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  • Can I use @table variable in SQL Server Report Builder?

    - by edosoft
    Using MS SQL 2008 Reporting services: I'm trying to write a report that displays some correlated data so I thought to use a @table variable like so DECLARE @Results TABLE (Number int, Name nvarchar(250), Total1 money, Total2 money) insert into @Results(Number, Name, Total1) select number, name, sum(total) from table1 group by number, name update @Results set total2 = total from (select number, sum(total) from table2) s where s.number = number select from @results However, Report Builder keeps asking to enter a value for the variable @Results. It this at all possible?

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  • Using Distinct or Not

    - by RPS
    In the below SQL Statement, should I be using DISTINCT as I have a Group By in my Where Clause? Thoughts? SELECT [OrderUser].OrderUserId, ISNULL(SUM(total.FileSize), 0), ISNULL(SUM(total.CompressedFileSize), 0) FROM ( SELECT DISTINCT ProductSize.OrderUserId, ProductSize.FileInfoId, CAST(ProductSize.FileSize AS BIGINT) AS FileSize, CAST(ProductSize.CompressedFileSize AS BIGINT) AS CompressedFileSize FROM ProductSize WITH (NOLOCK) INNER JOIN [Version] ON ProductSize.VersionId = [Version].VersionId ) AS total RIGHT OUTER JOIN [OrderUser] WITH (NOLOCK) ON total.OrderUserId = [OrderUser].OrderUserId WHERE NOT ([OrderUser].isCustomer = 1 AND [OrderUser].isEndOrderUser = 0 OR [OrderUser].isLocation = 1) AND [OrderUser].OrderUserId = 1 GROUP BY [OrderUser].OrderUserId

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  • Date range intersection in SQL

    - by Will
    I have a table where each row has a start and stop date-time. These can be arbitrarily short or long spans. I want to query the sum duration of the intersection of all rows with two start and stop date-times. How can you do this in MySQL? Or do you have to select the rows that intersect the query start and stop times, then calculate the actual overlap of each row and sum it client-side?

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  • Calculated group-by fields in MongoDB

    - by Navin Viswanath
    For this example from the MongoDB documentation, how do I write the query using MongoTemplate? db.sales.aggregate( [ { $group : { _id : { month: { $month: "$date" }, day: { $dayOfMonth: "$date" }, year: { $year: "$date" } }, totalPrice: { $sum: { $multiply: [ "$price", "$quantity" ] } }, averageQuantity: { $avg: "$quantity" }, count: { $sum: 1 } } } ] ) Or in general, how do I group by a calculated field?

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  • Is it a Good Practice to Add two Conditions when using a JOIN keyword?

    - by Raúl Roa
    I'd like to know if having to conditionals when using a JOIN keyword is a good practice. I'm trying to filter this resultset by date but I'm unable to get all the branches listed even if there's no expense or income for a date using a WHERE clause. Is there a better way of doing this, if so how? SELECT Branches.Name ,SUM(Expenses.Amount) AS Expenses ,SUM(Incomes.Amount) AS Incomes FROM Branches LEFT JOIN Expenses ON Branches.Id = Expenses.BranchId AND Expenses.Date = '3/11/2010' LEFT JOIN Incomes ON Branches.Id = Incomes.BranchId AND Incomes.Date = '3/11/2010' GROUP BY Branches.Name

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  • Problems with real-valued input deep belief networks (of RBMs)

    - by Junier
    I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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  • CTE Join query issues

