Python: 获取两个多维数组的点积
Python: getting dot product for two multidimensional arrays
我意识到 numpy.dot 不处理多维矩阵。我的数据如下所示。我想让所有列（总共 42 个）计算除第一列以外的点积。
这是我的数据的样子（数据简化）
数据 1:
0 4 6
-0.276 4403 4403
-0.138 4640 4640
0 0 0
0.138 12 0
0.276 0 0
0.414 0 0
0.552 0 0
0.69 0 0
0.828 0 12
0.966 0 0
1.104 0 12
1.242 0 0
1.38 0 0
1.518 0 0
1.656 0 0
1.794 0 0
1.932 0 0
2.07 0 0
2.208 0 0
2.346 0 0
2.484 0 0
2.622 0 12
2.76 0 0
2.898 0 0
3.036 0 0
3.174 0 0
3.312 0 0
3.45 0 0
3.588 0 0
3.726 0 0
3.864 12 0
4.002 0 0
4.14 0 0
4.278 12 0
4.416 0 0
4.554 0 12
4.692 0 0
4.83 0 0
4.968 0 0
5.106 0 0
5.244 0 0
5.382 12 0
5.52 0 0
5.658 0 0
5.796 127 60
5.934 357 275
6.072 1882 2144
6.21 6726 6609
6.348 9398 11180
6.486 12784 18389
6.624 15863 20111
6.762 6739 10202
6.9 1684 1921
7.038 249 376
7.176 47 103
7.314 0 26
7.452 17 0
7.59 0 0
7.728 0 0
7.866 0 0
8.004 0 0
8.142 0 0
8.28 0 0
8.418 0 0
8.556 0 0
8.694 0 0
8.832 0 0
8.97 0 0
9.108 0 0
9.246 0 0
9.384 0 0
9.522 0 0
9.66 0 0
9.798 0 0
9.936 0 0
10.074 0 0
10.212 0 0
10.35 0 12
10.488 0 0
10.626 0 0
10.764 0 0
10.902 0 0
11.04 0 0
11.178 0 0
11.316 0 0
11.454 0 0
11.592 0 0
11.73 0 0
11.868 0 0
12.006 0 0
12.144 0 0
12.282 0 0
12.42 0 0
12.558 0 0
12.696 12 0
12.834 0 0
12.972 0 0
13.11 0 0
13.248 0 0
13.386 12 0
13.524 0 0
13.662 0 12
13.8 0 0
13.938 0 0
14.076 0 0
14.214 0 0
14.352 0 0
14.49 0 0
14.628 12 0
14.766 0 0
14.904 12 0
15.042 0 0
15.18 0 0
15.318 0 0
15.456 0 0
15.594 0 0
15.732 0 0
15.87 0 0
16.008 0 0
16.146 0 0
16.284 0 0
16.422 0 0
16.56 12 0
16.698 0 0
16.836 0 0
16.974 0 0
17.112 0 0
17.25 0 0
17.388 0 0
17.526 0 0
17.664 0 12
17.802 0 0
17.94 0 0
18.078 0 0
18.216 0 0
18.354 0 0
18.492 0 0
18.63 12 0
18.768 0 0
18.906 0 0
19.044 0 0
19.182 0 0
19.32 0 0
19.458 0 0
19.596 0 0
19.734 0 0
19.872 0 0
20.01 0 0
20.148 0 12
20.286 12 0
20.424 0 12
20.562 0 0
20.7 0 0
20.838 0 0
20.976 0 0
21.114 0 0
21.252 0 0
21.39 0 12
21.528 0 0
21.666 0 0
21.804 12 0
21.942 0 0
22.08 0 0
22.218 0 0
22.356 0 0
22.494 0 0
22.632 0 0
22.77 0 0
22.908 0 0
23.046 0 0
23.184 0 0
23.322 0 0
23.46 12 0
23.598 0 12
23.736 0 0
23.874 0 0
24.012 0 0
24.15 0 0
24.288 0 0
24.426 0 0
24.564 0 0
24.702 0 0
24.84 0 0
24.978 0 0
25.116 0 0
25.254 0 0
25.392 0 0
25.53 0 0
25.668 0 0
25.806 12 0
25.944 12 0
26.082 0 0
26.22 0 0
26.358 0 12
26.496 0 0
26.634 0 0
26.772 0 0
26.91 0 0
27.048 13 0
27.186 0 0
27.324 0 0
27.462 0 0
数据 2：
0 4 6
-0.276 4400 4400
-0.138 4750 4750
0 0 0
0.138 12 0
0.276 0 0
0.414 0 12
0.552 0 0
0.69 0 25
0.828 0 0
0.966 12 13
1.104 0 0
1.242 0 12
1.38 0 0
