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Apriori | Apriori algorithm implement

 by   BigPeng Java Version: Current License: No License

 by   BigPeng Java Version: Current License: No License

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kandi X-RAY | Apriori Summary

Apriori is a Java library. Apriori has no bugs, it has no vulnerabilities and it has low support. However Apriori build file is not available. You can download it from GitHub.
Apriori algorithm implement
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • Apriori has a low active ecosystem.
  • It has 47 star(s) with 65 fork(s). There are 8 watchers for this library.
  • It had no major release in the last 12 months.
  • Apriori has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Apriori is current.
Apriori Support
Best in #Java
Average in #Java
Apriori Support
Best in #Java
Average in #Java

quality kandi Quality

  • Apriori has 0 bugs and 0 code smells.
Apriori Quality
Best in #Java
Average in #Java
Apriori Quality
Best in #Java
Average in #Java

securitySecurity

  • Apriori has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • Apriori code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
Apriori Security
Best in #Java
Average in #Java
Apriori Security
Best in #Java
Average in #Java

license License

  • Apriori does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
Apriori License
Best in #Java
Average in #Java
Apriori License
Best in #Java
Average in #Java

buildReuse

  • Apriori releases are not available. You will need to build from source code and install.
  • Apriori has no build file. You will be need to create the build yourself to build the component from source.
  • Apriori saves you 103 person hours of effort in developing the same functionality from scratch.
  • It has 261 lines of code, 14 functions and 2 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
Apriori Reuse
Best in #Java
Average in #Java
Apriori Reuse
Best in #Java
Average in #Java
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Apriori Key Features

Apriori algorithm implement

Apriori Examples and Code Snippets

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How to split the string in the list?

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for i, val in enumerate(trans_value_list):
    trans_value_list[i] = val[0].split(",")
def split_by_comma(data) :
   data_in_required_format = []
   for i in range(len(data)) :
      data_in_required_format.append(data[i][0].split(','))
   return data_in_required_format

Filter R dataframe to n most frequent cases and order by frequency

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library(dplyr)

adf %>%
  group_by(x) %>%
  mutate(size = n()) %>%
  ungroup() %>%
  filter(size >= sort(unique(size), TRUE)[2]) %>%
  arrange(desc(size))
#> # A tibble: 16 × 3
#>        x     y  size
#>    <dbl> <int> <int>
#>  1     4    13     9
#>  2     4    14     9
#>  3     4    15     9
#>  4     4    16     9
#>  5     4    17     9
#>  6     4    18     9
#>  7     4    19     9
#>  8     4    20     9
#>  9     4    21     9
#> 10     3     6     7
#> 11     3     7     7
#> 12     3     8     7
#> 13     3     9     7
#> 14     3    10     7
#> 15     3    11     7
#> 16     3    12     7
library(tidyverse)

adf %>%
  add_count(x) %>% 
  filter(n %in% tail(sort(unique(n)),2)) %>% 
  arrange(desc(n))
   x  y n
1  4 13 9
2  4 14 9
3  4 15 9
4  4 16 9
5  4 17 9
6  4 18 9
7  4 19 9
8  4 20 9
9  4 21 9
10 3  6 7
11 3  7 7
12 3  8 7
13 3  9 7
14 3 10 7
15 3 11 7
16 3 12 7
adf <- structure(list(x = c(2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 1), y = 1:22), class = "data.frame", row.names = c(NA, 
-22L))
library(tidyverse)

adf %>%
  add_count(x) %>% 
  filter(n %in% tail(sort(unique(n)),2)) %>% 
  arrange(desc(n))
   x  y n
1  4 13 9
2  4 14 9
3  4 15 9
4  4 16 9
5  4 17 9
6  4 18 9
7  4 19 9
8  4 20 9
9  4 21 9
10 3  6 7
11 3  7 7
12 3  8 7
13 3  9 7
14 3 10 7
15 3 11 7
16 3 12 7
adf <- structure(list(x = c(2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 1), y = 1:22), class = "data.frame", row.names = c(NA, 
-22L))
library(tidyverse)

