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  • Linear Layout Issue at Runtime

    - by George
    Hi all, I am trying to build a layout dynamically which display some text and image for the most part, but has a series of buttons placed next to each other in the bottom. I have a linear layout that carries the text, another linear layout that carries the image. And yet another linear layout that carries the buttons that get created in a for loop. I have a main layout aligned vertical that adds the text, image and buttons layout, in that order. To finally generate something like this: Text .... Image ... Button1 Button2 Button3.... The problem is the number of buttons get decided at runtime, so if there are more than 4 buttons, the 5th button gets displayed really tiny. Also, when I tilt the phone, I get only the text and image showing, but no buttons coz the image covers the entire screen. Layoutting seems to be pretty complicated to me, any help is appreciated! Thanks George

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  • -moz-linear-gradient with PNG baclground over top

    - by Alex
    So Firefox supports gradient Backgrounds. Also supports multiple Background images.. So why does this not work?? background:-moz-linear-gradient(top, #5989bd,#336296), url(Active-Arrow.png) right center no-repeat; Also tried: background-color:-moz-linear-gradient(top, #5989bd,#336296); background:url(Active-Arrow.png) right center no-repeat; Can this be done??

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  • System of linear equations in C++?

    - by Archagon
    I need to solve a system of linear equations in my program. Is there a simple linear algebra library for C++, preferably comprised of no more than a few headers? I've been looking for nearly an hour, and all the ones I found require messing around with Linux, compiling DLLs in MinGW, etc. etc. etc. (I'm using Visual Studio 2008.)

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  • Optimal two variable linear regression SQL statement (censoring outliers)

    - by Dave Jarvis
    Problem Am looking to apply the y = mx + b equation (where m is SLOPE, b is INTERCEPT) to a data set, which is retrieved as shown in the SQL code. The values from the (MySQL) query are: SLOPE = 0.0276653965651912 INTERCEPT = -57.2338357550468 SQL Code SELECT ((sum(t.YEAR) * sum(t.AMOUNT)) - (count(1) * sum(t.YEAR * t.AMOUNT))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as SLOPE, ((sum( t.YEAR ) * sum( t.YEAR * t.AMOUNT )) - (sum( t.AMOUNT ) * sum(power(t.YEAR, 2)))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as INTERCEPT FROM (SELECT D.AMOUNT, Y.YEAR FROM CITY C, STATION S, YEAR_REF Y, MONTH_REF M, DAILY D WHERE -- For a specific city ... -- C.ID = 8590 AND -- Find all the stations within a 15 unit radius ... -- SQRT( POW( C.LATITUDE - S.LATITUDE, 2 ) + POW( C.LONGITUDE - S.LONGITUDE, 2 ) ) <15 AND -- Gather all known years for that station ... -- S.STATION_DISTRICT_ID = Y.STATION_DISTRICT_ID AND -- The data before 1900 is shaky; insufficient after 2009. -- Y.YEAR BETWEEN 1900 AND 2009 AND -- Filtered by all known months ... -- M.YEAR_REF_ID = Y.ID AND -- Whittled down by category ... -- M.CATEGORY_ID = '001' AND -- Into the valid daily climate data. -- M.ID = D.MONTH_REF_ID AND D.DAILY_FLAG_ID <> 'M' GROUP BY Y.YEAR ORDER BY Y.YEAR ) t Data The data is visualized here (with five outliers highlighted): Questions How do I return the y value against all rows without repeating the same query to collect and collate the data? That is, how do I "reuse" the list of t values? How would you change the query to eliminate outliers (at an 85% confidence interval)? The following results (to calculate the start and end points of the line) appear incorrect. Why are the results off by ~10 degrees (e.g., outliers skewing the data)? (1900 * 0.0276653965651912) + (-57.2338357550468) = -4.66958228 (2009 * 0.0276653965651912) + (-57.2338357550468) = -1.65405406 I would have expected the 1900 result to be around 10 (not -4.67) and the 2009 result to be around 11.50 (not -1.65). Thank you!

