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ML之FE：基于單個csv檔案資料集(自動切分為兩個dataframe表)利用featuretools工具實作自動特征生成/特征衍生

2021-04-07 11:06:47 其他

ML之FE：基于單個csv檔案資料集(自動切分為兩個dataframe表)利用featuretools工具實作自動特征生成/特征衍生

目錄

基于單個csv檔案資料集(自動切分為兩個dataframe表)利用featuretools工具實作自動特征生成/特征衍生

設計思路

1、定義資料集

2、DFS設計

輸出結果

feature_matrix_cats_df.csv

feature_matrix_nums.csv

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ML之FE：基于單個csv檔案資料集(自動切分為兩個dataframe表)利用featuretools工具實作自動特征生成/特征衍生
ML之FE：基于單個csv檔案資料集(自動切分為兩個dataframe表)利用featuretools工具實作自動特征生成/特征衍生實作

基于單個csv檔案資料集(自動切分為兩個dataframe表)利用featuretools工具實作自動特征生成/特征衍生

設計思路

1、定義資料集

contents={"name": ['Bob', 'LiSa', 'Mary', 'Alan'],
"ID": [1, 2, 3, 4], # 輸出 NaN
"age": [np.nan, 28, 38 , '' ], # 輸出
"born": [pd.NaT, pd.Timestamp("1990-01-01"), pd.Timestamp("1980-01-01"), ''], # 輸出 NaT
"sex": ['男', '女', '女', '男',], # 輸出 None
"hobbey":['打籃球', '打羽毛球', '打乒乓球', '',], # 輸出
"money":[200.0, 240.0, 290.0, 300.0], # 輸出
"weight":[140.5, 120.8, 169.4, 155.6], # 輸出
}

2、DFS設計

  • (1)、指定一個包含資料集中所有物體的字典
  • (2)、指定物體間如何關聯：當兩個物體有一對多關系時，我們稱之為“one”物體，即“parent entity”，
  • (3)、運行深度特征合成：DFS的最小輸入是一組物體、一組關系和計算特性的“target_entity”，DFS的輸出是一個特征矩陣和相應的特征定義串列，
    讓我們首先為資料中的每個客戶創建一個特性矩陣，那么現在有幾十個新特性來描述客戶的行為，
  • (4)、改變目標的物體：DFS如此強大的原因之一是它可以為我們的資料中的任何物體創建一個特征矩陣，例如，如果我們想為會話構建特性
  • (5)、理解特征輸出：一般來說，Featuretools通過特性名稱參考生成的特性，
    為了讓特性更容易理解，Featuretools提供了兩個額外的工具，Featuretools .graph_feature()和Featuretools .describe_feature()，
    來幫助解釋什么是特性以及Featuretools生成特性的步驟，
  • (6)、特征譜系圖
    特征譜系圖可視地遍歷功能生成程序，從基本資料開始，它們一步一步地展示應用的原語和生成的中間特征，以創建最終特征，
  • (7)、特征描述：功能工具還可以自動生成功能的英文句子描述，特性描述有助于解釋什么是特性，并且可以通過包含手動定義的自定義來進一步改進，
    有關如何自定義自動生成的特性描述的詳細資訊，請參見生成特性描述，

輸出結果

   name  ID  age       born sex hobbey  money  weight
0   Bob   1  NaN        NaT   男    打籃球  200.0   140.5
1  LiSa   2   28 1990-01-01   女   打羽毛球  240.0   120.8
2  Mary   3   38 1980-01-01   女   打乒乓球  290.0   169.4
3  Alan   4             NaT   男         300.0   155.6
-------------------------------------------
nums_df：----------------------------------
   name  ID   age  money  weight
0   Bob   1   NaN  200.0   140.5
1  LiSa   2  28.0  240.0   120.8
2  Mary   3  38.0  290.0   169.4
3  Alan   4   NaN  300.0   155.6
cats_df：----------------------------------
   ID hobbey sex        born
0   4    NaN   男         NaN
1   1    打籃球   男         NaN
2   2   打羽毛球   女  1990-01-01
---------------------------------DFS設計：-----------------------------------
feature_matrix_nums 
       ID   age  money  weight cats.hobbey cats.sex  cats.COUNT(nums)  \
name                                                                   
Bob    1   NaN  200.0   140.5         打籃球        男               1.0   
LiSa   2  28.0  240.0   120.8        打羽毛球        女               1.0   
Mary   3  38.0  290.0   169.4         NaN      NaN               NaN   

