__, __slots__, __init__

pyttb/ktensor.py

@@ -43,9 +43,8 @@ class ktensor:
     dimension i and r is the rank of the tensor (as well as the length of the
     weights vector).
 
-    Although the constructor `__init__()` can be used to create an empty
-    :class:`pyttb.ktensor`, there are several class methods that can be used
-    to create an instance of this class:
+    Instances of :class:`pyttb.ktensor` can be created using `__init__()` or
+    one of the following methods:
 
       * :meth:`from_function`
       * :meth:`from_vector`
@@ -1279,11 +1278,11 @@ def normalize(
 
     def nvecs(self, n: int, r: int, flipsign: bool = True) -> np.ndarray:
         """
-        Compute the leading mode-n vectors for a :class:`pyttb.ktensor`.
+        Compute the leading mode-n vectors of the ktensor.
 
         Computes the `r` leading eigenvectors of Xn*Xn.T (where Xn is the
         mode-`n` matricization/unfolding of self), which provides information
-        about the mode-N fibers. In two-dimensions, the `r` leading mode-1
+        about the mode-n fibers. In two-dimensions, the `r` leading mode-1
         vectors are the same as the `r` left singular vectors and the `r`
         leading mode-2 vectors are the same as the `r` right singular
         vectors. By default, this method computes the top `r` eigenvectors

pyttb/sptensor.py

@@ -2511,15 +2511,14 @@ def __repr__(self):  # pragma: no cover
             return s
 
         s = f"Sparse tensor of shape {self.shape}"
-        s += f" with {nz} nonzeros \n"
+        s += f" with {nz} nonzeros\n"
 
         # Stop insane printouts
         if nz > 10000:
             r = input("Are you sure you want to print all nonzeros? (Y/N)")
             if r.upper() != "Y":
                 return s
         for i in range(0, self.subs.shape[0]):
-            s += "\t"
             s += "["
             idx = self.subs[i, :]
             s += str(idx.tolist())[1:]