Hướng dẫn how do you do scalar multiplication in python? - làm thế nào để bạn thực hiện phép nhân vô hướng trong python?

Question

Có cách nào hơn 'toán học' để làm như sau không:

Nội dung chính Show

1.2738 * (list_of_items) 61.2738 * (list_of_items) 2 1.2738 * (list_of_items) 3[1.2738 * item for item in list_of_items] 6 import numpy 1.2738 * numpy.array(list_of_items) 0
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Quá trình nhân vô hướng là gì?
Sản phẩm DOT trong Python là gì?
Làm thế nào để phép nhân ma trận hoạt động trong Python?

1.2738 * (list_of_items)

Vì vậy, cho những gì tôi đang làm là:

[1.2738 * item for item in list_of_items]

Đã hỏi ngày 2 tháng 3 năm 2015 lúc 23:40Mar 2, 2015 at 23:40

David542David542David542

105K163 Huy hiệu vàng447 Huy hiệu bạc765 Huy hiệu Đồng163 gold badges447 silver badges765 bronze badges

2

Tương đương toán học của những gì bạn mô tả là hoạt động của phép nhân bằng vô hướng cho một vectơ. Do đó, đề xuất của tôi sẽ là chuyển đổi danh sách các phần tử của bạn thành "vectơ" và sau đó nhân đó với vô hướng.

Một cách tiêu chuẩn để làm điều đó sẽ là sử dụng numpy.

Thay vì

1.2738 * (list_of_items)

Bạn có thể dùng

import numpy
1.2738 * numpy.array(list_of_items)

Đầu ra mẫu:

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

Đã trả lời ngày 2 tháng 3 năm 2015 lúc 23:42Mar 2, 2015 at 23:42

Cách tiếp cận khác

map(lambda x:x*1.2738,list_of_items)

Đã trả lời ngày 2 tháng 3 năm 2015 lúc 23:43Mar 2, 2015 at 23:43

Levilevilevi

21.1k7 Huy hiệu vàng66 Huy hiệu bạc72 Huy hiệu đồng7 gold badges66 silver badges72 bronze badges

Cải thiện bài viết

Lưu bài viết

Đọc

Bàn luận

Cải thiện bài viết

Lưu bài viết

Đọc
Examples:

Input : mat[][] = {{2, 3}
                   {5, 4}}
        k = 5
Output : 10 15 
         25 20 
We multiply 5 with every element.

Input : 1 2 3 
        4 5 6
        7 8 9
        k = 4
Output :  4 8  12
          16 20 24
          28 32 36

Bàn luậnscalar multiplication of a number k(scalar), multiply it on every entry in the matrix. and a matrix A is the matrix kA.

C++

#include <bits/stdc++.h>

Đưa ra một ma trận và phần tử vô hướng K, nhiệm vụ của chúng tôi là tìm ra sản phẩm vô hướng của ma trận đó. & Nbsp; ví dụ: & nbsp; & nbsp;

[1.2738 * item for item in list_of_items]

3

Sự nhân vô hướng của một số k (vô hướng), nhân nó trên mỗi mục nhập trong ma trận. và một ma trận A là ma trận ka. & nbsp;

1.2738 * (list_of_items)

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[1.2738 * item for item in list_of_items]

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import numpy
1.2738 * numpy.array(list_of_items)

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import numpy
1.2738 * numpy.array(list_of_items)

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import numpy
1.2738 * numpy.array(list_of_items)

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1.2738 * (list_of_items)

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import numpy
1.2738 * numpy.array(list_of_items)

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[1.2738 * item for item in list_of_items]

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import numpy
1.2738 * numpy.array(list_of_items)

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In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

0

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

3

1.2738 * (list_of_items)

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[1.2738 * item for item in list_of_items]

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import numpy
1.2738 * numpy.array(list_of_items)

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1.2738 * (list_of_items)

1

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

8

1.2738 * (list_of_items)

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map(lambda x:x*1.2738,list_of_items)

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1.2738 * (list_of_items)

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map(lambda x:x*1.2738,list_of_items)

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map(lambda x:x*1.2738,list_of_items)

3

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

0

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

1

[1.2738 * item for item in list_of_items]

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[1.2738 * item for item in list_of_items]

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import numpy
1.2738 * numpy.array(list_of_items)

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map(lambda x:x*1.2738,list_of_items)

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1.2738 * (list_of_items)

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Input : mat[][] = {{2, 3}
                   {5, 4}}
        k = 5
Output : 10 15 
         25 20 
We multiply 5 with every element.

Input : 1 2 3 
        4 5 6
        7 8 9
        k = 4
Output :  4 8  12
          16 20 24
          28 32 36

7

Input : mat[][] = {{2, 3}
                   {5, 4}}
        k = 5
Output : 10 15 
         25 20 
We multiply 5 with every element.