adf %>%
  add_count(x) %>% 
  filter(n %in% tail(sort(unique(n)),2)) %>% 
  arrange(desc(n))
   x  y n
1  4 13 9
2  4 14 9
3  4 15 9
4  4 16 9
5  4 17 9
6  4 18 9
7  4 19 9
8  4 20 9
9  4 21 9
10 3  6 7
11 3  7 7
12 3  8 7
13 3  9 7
14 3 10 7
15 3 11 7
16 3 12 7
adf <- structure(list(x = c(2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 1), y = 1:22), class = "data.frame", row.names = c(NA, 
-22L))

sql server ML service gives low memory error for large data (Association Rule Mining Project)

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ALTER EXTERNAL RESOURCE POOL [default] WITH (max_memory_percent=94, AFFINITY CPU = AUTO)
GO
ALTER RESOURCE GOVERNOR RECONFIGURE;

GO

finding alternate versions of a tag when iteratively scraping many links with rvest

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library(xml2)
library(httr)

url <- "https://www.sec.gov/Archives/edgar/data/1002152/000108514621000479/infotable.xml"

info_tables <- httr::GET(url, user_agent("Mozilla/5.0")) |> 
  httr::content(encoding="UTF-8") |>
  xml2::xml_find_all(xpath = "//*[local-name()='infoTable']")
df_list <- lapply(info_tables, function(r) {
  vals <- xml2::xml_children(r)
  
  other_vals <- xml2::xml_find_all(r, "*") |>
    xml2::xml_children()

  child_df <- setNames(
    c(xml2::xml_text(vals)), 
    c(xml2::xml_name(vals))
  ) |> rbind() |> data.frame()
  
  grand_df <- setNames(
    c(xml2::xml_text(other_vals)), 
    c(xml2::xml_name(other_vals))
  ) |> rbind() |> data.frame()
  
  cbind.data.frame(child_df, grand_df)
})

final_df <- do.call(rbind.data.frame, df_list)
final_df
                   nameOfIssuer titleOfClass     cusip value shrsOrPrnAmt investmentDiscretion votingAuthority sshPrnamt sshPrnamtType Sole Shared   None
1         ACCENTURE PLC IRELAND  SHS CLASS A G1151C101 47837     183135SH                 SOLE        00183135    183135            SH    0      0 183135
2                  ALPHABET INC CAP STK CL A 02079K305 43695      24931SH                 SOLE         0024931     24931            SH    0      0  24931
3                     APPLE INC          COM 037833100  3229      24334SH                 SOLE         0024334     24334            SH    0      0  24334
4    BERKSHIRE HATHAWAY INC DEL         CL A 084670108  2783          8SH                 SOLE             008         8            SH    0      0      8
5           CANADIAN NATL RY CO          COM 136375102   218       1985SH                 SOLE          001985      1985            SH    0      0   1985
6  CHECK POINT SOFTWARE TECH LT          ORD M22465104 45505     342375SH                 SOLE        00342375    342375            SH    0      0 342375
7           CHURCH & DWIGHT INC          COM 171340102 42500     487221SH                 SOLE        00487221    487221            SH    0      0 487221
8  COGNIZANT TECHNOLOGY SOLUTIO         CL A 192446102 46076     562243SH                 SOLE        00562243    562243            SH    0      0 562243
9               CVS HEALTH CORP          COM 126650100 44311     648773SH                 SOLE        00648773    648773            SH    0      0 648773
10          DANAHER CORPORATION          COM 235851102 44200     198974SH                 SOLE        00198974    198974            SH    0      0 198974
library(xml2)
library(httr)

url <- "https://www.sec.gov/Archives/edgar/data/1002152/000108514621000479/infotable.xml"