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  • Optimal two variable linear regression SQL statement

    - by Dave Jarvis
    Problem Am looking to apply the y = mx + b equation (where m is SLOPE, b is INTERCEPT) to a data set, which is retrieved as shown in the SQL code. The values from the (MySQL) query are: SLOPE = 0.0276653965651912 INTERCEPT = -57.2338357550468 SQL Code SELECT ((sum(t.YEAR) * sum(t.AMOUNT)) - (count(1) * sum(t.YEAR * t.AMOUNT))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as SLOPE, ((sum( t.YEAR ) * sum( t.YEAR * t.AMOUNT )) - (sum( t.AMOUNT ) * sum(power(t.YEAR, 2)))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as INTERCEPT FROM (SELECT D.AMOUNT, Y.YEAR FROM CITY C, STATION S, YEAR_REF Y, MONTH_REF M, DAILY D WHERE -- For a specific city ... -- C.ID = 8590 AND -- Find all the stations within a 5 unit radius ... -- SQRT( POW( C.LATITUDE - S.LATITUDE, 2 ) + POW( C.LONGITUDE - S.LONGITUDE, 2 ) ) <15 AND -- Gather all known years for that station ... -- S.STATION_DISTRICT_ID = Y.STATION_DISTRICT_ID AND -- The data before 1900 is shaky; and insufficient after 2009. -- Y.YEAR BETWEEN 1900 AND 2009 AND -- Filtered by all known months ... -- M.YEAR_REF_ID = Y.ID AND -- Whittled down by category ... -- M.CATEGORY_ID = '001' AND -- Into the valid daily climate data. -- M.ID = D.MONTH_REF_ID AND D.DAILY_FLAG_ID <> 'M' GROUP BY Y.YEAR ORDER BY Y.YEAR ) t Data The data is visualized here: Questions How do I return the y value against all rows without repeating the same query to collect and collate the data? That is, how do I "reuse" the list of t values? How would you change the query to eliminate outliers (at an 85% confidence interval)? The following results (to calculate the start and end points of the line) appear incorrect. Why are the results off by ~10 degrees (e.g., outliers skewing the data)? (1900 * 0.0276653965651912) + (-57.2338357550468) = -4.66958228 (2009 * 0.0276653965651912) + (-57.2338357550468) = -1.65405406 I would have expected the 1900 result to be around 10 (not -4.67) and the 2009 result to be around 11.50 (not -1.65). Thank you!

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  • Looping through covariates in regression using R

    - by Kyle Peyton
    I'm trying to run 96 regressions and save the results as 96 different objects. To complicate things, I want the subscript on one of the covariates in the model to also change 96 times. I've almost solved the problem but I've unfortunately hit a wall. The code so far is, for(i in 1:96){ assign(paste("z.out", i,sep=""), lm(rMonExp_EGM~ TE_i+ Month2+Month3+Month4+Month5+Month6+Month7+Month8+Month9+ Month10+Month11+Month12+Yrs_minus_2004 + as.factor(LGA),data=Pokies)) } This works on the object creation side (e.g. I have z.out1 - z.out96) but I can't seem to get the subscript on the covariate to change as well. I have 96 variables called TE_1, TE_2 ... TE_96 in the dataset. As such, the subscript on TE_, the "i" needs to change to correspond to each of the objects I create. That is, z.out1 should hold the results from this model: z.out1 <- lm(rMonExp_EGM~ TE_1 + Month2+Month3+Month4+Month5+Month6+Month7+Month8+Month9+ Month10+Month11+Month12+Yrs_minus_2004 + as.factor(LGA),data=Pokies) And z.out96 should be: z.out96 <- lm(rMonExp_EGM~ TE_96+ Month2+Month3+Month4+Month5+Month6+Month7+Month8+Month9+ Month10+Month11+Month12+Yrs_minus_2004 + as.factor(LGA),data=Pokies) Hopefully this makes sense. I'm grateful for any tips/advice. cheers, kyle