1.518 12 0
1.656 0 0
1.794 0 12
1.932 0 0
2.07 12 0
2.208 0 0
2.346 0 0
2.484 12 0
2.622 0 0
2.76 24 0
2.898 0 0
3.036 0 0
3.174 12 0
3.312 0 0
3.45 0 0
3.588 0 12
3.726 39 0
3.864 0 12
4.002 0 0
4.14 0 12
4.278 0 0
4.416 0 0
4.554 0 0
4.692 0 0
4.83 0 0
4.968 0 0
5.106 0 0
5.244 0 0
5.382 0 0
5.52 0 12
5.658 0 0
5.796 0 0
5.934 0 0
6.072 43 46
6.21 6711 11323
6.348 91043 116679
6.486 241572 307822
6.624 250588 309749
6.762 105123 139651
6.9 16143 21264
7.038 2521 3648
7.176 1042 1022
7.314 576 910
7.452 482 552
7.59 229 416
7.728 210 227
7.866 120 149
8.004 69 55
8.142 47 0
8.28 26 65
8.418 0 20
8.556 0 0
8.694 0 0
8.832 0 12
8.97 12 38
9.108 0 0
9.246 18 0
9.384 0 0
9.522 0 13
9.66 0 0
9.798 0 18
9.936 16 0
10.074 12 0
10.212 0 0
10.35 12 0
10.488 0 0
10.626 0 23
10.764 0 0
10.902 0 0
11.04 20 0
11.178 0 0
11.316 0 0
11.454 0 0
11.592 0 0
11.73 0 12
11.868 14 12
12.006 0 0
12.144 0 0
12.282 0 0
12.42 0 0
12.558 0 12
12.696 0 0
12.834 0 0
12.972 12 0
13.11 0 0
13.248 0 0
13.386 0 18
13.524 0 0
13.662 12 0
13.8 12 0
13.938 13 0
14.076 0 0
14.214 0 0
14.352 0 0
14.49 0 0
14.628 24 0
14.766 0 15
14.904 0 16
15.042 0 12
15.18 12 0
15.318 0 12
15.456 0 0
15.594 0 0
15.732 14 13
15.87 0 23
16.008 0 0
16.146 0 0
16.284 0 16
16.422 0 12
16.56 0 0
16.698 0 0
16.836 0 0
16.974 0 13
17.112 0 0
17.25 0 0
17.388 16 0
17.526 0 12
17.664 0 0
17.802 0 0
17.94 0 12
18.078 0 0
18.216 0 0
18.354 0 19
18.492 0 0
18.63 0 0
18.768 0 12
18.906 0 0
19.044 0 12
19.182 0 12
19.32 0 0
19.458 0 0
19.596 12 24
19.734 0 0
19.872 0 0
20.01 0 0
20.148 0 0
20.286 0 0
20.424 0 12
20.562 12 0
20.7 0 0
20.838 0 0
20.976 0 0
21.114 0 0
21.252 0 0
21.39 0 12
21.528 12 12
21.666 0 0
21.804 12 0
21.942 0 0
22.08 0 0
22.218 0 0
22.356 0 12
22.494 0 0
22.632 12 0
22.77 0 0
22.908 0 0
23.046 12 0
23.184 0 0
23.322 12 0
23.46 0 0
23.598 13 16
23.736 24 17
23.874 0 0
24.012 12 0
24.15 0 0
24.288 0 0
24.426 12 0
24.564 0 0
24.702 0 0
24.84 0 0
24.978 0 0
25.116 0 0
25.254 0 0
25.392 14 12
25.53 25 0
25.668 0 12
25.806 0 0
25.944 0 15
26.082 0 0
26.22 12 0
26.358 0 0
26.496 0 0
26.634 0 0
26.772 27 0
26.91 0 12
27.048 0 22
27.186 0 0
27.324 0 0
27.462 0 0
然后我有下面的代码
import pandas as pd
import numpy as np
first_y= np.array(firt_df.iloc[:,1:])
second_y= np.array(second_df.iloc[:,1:])
#dot product
dot_product_both=np.dot(first_y, second_y)
使用上面的代码，我得到以下错误
shapes (200,42) and (200,42) not aligned: 42 (dim 1) != 200 (dim 0)
我收到此错误是因为 np.dot 无法处理大于一维的数组。
我在想也许可以创建一个 numpy 函数，以便它可以解构我的数据框并一一处理所有内容。在我这样做之前，我想看看你们中是否有人有聪明的方法来解决这个问题..
