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  • how to use the results of a method in another method in a different class initialization at vb.net

    - by singgih
    I have a class which has the following methods: Public Function rumusbuffer () As Decimal buffer = (ukuranblok - pntrblok) / (ukrnrecord + pntrblok) Return buffer End Function Public Function rumusW () As Decimal interblock = pntrblok + ((pntrblok + intrblok) / buffer) Return interblock End Function how can I make the buffer can be used on its function rumusw but different forms so that her class should be re-initialization .. but the calculation method can rumusbuffer rumusw d use in the method?

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  • Matrix Multiplication with C++ AMP

    - by Daniel Moth
    As part of our API tour of C++ AMP, we looked recently at parallel_for_each. I ended that post by saying we would revisit parallel_for_each after introducing array and array_view. Now is the time, so this is part 2 of parallel_for_each, and also a post that brings together everything we've seen until now. The code for serial and accelerated Consider a naïve (or brute force) serial implementation of matrix multiplication  0: void MatrixMultiplySerial(std::vector<float>& vC, const std::vector<float>& vA, const std::vector<float>& vB, int M, int N, int W) 1: { 2: for (int row = 0; row < M; row++) 3: { 4: for (int col = 0; col < N; col++) 5: { 6: float sum = 0.0f; 7: for(int i = 0; i < W; i++) 8: sum += vA[row * W + i] * vB[i * N + col]; 9: vC[row * N + col] = sum; 10: } 11: } 12: } We notice that each loop iteration is independent from each other and so can be parallelized. If in addition we have really large amounts of data, then this is a good candidate to offload to an accelerator. First, I'll just show you an example of what that code may look like with C++ AMP, and then we'll analyze it. It is assumed that you included at the top of your file #include <amp.h> 13: void MatrixMultiplySimple(std::vector<float>& vC, const std::vector<float>& vA, const std::vector<float>& vB, int M, int N, int W) 14: { 15: concurrency::array_view<const float,2> a(M, W, vA); 16: concurrency::array_view<const float,2> b(W, N, vB); 17: concurrency::array_view<concurrency::writeonly<float>,2> c(M, N, vC); 18: concurrency::parallel_for_each(c.grid, 19: [=](concurrency::index<2> idx) restrict(direct3d) { 20: int row = idx[0]; int col = idx[1]; 21: float sum = 0.0f; 22: for(int i = 0; i < W; i++) 23: sum += a(row, i) * b(i, col); 24: c[idx] = sum; 25: }); 26: } First a visual comparison, just for fun: The beginning and end is the same, i.e. lines 0,1,12 are identical to lines 13,14,26. The double nested loop (lines 2,3,4,5 and 10,11) has been transformed into a parallel_for_each call (18,19,20 and 25). The core algorithm (lines 6,7,8,9) is essentially the same (lines 21,22,23,24). We have extra lines in the C++ AMP version (15,16,17). Now let's dig in deeper. Using array_view and extent When we decided to convert this function to run on an accelerator, we knew we couldn't use the std::vector objects in the restrict(direct3d) function. So we had a choice of copying the data to the the concurrency::array<T,N> object, or wrapping the vector container (and hence its data) with a concurrency::array_view<T,N> object from amp.h – here we used the latter (lines 15,16,17). Now we can access the same data through the array_view objects (a and b) instead of the vector objects (vA and vB), and the added benefit is that we can capture the array_view objects in the lambda (lines 19-25) that we pass to the parallel_for_each call (line 18) and the data will get copied on demand for us to the accelerator. Note that line 15 (and ditto for 16 and 17) could have been written as two lines instead of one: extent<2> e(M, W); array_view<const float, 2> a(e, vA); In other words, we could have explicitly created the extent object instead of letting the array_view create it for us under the covers through the constructor overload we chose. The benefit of the extent object in this instance is that we can express that the data is indeed two dimensional, i.e a matrix. When we were using a vector object we could not do that, and instead we had to track via additional unrelated variables the dimensions of the matrix (i.e. with the integers M and W) – aren't you loving C++ AMP already? Note that the const before the float when creating a and b, will result in the underling data only being copied to the accelerator and not be copied back – a nice optimization. A similar thing is happening on line 17 when creating array_view c, where we have indicated that we do not need to copy the data to the accelerator, only copy it back. The kernel dispatch On line 18 we make the call to the C++ AMP entry point (parallel_for_each) to invoke our parallel loop or, as some may say, dispatch our kernel. The first argument we need to pass describes how many threads we want for this computation. For this algorithm we decided that we want exactly the same number of threads as the number of elements in the output matrix, i.e. in array_view c which will eventually update the vector vC. So each thread will compute exactly one result. Since the elements in c are organized in a 2-dimensional manner we can organize our threads in a two-dimensional manner too. We don't have to think too much about how to create the first argument (a grid) since the array_view object helpfully exposes that as a property. Note that instead of c.grid we could have written grid<2>(c.extent) or grid<2>(extent<2>(M, N)) – the result is the same in that we have specified M*N threads to execute our lambda. The second argument is a restrict(direct3d) lambda that accepts an index object. Since we elected to use a two-dimensional extent as the first argument of parallel_for_each, the index will also be two-dimensional and as covered in the previous posts it represents the thread ID, which in our case maps perfectly to the index of each element in the resulting array_view. The kernel itself The lambda body (lines 20-24), or as some may say, the kernel, is the code that will actually execute on the accelerator. It will be called by M*N threads and we can use those threads to index into the two input array_views (a,b) and write results into the output array_view ( c ). The four lines (21-24) are essentially identical to the four lines of the serial algorithm (6-9). The only difference is how we index into a,b,c versus how we index into vA,vB,vC. The code we wrote with C++ AMP is much nicer in its indexing, because the dimensionality is a first class concept, so you don't have to do funny arithmetic calculating the index of where the next row starts, which you have to do when working with vectors directly (since they store all the data in a flat manner). I skipped over describing line 20. Note that we didn't really need to read the two components of the index into temporary local variables. This mostly reflects my personal choice, in some algorithms to break down the index into local variables with names that make sense for the algorithm, i.e. in this case row and col. In other cases it may i,j,k or x,y,z, or M,N or whatever. Also note that we could have written line 24 as: c(idx[0], idx[1])=sum  or  c(row, col)=sum instead of the simpler c[idx]=sum Targeting a specific accelerator Imagine that we had more than one hardware accelerator on a system and we wanted to pick a specific one to execute this parallel loop on. So there would be some code like this anywhere before line 18: vector<accelerator> accs = MyFunctionThatChoosesSuitableAccelerators(); accelerator acc = accs[0]; …and then we would modify line 18 so we would be calling another overload of parallel_for_each that accepts an accelerator_view as the first argument, so it would become: concurrency::parallel_for_each(acc.default_view, c.grid, ...and the rest of your code remains the same… how simple is that? Comments about this post by Daniel Moth welcome at the original blog.

