Working on the case statement below and keep getting a missing parenthesis error. any suggestions?
( CASE
WHEN XBAND = 4 AND TBAND = 0 AND YBAND >= 2
THEN 'A'
END
ELSE
CASE
WHEN XBAND = 4 AND TBAND = 0 AND YBAND >= 3
THEN 'B'
END
END ) XYT_BAND
There should only be one END per CASE expression. This should work:
CASE
WHEN XBAND = 4 AND TBAND = 0 AND YBAND >= 2
THEN 'A'
WHEN XBAND = 4 AND TBAND = 0 AND YBAND >= 3
THEN 'B'
END XYT_BAND
If you need to nest CASE expressions then:
CASE
WHEN XBAND = 4 AND TBAND = 0 AND YBAND >= 2
THEN 'A'
ELSE
CASE
WHEN XBAND = 4 AND TBAND = 0 AND YBAND >= 3
THEN 'B'
END
END XYT_BAND
In order to generate multiple rows for each entry you could use union or more flexible unpivot transposing columns to rows:
demo
select id, xyt_band
from (select id,
case when xband = 4 and yband = 0 and tband >= 2 then 'A' end c1,
case when xband = 4 and yband = 0 and tband >= 3 then 'B' end c2
from t)
unpivot (xyt_band for cx in (C1, C2))
Related
I'm currently working on an OpenVibe Session in which I must program a Lua Script. My problem is generating a random table with 2 values: 1s and 2s. If the value in table is 1, then send Stimulus through output 1. And if it's 2, then through output 2.
My question is how I can generate in Lua code a table of 52 1s and 2s (44 1s and 8 2s which correspond to 85% 1s and 15% 2s) in a way that you have at least 3 1s before the next 2s? Somehow like this: 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2.
I´m not an expert in Lua. So any help would be most appreciated.
local get_table_52
do
local cached_C = {}
local function C(n, k)
local idx = n * 9 + k
local value = cached_C[idx]
if not value then
if k == 0 or k == n then
value = 1
else
value = C(n-1, k-1) + C(n-1, k)
end
cached_C[idx] = value
end
return value
end
function get_table_52()
local result = {}
for j = 1, 52 do
result[j] = 1
end
local r = math.random(C(28, 8))
local p = 29
for k = 8, 1, -1 do
local b = 0
repeat
r = r - b
p = p - 1
b = C(p - 1, k - 1)
until r <= b
result[p + k * 3] = 2
end
return result
end
end
Usage:
local t = get_table_52()
-- t contains 44 ones and 8 twos, there are at least 3 ones before next two
Here is the logic.
You have 8 2s. Before each 2 there is a string of 3 1s. That's 32 of your numbers.
Those 8 groups of 1112 separate 9 spots that the remaining 20 1s can go.
So your problem is to randomly distribute 20 1s to 9 random places. And then take that collection of numbers and write out your list. So in untested code from a non-Lua programmer:
-- Populate buckets
local buckets = {0, 0, 0, 0, 0, 0, 0, 0, 0}
for k = 1, 20 do
local bucket = floor(rand(9))
buckets[bucket] = buckets[bucket] + 1
end
-- Turn that into an array
local result = {}
local i = 0
for bucket = 0, 8 do
-- Put buckets[bucket] 1s in result
if 0 < buckets[bucket] do
for j = 0, buckets[bucket] do
result[i] = 1
i = i + 1
end
end
-- Add our separating 1112?
if bucket < 8 do
result[i] = 1
result[i+1] = 1
result[i+2] = 1
result[i+3] = 2
i = i + 4
end
end
I have a matrix of N rows of binary vectors, i.e.
mymatrix = [ 1 0 0 1 0;
1 1 0 0 1;
0 1 1 0 1;
0 1 0 0 1;
0 0 1 0 0;
0 0 1 1 0;
.... ]
where I'd like to find the combinations of rows that, when added together, gets me exactly:
[1 1 1 1 1]
So in the above example, the combinations that would work are 1/3, 1/4/5, and 2/6.
The code I have for this right now is:
i = 1;
for j = 1:5
C = combnk([1:N],j); % Get every possible combination of rows
for c = 1:size(C,1)
if isequal(ones(1,5),sum(mymatrix(C(c,:),:)))
combis{i} = C(c,:);
i = i+1;
end
end
end
But as you would imagine, this takes a while, especially because of that combnk in there.
What might be a useful algorithm/function that can help me speed this up?
M = [
1 0 0 1 0;
1 1 0 0 1;
0 1 1 0 1;
0 1 0 0 1;
0 0 1 0 0;
0 0 1 1 0;
1 1 1 1 1
];
% Find all the unique combinations of rows...