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  • Which linear programming package should I use for high numbers of constraints and "warm starts"

    - by davidsd
    I have a "continuous" linear programming problem that involves maximizing a linear function over a curved convex space. In typical LP problems, the convex space is a polytope, but in this case the convex space is piecewise curved -- that is, it has faces, edges, and vertices, but the edges aren't straight and the faces aren't flat. Instead of being specified by a finite number of linear inequalities, I have a continuously infinite number. I'm currently dealing with this by approximating the surface by a polytope, which means discretizing the continuously infinite constraints into a very large finite number of constraints. I'm also in the situation where I'd like to know how the answer changes under small perturbations to the underlying problem. Thus, I'd like to be able to supply an initial condition to the solver based on a nearby solution. I believe this capability is called a "warm start." Can someone help me distinguish between the various LP packages out there? I'm not so concerned with user-friendliness as speed (for large numbers of constraints), high-precision arithmetic, and warm starts. Thanks!

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  • algorithm to combine data for linear fit?

    - by BoldlyBold
    I'm not sure if this is the best place to ask this, but you guys have been helpful with plenty of my CS homework in the past so I figure I'll give it a shot. I'm looking for an algorithm to blindly combine several dependent variables into an index that produces the best linear fit with an external variable. Basically, it would combine the dependent variables using different mathematical operators, include or not include each one, etc. until an index is developed that best correlates with my external variable. Has anyone seen/heard of something like this before? Even if you could point me in the right direction or to the right place to ask, I would appreciate it. Thanks.

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  • Why is a linked list implementation considered linear?

    - by VeeKay
    My apologies for asking such a simple question. Instead of posting such basic question in SO, I felt that this is more apt a question here. I tried finding an answer for this but none of them are logically appealing or convincing to my understanding. Typically, computer memory is always linear. So is the term non linear used for a data structure in a logical sense? If so, to logically achieve non linearity in a linear computer memory, we use pointers. Right? In that case, if pointers are virtual implementations for achieving non linearity, Why would a data structure like linked list be considered linear if in reality the nodes are never physically adjacent?

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  • Haskell Linear Algebra Matrix Library for Arbitrary Element Types

    - by Johannes Weiß
    I'm looking for a Haskell linear algebra library that has the following features: Matrix multiplication Matrix addition Matrix transposition Rank calculation Matrix inversion is a plus and has the following properties: arbitrary element (scalar) types (in particular element types that are not Storable instances). My elements are an instance of Num, additionally the multiplicative inverse can be calculated. The elements mathematically form a finite field (??2256). That should be enough to implement the features mentioned above. arbitrary matrix sizes (I'll probably need something like 100x100, but the matrix sizes will depend on the user's input so it should not be limited by anything else but the memory or the computational power available) as fast as possible, but I'm aware that a library for arbitrary elements will probably not perform like a C/Fortran library that does the work (interfaced via FFI) because of the indirection of arbitrary (non Int, Double or similar) types. At least one pointer gets dereferenced when an element is touched (written in Haskell, this is not a real requirement for me, but since my elements are no Storable instances the library has to be written in Haskell) I already tried very hard and evaluated everything that looked promising (most of the libraries on Hackage directly state that they wont work for me). In particular I wrote test code using: hmatrix, assumes Storable elements Vec, but the documentation states: Low Dimension : Although the dimensionality is limited only by what GHC will handle, the library is meant for 2,3 and 4 dimensions. For general linear algebra, check out the excellent hmatrix library and blas bindings I looked into the code and the documentation of many more libraries but nothing seems to suit my needs :-(. Update Since there seems to be nothing, I started a project on GitHub which aims to develop such a library. The current state is very minimalistic, not optimized for speed at all and only the most basic functions have tests and therefore should work. But should you be interested in using or helping out developing it: Contact me (you'll find my mail address on my web site) or send pull requests.