      cats.MAX(nums.age)  cats.MAX(nums.money)  cats.MAX(nums.weight)  \
name                                                                    
Bob                  NaN                 200.0                  140.5   
LiSa                28.0                 240.0                  120.8   
Mary                 NaN                   NaN                    NaN   

      cats.MEAN(nums.age)  cats.MEAN(nums.money)  cats.MEAN(nums.weight)  \
name                                                                       
Bob                   NaN                  200.0                   140.5   
LiSa                 28.0                  240.0                   120.8   
Mary                  NaN                    NaN                     NaN   

      cats.MIN(nums.age)  cats.MIN(nums.money)  cats.MIN(nums.weight)  \
name                                                                    
Bob                  NaN                 200.0                  140.5   
LiSa                28.0                 240.0                  120.8   
Mary                 NaN                   NaN                    NaN   

      cats.SKEW(nums.age)  cats.SKEW(nums.money)  cats.SKEW(nums.weight)  \
name                                                                       
Bob                   NaN                    NaN                     NaN   
LiSa                  NaN                    NaN                     NaN   
Mary                  NaN                    NaN                     NaN   

      cats.STD(nums.age)  cats.STD(nums.money)  cats.STD(nums.weight)  \
name                                                                    
Bob                  NaN                   NaN                    NaN   
LiSa                 NaN                   NaN                    NaN   
Mary                 NaN                   NaN                    NaN   

      cats.SUM(nums.age)  cats.SUM(nums.money)  cats.SUM(nums.weight)  \
name                                                                    
Bob                  0.0                 200.0                  140.5   
LiSa                28.0                 240.0                  120.8   
Mary                 NaN                   NaN                    NaN   

      cats.DAY(born)  cats.MONTH(born)  cats.WEEKDAY(born)  cats.YEAR(born)  
name                                                                         
Bob              NaN               NaN                 NaN              NaN  
LiSa             1.0               1.0                 0.0           1990.0  
Mary             NaN               NaN                 NaN              NaN  
features_defs_nums： 29 [<Feature: ID>, <Feature: age>, <Feature: money>, <Feature: weight>, <Feature: cats.hobbey>, <Feature: cats.sex>, <Feature: cats.COUNT(nums)>, <Feature: cats.MAX(nums.age)>, <Feature: cats.MAX(nums.money)>, <Feature: cats.MAX(nums.weight)>, <Feature: cats.MEAN(nums.age)>, <Feature: cats.MEAN(nums.money)>, <Feature: cats.MEAN(nums.weight)>, <Feature: cats.MIN(nums.age)>, <Feature: cats.MIN(nums.money)>, <Feature: cats.MIN(nums.weight)>, <Feature: cats.SKEW(nums.age)>, <Feature: cats.SKEW(nums.money)>, <Feature: cats.SKEW(nums.weight)>, <Feature: cats.STD(nums.age)>, <Feature: cats.STD(nums.money)>, <Feature: cats.STD(nums.weight)>, <Feature: cats.SUM(nums.age)>, <Feature: cats.SUM(nums.money)>, <Feature: cats.SUM(nums.weight)>, <Feature: cats.DAY(born)>, <Feature: cats.MONTH(born)>, <Feature: cats.WEEKDAY(born)>, <Feature: cats.YEAR(born)>]
feature_matrix_cats_df 
    hobbey sex  COUNT(nums)  MAX(nums.age)  MAX(nums.money)  MAX(nums.weight)  \
ID                                                                             
4     NaN   男            1            NaN            300.0             155.6   
1     打籃球   男            1            NaN            200.0             140.5   
2    打羽毛球   女            1           28.0            240.0             120.8   

