typo

README.md

@@ -28,6 +28,7 @@ The package consists of the following operations:
 * [**Scatter Mul**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/mul.html)
 * [**Scatter Div**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/div.html)
 * [**Scatter Mean**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/mean.html)
+* [**Scatter Std**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/std.html)
 * [**Scatter Min**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/min.html)
 * [**Scatter Max**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/max.html)
 

docs/source/_figures/build.sh

 #!/bin/bash
 
-files=(add sub mul div mean max min)
+files=(add sub mul div mean max min std)
 
 for name in "${files[@]}"; do
   pdflatex "$name"

docs/source/_figures/std.svg

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docs/source/_figures/std.tex

+\def\indices{{0, 0, 1, 0, 2, 2, 3, 3}}
+\def\inputs{{5, 1, 7, 2, 3, 2, 1, 3}}
+\def\outputs{{2.1, 0, 0.7, 1.4}}
+\def\colors{{"cyan", "orange", "olive", "magenta"}}
+\def\numberInputs{7}
+\def\numberOutputs{3}
+\def\operation{std}
+\input{template}

docs/source/functions/std.rst

+Scatter Std
+===========
+
+.. automodule:: torch_scatter
+
+.. autofunction:: scatter_std

setup.py

@@ -15,7 +15,7 @@ if CUDA_HOME is not None:
                       ['cuda/scatter.cpp', 'cuda/scatter_kernel.cu'])
     ]
 
-__version__ = '1.0.5'
+__version__ = '1.1.0'
 url = 'https://github.com/rusty1s/pytorch_scatter'
 
 install_requires = []
@@ -36,5 +36,4 @@ setup(
     tests_require=tests_require,
     ext_modules=ext_modules,
     cmdclass=cmdclass,
-    packages=find_packages(),
-)
+    packages=find_packages(), )

test/test_std.py

+from itertools import product
+
+import pytest
+import torch
+from torch_scatter import scatter_std
+
+from .utils import grad_dtypes as dtypes, devices, tensor
+
+biass = [True, False]
+
+
+@pytest.mark.parametrize('dtype,device,bias', product(dtypes, devices, biass))
+def test_std(dtype, device, bias):
+    src = tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], dtype, device)
+    index = tensor([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], torch.long, device)
+
+    out = scatter_std(src, index, dim=-1, unbiased=bias)
+    expected = src.std(dim=-1, unbiased=bias)
+    assert out.tolist() == [[expected[0].item(), 0], [0, expected[0].item()]]

torch_scatter/__init__.py

@@ -7,7 +7,7 @@ from .std import scatter_std
 from .max import scatter_max
 from .min import scatter_min
 
-__version__ = '1.0.5'
+__version__ = '1.1.0'
 
 __all__ = [
     'scatter_add',

torch_scatter/add.py

@@ -13,7 +13,7 @@ def scatter_add(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
     |
 
     Sums all values from the :attr:`src` tensor into :attr:`out` at the indices
-    specified in the :attr:`index` tensor along an given axis :attr:`dim`. For
+    specified in the :attr:`index` tensor along a given axis :attr:`dim`. For
     each value in :attr:`src`, its output index is specified by its index in
     :attr:`input` for dimensions outside of :attr:`dim` and by the
     corresponding value in :attr:`index` for dimension :attr:`dim`. If

torch_scatter/div.py

@@ -39,7 +39,7 @@ def scatter_div(src, index, dim=-1, out=None, dim_size=None, fill_value=1):
     |
 
     Divides all values from the :attr:`src` tensor into :attr:`out` at the
-    indices specified in the :attr:`index` tensor along an given axis
+    indices specified in the :attr:`index` tensor along a given axis
     :attr:`dim`.If multiple indices reference the same location, their
     **contributions divide** (`cf.` :meth:`~torch_scatter.scatter_add`).
 

torch_scatter/max.py

@@ -42,7 +42,7 @@ def scatter_max(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
     |
 
     Maximizes all values from the :attr:`src` tensor into :attr:`out` at the
-    indices specified in the :attr:`index` tensor along an given axis
+    indices specified in the :attr:`index` tensor along a given axis
     :attr:`dim`.If multiple indices reference the same location, their
     **contributions maximize** (`cf.` :meth:`~torch_scatter.scatter_add`).
     The second return tensor contains index location in :attr:`src` of each

torch_scatter/mean.py

@@ -15,7 +15,7 @@ def scatter_mean(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
     |
 