    - by Lee_McIntosh
    Hi everyone, this problem has me head going round in circles at the moment and i wondering if anyone could give any pointers as to where im going wrong. Im trying to produce a SPROC that produces a dataset to be called by SSRS for graphs spanning the last 6 months. The data for example purposes uses three tables (theres more but the it wont change the issue at hand) and are as follows: tbl_ReportList: Report Site ---------------- North abc North def East bbb East ccc East ddd South poa South pob South poc South pod West xyz tbl_TicketsRaisedThisMonth: Date Site Type NoOfTickets --------------------------------------------------------- 2010-07-01 00:00:00.000 abc Support 101 2010-07-01 00:00:00.000 abc Complaint 21 2010-07-01 00:00:00.000 def Support 6 ... 2010-12-01 00:00:00.000 abc Support 93 2010-12-01 00:00:00.000 xyz Support 5 tbl_FeedBackRequests: Date Site NoOfFeedBackR ---------------------------------------------------------------- 2010-07-01 00:00:00.000 abc 101 2010-07-01 00:00:00.000 def 11 ... 2010-12-01 00:00:00.000 abc 63 2010-12-01 00:00:00.000 xyz 4 I'm using CTE's to simplify the code, which is as follows: DECLARE @ReportName VarChar(200) SET @ReportName = 'North'; WITH TicketsRaisedThisMonth AS ( SELECT [Date], Site, SUM(NoOfTickets) AS NoOfTickets FROM tbl_TicketsRaisedThisMonth WHERE [Date] >= DATEADD(mm, DATEDIFF(m,0,GETDATE())-6,0) GROUP BY [Date], Site ), FeedBackRequests AS ( SELECT [Date], Site, SUM(NoOfFeedBackR) AS NoOfFeedBackR FROM tbl_FeedBackRequests WHERE [Date] >= DATEADD(mm, DATEDIFF(m,0,GETDATE())-6,0) GROUP BY [Date], Site ), SELECT trtm.[Date] SUM(trtm.NoOfTickets) AS NoOfTickets, SUM(fbr.NoOfFeedBackR) AS NoOfFeedBackR, FROM Reports rpts LEFT OUTER JOIN TotalIncidentsDuringMonth trtm ON rpts.Site = trtm.Site LEFT OUTER JOIN LoggedComplaints fbr ON rpts.Site = fbr.Site WHERE rpts.report = @ReportName GROUP BY trtm.[Date] And the output when the sproc is pass a parameter such as 'North' to be as follows: Date NoOfTickets NoOfFeedBackR ----------------------------------------------------------------------------------- 2010-07-01 00:00:00.000 128 112 2010-08-01 00:00:00.000 <data for that month> <data for that month> 2010-09-01 00:00:00.000 <data for that month> <data for that month> 2010-10-01 00:00:00.000 <data for that month> <data for that month> 2010-11-01 00:00:00.000 <data for that month> <data for that month> 2010-12-01 00:00:00.000 122 63 The issue I'm having is that when i execute the query I'm given a repeated list of values of each month, such as 128 will repeat 6 times then another value for the next months value repeated 6 times, etc. argh!

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  • Error in creating alias in formula tag

    - by Senthilnathan
    Hi all I have a sql query in formula tag inside property tag. In that query i am creating alias name but the hibernate appends table name and throwing me error. select sum(e.salary) as sal from employee e but hibernate changes to select sum(e.salary) as employee.sal from employee e how to avoid this .... it should recognise as sal inside of employee.sal !!!

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  • Get value of "data"

    - by Nicole Loyal-Windham
    Hi, I need to figure out the value of data strings with jquery, for example like this: { label: "Beginner", data: 2}, { label: "Advanced", data: 12}, { label: "Expert", data: 22}, to add them up. Something like: var sum = data1+data2+data3; alert(sum); So the result for this example would be 36. Appreciate your help! Nicole

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  • Datatype Conversion

    - by user87
    I am trying to execute the following Query select distinct pincode as Pincode,CAST(Date_val as DATE) as Date, SUM(cast(megh_38 as int)) as 'Postage Realized in Cash', SUM(cast(megh_39 as int)) as 'MO Commission', from dbo.arrow_dtp_upg group by pincode,Date_Val but I am getting an error "Conversion failed when converting the nvarchar value '82.25' to data type int." Am I using a wrong data type?

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  • How to refactor this MySQL code?

    - by Jader Dias
    SELECT * ( SELECT * FROM `table1` WHERE `id` NOT IN ( SELECT `id` FROM `table2` WHERE `col4` = 5 ) group by `col2` having sum(`col3`) > 0 UNION SELECT * FROM `table1` WHERE `id` NOT IN ( SELECT `id` FROM `table2` WHERE `col4` = 5 ) group by `col2` having sum(`col3`) = 0 ) t1; For readability and performance reasons, I think this code could be refactored. But how?