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  • C++ Pointers, objects, etc

    - by Zeee
    It may be a bit confusing, but... Let's say I have a vector type in a class to store objects, something like vector, and I have methods on my class that will later return Operators from this vector. Now if any of my methods receives an Operator, will I have any trouble to insert it directly into the vector? Or should I use the copy constructor to create a new Operator and put this new one on the vector?

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  • code-style: Is inline initialization of JS objects ok?

    - by michael
    I often find myself using inline initialization (see example below), especially in a switch statement when I don't know which case loop will hit. I find it easier to read than if statements. But is this good practice or will it incur side-effects or a performance hit? for (var i in array) { var o = o ? o : {}; // init object if it doesn't exist o[array[i]] = 1; // add key-values } Is there a good website to go to get coding style tips?

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  • What's the Difference Between These Two Ruby Class Initialization Definitions?

    - by michaelmichael
    I'm working through a book on Ruby, and the author used a slightly different form for writing a class initialization definition than he has in previous sections of the book. It looks like this: class Ticket attr_accessor :venue, :date def initialize(venue, date) self.venue = venue self.date = date end end In previous sections of the book, it would've been defined like this: class Ticket attr_accessor :venue, :date def initialize(venue, date) @venue = venue @date = date end end Is there any functional difference between using the setter method, as in the first example, vs. using the instance variable as in the second? They both seem to work. Even mixing them up works: class Ticket attr_accessor :venue, :date def initialize(venue, date) @venue = venue self.date = date end end

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  • Some optimization about the code (computing ranks of a vector)?

    - by user1748356
    The following code is a function (performance-critical) to compute tied ranks of a vector: mergeSort(x,inds,ci); //a sort function to sort vector x of length ci, also returns keys (inds) of x. int tj=0; double xi=x[0]; for (int j = 1; j < ci; ++j) { if (x[j] > xi) { double rankvalue = 0.5 * (j - 1 + tj); for (int k = tj; k < j; ++k) { ranks[inds[k]]=rankvalue; }; tj = j; xi = x[j]; }; }; double rankvalue = 0.5 * (ci - 1 + tj); for (int k = tj; k < ci; ++k) { ranks[inds[k]]=rankvalue; }; The problem is, the supposed performance bottleneck mergeSort(), which is O(NlogN) is several times faster than the other part of codes (which is O(N)), which suggests there is room for huge improvment with the other part of the codes, any advices?