     Averages all values from the :attr:`src` tensor into :attr:`out` at the
-    indices specified in the :attr:`index` tensor along an given axis
+    indices specified in the :attr:`index` tensor along a given axis
     :attr:`dim`.If multiple indices reference the same location, their
     **contributions average** (`cf.` :meth:`~torch_scatter.scatter_add`).
 

torch_scatter/min.py

@@ -42,7 +42,7 @@ def scatter_min(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
     |
 
     Minimizes all values from the :attr:`src` tensor into :attr:`out` at the
-    indices specified in the :attr:`index` tensor along an given axis
+    indices specified in the :attr:`index` tensor along a given axis
     :attr:`dim`.If multiple indices reference the same location, their
     **contributions maximize** (`cf.` :meth:`~torch_scatter.scatter_add`).
     The second return tensor contains index location in :attr:`src` of each

torch_scatter/mul.py

@@ -39,7 +39,7 @@ def scatter_mul(src, index, dim=-1, out=None, dim_size=None, fill_value=1):
     |
 
     Multiplies all values from the :attr:`src` tensor into :attr:`out` at the
-    indices specified in the :attr:`index` tensor along an given axis
+    indices specified in the :attr:`index` tensor along a given axis
     :attr:`dim`.If multiple indices reference the same location, their
     **contributions multiply** (`cf.` :meth:`~torch_scatter.scatter_add`).
 

torch_scatter/std.py

@@ -5,6 +5,45 @@ from torch_scatter.utils.gen import gen
 
 
 def scatter_std(src, index, dim=-1, out=None, dim_size=None, unbiased=True):
+    r"""
+    |
+
+    .. image:: https://raw.githubusercontent.com/rusty1s/pytorch_scatter/
+            master/docs/source/_figures/std.svg?sanitize=true
+        :align: center
+        :width: 400px
+
+    |
+
+    Computes the standard-deviation from all values from the :attr:`src` tensor
+    into :attr:`out` at the indices specified in the :attr:`index` tensor along
+    a given axis :attr:`dim` (`cf.` :meth:`~torch_scatter.scatter_add`).
+
+    For one-dimensional tensors, the operation computes
+
+    .. math::
+        \mathrm{out}_i = \sqrt{\frac{\sum_j {\left( x_j - \overline{x}_i
+        \right)}^2}{N_i - 1}}
+
+    where :math:`\sum_j` is over :math:`j` such that
+    :math:`\mathrm{index}_j = i`. :math:`N_i` and :math:`\overline{x}_i`
+    indicate the number of indices referencing :math:`i` and their mean value,
+    respectively.
+
+    Args:
+        src (Tensor): The source tensor.
+        index (LongTensor): The indices of elements to scatter.
+        dim (int, optional): The axis along which to index.
+            (default: :obj:`-1`)
+        out (Tensor, optional): The destination tensor. (default: :obj:`None`)
+        dim_size (int, optional): If :attr:`out` is not given, automatically
+            create output with size :attr:`dim_size` at dimension :attr:`dim`.
+            If :attr:`dim_size` is not given, a minimal sized output tensor is
+            returned. (default: :obj:`None`)
+        unbiased (bool, optional): If set to :obj:`False`, then the standard-
+            deviation will be calculated via the biased estimator.
+            (default: :obj:`True`)
+    """
     src, out, index, dim = gen(src, index, dim, out, dim_size, fill_value=0)
 
     tmp = scatter_add(src, index, dim, None, dim_size)
@@ -15,6 +54,6 @@ def scatter_std(src, index, dim=-1, out=None, dim_size=None, unbiased=True):
     var = var * var
     out = scatter_add(var, index, dim, out, dim_size)
     out = out / (count - 1 if unbiased else count).clamp(min=1)
-    out = torch.sqrt(out.clamp(min=1e-12))
+    out = torch.sqrt(out)
 
     return out

torch_scatter/sub.py

@@ -13,7 +13,7 @@ def scatter_sub(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
     |
 
     Subtracts all values from the :attr:`src` tensor into :attr:`out` at the
-    indices specified in the :attr:`index` tensor along an given axis
+    indices specified in the :attr:`index` tensor along a given axis
     :attr:`dim`.If multiple indices reference the same location, their
     **negated contributions add** (`cf.` :meth:`~torch_scatter.scatter_add`).