info_tables <- httr::GET(url, user_agent("Mozilla/5.0")) |> 
  httr::content(encoding="UTF-8") |>
  xml2::xml_find_all(xpath = "//*[local-name()='infoTable']")
df_list <- lapply(info_tables, function(r) {
  vals <- xml2::xml_children(r)
  
  other_vals <- xml2::xml_find_all(r, "*") |>
    xml2::xml_children()

  child_df <- setNames(
    c(xml2::xml_text(vals)), 
    c(xml2::xml_name(vals))
  ) |> rbind() |> data.frame()
  
  grand_df <- setNames(
    c(xml2::xml_text(other_vals)), 
    c(xml2::xml_name(other_vals))
  ) |> rbind() |> data.frame()
  
  cbind.data.frame(child_df, grand_df)
})

final_df <- do.call(rbind.data.frame, df_list)
final_df
                   nameOfIssuer titleOfClass     cusip value shrsOrPrnAmt investmentDiscretion votingAuthority sshPrnamt sshPrnamtType Sole Shared   None
1         ACCENTURE PLC IRELAND  SHS CLASS A G1151C101 47837     183135SH                 SOLE        00183135    183135            SH    0      0 183135
2                  ALPHABET INC CAP STK CL A 02079K305 43695      24931SH                 SOLE         0024931     24931            SH    0      0  24931
3                     APPLE INC          COM 037833100  3229      24334SH                 SOLE         0024334     24334            SH    0      0  24334
4    BERKSHIRE HATHAWAY INC DEL         CL A 084670108  2783          8SH                 SOLE             008         8            SH    0      0      8
5           CANADIAN NATL RY CO          COM 136375102   218       1985SH                 SOLE          001985      1985            SH    0      0   1985
6  CHECK POINT SOFTWARE TECH LT          ORD M22465104 45505     342375SH                 SOLE        00342375    342375            SH    0      0 342375
7           CHURCH & DWIGHT INC          COM 171340102 42500     487221SH                 SOLE        00487221    487221            SH    0      0 487221
8  COGNIZANT TECHNOLOGY SOLUTIO         CL A 192446102 46076     562243SH                 SOLE        00562243    562243            SH    0      0 562243
9               CVS HEALTH CORP          COM 126650100 44311     648773SH                 SOLE        00648773    648773            SH    0      0 648773
10          DANAHER CORPORATION          COM 235851102 44200     198974SH                 SOLE        00198974    198974            SH    0      0 198974

Counting datetime occurrences in a pandas series, and storing them back as a value for each date in a dictionary

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df['datetime'].value_counts().to_dict()
# adds dates with no value 
from datetime import date, timedelta
import pandas as pd

#Start date and end_date
start_date = pd.to_datetime("2019-12-05")
end_date = pd.to_datetime("2021-09-15") - timedelta(days=1) #Excluding last

#List of all dates
all_date = pd.date_range(start_date, end_date, freq='d')

#Left join your main data on dates data
all_date_df = pd.DataFrame({'Date':all_date})
tdf = df.groupby('Date', as_index=False)['Count'].sum()
tdf = pd.merge(all_date_df, tdf, on='Date', how="left")
tdf.fillna(0, inplace=True)
tdf["Count"] = [x+1 for x in tdfp["Count"]]
# adds dates with no value 
from datetime import date, timedelta
import pandas as pd

#Start date and end_date
start_date = pd.to_datetime("2019-12-05")
end_date = pd.to_datetime("2021-09-15") - timedelta(days=1) #Excluding last

#List of all dates
all_date = pd.date_range(start_date, end_date, freq='d')

#Left join your main data on dates data
all_date_df = pd.DataFrame({'Date':all_date})
tdf = df.groupby('Date', as_index=False)['Count'].sum()
tdf = pd.merge(all_date_df, tdf, on='Date', how="left")
tdf.fillna(0, inplace=True)
tdf["Count"] = [x+1 for x in tdfp["Count"]]