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  • How can I override list methods to do vector addition and subtraction in python?

    - by Bobble
    I originally implemented this as a wrapper class around a list, but I was annoyed by the number of operator() methods I needed to provide, so I had a go at simply subclassing list. This is my test code: class CleverList(list): def __add__(self, other): copy = self[:] for i in range(len(self)): copy[i] += other[i] return copy def __sub__(self, other): copy = self[:] for i in range(len(self)): copy[i] -= other[i] return copy def __iadd__(self, other): for i in range(len(self)): self[i] += other[i] return self def __isub__(self, other): for i in range(len(self)): self[i] -= other[i] return self a = CleverList([0, 1]) b = CleverList([3, 4]) print('CleverList does vector arith: a, b, a+b, a-b = ', a, b, a+b, a-b) c = a[:] print('clone test: e = a[:]: a, e = ', a, c) c += a print('OOPS: augmented addition: c += a: a, c = ', a, c) c -= b print('OOPS: augmented subtraction: c -= b: b, c, a = ', b, c, a) Normal addition and subtraction work in the expected manner, but there are problems with the augmented addition and subtraction. Here is the output: >>> CleverList does vector arith: a, b, a+b, a-b = [0, 1] [3, 4] [3, 5] [-3, -3] clone test: e = a[:]: a, e = [0, 1] [0, 1] OOPS: augmented addition: c += a: a, c = [0, 1] [0, 1, 0, 1] Traceback (most recent call last): File "/home/bob/Documents/Python/listTest.py", line 35, in <module> c -= b TypeError: unsupported operand type(s) for -=: 'list' and 'CleverList' >>> Is there a neat and simple way to get augmented operators working in this example?

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  • Arcball Problems with UDK

    - by opdude
    I'm trying to re-create an arcball example from a Nehe, where an object can be rotated in a more realistic way while floating in the air (in my game the object is attached to the player at a distance like for example the Physics Gun) however I'm having trouble getting this to work with UDK. I have created an LGArcBall which follows the example from Nehe and I've compared outputs from this with the example code. I think where my problem lies is what I do to the Quaternion that is returned from the LGArcBall. Currently I am taking the returned Quaternion converting it to a rotation matrix. Getting the product of the last rotation (set when the object is first clicked) and then returning that into a Rotator and setting that to the objects rotation. If you could point me in the right direction that would be great, my code can be found below. class LGArcBall extends Object; var Quat StartRotation; var Vector StartVector; var float AdjustWidth, AdjustHeight, Epsilon; function SetBounds(float NewWidth, float NewHeight) { AdjustWidth = 1.0f / ((NewWidth - 1.0f) * 0.5f); AdjustHeight = 1.0f / ((NewHeight - 1.0f) * 0.5f); } function StartDrag(Vector2D startPoint, Quat rotation) { StartVector = MapToSphere(startPoint); } function Quat Update(Vector2D currentPoint) { local Vector currentVector, perp; local Quat newRot; //Map the new point to the sphere currentVector = MapToSphere(currentPoint); //Compute the vector perpendicular to the start and current perp = startVector cross currentVector; //Make sure our length is larger than Epsilon if (VSize(perp) > Epsilon) { //Return the perpendicular vector as the transform newRot.X = perp.X; newRot.Y = perp.Y; newRot.Z = perp.Z; //In the quaternion values, w is cosine (theta / 2), where //theta is the rotation angle newRot.W = startVector dot currentVector; } else { //The two vectors coincide, so return an identity transform newRot.X = 0.0f; newRot.Y = 0.0f; newRot.Z = 0.0f; newRot.W = 0.0f; } return newRot; } function Vector MapToSphere(Vector2D point) { local float x, y, length, norm; local Vector result; //Transform the mouse coords to [-1..1] //and inverse the Y coord x = (point.X * AdjustWidth) - 1.0f; y = 1.0f - (point.Y * AdjustHeight); length = (x * x) + (y * y); //If the point is mapped outside of the sphere //( length > radius squared) if (length > 1.0f) { norm = 1.0f / Sqrt(length); //Return the "normalized" vector, a point on the sphere result.X = x * norm; result.Y = y * norm; result.Z = 0.0f; } else //It's inside of the sphere { //Return a vector to the point mapped inside the sphere //sqrt(radius squared - length) result.X = x; result.Y = y; result.Z = Sqrt(1.0f - length); } return result; } DefaultProperties { Epsilon = 0.000001f } I'm then attempting to rotate that object when the mouse is dragged, with the following update code in my PlayerController. //Get Mouse Position MousePosition.X = LGMouseInterfacePlayerInput(PlayerInput).MousePosition.X; MousePosition.Y = LGMouseInterfacePlayerInput(PlayerInput).MousePosition.Y; newQuat = ArcBall.Update(MousePosition); rotMatrix = MakeRotationMatrix(QuatToRotator(newQuat)); rotMatrix = rotMatrix * LastRot; LGMoveableActor(movingPawn.CurrentUseableObject).SetPhysics(EPhysics.PHYS_Rotating); LGMoveableActor(movingPawn.CurrentUseableObject).SetRotation(MatrixGetRotator(rotMatrix));