    MEAN(nums.age)  MEAN(nums.money)  MEAN(nums.weight)  MIN(nums.age)  \
ID                                                                       
4              NaN             300.0              155.6            NaN   
1              NaN             200.0              140.5            NaN   
2             28.0             240.0              120.8           28.0   

    MIN(nums.money)  MIN(nums.weight)  SKEW(nums.age)  SKEW(nums.money)  \
ID                                                                        
4             300.0             155.6             NaN               NaN   
1             200.0             140.5             NaN               NaN   
2             240.0             120.8             NaN               NaN   

    SKEW(nums.weight)  STD(nums.age)  STD(nums.money)  STD(nums.weight)  \
ID                                                                        
4                 NaN            NaN              NaN               NaN   
1                 NaN            NaN              NaN               NaN   
2                 NaN            NaN              NaN               NaN   

    SUM(nums.age)  SUM(nums.money)  SUM(nums.weight)  DAY(born)  MONTH(born)  \
ID                                                                             
4             0.0            300.0             155.6        NaN          NaN   
1             0.0            200.0             140.5        NaN          NaN   
2            28.0            240.0             120.8        1.0          1.0   

    WEEKDAY(born)  YEAR(born)  
ID                             
4             NaN         NaN  
1             NaN         NaN  
2             0.0      1990.0  
features_defs_cats_df： 25 [<Feature: hobbey>, <Feature: sex>, <Feature: COUNT(nums)>, <Feature: MAX(nums.age)>, <Feature: MAX(nums.money)>, <Feature: MAX(nums.weight)>, <Feature: MEAN(nums.age)>, <Feature: MEAN(nums.money)>, <Feature: MEAN(nums.weight)>, <Feature: MIN(nums.age)>, <Feature: MIN(nums.money)>, <Feature: MIN(nums.weight)>, <Feature: SKEW(nums.age)>, <Feature: SKEW(nums.money)>, <Feature: SKEW(nums.weight)>, <Feature: STD(nums.age)>, <Feature: STD(nums.money)>, <Feature: STD(nums.weight)>, <Feature: SUM(nums.age)>, <Feature: SUM(nums.money)>, <Feature: SUM(nums.weight)>, <Feature: DAY(born)>, <Feature: MONTH(born)>, <Feature: WEEKDAY(born)>, <Feature: YEAR(born)>]
<Feature: SUM(nums.age)>
The sum of the "age" of all instances of "nums" for each "ID" in "cats".

feature_matrix_cats_df.csv

features_defs_cats_df： 25
[<Feature: hobbey>, <Feature: sex>, <Feature: COUNT(nums)>, <Feature: MAX(nums.age)>, <Feature: MAX(nums.money)>, <Feature: MAX(nums.weight)>, <Feature: MEAN(nums.age)>, <Feature: MEAN(nums.money)>, <Feature: MEAN(nums.weight)>, <Feature: MIN(nums.age)>, <Feature: MIN(nums.money)>, <Feature: MIN(nums.weight)>, <Feature: SKEW(nums.age)>, <Feature: SKEW(nums.money)>, <Feature: SKEW(nums.weight)>, <Feature: STD(nums.age)>, <Feature: STD(nums.money)>, <Feature: STD(nums.weight)>, <Feature: SUM(nums.age)>, <Feature: SUM(nums.money)>, <Feature: SUM(nums.weight)>, <Feature: DAY(born)>, <Feature: MONTH(born)>, <Feature: WEEKDAY(born)>, <Feature: YEAR(born)>]

