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Divide table into groups of 5000 based on rank


Divide table into groups of 5000 based on rank

By : user2955108
Date : November 22 2020, 10:38 AM
With these it helps I have a table with three columns Customer_ID, Rank, Age and 25000 records. , Use Window function to create the group
code :
SELECT Avg(age) As AvgAge,
       '5000' As Tilecount
FROM  (SELECT ( ( Row_number()OVER(ORDER BY Rank ) - 1 ) / 5000 ) + 1 Group_Id,
              *
       FROM   tablename) a
GROUP  BY Group_Id 


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Sort table entries then divide into groups

Sort table entries then divide into groups


By : Agustin Jose
Date : March 29 2020, 07:55 AM
this one helps. The meat of your needs are met by Enumerable#group_by:
code :
my_lessons.sort_by{ |lesson|
  # Or use ActiveRecord to sort your data
  lesson.title
}.group_by{ |lesson|
  lesson.subject
}.each do |subject,subj_lessons|
  puts "subject: #{subject}"
  subj_lessons.each do |lesson|
    puts lesson.title
  end
end
Is there a way to name proc rank groups based on values within the group?

Is there a way to name proc rank groups based on values within the group?


By : Chris Mac
Date : March 29 2020, 07:55 AM
wish helps you It's not clear from your question if you want to store the min-max values or just format the rank columns with them. My solution below formats the rank column and utilises the ability of SAS to create formats from a dataset. I've obviously only used 1 variable to rank, for your data it will be a simple matter to wrap a macro around the code and run for each of your 40 or so variables. Hope this helps.
code :
/* create ranked dataset */
proc rank data=sashelp.steel groups=10 out=want;
var steel;
ranks steel_rank;
run;

/* calculate minimum and maximum values per rank */
proc summary data=want nway;
class steel_rank;
var steel;
output out=want_min_max (drop=_:) min= max= / autoname;
run;

/* create dataset with formatted values */
data steel_rank_fmt;
set want_min_max (rename=(steel_rank=start));
retain fmtname 'stl_fmt' type 'N';
label=catx('-',steel_min,steel_max);
run;

/* create format from previous dataset */
proc format cntlin=steel_rank_fmt;
run;

/* apply formatted value to rank column */
proc datasets lib=work nodetails nolist;
modify want;
format steel_rank stl_fmt10.;
quit;
MySQL - Rank users based on name occurrence in table; Find rank from name

MySQL - Rank users based on name occurrence in table; Find rank from name


By : Repellion
Date : March 29 2020, 07:55 AM
To fix the issue you can do Based on the answer that i linked in the comment, i was fiddling with it and came up with this:
code :
SELECT *
FROM (
  SELECT NAME, occurrences, @rownum := @rownum + 1 AS position
  FROM (
    SELECT NAME, COUNT(*) AS occurrences
    FROM users
    GROUP BY NAME
    ORDER BY occurrences DESC LIMIT 10
    ) a
  JOIN (
    SELECT @rownum := 0
    ) r
  ) b
WHERE NAME = "bbb"
SELECT name,occurrences,@rownum := @rownum + 1 AS position
FROM (
    SELECT name, COUNT(*) AS occurrences 
    FROM users 
    GROUP BY name 
    ORDER BY occurrences DESC LIMIT 10
) a
JOIN (SELECT @rownum := 0) r;
Divide a table into groups based on an attribute in R

Divide a table into groups based on an attribute in R


By : DAVID
Date : March 29 2020, 07:55 AM
I hope this helps you . I have a table(csv file) where first two attributes are store and dept and then there are other attributes like Date, Sales etc. The table is as follows:- , Making up some data:
code :
df <- data.frame(Store = sample(1:2, 20, replace = TRUE), 
                 Dept  = sample(1:2, 20, replace = TRUE))
lapply(split(df, as.factor(df$Store)), FUN = function(x) split(x, x$Dept))
RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[4, 5000, 5000, 3] to have 3 channels, but got

RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[4, 5000, 5000, 3] to have 3 channels, but got


By : Jamie
Date : March 29 2020, 07:55 AM
around this issue The order of dimensions in pytorch is different than what you expect. Your input tensor has shape of 4x5000x5000x3 which you interpret as a batch of size 4, with images of 5000x5000 pixels, each pixel has 3 channels. That is, your dimensions are batch-height-width-channel.
However, pytorch expects tensor dimensions to be in a different order: batch-channel-height-width. That is, the channel dimension should precede the width and height spatial dimensions.
code :
model(inputs.permute(0, 3, 1, 2))
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