我想要的结果是..（对于第二列）
4403*4400+4640*4750+0*0+12*12....
IIUC，你要
(x*y).sum(axis=0)
转换为 4403*4400+4640*4750+0*0+12*12 + ...
看看这个可重现的例子：
x = np.array([
[1,2,3],
[4,5,6]
])
y = np.array([
[1,10,1],
[2,2,3]
])
然后
>>> (x*y)
array([[ 1, 20, 3],
[ 8, 10, 18]])
和
>>> (x*y).sum(axis=1)
array([24, 36])
>>> (x*y).sum(axis=0)
array([ 9, 30, 21])
请注意，这些值实际上只是 np.dot 产品
的 对角线
>>> np.dot(x,y.T)
array([[24, 15],
[60, 36]])
>>> np.dot(x.T,y)
array([[ 9, 18, 13],
[12, 30, 17],
[15, 42, 21]])
我意识到 numpy.dot 不处理多维矩阵。我的数据如下所示。我想让所有列（总共 42 个）计算除第一列以外的点积。
这是我的数据的样子（数据简化） 数据 1:
0 4 6
-0.276 4403 4403
-0.138 4640 4640
0 0 0
0.138 12 0
0.276 0 0
0.414 0 0
0.552 0 0
0.69 0 0
0.828 0 12
0.966 0 0
1.104 0 12
1.242 0 0
1.38 0 0
1.518 0 0
1.656 0 0
1.794 0 0
1.932 0 0
2.07 0 0
2.208 0 0
2.346 0 0
2.484 0 0
2.622 0 12
2.76 0 0
2.898 0 0
3.036 0 0
3.174 0 0
3.312 0 0
3.45 0 0
3.588 0 0
3.726 0 0
3.864 12 0
4.002 0 0
4.14 0 0
4.278 12 0
4.416 0 0
4.554 0 12
4.692 0 0
4.83 0 0
4.968 0 0
5.106 0 0
5.244 0 0
5.382 12 0
5.52 0 0
5.658 0 0
5.796 127 60
5.934 357 275
6.072 1882 2144
6.21 6726 6609
6.348 9398 11180
6.486 12784 18389
6.624 15863 20111
6.762 6739 10202
6.9 1684 1921
7.038 249 376
7.176 47 103
7.314 0 26
7.452 17 0
7.59 0 0
7.728 0 0
7.866 0 0
8.004 0 0
8.142 0 0
8.28 0 0
8.418 0 0
8.556 0 0
8.694 0 0
8.832 0 0
8.97 0 0
9.108 0 0
9.246 0 0
9.384 0 0
9.522 0 0
9.66 0 0
9.798 0 0
9.936 0 0
10.074 0 0
10.212 0 0
10.35 0 12
10.488 0 0
10.626 0 0
10.764 0 0
10.902 0 0
11.04 0 0
11.178 0 0
11.316 0 0
11.454 0 0
11.592 0 0
11.73 0 0
11.868 0 0
12.006 0 0
12.144 0 0
12.282 0 0
12.42 0 0
12.558 0 0
12.696 12 0
12.834 0 0
12.972 0 0
13.11 0 0
13.248 0 0
13.386 12 0
13.524 0 0
13.662 0 12
13.8 0 0
13.938 0 0
14.076 0 0
14.214 0 0
14.352 0 0
14.49 0 0
14.628 12 0
14.766 0 0
14.904 12 0
15.042 0 0
15.18 0 0
15.318 0 0
15.456 0 0
15.594 0 0
15.732 0 0
15.87 0 0
16.008 0 0
16.146 0 0
16.284 0 0
16.422 0 0
16.56 12 0
16.698 0 0
16.836 0 0
16.974 0 0
17.112 0 0
17.25 0 0
17.388 0 0