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  • how to Compute the average probe length for success and failure - Linear probe (Hash Tables)

    - by fang_dejavu
    hi everyone, I'm doing an assignment for my Data Structures class. we were asked to to study linear probing with load factors of .1, .2 , .3, ...., and .9. The formula for testing is: The average probe length using linear probing is roughly Success-- ( 1 + 1/(1-L)**2)/2 or Failure-- (1+1(1-L))/2. we are required to find the theoretical using the formula above which I did(just plug the load factor in the formula), then we have to calculate the empirical (which I not quite sure how to do). here is the rest of the requirements **For each load factor, 10,000 randomly generated positive ints between 1 and 50000 (inclusive) will be inserted into a table of the "right" size, where "right" is strictly based upon the load factor you are testing. Repeats are allowed. Be sure that your formula for randomly generated ints is correct. There is a class called Random in java.util. USE it! After a table of the right (based upon L) size is loaded with 10,000 ints, do 100 searches of newly generated random ints from the range of 1 to 50000. Compute the average probe length for each of the two formulas and indicate the denominators used in each calculationSo, for example, each test for a .5 load would have a table of size approximately 20,000 (adjusted to be prime) and similarly each test for a .9 load would have a table of approximate size 10,000/.9 (again adjusted to be prime). The program should run displaying the various load factors tested, the average probe for each search (the two denominators used to compute the averages will add to 100), and the theoretical answers using the formula above. .** how do I calculate the empirical success?

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  • how does linear probing handle this?

    - by Weadadada Awda
    • the hash function: h(x) = | 2x + 5 | mod M • a bucket array of capacity N • a set of objects with keys: 12, 44, 13, 88, 23, 94, 11, 39, 20, 16, 5 (to input from left to right) 4.a [5 pts] Write the hash table where M=N=11 and collisions are handled using linear probing. So I got up to here x x x x x 44 88 12 23 13 94 but the next variable should go after the 94 now, (the 11) but does it start from the beggining or what? thx

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  • Solving linear system over integers with numpy

    - by A. R. S.
    I'm trying to solve an overdetermined linear system of equations with numpy. Currently, I'm doing something like this (as a simple example): a = np.array([[1,0], [0,1], [-1,1]]) b = np.array([1,1,0]) print np.linalg.lstsq(a,b)[0] [ 1. 1.] This works, but uses floats. Is there any way to solve the system over integers only? I've tried something along the lines of print map(int, np.linalg.lstsq(a,b)[0]) [0, 1] in order to convert the solution to an array of ints, expecting [1, 1], but clearly I'm missing something. Could anyone point me in the right direction?

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  • average case running time of linear search algorithm

    - by Brahadeesh
    Hi all. I am trying to derive the average case running time for deterministic linear search algorithm. The algorithm searches an element x in an unsorted array A in the order A[1], A[2], A[3]...A[n]. It stops when it finds the element x or proceeds until it reaches the end of the array. I searched on wikipedia and the answer given was (n+1)/(k+1) where k is the number of times x is present in the array. I approached in another way and am getting a different answer. Can anyone please give me the correct proof and also let me know whats wrong with my method? E(T)= 1*P(1) + 2*P(2) + 3*P(3) ....+ n*P(n) where P(i) is the probability that the algorithm runs for 'i' time (i.e. compares 'i' elements). P(i)= (n-i)C(k-1) * (n-k)! / n! Here, (n-i)C(k-1) is (n-i) Choose (k-1). As the algorithm has reached the ith step, the rest of k-1 x's must be in the last n-i elements. Hence (n-i)C(k-i). (n-k)! is the total number of ways of arranging the rest non x numbers, and n! is the total number of ways of arranging the n elements in the array. I am not getting (n+1)/(k+1) on simplifying.