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  • Multi-threading does not work correctly using std::thread (C++ 11)

    - by user1364743
    I coded a small c++ program to try to understand how multi-threading works using std::thread. Here's the step of my program execution : Initialization of a 5x5 matrix of integers with a unique value '42' contained in the class 'Toto' (initialized in the main). I print the initialized 5x5 matrix. Declaration of std::vector of 5 threads. I attach all threads respectively with their task (threadTask method). Each thread will manipulate a std::vector<int> instance. I join all threads. I print the new state of my 5x5 matrix. Here's the output : 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 It should be : 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 Here's the code sample : #include <iostream> #include <vector> #include <thread> class Toto { public: /* ** Initialize a 5x5 matrix with the 42 value. */ void initData(void) { for (int y = 0; y < 5; y++) { std::vector<int> vec; for (int x = 0; x < 5; x++) { vec.push_back(42); } this->m_data.push_back(vec); } } /* ** Display the whole matrix. */ void printData(void) const { for (int y = 0; y < 5; y++) { for (int x = 0; x < 5; x++) { printf("%d ", this->m_data[y][x]); } printf("\n"); } printf("\n"); } /* ** Function attached to the thread (thread task). ** Replace the original '42' value by another one. */ void threadTask(std::vector<int> &list, int value) { for (int x = 0; x < 5; x++) { list[x] = value; } } /* ** Return the m_data instance propertie. */ std::vector<std::vector<int> > &getData(void) { return (this->m_data); } private: std::vector<std::vector<int> > m_data; }; int main(void) { Toto toto; toto.initData(); toto.printData(); //Display the original 5x5 matrix (first display). std::vector<std::thread> threadList(5); //Initialization of vector of 5 threads. for (int i = 0; i < 5; i++) { //Threads initializationss std::vector<int> vec = toto.getData()[i]; //Get each sub-vectors. threadList.at(i) = std::thread(&Toto::threadTask, toto, vec, i); //Each thread will be attached to a specific vector. } for (int j = 0; j < 5; j++) { threadList.at(j).join(); } toto.printData(); //Second display. getchar(); return (0); } However, in the method threadTask, if I print the variable list[x], the output is correct. I think I can't print the correct data in the main because the printData() call is in the main thread and the display in the threadTask function is correct because the method is executed in its own thread (not the main one). It's strange, it means that all threads created in a parent processes can't modified the data in this parent processes ? I think I forget something in my code. I'm really lost. Does anyone can help me, please ? Thank a lot in advance for your help.

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  • Kernel compile error with iw_ndis.c