S = (dec2bin(1:2^size(M,1)-1) == '1');
% Find the matching combinations...
matches = cell(0,1);
for i = 1:size(S,1)
S_curr = S(i,:);
rows = M(S_curr,:);
rows_sum = sum(rows,1);
if (all(rows_sum == 1))
matches = [matches; {find(S_curr)}];
end
end
To display your matches in a good stylized way:
for i = 1:numel(matches)
match = matches{i};
if (numel(match) == 1)
disp(['Match found for row: ' mat2str(match) '.']);
else
disp(['Match found for rows: ' mat2str(match) '.']);
end
end
This will produce:
Match found for row: 7.
Match found for rows: [2 6].
Match found for rows: [1 4 5].
Match found for rows: [1 3].
In terms of efficiency, in my machine this algoritm is completing the detection of matches in about 2 milliseconds.
I have query for query data. That is document.
"SELECT
zk_z_hako * CASE WHEN zk_n_iri> 0 THEN zk_n_iri ELSE 1 END
+ zk_z_bara
- ifnull(
sum(
ns_hako
* CASE WHEN zk_n_iri> 0 THEN zk_n_iri ELSE 1 END
* CASE WHEN ns_tr_kbn in (0,6) OR ( ns_tr_kbn = 1 AND ns_ns_kbn = 7) THEN 1
WHEN ns_tr_kbn in (1,7) THEN (-1)
ELSE 0
END
+ ns_bara
* CASE WHEN ns_tr_kbn in (0,6) OR ( ns_tr_kbn = 1 AND ns_ns_kbn = 7) THEN 1
WHEN ns_tr_kbn in (1,7) THEN (-1)
ELSE 0
END )
,0 ) AS TOTAL_BARA
FROM t_table1
LEFT JOIN t_table2
ON ns_kno = zk_kno
AND ns_show_flg = 0
AND ns_ymd > 'Date param'
WHERE zk_kno = Value param;
So I am not a master of Laravel. Now I need to convert this query for work with laravel. Anyone can help me?
And i have to try this query.
$squery = 'zk_z_hako * CASE WHEN zk_n_iri> 0 THEN zk_n_iri ELSE 1 END
+ zk_z_bara
ifnull(
sum(
ns_hako
* CASE WHEN zk_n_iri> 0 THEN zk_n_iri ELSE 1 END
* CASE WHEN ns_tr_kbn in (0,6) OR ( ns_tr_kbn = 1 AND ns_ns_kbn = 7) THEN 1
WHEN ns_tr_kbn in (1,7) THEN (-1)
ELSE 0
END
+ ns_bara
* CASE WHEN ns_tr_kbn in (0,6) OR ( ns_tr_kbn = 1 AND ns_ns_kbn = 7) THEN 1
WHEN ns_tr_kbn in (1,7) THEN (-1)
ELSE 0
END )
,0 ) AS TOTAL_BARA ';
$param1= '20160310';
$param2= '1972640100';
$results = DB::table('table1')
->select($squery)
->leftJoin('table2', function($join) use ($param1)
{
$join->on('table1.ns_kno', '=', 'table2.zk_kno');
$join->on('table1.ns_show_flg', '=', DB::raw(0));
$join->on('ns_ymd','>',DB::raw("'".$param1."'"));
})
->where('zk_kno', DB::raw($param2))
->toSql()
But it's return sql
"select `zk_z_hako` as `CASE` from `t_zaikmst` left join `t_nsyutrn` on `t_nsyutrn`.`ns_kno` = `t_zaikmst`.`zk_kno` and `t_nsyutrn`.`ns_show_flg` = 0 and `ns_ymd` > '20160310' where `zk_kno` = 1972640100"
i don't sure it true.
If you want to make custom select than you need to use raw queries as select parameter like this:
->select(\DB::raw($squery))
Assume the following matrix:
myMatrix = [
1 0 1
1 0 0
1 1 1
1 1 1
0 1 1
0 0 0
0 0 0
0 1 0
1 0 0
0 0 0
0 0 0
0 0 1
0 0 1
0 0 1
];
Given the above (and treating each column independently), I'm trying to create a matrix that will contain the number of rows since the last value of 1 has "shown up". For example, in the first column, the first four values would become 0 since there are 0 rows between each of those rows and the previous value of 1.
Row 5 would become 1, row 6 = 2, row 7 = 3, row 8 = 4. Since row 9 contains a 1, it would become 0 and the count starts again with row 10. The final matrix should look like this:
FinalMatrix = [
0 1 0
0 2 1
0 0 0
0 0 0
1 0 0
2 1 1
3 2 2
4 0 3
0 1 4
1 2 5
2 3 6
3 4 0
4 5 0
5 6 0
];
What is a good way of accomplishing something like this?
EDIT: I'm currently using the following code:
[numRow,numCol] = size(myMatrix);
oneColumn = 1:numRow;
FinalMatrix = repmat(oneColumn',1,numCol);
toSubtract = zeros(numRow,numCol);
for m=1:numCol
rowsWithOnes = find(myMatrix(:,m));
for mm=1:length(rowsWithOnes);
toSubtract(rowsWithOnes(mm):end,m) = rowsWithOnes(mm);
end
end
FinalMatrix = FinalMatrix - toSubtract;
which runs about 5 times faster than the bsxfun solution posted over many trials and data sets (which are about 1500 x 2500 in size). Can the code above be optimized?