Input : 1 2 3 
        4 5 6
        7 8 9
        k = 4
Output :  4 8  12
          16 20 24
          28 32 36

8

1.2738 * (list_of_items)

6

map(lambda x:x*1.2738,list_of_items)

0

1.2738 * (list_of_items)

3

Scalar Product Matrix is : 
4 8 12 
16 20 24 
28 32 36

2

map(lambda x:x*1.2738,list_of_items)

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1.2738 * (list_of_items)

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import numpy
1.2738 * numpy.array(list_of_items)

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[1.2738 * item for item in list_of_items]

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1.2738 * (list_of_items)

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import numpy
1.2738 * numpy.array(list_of_items)

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[1.2738 * item for item in list_of_items]

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import numpy
1.2738 * numpy.array(list_of_items)

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1.2738 * (list_of_items)

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import numpy
1.2738 * numpy.array(list_of_items)

9

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

0

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

1

1.2738 * (list_of_items)

1

[1.2738 * item for item in list_of_items]

6

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

6

1.2738 * (list_of_items)

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1.2738 * (list_of_items)

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map(lambda x:x*1.2738,list_of_items)

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1.2738 * (list_of_items)

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1.2738 * (list_of_items)

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Scalar Product Matrix is : 
4 8 12 
16 20 24 
28 32 36

7

Scalar Product Matrix is : 
4 8 12 
16 20 24 
28 32 36

8

Java

import numpy
1.2738 * numpy.array(list_of_items)

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In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

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In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

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In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

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In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

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Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

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1

import numpy
1.2738 * numpy.array(list_of_items)

43

import numpy
1.2738 * numpy.array(list_of_items)

76

import numpy
1.2738 * numpy.array(list_of_items)

55

import numpy
1.2738 * numpy.array(list_of_items)

78

import numpy
1.2738 * numpy.array(list_of_items)

66

import numpy
1.2738 * numpy.array(list_of_items)

80

import numpy
1.2738 * numpy.array(list_of_items)

43

import numpy
1.2738 * numpy.array(list_of_items)

76

import numpy
1.2738 * numpy.array(list_of_items)

55

import numpy
1.2738 * numpy.array(list_of_items)

78

import numpy
1.2738 * numpy.array(list_of_items)

66

import numpy
1.2738 * numpy.array(list_of_items)

86

import numpy
1.2738 * numpy.array(list_of_items)

46numpy9

1.2738 * (list_of_items)

1

import numpy
1.2738 * numpy.array(list_of_items)

50

import numpy
1.2738 * numpy.array(list_of_items)

51

import numpy
1.2738 * numpy.array(list_of_items)

3

1.2738 * (list_of_items)

1

1.2738 * (list_of_items)

2

import numpy
1.2738 * numpy.array(list_of_items)

54

import numpy
1.2738 * numpy.array(list_of_items)

55

import numpy
1.2738 * numpy.array(list_of_items)

56__

1.2738 * (list_of_items)

6

1.2738 * (list_of_items)

2

1.2738 * (list_of_items)

3

import numpy
1.2738 * numpy.array(list_of_items)

66

import numpy
1.2738 * numpy.array(list_of_items)

56__

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

02

import numpy
1.2738 * numpy.array(list_of_items)

98

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

04

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

02

import numpy
1.2738 * numpy.array(list_of_items)

98

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

07

1.2738 * (list_of_items)

1

Scalar Product Matrix is : 
4 8 12 
16 20 24 
28 32 36

7

import numpy
1.2738 * numpy.array(list_of_items)

43numpy9

import numpy
1.2738 * numpy.array(list_of_items)

50

import numpy
1.2738 * numpy.array(list_of_items)

51

import numpy
1.2738 * numpy.array(list_of_items)

43

import numpy
1.2738 * numpy.array(list_of_items)

97

import numpy
1.2738 * numpy.array(list_of_items)

98

1.2738 * (list_of_items)

3

import numpy
1.2738 * numpy.array(list_of_items)

98

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

01

import numpy
1.2738 * numpy.array(list_of_items)

46

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

09

1.2738 * (list_of_items)

0

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

10

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

11

import numpy
1.2738 * numpy.array(list_of_items)

43

[1.2738 * item for item in list_of_items]

25

import numpy
1.2738 * numpy.array(list_of_items)

46

map(lambda x:x*1.2738,list_of_items)

3

1.2738 * (list_of_items)

6

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

16

1.2738 * (list_of_items)

3

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

10

import numpy
1.2738 * numpy.array(list_of_items)

76

import numpy
1.2738 * numpy.array(list_of_items)

55

import numpy
1.2738 * numpy.array(list_of_items)

78

import numpy
1.2738 * numpy.array(list_of_items)

66

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

52

[1.2738 * item for item in list_of_items]

75

map(lambda x:x*1.2738,list_of_items)

3

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

16

1.2738 * (list_of_items)

3

[1.2738 * item for item in list_of_items]