    - by James
    Hi, I have a hp pavilion dm3t with intel HD graphics running ubuntu 10.10 64 bit. I'm trying to compile and install a patched kernel according to this, https://launchpad.net/~kamalmostafa/+archive/linux-kamal-mjgbacklight So I downloaded the tarball from here (linked to from the page above): http://kernel.ubuntu.com/git?p=kamal/ubuntu-maverick.git;a=shortlog;h=refs/heads/mjg-backlight I untar'd it to a directory, entered the directory and did: make defconfig I'm not sure if that's what I should have done but it was successful, so I did: make which seemed to work fine until it gave these errors: ubuntu/ndiswrapper/iw_ndis.c:1966: error: unknown field ‘num_private’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1966: warning: initialization makes pointer from integer without a cast ubuntu/ndiswrapper/iw_ndis.c:1967: error: unknown field ‘num_private_args’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1967: warning: excess elements in struct initializer ubuntu/ndiswrapper/iw_ndis.c:1967: warning: (near initialization for ‘ndis_handler_def’) ubuntu/ndiswrapper/iw_ndis.c:1970: error: unknown field ‘private’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1970: warning: initialization makes integer from pointer without a cast ubuntu/ndiswrapper/iw_ndis.c:1970: error: initializer element is not computable at load time ubuntu/ndiswrapper/iw_ndis.c:1970: error: (near initialization for ‘ndis_handler_def.num_standard’) ubuntu/ndiswrapper/iw_ndis.c:1971: error: unknown field ‘private_args’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1971: warning: initialization from incompatible pointer type make[2]: *** [ubuntu/ndiswrapper/iw_ndis.o] Error 1 make[1]: *** [ubuntu/ndiswrapper] Error 2 make: *** [ubuntu] Error 2 How can I compile and install this kernel successfully? I'm new to this and would appreciate any help.

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  • Extreme Optimization – Numerical Algorithm Support

    - by JoshReuben
    Function Delegates Many calculations involve the repeated evaluation of one or more user-supplied functions eg Numerical integration. The EO MathLib provides delegate types for common function signatures and the FunctionFactory class can generate new delegates from existing ones. RealFunction delegate - takes one Double parameter – can encapsulate most of the static methods of the System.Math class, as well as the classes in the Extreme.Mathematics.SpecialFunctions namespace: var sin = new RealFunction(Math.Sin); var result = sin(1); BivariateRealFunction delegate - takes two Double parameters: var atan2 = new BivariateRealFunction (Math.Atan2); var result = atan2(1, 2); TrivariateRealFunction delegate – represents a function takes three Double arguments ParameterizedRealFunction delegate - represents a function taking one Integer and one Double argument that returns a real number. The Pow method implements such a function, but the arguments need order re-arrangement: static double Power(int exponent, double x) { return ElementaryFunctions.Pow(x, exponent); } ... var power = new ParameterizedRealFunction(Power); var result = power(6, 3.2); A ComplexFunction delegate - represents a function that takes an Extreme.Mathematics.DoubleComplex argument and also returns a complex number. MultivariateRealFunction delegate - represents a function that takes an Extreme.Mathematics.LinearAlgebra.Vector argument and returns a real number. MultivariateVectorFunction delegate - represents a function that takes a Vector argument and returns a Vector. FastMultivariateVectorFunction delegate - represents a function that takes an input Vector argument and an output Matrix argument – avoiding object construction  The FunctionFactory class RealFromBivariateRealFunction and RealFromParameterizedRealFunction helper methods - transform BivariateRealFunction or a ParameterizedRealFunction into a RealFunction delegate by fixing one of the arguments, and treating this as a new function of a single argument. var tenthPower = FunctionFactory.RealFromParameterizedRealFunction(power, 10); var result = tenthPower(x); Note: There is no direct way to do this programmatically in C# - in F# you have partial value functions where you supply a subset of the arguments (as a travelling closure) that the function expects. When you omit arguments, F# generates a new function that holds onto/remembers the arguments you passed in and "waits" for the other parameters to be supplied. let sumVals x y = x + y     let sumX = sumVals 10     // Note: no 2nd param supplied.     // sumX is a new function generated from partially applied sumVals.     // ie "sumX is a partial application of sumVals." let sum = sumX 20     // Invokes sumX, passing in expected int (parameter y from original)  val sumVals : int -> int -> int val sumX : (int -> int) val sum : int = 30 RealFunctionsToVectorFunction and RealFunctionsToFastVectorFunction helper methods - combines an array of delegates returning a real number or a vector into vector or matrix functions. The resulting vector function returns a vector whose components are the function values of the delegates in the array. var funcVector = FunctionFactory.RealFunctionsToVectorFunction(     new MultivariateRealFunction(myFunc1),     new MultivariateRealFunction(myFunc2));  The IterativeAlgorithm<T> abstract base class Iterative algorithms are common in numerical computing - a method is executed repeatedly until a certain condition is reached, approximating the result of a calculation with increasing accuracy until a certain threshold is reached. If the desired accuracy is achieved, the algorithm is said to converge. This base class is derived by many classes in the Extreme.Mathematics.EquationSolvers and Extreme.Mathematics.Optimization namespaces, as well as the ManagedIterativeAlgorithm class which contains a driver method that manages the iteration process.  The ConvergenceTest abstract base class This class is used to specify algorithm Termination , convergence and results - calculates an estimate for the error, and signals termination of the algorithm when the error is below a specified tolerance. Termination Criteria - specify the success condition as the difference between some quantity and its actual value is within a certain tolerance – 2 ways: absolute error - difference between the result and the actual value. relative error is the difference between the result and the actual value relative to the size of the result. Tolerance property - specify trade-off between accuracy and execution time. The lower the tolerance, the longer it will take for the algorithm to obtain a result within that tolerance. Most algorithms in the EO NumLib have a default value of MachineConstants.SqrtEpsilon - gives slightly less than 8 digits of accuracy. ConvergenceCriterion property - specify under what condition the algorithm is assumed to converge. Using the ConvergenceCriterion enum: WithinAbsoluteTolerance / WithinRelativeTolerance / WithinAnyTolerance / NumberOfIterations Active property - selectively ignore certain convergence tests Error property - returns the estimated error after a run MaxIterations / MaxEvaluations properties - Other Termination Criteria - If the algorithm cannot achieve the desired accuracy, the algorithm still has to end – according to an absolute boundary. Status property - indicates how the algorithm terminated - the AlgorithmStatus enum values:NoResult / Busy / Converged (ended normally - The desired accuracy has been achieved) / IterationLimitExceeded / EvaluationLimitExceeded / RoundOffError / BadFunction / Divergent / ConvergedToFalseSolution. After the iteration terminates, the Status should be inspected to verify that the algorithm terminated normally. Alternatively, you can set the ThrowExceptionOnFailure to true. Result property - returns the result of the algorithm. This property contains the best available estimate, even if the desired accuracy was not obtained. IterationsNeeded / EvaluationsNeeded properties - returns the number of iterations required to obtain the result, number of function evaluations.  Concrete Types of Convergence Test classes SimpleConvergenceTest class - test if a value is close to zero or very small compared to another value. VectorConvergenceTest class - test convergence of vectors. This class has two additional properties. The Norm property specifies which norm is to be used when calculating the size of the vector - the VectorConvergenceNorm enum values: EuclidianNorm / Maximum / SumOfAbsoluteValues. The ErrorMeasure property specifies how the error is to be measured – VectorConvergenceErrorMeasure enum values: Norm / Componentwise ConvergenceTestCollection class - represent a combination of tests. The Quantifier property is a ConvergenceTestQuantifier enum that specifies how the tests in the collection are to be combined: Any / All  The AlgorithmHelper Class inherits from IterativeAlgorithm<T> and exposes two methods for convergence testing. IsValueWithinTolerance<T> method - determines whether a value is close to another value to within an algorithm's requested tolerance. IsIntervalWithinTolerance<T> method - determines whether an interval is within an algorithm's requested tolerance.