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  • linear combinations in python/numpy

    - by nmaxwell
    greetings, I'm not sure if this is a dumb question or not. Lets say I have 3 numpy arrays, A1,A2,A3, and 3 floats, c1,c2,c3 and I'd like to evaluate B = A1*c1+ A2*c2+ A3*c3 will numpy compute this as for example, E1 = A1*c1 E2 = A2*c2 E3 = A3*c3 D1 = E1+E2 B = D1+E3 or is it more clever than that? In c++ I had a neat way to abstract this kind of operation. I defined series of general 'LC' template functions, LC for linear combination like: template<class T,class D> void LC( T & R, T & L0,D C0, T & L1,D C1, T & L2,D C2) { R = L0*C0 +L1*C1 +L2*C2; } and then specialized this for various types, so for instance, for an array the code looked like for (int i=0; i<L0.length; i++) R.array[i] = L0.array[i]*C0 + L1.array[i]*C1 + L2.array[i]*C2; thus avoiding having to create new intermediate arrays. This may look messy but it worked really well. I could do something similar in python, but I'm not sure if its nescesary. Thanks in advance for any insight. -nick

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  • Triangulation & Direct linear transform

    - by srand
    Following Hartley/Zisserman's Multiview Geometery, Algorithm 12: The optimal triangulation method (p318), I got the corresponding image points xhat1 and xhat2 (step 10). In step 11, one needs to compute the 3D point Xhat. One such method is Direct Linear Transform (DLT), mentioned in 12.2 (p312) and 4.1 (p88). The homogenous method (DLT), p312-313, states that it finds a solution as the unit singular vector corresponding to the smallest singular value of A, thus, A = [xhat1(1) * P1(3,:)' - P1(1,:)' ; xhat1(2) * P1(3,:)' - P1(2,:)' ; xhat2(1) * P2(3,:)' - P2(1,:)' ; xhat2(2) * P2(3,:)' - P2(2,:)' ]; [Ua Ea Va] = svd(A); Xhat = Va(:,end); plot3(Xhat(1),Xhat(2),Xhat(3), 'r.'); However, A is a 16x1 matrix, resulting in a Va that is 1x1. What am I doing wrong (and a fix) in getting the 3D point? For what its worth sample data: xhat1 = 1.0e+009 * 4.9973 -0.2024 0.0027 xhat2 = 1.0e+011 * 2.0729 2.6624 0.0098 P1 = 699.6674 0 392.1170 0 0 701.6136 304.0275 0 0 0 1.0000 0 P2 = 1.0e+003 * -0.7845 0.0508 -0.1592 1.8619 -0.1379 0.7338 0.1649 0.6825 -0.0006 0.0001 0.0008 0.0010 A = <- my computation 1.0e+011 * -0.0000 0 0.0500 0 0 -0.0000 -0.0020 0 -1.3369 0.2563 1.5634 2.0729 -1.7170 0.3292 2.0079 2.6624

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  • Java - Using Linear Coordinates to Check Against AI [closed]

    - by Oliver Jones
    I'm working on some artificial intelligence, and I want my AI not to run into given coordinates as these are references of a wall/boundary. To begin with, every time my AI hits a wall, it makes a reference to that position (x,y). When it hits the same wall three times, it uses linear check points to 'imagine' there is a wall going through these coordinates. I want to now prevent my AI from going into that wall again. To detect if my coordinates make a straight line, i use: private boolean collinear(double x1, double y1, double x2, double y2, double x3, double y3) { return (y1 - y2) * (x1 - x3) == (y1 - y3) * (x1 - x2); } This returns true is the given points are linear to one another. So my problems are: How do I determine whether my robot is approaching the wall from its current trajectory? Instead of Java 'imagining' theres a line from 1, to 3. But to 'imagine' a line all the way through these linear coordinantes, until infinity (or close). I have a feeling this is going to require some confusing trigonometry? (REPOST: http://stackoverflow.com/questions/13542592/java-using-linear-coordinates-to-check-against-ai)

