Small changes

Author: rawnhenry

sdh_documentation/docs/pycomputations.md

@@ -81,8 +81,9 @@ and demonstrates how matrix-vector multiplication can be expressed in index
 notation.  Both forms are supported by TACO:
 
 ```python
+i, j = pytaco.get_index_vars(2)
 y[i] = A[i,j] * x[j]
-y[i] = sum(j, A[i,j] * x[j])
+y[i] = pytaco.sum(j, A[i,j] * x[j])
 ```
 
 Reductions that are not explicitly expressed are assumed to be over the
@@ -135,6 +136,7 @@ stores the result in matrix `A`:
 A = pt.tensor([3,3], pt.format([dense, dense]))
 B = pt.tensor([3,3], pt.format([dense, dense]))
 C = pt.tensor([3,3], pt.format([dense, dense]))
+i, j = pt.get_index_vars(2)
 
 A[i,j] = B[i,j] + C[j,i]
 ```
@@ -153,6 +155,7 @@ before `i`:
 A = pt.tensor([3,3], pt.format([dense, compressed]))
 B = pt.tensor([3,3], pt.format([dense, compressed]))
 C = pt.tensor([3,3], pt.format([dense, compressed]))
+i, j = pt.get_index_vars(2)
 
 A[i,j] = B[i,j] + C[j,i]
 ```
@@ -165,6 +168,7 @@ addition with the already-transposed temporary:
 A = pt.tensor([3,3], pt.format([dense, compressed]))
 B = pt.tensor([3,3], pt.format([dense, compressed]))
 C = pt.tensor([3,3], pt.format([dense, compressed]))
+i, j = pt.get_index_vars(2)
 
 Ct = C.transpose([1, 0])  # Ct is also stored in the CSR format
 A[i,j] = B[i,j] + Ct[i,j]
@@ -179,6 +183,7 @@ order, requires `j` to be accessed before `i`:
 A = pt.tensor([3,3], pt.format([dense, compressed]))
 B = pt.tensor([3,3], pt.format([dense, compressed]))
 C = pt.tensor([3,3], pt.format([dense, compressed], [1, 0]))
+i, j = pt.get_index_vars(2)
 
 A[i,j] = B[i,j] + C[i,j]
 ```
@@ -191,6 +196,7 @@ addition:
 A = pt.tensor([3,3], pt.format([dense, compressed]))
 B = pt.tensor([3,3], pt.format([dense, compressed]))
 C = pt.tensor([3,3], pt.format([dense, compressed], [1, 0]))
+i, j = pt.get_index_vars(2)
 
 Cp = C.transpose([0, 1], pt.format([dense, compressed]))  # Store a copy of C in the CSR format
 A[i,j] = B[i,j] + Cp[i,j]

sdh_documentation/docs/pytensors.md

@@ -76,7 +76,7 @@ As demonstrated above, you can define a new tensor storage format by creating a
 `pytaco.format` object.  This requires specifying whether each tensor dimension
 is dense or sparse as well as (optionally) the order in which dimensions should
 be stored.  TACO also predefines some common tensor formats (including 
-```pt.csr```, ```pt.csc``` and ```pt.csf```) that you can use out of the box.
+```pt.csr``` and ```pt.csc```) that you can use out of the box.
 
 # Initializing Tensors
 
@@ -120,7 +120,7 @@ A.pack()
 You can then iterate over the nonzero elements of the tensor as follows:
 
 ```python
-for elem in A:
+for coordinate, elem in A:
   print(elem)
 ```