IDhobbeysexCOUNT(nums)MAX(nums.age)MAX(nums.money)MAX(nums.weight)MEAN(nums.age)MEAN(nums.money)MEAN(nums.weight)MIN(nums.age)MIN(nums.money)MIN(nums.weight)SKEW(nums.age)SKEW(nums.money)SKEW(nums.weight)STD(nums.age)STD(nums.money)STD(nums.weight)SUM(nums.age)SUM(nums.money)SUM(nums.weight)DAY(born)MONTH(born)WEEKDAY(born)YEAR(born)
4 男1 300155.6 300155.6 300155.6 0300155.6
1打籃球男1 200140.5 200140.5 200140.5 0200140.5
2打羽毛球女128240120.828240120.828240120.8 28240120.81101990

IDhobbeysexCOUNT(nums)
4 男1
1打籃球男1
2打羽毛球女1
MAX(nums.age)MAX(nums.money)MAX(nums.weight)MEAN(nums.age)MEAN(nums.money)MEAN(nums.weight)MIN(nums.age)MIN(nums.money)MIN(nums.weight)
300155.6 300155.6 300155.6
200140.5 200140.5 200140.5
28240120.828240120.828240120.8
SKEW(nums.age)SKEW(nums.money)SKEW(nums.weight)STD(nums.age)STD(nums.money)STD(nums.weight)SUM(nums.age)SUM(nums.money)SUM(nums.weight)
0300155.6
0200140.5
28240120.8
DAY(born)MONTH(born)WEEKDAY(born)YEAR(born)
1101990

欄位解釋：

  1. <Feature: hobbey> ： The "hobbey".
  2. <Feature: sex> ： The "sex".
  3. <Feature: COUNT(nums)> ： The number of all instances of "nums" for each "ID" in "cats".
  4. <Feature: MAX(nums.age)> ： The maximum of the "age" of all instances of "nums" for each "ID" in "cats".
  5. <Feature: MAX(nums.money)> ： The maximum of the "money" of all instances of "nums" for each "ID" in "cats".
  6. <Feature: MAX(nums.weight)> ： The maximum of the "weight" of all instances of "nums" for each "ID" in "cats".
  7. <Feature: MEAN(nums.age)> ： The average of the "age" of all instances of "nums" for each "ID" in "cats".
  8. <Feature: MEAN(nums.money)> ： The average of the "money" of all instances of "nums" for each "ID" in "cats".
  9. <Feature: MEAN(nums.weight)> ： The average of the "weight" of all instances of "nums" for each "ID" in "cats".
  10. <Feature: MIN(nums.age)> ： The minimum of the "age" of all instances of "nums" for each "ID" in "cats".
  11. <Feature: MIN(nums.money)> ： The minimum of the "money" of all instances of "nums" for each "ID" in "cats".
  12. <Feature: MIN(nums.weight)> ： The minimum of the "weight" of all instances of "nums" for each "ID" in "cats".
  13. <Feature: SKEW(nums.age)> ： The skewness of the "age" of all instances of "nums" for each "ID" in "cats".
  14. <Feature: SKEW(nums.money)> ： The skewness of the "money" of all instances of "nums" for each "ID" in "cats".
  15. <Feature: SKEW(nums.weight)> ： The skewness of the "weight" of all instances of "nums" for each "ID" in "cats".
  16. <Feature: STD(nums.age)> ： The standard deviation of the "age" of all instances of "nums" for each "ID" in "cats".
  17. <Feature: STD(nums.money)> ： The standard deviation of the "money" of all instances of "nums" for each "ID" in "cats".
  18. <Feature: STD(nums.weight)> ： The standard deviation of the "weight" of all instances of "nums" for each "ID" in "cats".
  19. <Feature: SUM(nums.age)> ： The sum of the "age" of all instances of "nums" for each "ID" in "cats".
  20. <Feature: SUM(nums.money)> ： The sum of the "money" of all instances of "nums" for each "ID" in "cats".
  21. <Feature: SUM(nums.weight)> ： The sum of the "weight" of all instances of "nums" for each "ID" in "cats".
  22. <Feature: DAY(born)> ： The day of the month of the "born".
  23. <Feature: MONTH(born)> ： The month of the "born".
  24. <Feature: WEEKDAY(born)> ： The day of the week of the "born".
  25. <Feature: YEAR(born)> ： The year of the "born".

