update (#5215)

Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

docs/source/api/python/dgl.sparse_v0.rst

@@ -11,55 +11,17 @@ Sparse matrix class
 
 .. class:: SparseMatrix
 
-    Class for creating a sparse matrix representation
+    A SparseMatrix can be created from Coordinate format indices using the
+    :func:`spmatrix` constructor:
 
-    There are a few ways to create a sparse matrix:
-
-    * In COO format using row and col indices, use :func:`from_coo`.
-    * In CSR format using row pointers and col indices, use :func:`from_csr`.
-    * In CSC format using col pointers and row indices, use :func:`from_csc`.
-
-    For example, one can create COO matrices as follows:
-
-    Case1: Sparse matrix with row and column indices without values
-
-        >>> row = torch.tensor([1, 1, 2])
-        >>> col = torch.tensor([2, 4, 3])
-        >>> A = from_coo(row, col)
-        >>> A
+        >>> indices = torch.tensor([[1, 1, 2],
+        >>>                         [2, 4, 3]])
+        >>> A = dglsp.spmatrix(indices)
         SparseMatrix(indices=tensor([[1, 1, 2],
                                      [2, 4, 3]]),
                      values=tensor([1., 1., 1.]),
                      shape=(3, 5), nnz=3)
 
-    Case2: Sparse matrix with scalar/vector values
-
-        >>> # vector values
-        >>> val = torch.tensor([[1, 1], [2, 2], [3, 3]])
-        >>> A = from_coo(row, col, val)
-        SparseMatrix(indices=tensor([[1, 1, 2],
-                                     [2, 4, 3]]),
-                     values=tensor([[1, 1],
-                                    [2, 2],
-                                    [3, 3]]),
-                     shape=(3, 5), nnz=3)
-
-    Similarly, one can create a CSR matrix as follows:
-
-        >>> indptr = torch.tensor([0, 1, 2, 5])
-        >>> indices = torch.tensor([1, 2, 0, 1, 2])
-        >>> val = torch.tensor([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]])
-        >>> A = from_csr(indptr, indices, val)
-        >>> A
-        SparseMatrix(indices=tensor([[0, 1, 2, 2, 2],
-                                     [1, 2, 0, 1, 2]]),
-                     values=tensor([[1, 1],
-                                    [2, 2],
-                                    [3, 3],
-                                    [4, 4],
-                                    [5, 5]]),
-                     shape=(3, 3), nnz=5)
-
 Creation Ops
 ````````