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  • Some OBI EE Tricks and Tips in the Admin Tool By Gerry Langton

    - by hamsun
    How to set the log level from a Session variable Initialization block As we know it is normal to set the log level non-zero for a particular user when we wish to debug problems. However sometimes it is inconvenient to go into each user’s properties in the Admin tool and update the log level. So I am showing a method which allows the log level to be set for all users via a session initialization block. This is particularly useful for anyone wanting an alternative way to set the log level. The screen shots shown are using the OBIEE 11g SampleApp demo but are applicable to any environment. Open the appropriate rpd in on-line mode and navigate to Manage Variables. Select Session Initialization Blocks, right click in the white space and create a New Initialization Block. I called the Initialization block Set_Loglevel . Now click on ‘Edit Data Source’ to enter the SQL. Chose the ‘Use OBI EE Server’ option for the SQL. This means that the SQL provided must use tables which have been defined in the Physical layer of the RPD, and whilst there is no need to provide a connection pool you must work in On-Line mode. The SQL can access any of the RPD tables and is purely used to return a value of 2. The ‘Test’ button confirms that the SQL is valid. Next, click on the ‘Edit Data Target’ button to add the LOGLEVEL variable to the initialization block. Check the ‘Enable any user to set the value’ option so that this will work for any user. Click OK and the following message will display as LOGLEVEL is a system session variable: Click ‘Yes’. Click ‘OK’ to save the Initialization block. Then check in the On-LIne changes. To test that LOGLEVEL has been set, log in to OBIEE using an administrative login (e.g. weblogic) and reload server metadata, either from the Analysis editor or from Administration > Reload Files and Metadata link. Run a query then navigate to Administration > Manage Sessions and click ‘View Log’ for the query just issued (which should be approximately the last in the list). A log file should exist and with LOGLEVEL set to 2 should include both logical and physical sql. If more diagnostic information is required then set LOGLEVEL to a higher value. If logging is required only for a particular analysis then an alternative method can be used directly from the Analysis editor. Edit the analysis for which debugging is required and click on the Advanced tab. Scroll down to the Advanced SQL clauses section and enter the following in the Prefix box: SET VARIABLE LOGLEVEL = 2; Click the ‘Apply SQL’ button. The SET VARIABLE statement will now prefix the Analysis’s logical SQL. So that any time this analysis is run it will produce a log. You can find information about training for Oracle BI EE products here or in the OU Learning Paths. Please send me an email at [email protected] if you have any further questions. About the Author: Gerry Langton started at Siebel Systems in 1999 working as a technical instructor teaching both Siebel application development and also Siebel Analytics (which subsequently became Oracle BI EE). From 2006 Gerry has worked as Senior Principal Instructor within Oracle University specialising in Oracle BI EE, Oracle BI Publisher and Oracle Data Warehouse development for BI.

