<p>如果<em>窗口中没有重叠,则可以根据需要重新调整数据形状:</p>
<p>求9x9数组的3x3个窗口的平均值。在</p>
<pre><code>import numpy as np
>>> a
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8],
[ 9, 10, 11, 12, 13, 14, 15, 16, 17],
[18, 19, 20, 21, 22, 23, 24, 25, 26],
[27, 28, 29, 30, 31, 32, 33, 34, 35],
[36, 37, 38, 39, 40, 41, 42, 43, 44],
[45, 46, 47, 48, 49, 50, 51, 52, 53],
[54, 55, 56, 57, 58, 59, 60, 61, 62],
[63, 64, 65, 66, 67, 68, 69, 70, 71],
[72, 73, 74, 75, 76, 77, 78, 79, 80]])
</code></pre>
<p>找到新形状</p>
^{pr2}$
<p>沿第一轴和第三轴求平均值。在</p>
<pre><code>>>> b.mean(axis = (1,3))
array([[ 10., 13., 16.],
[ 37., 40., 43.],
[ 64., 67., 70.]])
>>>
</code></pre>
<p>4x4阵列的2x2个窗口:</p>
<pre><code>>>> a = np.arange(16).reshape((4,4))
>>> window_size = (2,2)
>>> tuple(np.array(a.shape) / window_size) + window_size
(2, 2, 2, 2)
>>> b = a.reshape(2,2,2,2)
>>> b.mean(axis = (1,3))
array([[ 2.5, 4.5],
[ 10.5, 12.5]])
>>>
</code></pre>
<p>如果窗口大小不均匀地划分为数组大小,它将不起作用。在这种情况下,您需要在窗口中进行一些重叠,或者如果您只希望重叠<code>numpy.lib.stride_tricks.as_strided</code>是一种可行的方法,一个通用的N-D函数可以在<a href="http://www.johnvinyard.com/blog/?p=268" rel="nofollow noreferrer">Efficient Overlapping Windows with Numpy</a>找到</p>
<hr/>
<p>2d数组的另一个选项是<a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html#sklearn-feature-extraction-image-extract-patches-2d" rel="nofollow noreferrer">sklearn.feature_extraction.image.extract_patches_2d</a>和ndarray的-<a href="https://github.com/scikit-learn/scikit-learn/blob/412996f/sklearn/feature_extraction/image.py#L242" rel="nofollow noreferrer">sklearn.feature_extraction.image.extract_patches</a>。每一个操作数组的步进以生成补丁/窗口。在</p>