Better solution for sandialabs#380 and also sandialabs#368. The function isn't changed but the instructions are.

Author: tgkolda

pyttb/tensor.py

@@ -217,15 +217,17 @@ def from_function(
         function_handle: Callable[[Tuple[int, ...]], np.ndarray],
         shape: Shape,
     ) -> tensor:
-        """Construct a :class:`pyttb.tensor` with data generated by function.
+        """Construct :class:`pyttb.tensor` with data generated by given function.
 
         Parameters
         ----------
         function_handle:
-            A function that can accept an integer length and
-            return a :class:`numpy.ndarray` vector of that length.
+            A function that takes a tuple of integers and returns a
+            :class:`numpy.ndarray`. The array should be in Fortran order to avoid
+            warnings of data being copied. The data will be reshaped to the shape,
+            so returning a vector of length equal to the product of the shape is fine.
         shape:
-            Shape of the resulting tensor.
+            Shape of the resulting tensor; e.g., a tuple of integers.
 
         Returns
         -------
@@ -236,46 +238,46 @@ def from_function(
         Create a :class:`pyttb.tensor` with entries drawn from a normal distribution
         using :func:`numpy.random.randn`::
 
-            >>> np.random.seed(0)
-            >>> T = ttb.tensor.from_function(np.random.randn, (4, 3, 2))
+            >>> randn = lambda s : np.random.randn(np.prod(s))
+            >>> np.random.seed(0) # reproducibility
+            >>> T = ttb.tensor.from_function(randn, (4, 3, 2))
             >>> print(T)
             tensor of shape (4, 3, 2) with order F
             data[:, :, 0] =
             [[ 1.76405235  1.86755799 -0.10321885]
-            [ 0.40015721 -0.97727788  0.4105985 ]
-            [ 0.97873798  0.95008842  0.14404357]
-            [ 2.2408932  -0.15135721  1.45427351]]
+             [ 0.40015721 -0.97727788  0.4105985 ]
+             [ 0.97873798  0.95008842  0.14404357]
+             [ 2.2408932  -0.15135721  1.45427351]]
             data[:, :, 1] =
             [[ 0.76103773  1.49407907 -2.55298982]
-            [ 0.12167502 -0.20515826  0.6536186 ]
-            [ 0.44386323  0.3130677   0.8644362 ]
-            [ 0.33367433 -0.85409574 -0.74216502]]
+             [ 0.12167502 -0.20515826  0.6536186 ]
+             [ 0.44386323  0.3130677   0.8644362 ]
+             [ 0.33367433 -0.85409574 -0.74216502]]
 
         Create a :class:`pyttb.tensor` with all entries equal to 1
         using :func:`numpy.ones`::
 
-            >>> T = ttb.tensor.from_function(np.ones, (2, 3, 4))
+            >>> T = ttb.tensor.from_function(lambda s: np.ones(s,order='F'), (2, 3, 4))
             >>> print(T)
             tensor of shape (2, 3, 4) with order F
             data[:, :, 0] =
             [[1. 1. 1.]
-            [1. 1. 1.]]
+             [1. 1. 1.]]
             data[:, :, 1] =
             [[1. 1. 1.]
-            [1. 1. 1.]]
+             [1. 1. 1.]]
             data[:, :, 2] =
             [[1. 1. 1.]
-            [1. 1. 1.]]
+             [1. 1. 1.]]
             data[:, :, 3] =
             [[1. 1. 1.]
-            [1. 1. 1.]]
+             [1. 1. 1.]]
         """
         # Check size
         shape = parse_shape(shape)
 
         # Generate data
-        totalsize = prod(shape)
-        data = function_handle(totalsize)
+        data = function_handle(shape)
 
         # Create the tensor
         return cls(data, shape, copy=False)