feature_matrix_nums.csv

features_defs_nums： 29
[<Feature: ID>, <Feature: age>, <Feature: money>, <Feature: weight>, <Feature: cats.hobbey>, <Feature: cats.sex>, <Feature: cats.COUNT(nums)>, <Feature: cats.MAX(nums.age)>, <Feature: cats.MAX(nums.money)>, <Feature: cats.MAX(nums.weight)>, <Feature: cats.MEAN(nums.age)>, <Feature: cats.MEAN(nums.money)>, <Feature: cats.MEAN(nums.weight)>, <Feature: cats.MIN(nums.age)>, <Feature: cats.MIN(nums.money)>, <Feature: cats.MIN(nums.weight)>, <Feature: cats.SKEW(nums.age)>, <Feature: cats.SKEW(nums.money)>, <Feature: cats.SKEW(nums.weight)>, <Feature: cats.STD(nums.age)>, <Feature: cats.STD(nums.money)>, <Feature: cats.STD(nums.weight)>, <Feature: cats.SUM(nums.age)>, <Feature: cats.SUM(nums.money)>, <Feature: cats.SUM(nums.weight)>, <Feature: cats.DAY(born)>, <Feature: cats.MONTH(born)>, <Feature: cats.WEEKDAY(born)>, <Feature: cats.YEAR(born)>]

nameIDagemoneyweightcats.hobbeycats.sexcats.COUNT(nums)cats.MAX(nums.age)cats.MAX(nums.money)cats.MAX(nums.weight)cats.MEAN(nums.age)cats.MEAN(nums.money)cats.MEAN(nums.weight)cats.MIN(nums.age)cats.MIN(nums.money)cats.MIN(nums.weight)cats.SKEW(nums.age)cats.SKEW(nums.money)cats.SKEW(nums.weight)cats.STD(nums.age)cats.STD(nums.money)cats.STD(nums.weight)cats.SUM(nums.age)cats.SUM(nums.money)cats.SUM(nums.weight)cats.DAY(born)cats.MONTH(born)cats.WEEKDAY(born)cats.YEAR(born)
Bob1 200140.5打籃球男1 200140.5 200140.5 200140.5 0200140.5
LiSa228240120.8打羽毛球女128240120.828240120.828240120.8 28240120.81101990
Mary338290169.4
Alan4 300155.6 男1 300155.6 300155.6 300155.6 0300155.6

nameIDagemoneyweight
Bob1 200140.5
LiSa228240120.8
Mary338290169.4
Alan4 300155.6
cats.hobbeycats.sexcats.COUNT(nums)
打籃球男1
打羽毛球女1
男1
cats.MAX(nums.age)cats.MAX(nums.money)cats.MAX(nums.weight)cats.MEAN(nums.age)cats.MEAN(nums.money)cats.MEAN(nums.weight)cats.MIN(nums.age)cats.MIN(nums.money)cats.MIN(nums.weight)
200140.5 200140.5 200140.5
28240120.828240120.828240120.8
300155.6 300155.6 300155.6
cats.SKEW(nums.age)cats.SKEW(nums.money)cats.SKEW(nums.weight)cats.STD(nums.age)cats.STD(nums.money)cats.STD(nums.weight)cats.SUM(nums.age)cats.SUM(nums.money)cats.SUM(nums.weight)
0200140.5
28240120.8
0300155.6
cats.DAY(born)cats.MONTH(born)cats.WEEKDAY(born)cats.YEAR(born)
1101990

