I have a 1 column matrix with the following values:
*-------*
| 6 |
| 4 |
| 3 |
| 1 |
| 1 |
*-------*
With this function, starting from the first value, I subtract the value in the following row and place 0 at the end. This is the result:
Delta = Ctv_ds_universal(1:(end-1),1)-Ctv_ds_universal(2:end,1);
Delta(end+1)=0;
*-----------*
| 2 (6-4) |
| 1 (4-3) |
| 2 (3-1) |
| 0 (1-1) |
| 0 |
*-----------*
Now, I would like to reverse the order and start subtracting from down to the top, placing 0 at the beginning. How can I modify the function?
*------------*
| 0 |
| -2 (4-6) |
| -1 (3-4) |
| -2 (1-3) |
| 0 (1-1) |
*------------*
Delta = 0;
Delta = [Delta; Ctv_ds_universal(2:end,1)-Ctv_ds_universal(1:end-1,1)];
Related
I have 2 table duty_sheets
centerId | centerName | p1 | p2 | p3 | p4 | ...p22 | examiId
1 | xyz | 1 | 5 | 8 | 7 | 1 | 1
2 | abc | 9 | 1 | 6 | 6 | 1 | 1
and feedback
id | centerId | inspectorId | A | B | C | examiId
1 | 1 | 1 | 1 | 5 | 8 | 1
2 | 2 | 9 | 9 | 1 | 6 | 1
here is my code
$center = DutySheet::select('duty_sheets.centerId', 'duty_sheets.centerName','feedback.id')
->leftJoin('feedback', function ($leftJoin) {
$leftJoin->on('duty_sheets.examId', 'feedback.examId')
->where("duty_sheets.centerId", 'feedback.centerId')
->where("feedback.inspectorId", 1);
})
->where("duty_sheets.examId", 1)
->where("p20", 1)
->get();
dd($center);
to retrieve "All rows from DutySheet where p20 = 1 and dutysheet.examId = 1, and relevant rows from feedback depend on centerId, inspectorId and examId.
The problem is that the query return feedback.id as null while the record exist in feedback table with the ids.
Laravel version = 9
The problem is in left Join
->where("duty_sheets.centerId", 'feedback.centerId')
This build a where against the value 'feedback.centerId'
duty_sheets.centerId='feedback.centerId'
You need use
->on("duty_sheets.centerId",'=', 'feedback.centerId')
Or
->whereColumn("duty_sheets.centerId", 'feedback.centerId')
I have a number of tables that looks as follows:
time | node | left |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr| dualbound | primalbound | gap | compl.
0.0s| 1 | 0 | 100 | - | 1046k | 0 | 100 | 102 | 100 | 0 | 0 | 0 | 0 | -- | 9.999990e+05*| Inf | unknown
* 0.3s| 1 | 0 | 100 | - | LP | 0 | 200 | 102 | 100 | 0 | 0 | 0 | 0 | -- | 5.587300e+04 | Inf | unknown
12.0s| 1 | 0 | 239 | - | 1781k | 0 | 239 | 102 | 100 | 0 | 0 | 0 | 0 | 5.577800e+04 | 5.587300e+04 | 0.17%| unknown
12.1s| 1 | 0 | 287 | - | 2595k | 0 | 239 | 102 | 935 | 835 | 1 | 0 | 0 | 5.577800e+04 | 5.587300e+04 | 0.17%| unknown
66.8s| 1 | 0 | 422 | - | 3061k | 0 | 336 | 102 | 935 | 835 | 1 | 0 | 0 | 5.577800e+04 | 5.587300e+04 | 0.17%| unknown
89.4s| 1 | 0 | 481 | - | 3218k | 0 | 361 | 102 | 935 | 835 | 1 | 0 | 0 | 5.580100e+04 | 5.587300e+04 | 0.13%| unknown
89.5s| 1 | 0 | 579 | - | 3513k | 0 | 361 | 102 |1335 |1235 | 2 | 0 | 0 | 5.580100e+04 | 5.587300e+04 | 0.13%| unknown
100s| 1 | 0 | 715 | - | 3837k | 0 | 403 | 102 |1335 |1235 | 2 | 0 | 0 | 5.583250e+04 | 5.587300e+04 | 0.07%| unknown
I'm interested in recording the first numeric value in the gap column (second last column of the table). The gap column could either have Inf or x.xx% values in it. If all the values in the gap column are Inf, then I would simply record Inf, otherwise, I would like to record the first numeric value. For e.g. in the above table, the value that I would like to record is 0.17. I tried many different ways but couldn't achieve any success. It would be really great if someone could provide some guidance as to how to achieve the above-mentioned objective. Thanks !
You may use this awk solution:
awk -F '[[:blank:]]*\\|[[:blank:]]*' '
NR > 1 && (!v || v == "Inf") {
v = ($(NF-1) == "Inf" ? $(NF-1) : $(NF-1)+0)
}
END {
print v
}' file
0.17
I just want to ask on how to create an LINQ code that can fill up my html table.