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  • Compiling kernal problem

    - by James
    Hi, I have a hp pavilion dm3t with intel HD graphics running ubuntu 10.10 64 bit. I'm trying to compile and install a patched kernel according to this, https://launchpad.net/~kamalmostafa/+archive/linux-kamal-mjgbacklight So I downloaded the tarball from here (linked to from the page above): http://kernel.ubuntu.com/git?p=kamal/ubuntu-maverick.git;a=shortlog;h=refs/heads/mjg-backlight I untar'd it to a directory, entered the directory and did: make defconfig which was successful, so I did: make which seemed to work fine until it gave these errors: ubuntu/ndiswrapper/iw_ndis.c:1966: error: unknown field ‘num_private’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1966: warning: initialization makes pointer from integer without a cast ubuntu/ndiswrapper/iw_ndis.c:1967: error: unknown field ‘num_private_args’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1967: warning: excess elements in struct initializer ubuntu/ndiswrapper/iw_ndis.c:1967: warning: (near initialization for ‘ndis_handler_def’) ubuntu/ndiswrapper/iw_ndis.c:1970: error: unknown field ‘private’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1970: warning: initialization makes integer from pointer without a cast ubuntu/ndiswrapper/iw_ndis.c:1970: error: initializer element is not computable at load time ubuntu/ndiswrapper/iw_ndis.c:1970: error: (near initialization for ‘ndis_handler_def.num_standard’) ubuntu/ndiswrapper/iw_ndis.c:1971: error: unknown field ‘private_args’ specified in initializer ubuntu/ndiswrapper/iw_ndis.c:1971: warning: initialization from incompatible pointer type make[2]: *** [ubuntu/ndiswrapper/iw_ndis.o] Error 1 make[1]: *** [ubuntu/ndiswrapper] Error 2 make: *** [ubuntu] Error 2 How can I compile and install this kernel successfully? I'm new to this and would appreciate any help.

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  • Lazy Initailization in .NET 4.0

    Lazy initialization or lazy instantiation means that an object is not created until it is first referenced. Lazy initialization is used to reduce wasteful computation, memory requirements. Following is an example where Lazy initialization is particularly useful.

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  • Visual C++ doesn't operator<< overload

    - by PierreBdR
    I have a vector class that I want to be able to input/output from a QTextStream object. The forward declaration of my vector class is: namespace util { template <size_t dim, typename T> class Vector; } I define the operator<< as: namespace util { template <size_t dim, typename T> QTextStream& operator<<(QTextStream& out, const util::Vector<dim,T>& vec) { ... } template <size_t dim, typename T> QTextStream& operator>>(QTextStream& in,util::Vector<dim,T>& vec) { .. } } However, if I ty to use these operators, Visual C++ returns this error: error C2678: binary '<<' : no operator found which takes a left-hand operand of type 'QTextStream' (or there is no acceptable conversion) A few things I tried: Originaly, the methods were defined as friends of the template, and it is working fine this way with g++. The methods have been moved outside the namespace util I changed the definition of the templates to fit what I found on various Visual C++ websites. The original friend declaration is: friend QTextStream& operator>>(QTextStream& ss, Vector& in) { ... } The "Visual C++ adapted" version is: friend QTextStream& operator>> <dim,T>(QTextStream& ss, Vector<dim,T>& in); with the function pre-declared before the class and implemented after. I checked the file is correctly included using: #pragma message ("Including vector header") And everything seems fine. Doesn anyone has any idea what might be wrong?