欄位解釋：

  1. <Feature: ID> ： The "ID".
  2. <Feature: age> ： The "age".
  3. <Feature: money> ： The "money".
  4. <Feature: weight> ： The "weight".
  5. <Feature: cats.sex> ： The "sex" for the instance of "cats" associated with this instance of "nums".
  6. <Feature: cats.hobbey> ： The "hobbey" for the instance of "cats" associated with this instance of "nums".
  7. <Feature: cats.COUNT(nums)> ： The number of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  8. <Feature: cats.MAX(nums.age)> ： The maximum of the "age" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  9. <Feature: cats.MAX(nums.money)> ： The maximum of the "money" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  10. <Feature: cats.MAX(nums.weight)> ： The maximum of the "weight" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  11. <Feature: cats.MEAN(nums.age)> ： The average of the "age" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  12. <Feature: cats.MEAN(nums.money)> ： The average of the "money" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  13. <Feature: cats.MEAN(nums.weight)> ： The average of the "weight" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  14. <Feature: cats.MIN(nums.age)> ： The minimum of the "age" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  15. <Feature: cats.MIN(nums.money)> ： The minimum of the "money" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  16. <Feature: cats.MIN(nums.weight)> ： The minimum of the "weight" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  17. <Feature: cats.SKEW(nums.age)> ： The skewness of the "age" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  18. <Feature: cats.SKEW(nums.money)> ： The skewness of the "money" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  19. <Feature: cats.SKEW(nums.weight)> ： The skewness of the "weight" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  20. <Feature: cats.STD(nums.age)> ： The standard deviation of the "age" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  21. <Feature: cats.STD(nums.money)> ： The standard deviation of the "money" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  22. <Feature: cats.STD(nums.weight)> ： The standard deviation of the "weight" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  23. <Feature: cats.SUM(nums.age)> ： The sum of the "age" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  24. <Feature: cats.SUM(nums.money)> ： The sum of the "money" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  25. <Feature: cats.SUM(nums.weight)> ： The sum of the "weight" of all instances of "nums" for each "ID" in "cats" for the instance of "cats" associated with this instance of "nums".
  26. <Feature: cats.DAY(born)> ： The day of the month of the "born" for the instance of "cats" associated with this instance of "nums".
  27. <Feature: cats.MONTH(born)> ： The month of the "born" for the instance of "cats" associated with this instance of "nums".
  28. <Feature: cats.WEEKDAY(born)> ： The day of the week of the "born" for the instance of "cats" associated with this instance of "nums".
  29. <Feature: cats.YEAR(born)> ： The year of the "born" for the instance of "cats" associated with this instance of "nums".

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    uj5u.com 2020-09-10 02:00:44 more
  • 如何從xshell上傳檔案到centos linux虛擬機里

    如何從xshell上傳檔案到centos linux虛擬機里及：虛擬機CentOs下執行 yum -y install lrzsz命令，出現錯誤：鏡像無法找到軟體包 前言 一、安裝lrzsz步驟 二、上傳檔案 三、遇到的問題及解決方案 總結 前言 提示：其實很簡單，往虛擬機上安裝一個上傳檔案的工具 ......

    uj5u.com 2020-09-10 02:00:47 more
  • 一、SQLMAP入門

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    uj5u.com 2020-09-10 02:00:50 more
  • Metasploit 簡單使用教程

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    uj5u.com 2020-09-10 02:00:53 more
  • 游戲逆向之驅動層與用戶層通訊

    驅動層代碼： #pragma once #include <ntifs.h> #define add_code CTL_CODE(FILE_DEVICE_UNKNOWN,0x800,METHOD_BUFFERED,FILE_ANY_ACCESS) /* 更多游戲逆向視頻www.yxfzedu.com ......

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  • 北斗電力時鐘（北斗授時服務器）讓網路資料更精準

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    uj5u.com 2020-09-10 02:01:03 more
  • 【CTF】CTFHub 技能樹 彩蛋 writeup

    ?碎碎念 CTFHub：https://www.ctfhub.com/ 筆者入門CTF時時剛開始刷的是bugku的舊平臺，后來才有了CTFHub。 感覺不論是網頁UI設計，還是題目質量，賽事跟蹤，工具軟體都做得很不錯。 而且因為獨到的金幣制度的確讓人有一種想去刷題賺金幣的感覺。 個人還是非常喜歡這個 ......