17.526 0 0
17.664 0 12
17.802 0 0
17.94 0 0
18.078 0 0
18.216 0 0
18.354 0 0
18.492 0 0
18.63 12 0
18.768 0 0
18.906 0 0
19.044 0 0
19.182 0 0
19.32 0 0
19.458 0 0
19.596 0 0
19.734 0 0
19.872 0 0
20.01 0 0
20.148 0 12
20.286 12 0
20.424 0 12
20.562 0 0
20.7 0 0
20.838 0 0
20.976 0 0
21.114 0 0
21.252 0 0
21.39 0 12
21.528 0 0
21.666 0 0
21.804 12 0
21.942 0 0
22.08 0 0
22.218 0 0
22.356 0 0
22.494 0 0
22.632 0 0
22.77 0 0
22.908 0 0
23.046 0 0
23.184 0 0
23.322 0 0
23.46 12 0
23.598 0 12
23.736 0 0
23.874 0 0
24.012 0 0
24.15 0 0
24.288 0 0
24.426 0 0
24.564 0 0
24.702 0 0
24.84 0 0
24.978 0 0
25.116 0 0
25.254 0 0
25.392 0 0
25.53 0 0
25.668 0 0
25.806 12 0
25.944 12 0
26.082 0 0
26.22 0 0
26.358 0 12
26.496 0 0
26.634 0 0
26.772 0 0
26.91 0 0
27.048 13 0
27.186 0 0
27.324 0 0
27.462 0 0
数据 2：
0 4 6
-0.276 4400 4400
-0.138 4750 4750
0 0 0
0.138 12 0
0.276 0 0
0.414 0 12
0.552 0 0
0.69 0 25
0.828 0 0
0.966 12 13
1.104 0 0
1.242 0 12
1.38 0 0
1.518 12 0
1.656 0 0
1.794 0 12
1.932 0 0
2.07 12 0
2.208 0 0
2.346 0 0
2.484 12 0
2.622 0 0
2.76 24 0
2.898 0 0
3.036 0 0
3.174 12 0
3.312 0 0
3.45 0 0
3.588 0 12
3.726 39 0
3.864 0 12
4.002 0 0
4.14 0 12
4.278 0 0
4.416 0 0
4.554 0 0
4.692 0 0
4.83 0 0
4.968 0 0
5.106 0 0
5.244 0 0
5.382 0 0
5.52 0 12
5.658 0 0
5.796 0 0
5.934 0 0
6.072 43 46
6.21 6711 11323
6.348 91043 116679
6.486 241572 307822
6.624 250588 309749
6.762 105123 139651
6.9 16143 21264
7.038 2521 3648
7.176 1042 1022
7.314 576 910
7.452 482 552
7.59 229 416
7.728 210 227
7.866 120 149
8.004 69 55
8.142 47 0
8.28 26 65
8.418 0 20
8.556 0 0
8.694 0 0
8.832 0 12
8.97 12 38
9.108 0 0
9.246 18 0
9.384 0 0
9.522 0 13
9.66 0 0
9.798 0 18
9.936 16 0
10.074 12 0
10.212 0 0
10.35 12 0
10.488 0 0
10.626 0 23
10.764 0 0
10.902 0 0
11.04 20 0
11.178 0 0
11.316 0 0
11.454 0 0
11.592 0 0
11.73 0 12
11.868 14 12
12.006 0 0
12.144 0 0
12.282 0 0
12.42 0 0
12.558 0 12
12.696 0 0
12.834 0 0
12.972 12 0
13.11 0 0
13.248 0 0
13.386 0 18
13.524 0 0
13.662 12 0