55

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

19

Scalar Product Matrix is : 
4 8 12 
16 20 24 
28 32 36

2

map(lambda x:x*1.2738,list_of_items)

3

import numpy
1.2738 * numpy.array(list_of_items)

3

1.2738 * (list_of_items) 2 1.2738 * (list_of_items) 3import numpy 1.2738 * numpy.array(list_of_items) 55 import numpy 1.2738 * numpy.array(list_of_items) 56import numpy 1.2738 * numpy.array(list_of_items) 55 import numpy 1.2738 * numpy.array(list_of_items) 58__

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

60

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

61

1.2738 * (list_of_items)

1

1.2738 * (list_of_items)

2

1.2738 * (list_of_items)

3

import numpy
1.2738 * numpy.array(list_of_items)

66

import numpy
1.2738 * numpy.array(list_of_items)

56__

1.2738 * (list_of_items)

1

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

16

Scalar Product Matrix is : 
4 8 12 
16 20 24 
28 32 36

2numpy9

JavaScript

import numpy
1.2738 * numpy.array(list_of_items)

41

1.2738 * (list_of_items)

43

import numpy
1.2738 * numpy.array(list_of_items)

1

import numpy
1.2738 * numpy.array(list_of_items)

2

import numpy
1.2738 * numpy.array(list_of_items)

3

1.2738 * (list_of_items)

0

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

80

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

81

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

80

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

83

1.2738 * (list_of_items)

1

1.2738 * (list_of_items)

2

1.2738 * (list_of_items)

3

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

68

1.2738 * (list_of_items)

5

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

8

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

87

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

88

map(lambda x:x*1.2738,list_of_items)

3

1.2738 * (list_of_items)

6

1.2738 * (list_of_items)

2

1.2738 * (list_of_items)

3

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

68

import numpy
1.2738 * numpy.array(list_of_items)

0

1.2738 * (list_of_items)

0

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

68

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

79

1.2738 * (list_of_items)

6

map(lambda x:x*1.2738,list_of_items)

01

[1.2738 * item for item in list_of_items]

75

map(lambda x:x*1.2738,list_of_items)

3

1.2738 * (list_of_items)

1

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

87

map(lambda x:x*1.2738,list_of_items)

06

map(lambda x:x*1.2738,list_of_items)

3

import numpy
1.2738 * numpy.array(list_of_items)

3

map(lambda x:x*1.2738,list_of_items)

09

Output:

Scalar Product Matrix is : 
4 8 12 
16 20 24 
28 32 36

In [8]: list_of_items
Out[8]: [1, 2, 4, 5]

In [9]: import numpy

In [10]: 1.2738 * numpy.array(list_of_items)
Out[10]: array([ 1.2738,  2.5476,  5.0952,  6.369 ])

68
In [8]: list_of_items Out[8]: [1, 2, 4, 5] In [9]: import numpy In [10]: 1.2738 * numpy.array(list_of_items) Out[10]: array([ 1.2738, 2.5476, 5.0952, 6.369 ])
6 O(n2),

1.2738 * (list_of_items)

2
1.2738 * (list_of_items)
3
In [8]: list_of_items Out[8]: [1, 2, 4, 5] In [9]: import numpy In [10]: 1.2738 * numpy.array(list_of_items) Out[10]: array([ 1.2738, 2.5476, 5.0952, 6.369 ])
68
1.2738 * (list_of_items)
5 O(1), since no extra space has been taken.

Quá trình nhân vô hướng là gì?

Khi chúng tôi làm việc với ma trận, chúng tôi gọi số thực là vô hướng.Thuật ngữ phép nhân vô hướng đề cập đến sản phẩm của một số thực và ma trận.Trong phép nhân vô hướng, mỗi mục trong ma trận được nhân với vô hướng đã cho.each entry in the matrix is multiplied by the given scalar.

Sản phẩm DOT trong Python là gì?

DOT (A, B, OUT = Không) DOT Sản phẩm của hai mảng.Cụ thể, nếu cả A và B là mảng 1-D, thì đó là sản phẩm bên trong của các vectơ (không liên hợp phức tạp).Nếu cả A và B là mảng 2-D, thì đó là phép nhân ma trận, nhưng sử dụng matmul hoặc a @ b được ưa thích.inner product of vectors (without complex conjugation). If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.

Làm thế nào để phép nhân ma trận hoạt động trong Python?

Sự nhân ma trận là một hoạt động lấy hai ma trận làm đầu vào và tạo ra ma trận đơn bằng cách nhân các hàng của ma trận thứ nhất với cột của ma trận thứ hai.của các hàng của ma trận thứ hai.takes two matrices as input and produces single matrix by multiplying rows of the first matrix to the column of the second matrix.In matrix multiplication make sure that the number of columns of the first matrix should be equal to the number of rows of the second matrix.

programming python Scalar matrix multiplication