Parallel-processing efficient_apriori code in Python

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from multiprocessing import Pool

length_of_input_file=len(raw_data_min)
total_offset_count=4  # number of parallel process to run
offset=int(length_of_input_file/total_offset_count // 1)
dataNew1=customer[0:offset-1]
dataNew2=customer[offset:2*offset-1]
dataNew3=customer[2*offset:3*offset-1]
dataNew4=customer[3*offset:4*offset-1]

def calculate_frequent_itemset(fractional_data):
    """Function that calculated the frequent dataset parallely"""
    itemsets, rules = apriori(fractional_data, min_support=MIN_SUPPORT, 
    min_confidence=MIN_CONFIDENCE)
    return itemsets, rules
        
p=Pool()
frequent_itemsets=p.map(calculate_frequent_itemset,(dataNew1,dataNew2,dataNew3,dataNew4))
p.close()
p.join()

itemsets1, rules1 =frequent_itemsets[0]
itemsets2, rules2=frequent_itemsets[1]
itemsets3, rules3=frequent_itemsets[2]
itemsets4, rules4=frequent_itemsets[3]

How to transform such a long to wide table?

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yourtable.pivot_table(index=["yourindex", "yourindex2"], 
                    columns='yourcollums', 
                    values='yourvalues')

How to setup a csv or txt file for uploading to weka?

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id,A,C,D,E,I,K,L,M,N,O,Y
1,,,,t,,t,,t,t,t,t
2,,,t,t,,t,,,t,t,t
3,t,,,t,,t,,t,,,
4,,t,,t,t,t,,,,t,
5,,,t,t,,,t,,,t,