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  • Efficient algorithm to generate all solutions of a linear diophantine equation with ai=1

    - by Ben
    I am trying to generate all the solutions for the following equations for a given H. With H=4 : 1) ALL solutions for x_1 + x_2 + x_3 + x_4 =4 2) ALL solutions for x_1 + x_2 + x_3 = 4 3) ALL solutions for x_1 + x_2 = 4 4) ALL solutions for x_1 =4 For my problem, there are always 4 equations to solve (independently from the others). There are a total of 2^(H-1) solutions. For the previous one, here are the solutions : 1) 1 1 1 1 2) 1 1 2 and 1 2 1 and 2 1 1 3) 1 3 and 3 1 and 2 2 4) 4 Here is an R algorithm which solve the problem. library(gtools) H<-4 solutions<-NULL for(i in seq(H)) { res<-permutations(H-i+1,i,repeats.allowed=T) resum<-apply(res,1,sum) id<-which(resum==H) print(paste("solutions with ",i," variables",sep="")) print(res[id,]) } However, this algorithm makes more calculations than needed. I am sure it is possible to go faster. By that, I mean not generating the permutations for which the sums is H Any idea of a better algorithm for a given H ?

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  • Recommended library for linear programming in .Net?

    - by tbone
    Can anyone recommend a library - free, or commercial but affordable ( There are some listed here: http://en.wikipedia.org/wiki/Linear_programming#Solvers_and_scripting_.28programming.29_languages I am just starting out with LP and hope someone can recommend something. I am trying to basically minimize pricing for cell phone subscription services.

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  • Sparse linear program solver

    - by Jacob
    This great SO answer points to a good sparse solver, but I've got constraints on x (for Ax = b) such that each element in x is >=0 an <=N. The first thing which comes to mind is an LP solver for large sparse matrices. Any ideas/recommendations?

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  • O'Reilly book clarification on 2d linear system

    - by Eric
    The Oreilly book "Learning openCV" states at page 356 : Quote Before we get totally lost, let’s consider a particular realistic situation of taking measurements on a car driving in a parking lot. We might imagine that the state of the car could be summarized by two position variables, x and y, and two velocities, vx and vy. These four variables would be the elements of the state vector xk. Th is suggests that the correct form for F is: x = [ x; y; vx; vy; ]k F = [ 1, 0, dt, 0; 0, 1, 0, dt; 0, 0, 1, 0; 0, 0, 0, 1; ] It seems natural to put 'dt' just there in the F matrix but I just don't get why. What if I have a n states system, how would I spray some "dt" in the F matrix?

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  • Regression testing with Selenium GRID