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  • 02windows基礎操作

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    uj5u.com 2020-09-10 02:04:18 more
  • 03.Linux基礎操作

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    uj5u.com 2023-04-20 08:48:24 more
  • web3 產品介紹：metamask 錢包 使用最多的瀏覽器插件錢包

    Metamask錢包是一種基于區塊鏈技術的數字貨幣錢包，它允許用戶在安全、便捷的環境下管理自己的加密資產。Metamask錢包是以太坊生態系統中最流行的錢包之一，它具有易于使用、安全性高和功能強大等優點。 本文將詳細介紹Metamask錢包的功能和使用方法。 一、 Metamask錢包的功能 數字資 ......

    uj5u.com 2023-04-20 08:47:46 more
  • vulnhub_Earth

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    uj5u.com 2023-04-20 07:46:20 more
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    uj5u.com 2023-04-20 07:44:00 more
  • 最新版本 Stable Diffusion 開源 AI 繪畫工具之中文自動提詞篇

    🎈 標簽生成器 由于輸入正向提示詞 prompt 和反向提示詞 negative prompt 都是使用英文，所以對學習母語的我們非常不友好 使用網址：https://tinygeeker.github.io/p/ai-prompt-generator 這個網址是為了讓大家在使用 AI 繪畫的時候 ......

    uj5u.com 2023-04-20 07:43:36 more
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    隨著前端技術的不斷發展和應用程式的日益復雜，前端自動化測驗也在不斷演進。隨著 Web 應用程式變得越來越復雜，自動化測驗的需求也越來越高。如今，自動化測驗已經成為 Web 應用程式開發程序中不可或缺的一部分，它們可以幫助開發人員更快地發現和修復錯誤，提高應用程式的性能和可靠性。 ......

    uj5u.com 2023-04-20 07:43:16 more
  • CANN開發實踐：4個DVPP記憶體問題的典型案例解讀

    摘要：由于DVPP媒體資料處理功能對存放輸入、輸出資料的記憶體有更高的要求（例如，記憶體首地址128位元組對齊），因此需呼叫專用的記憶體申請介面，那么本期就分享幾個關于DVPP記憶體問題的典型案例，并給出原因分析及解決方法。 本文分享自華為云社區《FAQ_DVPP記憶體問題案例》，作者：昇騰CANN。 DVPP ......

    uj5u.com 2023-04-20 07:43:03 more
  • msf學習

    msf學習 以kali自帶的msf為例 一、msf核心模塊與功能 msf模塊都放在/usr/share/metasploit-framework/modules目錄下 1、auxiliary 輔助模塊，輔助滲透（埠掃描、登錄密碼爆破、漏洞驗證等） 2、encoders 編碼器模塊，主要包含各種編碼 ......

    uj5u.com 2023-04-20 07:42:59 more
  • Halcon軟體安裝與界面簡介

    1. 下載Halcon17版本到到本地 2. 雙擊安裝包后 3. 步驟如下 1.2 Halcon軟體安裝 界面分為四大塊 1. Halcon的五個助手 1) 影像采集助手：與相機連接，設定相機引數，采集影像 2) 標定助手：九點標定或是其它的標定，生成標定檔案及內參外參，可以將像素單位轉換為長度單位 ......

    uj5u.com 2023-04-20 07:42:17 more
  • 在MacOS下使用Unity3D開發游戲

    第一次發博客，先發一下我的游戲開發環境吧。 去年2月份買了一臺MacBookPro2021 M1pro(以下簡稱mbp)，這一年來一直在用mbp開發游戲。我大致分享一下我的開發工具以及使用體驗。 1、Unity 官網鏈接： https://unity.cn/releases 我一般使用的Apple ......

    uj5u.com 2023-04-20 07:40:19 more

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