Indexing/reshaping Python 中的矩阵以匹配目标矩阵

Question

我有一个如下所示的 NumPy 数组：

>>> import numpy

>>> foo = numpy.array(
    [[ 1.    ,  0.3491,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       1.    ,  0.1648,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       1.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       1.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       1.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
     [ 0.4269,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.225 ,  0.1637,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.4269,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.2929,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.4078,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
     [ 0.4212,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.1719,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.3856,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.147 ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.2459,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
     [ 0.3581,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.1676,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.2545,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.0619,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.2195,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
     [ 0.3558,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.1658,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.2544,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
       0.2159,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ]])

我想重新索引并重塑它，使其成为以下内容：

>>> bar
array(
[[ 1.    ,  1.    ,  1.    ,  1.    ,  1.    ],
 [ 0.4269,  0.225 ,  0.4269,  0.2929,  0.4078],
 [ 0.4212,  0.1719,  0.3856,  0.147 ,  0.2459],
 [ 0.3581,  0.1676,  0.2545,  0.0619,  0.2195],
 [ 0.3558,  0.1658,  0.2544,  0.    ,  0.2159],
 [ 0.3491,  0.1648,  0.    ,  0.    ,  0.    ],
 [ 0.    ,  0.1637,  0.    ,  0.    ,  0.    ]])

有没有不使用 for 循环的有效方法？也许使用 broadcasting or strides?

Answer 1

您可以 reshape the array in Fortran order (or use a combination of transpositions) and then slice 数组仅提取前 7 行：

a.reshape(50, -1, order='F')[:7,:]

此处选择的新形状是基于阵列中形状的位置。当您展平数组、Fortran 顺序时，这一点变得很清楚，然后您会看到您的模式出现。

Answer 2

首先，创建一个numpy数组：

import numpy as np
arr = np.asarray(a)

arr

array([[ 1.    ,  0.3491,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  1.    ,  0.1648,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  1.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  1.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  1.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
   [ 0.4269,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.225 ,  0.1637,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.4269,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.2929,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.4078,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
   [ 0.4212,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.1719,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.3856,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.147 ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.2459,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
   [ 0.3581,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.1676,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.2545,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.0619,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.2195,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
   [ 0.3558,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.1658,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.2544,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.2159,  0.    ,
     0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ]])

然后，切片：

b = arr[:,0:50:10]

Out[13]: array([[ 1.    ,  1.    ,  1.    ,  1.    ,  1.    ],
   [ 0.4269,  0.225 ,  0.4269,  0.2929,  0.4078],
   [ 0.4212,  0.1719,  0.3856,  0.147 ,  0.2459],
   [ 0.3581,  0.1676,  0.2545,  0.0619,  0.2195],
   [ 0.3558,  0.1658,  0.2544,  0.    ,  0.2159]])

然后，堆叠你想保留的其他数据：

c = arr[:,1:50:10]

np.vstack((b,c))
Out[17]: 
array([[ 1.    ,  1.    ,  1.    ,  1.    ,  1.    ],
   [ 0.4269,  0.225 ,  0.4269,  0.2929,  0.4078],
   [ 0.4212,  0.1719,  0.3856,  0.147 ,  0.2459],
   [ 0.3581,  0.1676,  0.2545,  0.0619,  0.2195],
   [ 0.3558,  0.1658,  0.2544,  0.    ,  0.2159],
   [ 0.3491,  0.1648,  0.    ,  0.    ,  0.    ],
   [ 0.    ,  0.1637,  0.    ,  0.    ,  0.    ],
   [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
   [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
   [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ]])

并通过更多切片去掉最后几行。

Indexing/reshaping Python 中的矩阵以匹配目标矩阵

Indexing/reshaping matrix in Python to match target matrix

python

indexing

numpy

matrix