    - by Ben Adderson
    A lot of software teams out there are tasked with supporting and maintaining systems that have grown organically over time, and the web team here at Red Gate is no exception. We're about to embark on our first significant refactoring endeavour for some time, and as such its clearly paramount that the code be tested thoroughly for regressions. Unfortunately we currently find ourselves with a codebase that isn't very testable - the three layers (database, business logic and UI) are currently tightly coupled. This leaves us with the unfortunate problem that, in order to confidently refactor the code, we need unit tests. But in order to write unit tests, we need to refactor the code :S To try and ease the initial pain of decoupling these layers, I've been looking into the idea of using UI automation to provide a sort of system-level regression test suite. The idea being that these tests can help us identify regressions whilst we work towards a more testable codebase, at which point the more traditional combination of unit and integration tests can take over. Ending up with a strong battery of UI tests is also a nice bonus :) Following on from my previous posts (here, here and here) I knew I wanted to use Selenium. I also figured that this would be a good excuse to put my xUnit [Browser] attribute to good use. Pretty quickly, I had a raft of tests that looked like the following (this particular example uses Reflector Pro). In a nut shell the test traverses our shopping cart and, for a particular combination of number of users and months of support, checks that the price calculations all come up with the correct values. [BrowserTheory] [Browser(Browsers.Firefox3_6, "http://www.red-gate.com")] public void Purchase1UserLicenceNoSupport(SeleniumProvider seleniumProvider) {     //Arrange     _browser = seleniumProvider.GetBrowser();     _browser.Open("http://www.red-gate.com/dynamic/shoppingCart/ProductOption.aspx?Product=ReflectorPro");                  //Act     _browser = ShoppingCartHelpers.TraverseShoppingCart(_browser, 1, 0, ".NET Reflector Pro");     //Assert     var priceResult = PriceHelpers.GetNewPurchasePrice(db, "ReflectorPro", 1, 0, Currencies.Euros);         Assert.Equal(priceResult.Price, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl01_Price"));     Assert.Equal(priceResult.Tax, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Tax"));     Assert.Equal(priceResult.Total, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Total")); } These tests are pretty concise, with much of the common code in the TraverseShoppingCart() and GetNewPurchasePrice() methods. The (inevitable) problem arose when it came to execute these tests en masse. Selenium is a very slick tool, but it can't mask the fact that UI automation is very slow. To give you an idea, the set of cases that covers all of our products, for all combinations of users and support, came to 372 tests (for now only considering purchases in dollars). In the world of automated integration tests, that's a very manageable number. For unit tests, it's a trifle. However for UI automation, those 372 tests were taking just over two hours to run. Two hours may not sound like a lot, but those cases only cover one of the three currencies we deal with, and only one of the many different ways our systems can be asked to calculate a price. It was already pretty clear at this point that in order for this approach to be viable, I was going to have to find a way to speed things up. Up to this point I had been using Selenium Remote Control to automate Firefox, as this was the approach I had used previously and it had worked well. Fortunately,  the guys at SeleniumHQ also maintain a tool for executing multiple Selenium RC tests in parallel: Selenium Grid. Selenium Grid uses a central 'hub' to handle allocation of Selenium tests to individual RCs. The Remote Controls simply register themselves with the hub when they start, and then wait to be assigned work. The (for me) really clever part is that, as far as the client driver library is concerned, the grid hub looks exactly the same as a vanilla remote control. To create a new browser session against Selenium RC, the following C# code suffices: new DefaultSelenium("localhost", 4444, "*firefox", "http://www.red-gate.com"); This assumes that the RC is running on the local machine, and is listening on port 4444 (the default). Assuming the hub is running on your local machine, then to create a browser session in Selenium Grid, via the hub rather than directly against the control, the code is exactly the same! Behind the scenes, the hub will take this request and hand it off to one of the registered RCs that provides the "*firefox" execution environment. It will then pass all communications back and forth between the test runner and the remote control transparently. This makes running existing RC tests on a Selenium Grid a piece of cake, as the developers intended. For a more detailed description of exactly how Selenium Grid works, see this page. Once I had a test environment capable of running multiple tests in parallel, I needed a test runner capable of doing the same. Unfortunately, this does not currently exist for xUnit (boo!). MbUnit on the other hand, has the concept of concurrent execution baked right into the framework. So after swapping out my assembly references, and fixing up the resulting mismatches in assertions, my example test now looks like this: [Test] public void Purchase1UserLicenceNoSupport() {    //Arrange    ISelenium browser = BrowserHelpers.GetBrowser();    var db = DbHelpers.GetWebsiteDBDataContext();    browser.Start();    browser.Open("http://www.red-gate.com/dynamic/shoppingCart/ProductOption.aspx?Product=ReflectorPro");                 //Act     browser = ShoppingCartHelpers.TraverseShoppingCart(browser, 1, 0, ".NET Reflector Pro");    var priceResult = PriceHelpers.GetNewPurchasePrice(db, "ReflectorPro", 1, 0, Currencies.Euros);    //Assert     Assert.AreEqual(priceResult.Price, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl01_Price"));     Assert.AreEqual(priceResult.Tax, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Tax"));     Assert.AreEqual(priceResult.Total, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Total")); } This is pretty much the same as the xUnit version. The exceptions are that the attributes have changed,  the //Arrange phase now has to handle setting up the ISelenium object, as the attribute that previously did this has gone away, and the test now sets up its own database connection. Previously I was using a shared database connection, but this approach becomes more complicated when tests are being executed concurrently. To avoid complexity each test has its own connection, which it is responsible for closing. For the sake of readability, I snipped out the code that closes the browser session and the db connection at the end of the test. With all that done, there was only one more step required before the tests would execute concurrently. It is necessary to tell the test runner which tests are eligible to run in parallel, via the [Parallelizable] attribute. This can be done at the test, fixture or assembly level. Since I wanted to run all tests concurrently, I marked mine at the assembly level in the AssemblyInfo.cs using the following: [assembly: DegreeOfParallelism(3)] [assembly: Parallelizable(TestScope.All)] The second attribute marks all tests in the assembly as [Parallelizable], whilst the first tells the test runner how many concurrent threads to use when executing the tests. I set mine to three since I was using 3 RCs in separate VMs. With everything now in place, I fired up the Icarus* test runner that comes with MbUnit. Executing my 372 tests three at a time instead of one at a time reduced the running time from 2 hours 10 minutes, to 55 minutes, that's an improvement of about 58%! I'd like to have seen an improvement of 66%, but I can understand that either inefficiencies in the hub code, my test environment or the test runner code (or some combination of all three most likely) contributes to a slightly diminished improvement. That said, I'd love to hear about any experience you have in upping this efficiency. Ultimately though, it was a saving that was most definitely worth having. It makes regression testing via UI automation a far more plausible prospect. The other obvious point to make is that this approach scales far better than executing tests serially. So if ever we need to improve performance, we just register additional RC's with the hub, and up the DegreeOfParallelism. *This was just my personal preference for a GUI runner. The MbUnit/Gallio installer also provides a command line runner, a TestDriven.net runner, and a Resharper 4.5 runner. For now at least, Resharper 5 isn't supported.