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  • optimize output value using a class and public member

    - by wiso
    Suppose you have a function, and you call it a lot of times, every time the function return a big object. I've optimized the problem using a functor that return void, and store the returning value in a public member: #include <vector> const int N = 100; std::vector<double> fun(const std::vector<double> & v, const int n) { std::vector<double> output = v; output[n] *= output[n]; return output; } class F { public: F() : output(N) {}; std::vector<double> output; void operator()(const std::vector<double> & v, const int n) { output = v; output[n] *= n; } }; int main() { std::vector<double> start(N,10.); std::vector<double> end(N); double a; // first solution for (unsigned long int i = 0; i != 10000000; ++i) a = fun(start, 2)[3]; // second solution F f; for (unsigned long int i = 0; i != 10000000; ++i) { f(start, 2); a = f.output[3]; } } Yes, I can use inline or optimize in an other way this problem, but here I want to stress on this problem: with the functor I declare and construct the output variable output only one time, using the function I do that every time it is called. The second solution is two time faster than the first with g++ -O1 or g++ -O2. What do you think about it, is it an ugly optimization?

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  • How to speed-up a simple method (preferably without changing interfaces or data structures)?

    - by baol
    I have some data structures: all_unordered_m is a big vector containing all the strings I need (all different) ordered_m is a small vector containing the indexes of a subset of the strings (all different) in the former vector position_m maps the indexes of objects from the first vector to their position in the second one. The string_after(index, reverse) method returns the string referenced by ordered_m after all_unordered_m[index]. ordered_m is considered circular, and is explored in natural or reverse order depending on the second parameter. The code is something like the following: struct ordered_subset { // [...] std::vector<std::string>& all_unordered_m; // size = n >> 1 std::vector<size_t> ordered_m; // size << n std::tr1::unordered_map<size_t, size_t> position_m; const std::string& string_after(size_t index, bool reverse) const { size_t pos = position_m.find(index)->second; if(reverse) pos = (pos == 0 ? orderd_m.size() - 1 : pos - 1); else pos = (pos == ordered.size() - 1 ? 0 : pos + 1); return all_unordered_m[ordered_m[pos]]; } }; Given that: I do need all of the data-structures for other purposes; I cannot change them because I need to access the strings: by their id in the all_unordered_m; by their index inside the various ordered_m; I need to know the position of a string (identified by it's position in the first vector) inside ordered_m vector; I cannot change the string_after interface without changing most of the program. How can I speed up the string_after method that is called billions of times and is eating up about 10% of the execution time?

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  • How to speed-up a simple method? (possibily without changing interfaces or data structures)

    - by baol
    Hello. I have some data structures: all_unordered_mordered_m is a big vector containing all the strings I need (all different) ordered_m is a small vector containing the indexes of a subset of the strings (all different) in the former vector position_m maps the indexes of objects from the first vector to their position in the second one. The string_after(index, reverse) method returns the string referenced by ordered_m after all_unordered_m[index]. ordered_m is considered circular, and is explored in natural or reverse order depending on the second parameter. The code is something like the following: struct ordered_subset { // [...] std::vector<std::string>& all_unordered_m; // size = n >> 1 std::vector<size_t> ordered_m; // size << n std::map<size_t, size_t> position_m; // positions of strings in ordered_m const std::string& string_after(size_t index, bool reverse) const { size_t pos = position_m.find(index)->second; if(reverse) pos = (pos == 0 ? orderd_m.size() - 1 : pos - 1); else pos = (pos == ordered.size() - 1 ? 0 : pos + 1); return all_unordered_m[ordered_m[pos]]; } }; Given that: I do need all of the data-structures for other purposes; I cannot change them because I need to access the strings: by their id in the all_unordered_m; by their index inside the various ordered_m; I need to know the position of a string (identified by it's position in the first vector) inside ordered_m vector; I cannot change the string_after interface without changing most of the program. How can I speed up the string_after method that is called billions of times and is eating up about 10% of the execution time?

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  • Shapes-tool creating a vector mask every time, cannot seem to fix in CS3?

    - by Bryan
    Every time I create a shape using the shape tool, it places a vector mask on top of this. I don't know how I enabled this but it does not do it on my laptop version, only my desktop. I can seem to disable this problem I am having. Even reinstalling and restoring defaults I cannot seem to stop this. Very frustrating, anyone have a fix for this problem? Thanks in advance!

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