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Unverified Commit dffe722f authored by Mufei Li's avatar Mufei Li Committed by GitHub
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Update (#5196) 


Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-36-188.ap-northeast-1.compute.internal>
parent 995c1913
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Showing with 30 additions and 6 deletions
python/dgl/sparse/reduction.py
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...@@ -35,12 +35,14 @@ def reduce(input: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"): ...@@ -35,12 +35,14 @@ def reduce(input: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"):
Returns Returns
---------- ----------
Tensor torch.Tensor
Reduced tensor Reduced tensor
Examples Examples
---------- ----------
Case1: scalar-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([1, 1, 2]) >>> val = torch.tensor([1, 1, 2])
...@@ -56,6 +58,8 @@ def reduce(input: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"): ...@@ -56,6 +58,8 @@ def reduce(input: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"):
>>> print(dglsp.reduce(A, 1, 'smin')) >>> print(dglsp.reduce(A, 1, 'smin'))
tensor([1, 1, 0, 0]) tensor([1, 1, 0, 0])
Case2: vector-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]]) >>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]])
...@@ -99,12 +103,14 @@ def sum(input: SparseMatrix, dim: Optional[int] = None): ...@@ -99,12 +103,14 @@ def sum(input: SparseMatrix, dim: Optional[int] = None):
Returns Returns
---------- ----------
Tensor torch.Tensor
Reduced tensor Reduced tensor
Examples Examples
---------- ----------
Case1: scalar-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([1, 1, 2]) >>> val = torch.tensor([1, 1, 2])
...@@ -116,6 +122,8 @@ def sum(input: SparseMatrix, dim: Optional[int] = None): ...@@ -116,6 +122,8 @@ def sum(input: SparseMatrix, dim: Optional[int] = None):
>>> print(dglsp.sum(A, 1)) >>> print(dglsp.sum(A, 1))
tensor([1, 3, 0, 0]) tensor([1, 3, 0, 0])
Case2: vector-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([[1, 2], [2, 1], [2, 2]]) >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
...@@ -153,12 +161,14 @@ def smax(input: SparseMatrix, dim: Optional[int] = None): ...@@ -153,12 +161,14 @@ def smax(input: SparseMatrix, dim: Optional[int] = None):
Returns Returns
---------- ----------
Tensor torch.Tensor
Reduced tensor Reduced tensor
Examples Examples
---------- ----------
Case1: scalar-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([1, 1, 2]) >>> val = torch.tensor([1, 1, 2])
...@@ -170,6 +180,8 @@ def smax(input: SparseMatrix, dim: Optional[int] = None): ...@@ -170,6 +180,8 @@ def smax(input: SparseMatrix, dim: Optional[int] = None):
>>> print(dglsp.smax(A, 1)) >>> print(dglsp.smax(A, 1))
tensor([1, 2, 0, 0]) tensor([1, 2, 0, 0])
Case2: vector-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([[1, 2], [2, 1], [2, 2]]) >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
...@@ -208,12 +220,14 @@ def smin(input: SparseMatrix, dim: Optional[int] = None): ...@@ -208,12 +220,14 @@ def smin(input: SparseMatrix, dim: Optional[int] = None):
Returns Returns
---------- ----------
Tensor torch.Tensor
Reduced tensor Reduced tensor
Examples Examples
---------- ----------
Case1: scalar-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([1, 1, 2]) >>> val = torch.tensor([1, 1, 2])
...@@ -225,6 +239,8 @@ def smin(input: SparseMatrix, dim: Optional[int] = None): ...@@ -225,6 +239,8 @@ def smin(input: SparseMatrix, dim: Optional[int] = None):
>>> print(dglsp.smin(A, 1)) >>> print(dglsp.smin(A, 1))
tensor([1, 1, 0, 0]) tensor([1, 1, 0, 0])
Case2: vector-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([[1, 2], [2, 1], [2, 2]]) >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
...@@ -267,12 +283,14 @@ def smean(input: SparseMatrix, dim: Optional[int] = None): ...@@ -267,12 +283,14 @@ def smean(input: SparseMatrix, dim: Optional[int] = None):
Returns Returns
---------- ----------
Tensor torch.Tensor
Reduced tensor Reduced tensor
Examples Examples
---------- ----------
Case1: scalar-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([1., 1., 2.]) >>> val = torch.tensor([1., 1., 2.])
...@@ -284,6 +302,8 @@ def smean(input: SparseMatrix, dim: Optional[int] = None): ...@@ -284,6 +302,8 @@ def smean(input: SparseMatrix, dim: Optional[int] = None):
>>> print(dglsp.smean(A, 1)) >>> print(dglsp.smean(A, 1))
tensor([1.0000, 1.5000, 0.0000, 0.0000]) tensor([1.0000, 1.5000, 0.0000, 0.0000])
Case2: vector-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]]) >>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]])
...@@ -326,12 +346,14 @@ def sprod(input: SparseMatrix, dim: Optional[int] = None): ...@@ -326,12 +346,14 @@ def sprod(input: SparseMatrix, dim: Optional[int] = None):
Returns Returns
---------- ----------
Tensor torch.Tensor
Reduced tensor Reduced tensor
Examples Examples
---------- ----------
Case1: scalar-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([1, 1, 2]) >>> val = torch.tensor([1, 1, 2])
...@@ -343,6 +365,8 @@ def sprod(input: SparseMatrix, dim: Optional[int] = None): ...@@ -343,6 +365,8 @@ def sprod(input: SparseMatrix, dim: Optional[int] = None):
>>> print(dglsp.sprod(A, 1)) >>> print(dglsp.sprod(A, 1))
tensor([1, 2, 0, 0]) tensor([1, 2, 0, 0])
Case2: vector-valued sparse matrix
>>> row = torch.tensor([0, 1, 1]) >>> row = torch.tensor([0, 1, 1])
>>> col = torch.tensor([0, 0, 2]) >>> col = torch.tensor([0, 0, 2])
>>> val = torch.tensor([[1, 2], [2, 1], [2, 2]]) >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
......
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