For a single column you could do this:
col = 1; %// desired column
vals = bsxfun(#minus, 1:size(myMatrix,1), find(myMatrix(:,col)));
vals(vals<0) = inf;
result = min(vals, [], 1).';
Result for first column:
result =
0
0
0
0
1
2
3
4
0
1
2
3
4
5
find + diff + cumsum based approach -
offset_array = zeros(size(myMatrix));
for k1 = 1:size(myMatrix,2)
a = myMatrix(:,k1);
widths = diff(find(diff([1 ; a])~=0));
idx = find(diff(a)==1)+1;
offset_array(idx(idx<=numel(a)),k1) = widths(1:2:end);
end
FinalMatrix1 = cumsum(double(myMatrix==0) - offset_array);
Benchmarking
The benchmarking code for comparing the above mentioned approach against the one in the question is listed here -
clear all
myMatrix = round(rand(1500,2500)); %// create random input array
for k = 1:50000
tic(); elapsed = toc(); %// Warm up tic/toc
end
disp('------------- With FIND+DIFF+CUMSUM based approach') %//'#
tic
offset_array = zeros(size(myMatrix));
for k1 = 1:size(myMatrix,2)
a = myMatrix(:,k1);
widths = diff(find(diff([1 ; a])~=0));
idx = find(diff(a)==1)+1;
offset_array(idx(idx<=numel(a)),k1) = widths(1:2:end);
end
FinalMatrix1 = cumsum(double(myMatrix==0) - offset_array);
toc
clear FinalMatrix1 offset_array idx widths a
disp('------------- With original approach') %//'#
tic
[numRow,numCol] = size(myMatrix);
oneColumn = 1:numRow;
FinalMatrix = repmat(oneColumn',1,numCol); %//'#
toSubtract = zeros(numRow,numCol);
for m=1:numCol
rowsWithOnes = find(myMatrix(:,m));
for mm=1:length(rowsWithOnes);
toSubtract(rowsWithOnes(mm):end,m) = rowsWithOnes(mm);
end
end
FinalMatrix = FinalMatrix - toSubtract;
toc
The results I got were -
------------- With FIND+DIFF+CUMSUM based approach
Elapsed time is 0.311115 seconds.
------------- With original approach
Elapsed time is 7.587798 seconds.
I have this table
EquipmentId Value Date
1 2 11/04/2013
1 1 11/04/2013
2 3 11/04/2013
2 2 10/04/2013
2 5 10/04/2013
3 1 10/04/2013
3 3 11/04/2013
I want to group these items by date, and have a dictionary with the date as a key and the sum of the maxs of the all equipments values in that day
the result would be like this
[10/04/2013: 6] // 6 = 5 (as the max of values of the the equipmetId 2) + 1 (as the max of values of the the equipmetId 3)
[11/04/2013: 5] // 5 = 2(as the max of values of the the equipmetId 1) + 3(as the max of values of the the equipmetId 3)
I managed to make the query to get this without the sum, meaning for only one equipment.
var consumptionValues = (from c in context.ConsumptionSet
join pi in context.PropertiesInstanceSet on c.PropertiesInstanceID equals pi.PropertiesInstanceID
join ep in context.EquipmentPropertiesSet on pi.EquipmentPropertiesID equals ep.EquipmentPropertiesID
join e in context.EquipmentSet on ep.EquipmentID equals e.EquipmentID
where (e.EquipmentID == equipmentId && pi.ProprietesName == ProprietesName.Energy && c.Date <= DateTime.Now && c.Date >= firstDayDate)
group c by SqlFunctions.DatePart("weekday", c.Date) into grp
select new
{
dayOfWeek = (DayOfWeek)grp.Key.Value - 1,
value = grp.Max(c => c.Value),
}).ToDictionary(c => c.dayOfWeek.ToString(), c => c.value);
It's the complete query with all the joins, in the example I just gave a simplified example.
Is it possible to do this in one single query ?
I have to say I'm not sure it will work, but you should give it a shot:
var consumptionValues = (from c in context.ConsumptionSet
join pi in context.PropertiesInstanceSet on c.PropertiesInstanceID equals pi.PropertiesInstanceID
join ep in context.EquipmentPropertiesSet on pi.EquipmentPropertiesID equals ep.EquipmentPropertiesID
join e in context.EquipmentSet on ep.EquipmentID equals e.EquipmentID
where (e.EquipmentID == equipmentId && pi.ProprietesName == ProprietesName.Energy && c.Date <= DateTime.Now && c.Date >= firstDayDate)
group new { c, e } by SqlFunctions.DatePart("weekday", c.Date) into grp
select new
{
dayOfWeek = (DayOfWeek)grp.Key.Value - 1,
value = grp.GroupBy(i => i.e.EquipmentID).Sum(g => g.Max(i => i.c.Value)),
}).ToDictionary(c => c.dayOfWeek.ToString(), c => c.value);