Collapse specific rows/cases of dataframe

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library(dplyr)
df %>% 
    slice(1:2) %>% 
    mutate(across(1:4, mean),
           ID = paste(ID[1], ID[2], sep = "+"),
           across(3:4, sum)) %>% 
    slice(1) %>% 
    bind_rows(df) %>% 
    slice(c(1, 4:6))
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species        ID
1          5.0        3.25          2.8         0.4  setosa ID_1+ID_2
2          4.7        3.20          1.3         0.2  setosa      ID_3
3          4.6        3.10          1.5         0.2  setosa      ID_4
4          5.0        3.60          1.4         0.2  setosa      ID_5
library(dplyr)
df %>% 
    slice(1:2) %>% 
    mutate(across(1:4, mean),
           ID = paste(ID[1], ID[2], sep = "+"),
           across(3:4, sum)) %>% 
    slice(1) %>% 
    bind_rows(df) %>% 
    slice(c(1, 4:6))
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species        ID
1          5.0        3.25          2.8         0.4  setosa ID_1+ID_2
2          4.7        3.20          1.3         0.2  setosa      ID_3
3          4.6        3.10          1.5         0.2  setosa      ID_4
4          5.0        3.60          1.4         0.2  setosa      ID_5
library(dplyr)
library(stringr)
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number()), Species) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Species Sepal.Length Sepal.Width Petal.Length Petal.Width ID       
  <fct>          <dbl>       <dbl>        <dbl>       <dbl> <chr>    
1 setosa           5          3.25          2.8         0.4 ID_1+ID_2
2 setosa           4.7        3.2           1.3         0.2 ID_3     
3 setosa           4.6        3.1           1.5         0.2 ID_4     
4 setosa           5          3.6           1.4         0.2 ID_5     
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number())) %>%  
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     
library(forcats)
df1 %>%
   group_by(grp = fct_collapse(ID, other = c("ID_1", "ID_2"))) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     
library(dplyr)
library(stringr)
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number()), Species) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Species Sepal.Length Sepal.Width Petal.Length Petal.Width ID       
  <fct>          <dbl>       <dbl>        <dbl>       <dbl> <chr>    
1 setosa           5          3.25          2.8         0.4 ID_1+ID_2
2 setosa           4.7        3.2           1.3         0.2 ID_3     
3 setosa           4.6        3.1           1.5         0.2 ID_4     
4 setosa           5          3.6           1.4         0.2 ID_5     
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number())) %>%  
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     
library(forcats)
df1 %>%
   group_by(grp = fct_collapse(ID, other = c("ID_1", "ID_2"))) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     
library(dplyr)
library(stringr)
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number()), Species) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Species Sepal.Length Sepal.Width Petal.Length Petal.Width ID       
  <fct>          <dbl>       <dbl>        <dbl>       <dbl> <chr>    
1 setosa           5          3.25          2.8         0.4 ID_1+ID_2
2 setosa           4.7        3.2           1.3         0.2 ID_3     
3 setosa           4.6        3.1           1.5         0.2 ID_4     
4 setosa           5          3.6           1.4         0.2 ID_5     
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number())) %>%  
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     
library(forcats)
df1 %>%
   group_by(grp = fct_collapse(ID, other = c("ID_1", "ID_2"))) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     
library(dplyr)
library(stringr)
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number()), Species) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Species Sepal.Length Sepal.Width Petal.Length Petal.Width ID       
  <fct>          <dbl>       <dbl>        <dbl>       <dbl> <chr>    
1 setosa           5          3.25          2.8         0.4 ID_1+ID_2
2 setosa           4.7        3.2           1.3         0.2 ID_3     
3 setosa           4.6        3.1           1.5         0.2 ID_4     
4 setosa           5          3.6           1.4         0.2 ID_5     
df1 %>% 
   group_by(grp = case_when(ID %in% c("ID_1", "ID_2") ~ 0L, 
        TRUE ~ row_number())) %>%  
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     
library(forcats)
df1 %>%
   group_by(grp = fct_collapse(ID, other = c("ID_1", "ID_2"))) %>% 
   summarise(across(starts_with("Sepal"), mean), 
   across(starts_with("Petal"), sum), Species = first(Species), 
        ID = str_c(ID, collapse="+"), 
         .groups = 'drop') %>% 
   select(-grp)
# A tibble: 4 x 6
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID       
         <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>    
1          5          3.25          2.8         0.4 setosa  ID_1+ID_2
2          4.7        3.2           1.3         0.2 setosa  ID_3     
3          4.6        3.1           1.5         0.2 setosa  ID_4     
4          5          3.6           1.4         0.2 setosa  ID_5     

RadioListTile issue when used on PageView.builder

copy iconCopydownload iconDownload
import 'package:flutter/material.dart';

class TestScreen extends StatefulWidget {
  @override
  _TestScreenState createState() => _TestScreenState();
}

class _TestScreenState extends State<TestScreen> {
  PageController controller = PageController();
  int index = 0;