Please look at my Tables below
Table EMP: note* my "Male" is boolean
+----+---------------+--------+--------+
| id | Male| JS_REF |DEPT_ID | POS_ID |
+----+---------------+--------+--------+
| 1 | 1 | 1 | 2 | 3 |
| 2 | 0 | 2 | 2 | 3 |
| 3 | 1 | 3 | 1 | 2 |
| 4 | 1 | 2 | 4 | 2 |
| 5 | 1 | 1 | 5 | 5 |
| 6 | 0 | 4 | 6 | 1 |
| 7 | 1 | 1 | 1 | 1 |
| 8 | 0 | 2 | 2 | 3 |
+----+---------------+--------+--------+
Table:JOB_STATUS
+----+--------------------+
| id | JS_REF| JS_TITLE |
+----+--------------------+
| 1 | 1 |Undefined |
| 2 | 2 |Regular |
| 3 | 3 |Contructual |
| 4 | 4 |Probationary|
+----+--------------------+
Table:DEPTS
+----+--------------------+
| id | DEPT_ID| DEPT_NAME |
+----+--------------------+
| 1 | 1 |Admin |
| 2 | 2 |Accounting |
| 3 | 3 |Eginnering |
| 4 | 4 |HR |
+----+--------------------+
Table: POSITIONS
+----+--------------------+
| id | POS_ID| DEPT_NAME |
+----+--------------------+
| 1 | 1 |Clerk |
| 2 | 2 |Accountant |
| 3 | 3 |Bookeeper |
| 4 | 4 |Assistant |
| 5 | 5 |Mechanic |
| 6 | 6 |Staff |
+----+--------------------+
I'd made a static table on what will be the outcome of the LINQ code
Here's the picture:
Here's what i've tried so far:
SELECT tb.DEPT_NAME,TB.JS_TITLE, TB.Male, TB.Female, (TB.Male + TB.Female) AS 'Total Employees' FROM
(
SELECT JS_TITLE,DEPT_NAME,
SUM(CASE WHEN MALE = 1 THEN 1 ELSE 0 END) AS Male,
SUM(CASE WHEN MALE = 0 THEN 1 ELSE 0 END) AS Female
FROM EMP
left join JOB_STATUS on JOB_STATUS.JS_REF = EMP.JS_REF
left join DEPTS on DEPTS.DEPT_ID = EMP.DEPT_ID
GROUP BY JS_TITLE,DEPT_NAME
) AS TB
ORDER BY CASE WHEN TB.MALE IS NULL THEN 1 ELSE 0 END
If anyone can help me or give me some tips on how can I implement this im stuck in this part.
101 is total count for male, 23 for female. (the values are just copy and pasted, that's why the values are the same)
(Actual data result)
There are several billions rows like this
id | type | groupId
---+------+--------
1 | a |
1 | b |
2 | a |
2 | c |
1 | a |
2 | d |
2 | a |
1 | e |
5 | a |
1 | f |
4 | a |
1 | b |
4 | a |
1 | t |
8 | a |
3 | c |
6 | a |
I need to add groupId for these data, if id same or type same, then its a same groupId, the result like this:
id | type | group
---+------+--------
1 | a | 1
1 | b | 1
2 | a | 1
2 | c | 1
1 | a | 1
2 | d | 1
2 | a | 1
1 | e | 1
5 | a | 1
1 | f | 1
4 | a | 1
1 | b | 1
4 | a | 1
7 | t | 2
8 | g | 3
3 | c | 1
6 | a | 1
I try to use a loop to do this, but its very inefficiency, its need server weeks to finish all this.
This is a classic example where you can use a Quick-Union algorithm.
Computational Limits
Time complexity for grouping N rows : O(N log* N) where log* N is the "number of times needed to take the lg of a number until reaching 1" . eg Log* 10^100 = 3 (approx)
Space complexity : O(N)
Read more on this algorithm:
https://www.youtube.com/watch?v=MaNCMWhYIHo ,
https://www.cs.princeton.edu/~rs/AlgsDS07/01UnionFind.pdf
I am very new to statsample and having some basic questions. With this sample data:
[[1, 2, 3, 3],[2, 3, 3, 5],[4, 1, 3, 4]]
I create a 4x4 statsample dataaset called ds and get the following output for each call:
puts ds.summary
gets
= Dataset 1
Cases: 3
Element:[actuals]
== Vector 3
n :3
n valid:3
factors:3
mode: 3
Distribution
+---+---+---------+
| 3 | 3 | 100.00% |
+---+---+---------+
Element:[mids]
== Vector 2
n :3
n valid:3
factors:1,2,3
mode: 2
Distribution
+---+---+--------+
| 1 | 1 | 33.33% |
| 2 | 1 | 33.33% |
| 3 | 1 | 33.33% |
+---+---+--------+
Element:[predicteds]
== Vector 4
n :3
n valid:3
factors:3,4,5
mode: 3
Distribution
+---+---+--------+
| 3 | 1 | 33.33% |
| 4 | 1 | 33.33% |
| 5 | 1 | 33.33% |
+---+---+--------+
Element:[prediction_error]
== Vector 5
n :3
n valid:3
factors:0,1,2
mode: 0
Distribution
+---+---+--------+
| 0 | 1 | 33.33% |
| 1 | 1 | 33.33% |
| 2 | 1 | 33.33% |
+---+---+--------+
Element:[uids]
== Vector 1
n :3
n valid:3
factors:1,2,4
mode: 1
Distribution
+---+---+--------+
| 1 | 1 | 33.33% |
| 2 | 1 | 33.33% |
| 4 | 1 | 33.33% |
+---+---+--------+
Which seems reasonable but then:
cm = ds.correlation_matrix
puts cm.summary
gets this, which is confusing:
Correlation Matrix
+------------------+---------+-------+------------+------------------+-------+
| | actuals | mids | predicteds | prediction_error | uids |
+------------------+---------+-------+------------+------------------+-------+
| actuals | 1.000 | -- | -- | -- | -- |
| mids | -- | 1.000 | -- | -- | -- |
| predicteds | -- | -- | 1.000 | -- | -- |
| prediction_error | -- | -- | -- | 1.000 | -- |
| uids | -- | -- | -- | -- | 1.000 |
+------------------+---------+-------+------------+------------------+-------+
You created a dataset with nominal vectors, not scalar ones. So, correlations between not numeric vectors is always 0.