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  • Conditionally colour data points outside of confidence bands in R

    - by D W
    I need to colour datapoints that are outside of the the confidence bands on the plot below differently from those within the bands. Should I add a separate column to my dataset to record whether the data points are within the confidence bands? Can you provide an example please? Example dataset: ## Dataset from http://www.apsnet.org/education/advancedplantpath/topics/RModules/doc1/04_Linear_regression.html ## Disease severity as a function of temperature # Response variable, disease severity diseasesev<-c(1.9,3.1,3.3,4.8,5.3,6.1,6.4,7.6,9.8,12.4) # Predictor variable, (Centigrade) temperature<-c(2,1,5,5,20,20,23,10,30,25) ## For convenience, the data may be formatted into a dataframe severity <- as.data.frame(cbind(diseasesev,temperature)) ## Fit a linear model for the data and summarize the output from function lm() severity.lm <- lm(diseasesev~temperature,data=severity) jpeg('~/Desktop/test1.jpg') # Take a look at the data plot( diseasesev~temperature, data=severity, xlab="Temperature", ylab="% Disease Severity", pch=16, pty="s", xlim=c(0,30), ylim=c(0,30) ) title(main="Graph of % Disease Severity vs Temperature") par(new=TRUE) # don't start a new plot ## Get datapoints predicted by best fit line and confidence bands ## at every 0.01 interval xRange=data.frame(temperature=seq(min(temperature),max(temperature),0.01)) pred4plot <- predict( lm(diseasesev~temperature), xRange, level=0.95, interval="confidence" ) ## Plot lines derrived from best fit line and confidence band datapoints matplot( xRange, pred4plot, lty=c(1,2,2), #vector of line types and widths type="l", #type of plot for each column of y xlim=c(0,30), ylim=c(0,30), xlab="", ylab="" )

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