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  • How to update a single table using trigger in MS SQL 2008

    - by Yakob-Jack
    I have a table PeroidicDeduction and the fields are ID(auto-increment),TotalDeduction(e.g.it can be loan),Paid(on which the deduction for each month),RemainingAmount, What I want is when every time I insert or update the table---RemainingAmount will get the value of TotalDeduction-SUM(Paid)....and writ the following trigger...but dosen't work for me CREATE TRIGGER dbo.UpdatePD ON PeroidicDedcution AFTER INSERT,UPDATE AS BEGIN UPDATE PeroidicDedcution SET REmaininAmoubnt=(SELECT TotalDeduction-(SELECT SUM(Paid) FROM PeroidicDeduction) FROM PeroidicDeduction) END NOTE: it is on a Single table

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  • Problems with real-valued deep belief networks (of RBMs)

    - by Junier
    I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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  • Excel Functions

    - by dwyane
    =MAX(SUM(A1:A5)) How do i incorporate the above formula into =IF( AND( $H$14<F22, F22<=($H$14+$H$15) ), $I$15, IF( AND( $H$14+$H$15<F22, F22<($H$14+$H$15+$H$16) ), $I$16, IF( AND( $H$14+$H$15+$H$16<F22, F22<=($H$14+$H$15+$H$16+$H$17) ), $I$17, $I$14 ) ) ) It keeps running a circular reference error. Help! The sum value shouldnt exceed 150. If exceed, then replace the cell with zero value.

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  • How to update a table using a select group by in a second one and itself as the data source in MySQL

    - by Jader Dias
    I can do this: SELECT t2.value + sum(t3.value) FROM tableA t2, tableB t3 WHERE t2.somekey = t3.somekey GROUP BY t3.somekey But how to do this? UPDATE tableA t1 SET speed = ( SELECT t2.value + sum(t3.value) FROM tableA t2, tableB t3 WHERE t2.somekey = t3.somekey AND t1.somekey = t3.somekey GROUP BY t3.somekey ) ; MySQL says it's illegal since you can't specify target table t1 for update in FROM clause.

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  • MySQL Multiple Subquery on same table

    - by user1444980
    I have a table of the following structure ID | Amount | Bank (1 or 2) ---+--------+------ 1 | 100000 | 1 2 | 256415 | 2 3 | 142535 | 1 1 | 214561 | 2 2 | 123456 | 1 1 | 987654 | 2 I want a result like this (from the same table): ID | sum(Bank 1) | sum(Bank 2) ---+-------------+------------ 1 | 100000 | 1202215 2 | 123456 | 256415 3 | 142535 | 0 What will be the easiest query to achieve this?

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  • python-nth perfect square

    - by kasyap
    Write a program that computes the sum of the logarithms of all the primes from 2 to some number n, and print out the sum of the logs of the primes, the number n, and the ratio of these two quantities. Test this for different values of n.

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  • How to use an adjacency matrix to determine which rows to 'pass' to a function in r?

    - by dubhousing
    New to R, and I have a long-ish question: I have a shapefile/map, and I'm aiming to calculate a certain index for every polygon in that map, based on attributes of that polygon and each polygon that neighbors it. I have an adjacency matrix -- which I think is the same as a "1st-order queen contiguity weights matrix", although I'm not sure -- that describes which polygons border which other polygons, e.g., POLYID A B C D E A 0 0 1 0 1 B 0 0 1 0 0 C 1 1 0 1 0 D 0 0 1 0 1 E 1 0 0 1 0 The above indicates, for instance, that polygons 'C' and 'E' adjoin polygon 'A'; polygon 'B' adjoins only polygon 'C', etc. The attribute table I have has one polygon per row: POLYID TOT L10K 10_15K 15_20K ... A 500 24 30 77 ... Where TOT, L10K, etc. are the variables I use to calculate an index. There are 525 polygons/rows in my data, so I'd like to use the adjacency matrix to determine which rows' attributes to incorporate into the calculation of the index of interest. For now, I can calculate the index when I subset the rows that correspond to one 'bundle' of neighboring polygons, and then use a loop (if it's of interest, I'm calculating the Centile Gap Index, a measure of local income segregation). E.g., subsetting the 'neighborhood' of the Detroit City Schools: Detroit <- UNSD00[c(142,150,164,221,226,236,295,327,157,177,178,364,233,373,418,424,449,451,487),] Then record the marginal column proportions and a running total: catprops <- vector() for(i in 4:19) { catprops[(i-3)]<-sum(Detroit[,i])/sum(Detroit[,3]) } catprops <- as.data.frame(catprops) catprops[,2]<-cumsum(catprops[,1]) Columns 4:19 are the necessary ones in the attribute table. Then I use the following code to calculate the index -- note that the loop has "i in 1:19" because the Detroit subset has 19 polygons. cgidistsum <- 0 for(i in 1:19) { pranks <- vector() for(j in 4:19) { if (Detroit[i,j]==0) pranks <- append(pranks,0) else if (j == 4) pranks <- append(pranks,seq(0,catprops[1,2],by=catprops[1,2]/Detroit[i,j])) else pranks <- append(pranks,seq(catprops[j-4,2],catprops[j-3,2],by=catprops[j-3,1]/Detroit[i,j])) } distpranks <- vector() distpranks<-abs(pranks-median(pranks)) cgidistsum <- cgidistsum + sum(distpranks) } cgi <- (.25-(cgidistsum/sum(Detroit[,3])))/.25 My apologies if I've provided more information than is necessary. I would really like to exploit the adjacency matrix in order to calculate the CGI for each 'bundle' of these rows. If you happen to know how I could started with this, that would be great. and my apologies for any novice mistakes, I'm new to R!