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      backgroundColor: Colors.grey[100],
      appBar: AppBar(
        title: Text('Quiz'),
      ),
      body: Container(
        padding: EdgeInsets.all(10),
        child: Column(
          children: [
            Container(
              height: 105,
              child: GridView(
                physics: NeverScrollableScrollPhysics(),
                gridDelegate: SliverGridDelegateWithFixedCrossAxisCount(
                  crossAxisCount: 5,
                  childAspectRatio: 3 / 2,
                  mainAxisSpacing: 10,
                ),
                children: List.generate(
                  questions.length,
                  (index) => Container(
                    decoration: BoxDecoration(
                      color: Colors.white,
                      shape: BoxShape.circle,
                      border: Border.all(
                        color:
                            this.index == index ? Colors.red : Colors.lightBlue,
                        width: 2,
                      ),
                    ),
                    child: Center(
                      child: Text(
                        '${index + 1}',
                        style: TextStyle(
                          fontWeight: FontWeight.bold,
                          fontSize: 15,
                        ),
                      ),
                    ),
                  ),
                ),
              ),
            ),
            SizedBox(height: 10),
            Expanded(
              child: Card(
                shape: RoundedRectangleBorder(
                  borderRadius: BorderRadius.circular(16),
                ),
                child: Column(
                  children: [
                    Expanded(
                      child: PageView(
                        controller: controller,
                        physics: NeverScrollableScrollPhysics(),
                        children: List.generate(
                          questions.length,
                          (index) => Container(
                            padding: EdgeInsets.all(20),
                            child: Column(
                              crossAxisAlignment: CrossAxisAlignment.start,
                              children: [
                                Text(
                                  questions[index]['title'],
                                  style: TextStyle(
                                    fontSize: 18,
                                    fontWeight: FontWeight.bold,
                                  ),
                                ),
                                SizedBox(height: 10),
                                // questions[index]['title'].forEach().
                                // questions.forEach((element) { }).toList
                                Expanded(
                                  child: Column(
                                    children: questions[index]['answer']
                                        .map<Widget>(
                                          (answer) => Container(
                                            child: ListTile(
                                              onTap: () {
                                                nextPage();
                                              },
                                              leading: Icon(Icons
                                                  .radio_button_off_rounded),
                                              title: Text(
                                                answer['answer_text'],
                                              ),
                                            ),
                                          ),
                                        )
                                        .toList(),
                                  ),
                                ),
                              ],
                            ),
                          ),
                        ),
                      ),
                    ),
                    Container(
                      padding: EdgeInsets.symmetric(horizontal: 16),
                      child: Row(
                        mainAxisAlignment: MainAxisAlignment.spaceBetween,
                        children: [
                          ElevatedButton(
                            style: ButtonStyle(
                              shape: MaterialStateProperty.all(
                                RoundedRectangleBorder(
                                  borderRadius: BorderRadius.circular(30),
                                ),
                              ),
                              padding: MaterialStateProperty.all(
                                EdgeInsets.symmetric(
                                  horizontal: 20,
                                  vertical: 12,
                                ),
                              ),
                            ),
                            onPressed: () {
                              previousPage();
                            },
                            child: Text(
                              'Previous',
                              style: TextStyle(fontSize: 14),
                            ),
                          ),
                          ElevatedButton(
                            style: ButtonStyle(
                              shape: MaterialStateProperty.all(
                                RoundedRectangleBorder(
                                  borderRadius: BorderRadius.circular(30),
                                ),
                              ),
                              padding: MaterialStateProperty.all(
                                EdgeInsets.symmetric(
                                  horizontal: 20,
                                  vertical: 12,
                                ),
                              ),
                              backgroundColor: MaterialStateProperty.all(
                                Colors.red[600],
                              ),
                            ),
                            onPressed: () {
                              nextPage();
                            },
                            child: Text(
                              'Next',
                              style: TextStyle(fontSize: 14),
                            ),
                          ),
                        ],
                      ),
                    ),
                    SizedBox(height: 10),
                  ],
                ),
              ),
            ),
          ],
        ),
      ),
    );
  }

  void nextPage() {
    if (index < 9) {
      setState(() {
        index++;
      });
      controller.jumpToPage(index);
      print(index);
    } else {
      Navigator.of(context).pop();
    }
  }

  void previousPage() {
    if (index > 0) {
      setState(() {
        index--;
      });
      controller.jumpToPage(index);
      print(index);
    }
  }

