假设我有一个二维数组，我想要从中随机抽取（使用蒙特卡罗方法）较小的二维子数组，如下图中示黑色斑块所示。我正在寻找一种有效的方法来实现这一点。
在经过数小时的搜索后，我找到了一个函数，它部分地实现了我的需求，但缺少随机位置采样的能力。虽然它有一个random_state参数，但我不理解它是否可以根据其参数从随机位置取样。
sklearn.feature_extraction.image.extract_patches_2d(image, patch_size, max_patches=None, random_state=None)
随机选择补丁坐标（2D子数组），并使用它们从较大的数组中切割一个补丁，如上图所示。允许随机抽样的补丁重叠。
def box_sampler(arr,
loc_sampler_fn,
loc_dim_param,
loc_params,
shape_sampler_fn,
shape_dim_param,
shape_params):
'''
Extracts a sample cut from `arr`.
Parameters:
-----------
loc_sampler_fn : function
The function to determine the where the minimum coordinate
for each axis should be placed.
loc_dim_param : string or None
The parameter in `loc_sampler_fn` that should use the axes
dimension size
loc_params : dict
Parameters to pass to `loc_sampler_fn`.
shape_sampler_fn : function
The function to determine the width of the sample cut
along each axis.
shape_dim_param : string or None
The parameter in `shape_sampler_fn` that should use the
axes dimension size.
shape_params : dict
Parameters to pass to `shape_sampler_fn`.
Returns:
--------
(slices, x) : A tuple of the slices used to cut the sample as well as
the sampled subsection with the same dimensionality of arr.
slice :: list of slice objects
x :: array object with the same ndims as arr
'''
slices = []
for dim in arr.shape:
if loc_dim_param:
loc_params.update({loc_dim_param: dim})
if shape_dim_param:
shape_params.update({shape_dim_param: dim})
start = int(loc_sampler_fn(**loc_params))
stop = start + int(shape_sampler_fn(**shape_params))
slices.append(slice(start, stop))
return slices, arr[slices]
一个在宽度为3到9之间的二维数组上进行均匀切割的例子：
a = np.random.randint(0, 1+1, size=(100,150))
box_sampler(a,
np.random.uniform, 'high', {'low':0},
np.random.uniform, None, {'low':3, 'high':10})
# returns:
([slice(49, 55, None), slice(86, 89, None)],
array([[0, 0, 1],
[0, 1, 1],
[0, 0, 0],
[0, 0, 1],
[1, 1, 1],
[1, 1, 0]]))
从一个10x20x30的三维数组中提取2x2x2块的示例：
a = np.random.randint(0,2,size=(10,20,30))
box_sampler(a, np.random.uniform, 'high', {'low':0},
np.random.uniform, None, {'low':2, 'high':2})
# returns:
([slice(7, 9, None), slice(9, 11, None), slice(19, 21, None)],
array([[[0, 1],
[1, 0]],
[[0, 1],
[1, 1]]]))
针对您的具体目的，看起来您需要一个矩形样本，其中起始角落从数组中任何位置均匀采样，并且沿每个轴的样本宽度是均匀采样的，但可以受到限制。
这里有一个生成这些样本的函数。min_width和max_width可以接受整数的可迭代对象（例如元组）或单个整数。
def uniform_box_sampler(arr, min_width, max_width):
'''
Extracts a sample cut from `arr`.
Parameters:
-----------
arr : array
The numpy array to sample a box from
min_width : int or tuple
The minimum width of the box along a given axis.
If a tuple of integers is supplied, it my have the
same length as the number of dimensions of `arr`
max_width : int or tuple
The maximum width of the box along a given axis.
If a tuple of integers is supplied, it my have the
same length as the number of dimensions of `arr`
Returns:
--------
(slices, x) : A tuple of the slices used to cut the sample as well as
the sampled subsection with the same dimensionality of arr.
slice :: list of slice objects
x :: array object with the same ndims as arr
'''
if isinstance(min_width, (tuple, list)):
assert len(min_width)==arr.ndim, 'Dimensions of `min_width` and `arr` must match'
else:
min_width = (min_width,)*arr.ndim
if isinstance(max_width, (tuple, list)):
assert len(max_width)==arr.ndim, 'Dimensions of `max_width` and `arr` must match'
else:
max_width = (max_width,)*arr.ndim
slices = []
for dim, mn, mx in zip(arr.shape, min_width, max_width):
fn = np.random.uniform
start = int(np.random.uniform(0,dim))
stop = start + int(np.random.uniform(mn, mx+1))
slices.append(slice(start, stop))
return slices, arr[slices]
生成一个盒子的示例，该盒子从数组中任意位置开始均匀地切割，高度是从1到4的随机均匀抽取，宽度是从2到6的随机均匀抽取（仅供展示）。在这种情况下，盒子的大小为3乘4，从第66行和第19列开始。
x = np.random.randint(0,2,size=(100,100))
uniform_box_sampler(x, (1,2), (4,6))
# returns:
([slice(65, 68, None), slice(18, 22, None)],
array([[1, 0, 0, 0],
[0, 0, 1, 1],
[0, 1, 1, 0]]))
arr之后的函数参数方面我不太清楚。我尝试查看示例并将其与参数定义连接起来，但我无法理解。您能否帮助澄清这些参数或尝试将它们与您的示例连接起来？ - user11shape_* 参数呢？您介意也加上它们的说明吗？谢谢。 - user11sklearn.feature_extraction.image.extract_patches_2d的问题是它强制你指定一个单一的补丁大小，而你正在寻找不同大小的随机补丁。from itertools import product
def random_patches_2d(arr, n_patches):
# The all possible row and column slices from `arr` given its shape
row, col = arr.shape
row_comb = [(i, j) for i, j in product(range(row), range(row)) if i < j]
col_comb = [(i, j) for i, j in product(range(col), range(col)) if i < j]
# Pick randomly from the possible slices. The distribution will be
# random uniform from the given slices. We can't use
# np.random.choice because it only samples from a 1d array.
a = np.random.choice(np.arange(len(row_comb)), size=n_patches)
b = np.random.choice(np.arange(len(col_comb)), size=n_patches)
for i, j in zip(a, b):
yield arr[row_comb[i][0]:row_comb[i][1],
col_comb[i][0]:col_comb[i][1]]
例子：
np.random.seed(99)
arr = np.arange(49).reshape(7, 7)
res = list(random_patches_2d(arr, 5))
print(res[0])
print()
print(res[3])
[[0 1]
[7 8]]
[[ 8 9 10 11]
[15 16 17 18]
[22 23 24 25]
[29 30 31 32]]
简明：
def random_patches_2d(arr, n_patches):
row, col = arr.shape
row_comb = [(i, j) for i, j in product(range(row), range(row)) if i < j]
col_comb = [(i, j) for i, j in product(range(col), range(col)) if i < j]
a = np.random.choice(np.arange(len(row_comb)), size=n_patches)
b = np.random.choice(np.arange(len(col_comb)), size=n_patches)
for i, j in zip(a, b):
yield arr[row_comb[i][0]:row_comb[i][1],
col_comb[i][0]:col_comb[i][1]]
# `size` is just row x col
area = arr.size
patch_area = 0
while patch_area <= area: # or while patch_area <= 0.1 * area:
patch = random_patches_2d(arr, n_patches=1)
patch_area += patch
numpy.as_strided。它非常有效地形成数组上不同的“窗口”，但比基本索引要复杂一些。 - Brad Solomon