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  • passing back answers in prolog

    - by AhmadAssaf
    i have this code than runs perfectly .. returns a true .. when tracing the values are ok .. but its not returning back the answer .. it acts strangely when it ends and always return empty list .. uninstantiated variable .. test :- extend(4,12,[4,3,1,2],[[1,5],[3,4],[6]],_ExtendedBins). %printing basic information about the extend(NumBins,Capacity,RemainingNumbers,BinsSoFar,_ExtendedBins) :- getNumberofBins(BinsSoFar,NumberOfBins), msort(RemainingNumbers,SortedRemaining),nl, format("Current Number of Bins is :~w\n",[NumberOfBins]), format("Allowed Capacity is :~w\n",[Capacity]), format("maximum limit in bin is :~w\n",[NumBins]), format("Trying to fit :~w\n\n",[SortedRemaining]), format("Possible Solutions :\n\n"), fitElements(NumBins,NumberOfBins, Capacity,SortedRemaining,BinsSoFar,[]). %this is were the creation for possibilities will start %will check first if the number of bins allowed is less than then %we create a new list with all the possible combinations %after that we start matching to other bins with capacity constraint fitElements(NumBins,NumberOfBins, Capacity,RemainingNumbers,Bins,ExtendedBins) :- ( NumberOfBins < NumBins -> print('Creating new set: '); print('Sorry, Cannot create New Sets')), createNewList(Capacity,RemainingNumbers,Bins,ExtendedBins). createNewList(Capacity,RemainingNumbers,Bins,ExtendedBins) :- createNewList(Capacity,RemainingNumbers,Bins,[],ExtendedBins), print(ExtendedBins). createNewList(0,Bins,Bins,ExtendedBins,ExtendedBins). createNewList(_,[],_,ExtendedBins,ExtendedBins). createNewList(Capacity,[Element|Rest],Bins,Temp,ExtendedBins) :- conjunct_to_list(Element,ListedElement), append(ListedElement,Temp,NewList), sumlist(NewList,Sum), (Sum =< Capacity, append(ListedElement,ExtendedBins,Result); Capacity = 0), createNewList(Capacity,Rest,Bins,NewList,Result). fit(0,[],ExtendedBins,ExtendedBins). fit(Capacity,[Element|Rest],Bin,ExtendedBins) :- conjunct_to_list(Element,Listed), append(Listed,Bin,NewBin), sumlist(NewBin,Sum), (Sum =< Capacity -> fit(Capacity,Rest,NewBin,ExtendedBins); Capacity = 0, append(NewBin,ExtendedBins,NewExtendedBins), print(NewExtendedBins), fit(0,[],NewBin,ExtendedBins)). %get the number of bins provided getNumberofBins(List,NumberOfBins) :- getNumberofBins(List,0,NumberOfBins). getNumberofBins([],NumberOfBins,NumberOfBins). getNumberofBins([_List|Rest],TempCount,NumberOfBins) :- NewCount is TempCount + 1, %calculate the count getNumberofBins(Rest,NewCount,NumberOfBins). %recursive call %Convert set of terms into a list - used when needed to append conjunct_to_list((A,B), L) :- !, conjunct_to_list(A, L0), conjunct_to_list(B, L1), append(L0, L1, L). conjunct_to_list(A, [A]). Greatly appreciate the help

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