  final List<Map<String, dynamic>> questions = [
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 20,
      "title": "node.next -> node.next.next; will make",
      "answer": [
        {"id": 26, "answer_text": "node.next inaccessible"},
        {"id": 27, "answer_text": "node.next.next inaccessible"},
        {"id": 28, "answer_text": "this node inaccessible"},
        {"id": 29, "answer_text": "none of the above!"}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 15,
      "title":
          "For a binary search algorithm to work, it is necessary that the array (list) must be",
      "answer": [
        {"id": 8, "answer_text": "sorted"},
        {"id": 9, "answer_text": "Unsorted"},
        {"id": 10, "answer_text": "In a heap"},
        {"id": 11, "answer_text": "popped out of stack"}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 22,
      "title": "A pivot element to partition unsorted list is used in",
      "answer": [
        {"id": 34, "answer_text": "Merge Sort"},
        {"id": 35, "answer_text": "Quick Sort"},
        {"id": 36, "answer_text": "Insertion Sort"},
        {"id": 37, "answer_text": "Selection Sort"}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 17,
      "title": "Find the odd out",
      "answer": [
        {"id": 14, "answer_text": "Prim's Minimal Spanning Tree Algorithm"},
        {"id": 15, "answer_text": "Kruskal's Minimal Spanning Tree Algorithm"},
        {
          "id": 16,
          "answer_text": "Floyd-Warshall's All pair shortest path Algorithm"
        },
        {"id": 17, "answer_text": "Dijkstra's Minimal Spanning Tree Algorithm"}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 23,
      "title": "Apriori analysis of an algorithm assumes that −",
      "answer": [
        {
          "id": 38,
          "answer_text":
              "the algorithm has been tested before in real environment."
        },
        {
          "id": 39,
          "answer_text":
              "all other factors like CPU speed are constant and have no effect on implementation."
        },
        {"id": 40, "answer_text": "the algorithm needs not to be practical."},
        {"id": 41, "answer_text": "none of the above."}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 16,
      "title": "Postfix expression is just a reverse of prefix expression.",
      "answer": [
        {"id": 12, "answer_text": "Yes"},
        {"id": 13, "answer_text": "No"}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 18,
      "title":
          "If the array is already sorted, which of these algorithms will exhibit the best performance",
      "answer": [
        {"id": 18, "answer_text": "Merge Sort"},
        {"id": 19, "answer_text": "Insertion Sort"},
        {"id": 20, "answer_text": "Quick Sort"},
        {"id": 21, "answer_text": "Heap Sort"}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 21,
      "title":
          "Which of the following algorithm cannot be desiged without recursion −",
      "answer": [
        {"id": 30, "answer_text": "Tower of Hanoi"},
        {"id": 31, "answer_text": "Fibonacci Series"},
        {"id": 32, "answer_text": "Tree Traversal"},
        {"id": 33, "answer_text": "None of the above"}
      ]
    },
    {
      "question_type": "Multiple Choice",
      "difficulty": "Fundamental",
      "id": 19,
      "title": "Apriory algorithm analysis does not include −",
      "answer": [
        {"id": 22, "answer_text": "It is the easiest possible way."},
        {
          "id": 23,
          "answer_text": "To make sure that it is still complete binary tree."
        },
        {
          "id": 24,
          "answer_text": "Because left and right subtree might be missing."
        },
        {"id": 25, "answer_text": "None of the above!"}
      ]
    }
  ];
}

See all related Code Snippets

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QUESTION

How to split the string in the list?

Asked 2022-Feb-15 at 08:45

I am handing with the txt dataset, the slash words are codes, I used this code to convert the trans.txt into CSV format

import pandas as pd
trans=pd.read_csv("trans.txt",header=None,sep=' ')

#Then I used the code to find the values

trans_values=trans.values


# The values' output looks like this array
   ([['0,1,2'],
   ['3'],
   ['4,3'],
   ...,
   ['762'],
   ['775'],
   ['789']], dtype=object)

#I tried to convert this array to a list.

trans_values_list=trans_values.tolist()

The first array is ['0,1,2'], but I want the arrays to look like something like ['0','1','2'], how could I split the strings inside the list? Need it as it is needed to finish the apriori problem, many thanks!

ANSWER

Answered 2022-Feb-15 at 08:36

Just write below code

for i, val in enumerate(trans_value_list):
    trans_value_list[i] = val[0].split(",")

Source https://stackoverflow.com/questions/71123174

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Apriori

You can download it from GitHub.
You can use Apriori like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Apriori component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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