13.8 12 0
13.938 13 0
14.076 0 0
14.214 0 0
14.352 0 0
14.49 0 0
14.628 24 0
14.766 0 15
14.904 0 16
15.042 0 12
15.18 12 0
15.318 0 12
15.456 0 0
15.594 0 0
15.732 14 13
15.87 0 23
16.008 0 0
16.146 0 0
16.284 0 16
16.422 0 12
16.56 0 0
16.698 0 0
16.836 0 0
16.974 0 13
17.112 0 0
17.25 0 0
17.388 16 0
17.526 0 12
17.664 0 0
17.802 0 0
17.94 0 12
18.078 0 0
18.216 0 0
18.354 0 19
18.492 0 0
18.63 0 0
18.768 0 12
18.906 0 0
19.044 0 12
19.182 0 12
19.32 0 0
19.458 0 0
19.596 12 24
19.734 0 0
19.872 0 0
20.01 0 0
20.148 0 0
20.286 0 0
20.424 0 12
20.562 12 0
20.7 0 0
20.838 0 0
20.976 0 0
21.114 0 0
21.252 0 0
21.39 0 12
21.528 12 12
21.666 0 0
21.804 12 0
21.942 0 0
22.08 0 0
22.218 0 0
22.356 0 12
22.494 0 0
22.632 12 0
22.77 0 0
22.908 0 0
23.046 12 0
23.184 0 0
23.322 12 0
23.46 0 0
23.598 13 16
23.736 24 17
23.874 0 0
24.012 12 0
24.15 0 0
24.288 0 0
24.426 12 0
24.564 0 0
24.702 0 0
24.84 0 0
24.978 0 0
25.116 0 0
25.254 0 0
25.392 14 12
25.53 25 0
25.668 0 12
25.806 0 0
25.944 0 15
26.082 0 0
26.22 12 0
26.358 0 0
26.496 0 0
26.634 0 0
26.772 27 0
26.91 0 12
27.048 0 22
27.186 0 0
27.324 0 0
27.462 0 0
然后我有下面的代码
import pandas as pd
import numpy as np
first_y= np.array(firt_df.iloc[:,1:])
second_y= np.array(second_df.iloc[:,1:])
#dot product
dot_product_both=np.dot(first_y, second_y)
使用上面的代码，我得到以下错误
shapes (200,42) and (200,42) not aligned: 42 (dim 1) != 200 (dim 0)
我收到此错误是因为 np.dot 无法处理大于一维的数组。
我在想也许可以创建一个 numpy 函数，以便它可以解构我的数据框并一一处理所有内容。在我这样做之前，我想看看你们中是否有人有聪明的方法来解决这个问题..
我想要的结果是..（对于第二列） 4403*4400+4640*4750+0*0+12*12....
IIUC，你要
(x*y).sum(axis=0)
转换为 4403*4400+4640*4750+0*0+12*12 + ...
看看这个可重现的例子：
x = np.array([
[1,2,3],
[4,5,6]
])
y = np.array([
[1,10,1],
[2,2,3]
])
然后
>>> (x*y)
array([[ 1, 20, 3],
[ 8, 10, 18]])
和
>>> (x*y).sum(axis=1)
array([24, 36])
>>> (x*y).sum(axis=0)
array([ 9, 30, 21])
请注意，这些值实际上只是 np.dot 产品
>>> np.dot(x,y.T)
array([[24, 15],
[60, 36]])
>>> np.dot(x.T,y)
array([[ 9, 18, 13],
[12, 30, 17],
[15, 42, 21]])