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  • Why is the destructor of the class called twice ?

    - by dicaprio
    Apologies if the question sounds silly, I was following experts in SO and trying some examples myself, and this is one of them. I did try the search option but didn't find an answer for this kind. class A { public: A(){cout<<"A Contruction"<<endl;} ~A(){cout<<"A destruction"<<endl;} }; int main() { vector<A> t; t.push_back(A()); // After this line, when the scope of the object is lost. } Why is the destructor of the class called twice ?

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  • C++ Questions about vectors

    - by xbonez
    Hey guys, I have a CS exam tomorrow. Just want to get a few questions cleared up. Thanks a lot, and I really appreciate the help. Que 1. What are parallel vectors? Vectors of the same length that contain data that is meant to be processed together Vectors that are all of the same data type Vectors that are of the same length Any vector of data type parallel Que 2. Arrays are faster and more efficient than vectors. True False Que 3. Arrays can be a return type of a function call. True False Que 4. Vectors can be a return type of a function call. True False

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  • Twin edges - Half edge data structure

    - by Pradeep Kumar
    I have implemented a Half-edge data structure for loading 3d objects. I find that the part of assigning twin/pair edges takes the longest computation time (especially for objects which have hundreds of thousands half edges). The reason is that I use nested loops to accomplish this. Is there a simpler and efficient way of doing this? Below is the code which I've written. HE is the half-edge data structure. hearr is a vector containing all the half edges. vert is the starting vertex and end is the ending vertex. Thanks!! HE *e1,*e2; for(size_t i=0;i<hearr.size();i++){ e1=hearr[i]; for(size_t j=1;j<hearr.size();j++){ e2=hearr[j]; if((e1->vert==e2->end)&&(e2->vert==e1->end)){ e1->twin=e2; e2->twin=e1; } } }

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  • inputMismatchException Java reading doubles from plain text file

    - by user939287
    Using double variable = inputFile.nextDouble(); Gives the mismatch error and I can't figure out why... Anyone know what's up? The input file is just a bunch of doubles like 5.0... Okay here is the code snippet String fileName; Scanner scanner = new Scanner(System.in); System.out.println("\nEnter file name that contains the matrix and vector: "); fileName = scanner.nextLine(); Scanner inputFile = new Scanner(fileName); double a1 = inputFile.nextDouble(); the input file is a plain text document .txt in this format 5.0 4.0 -3.0 4.0 2.0 5.0 6.0 5.0 -2.0 -13.0 4.0 12.0 I don't understand why it wouldn't take those as doubles... As far as what its expecting the format of the file to be... I suppose binary? isn't that the default? I didn't specify in the code...

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  • Create a table of Quantiles in R for multiple Subsets of Data

    - by user1489719
    I'm trying to create append a table of quantiles in R for multiple subsets of data. Right now, I have a vector of ids (p_ids) in table DATA, which are not consecutive. For each value in p_ids, I am looking to list the quantile. So far, I've tried variations of: i <- 1 n <- 1 for (i in p_ids) { while(n <= nrow(data)) { quantiles[n] <- quantile(subset(alldata$variableA, alldata$variableB == i),probs = c(0,1,2,3)/3) n <- n + 1 } } I know my issue lies somewhere in the index, but I can't seem to get where the index should go. Suggestions?

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  • Why only random-access-iterator implements operator+ in C++?

    - by xopht
    I'd like get far next value for STL list iterator but it doesn't implement operator+, vector has it though. Why and how can I get the value where I want? I think I can do that if I call operator++ several times, but isn't that a little bit dirty? What I want to do is the following: list<int> l; ...omitted... list<int>::iterator itr = l.begin() + 3; // but, list iterator does not have // operator+ What is the best solution for what I want?

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  • C++: Copying to dereferenced pointer...

    - by bbb
    Hi. I currently have a weird problem with a program segfaulting but im not able to spot the error. I think the problem boils down to this. struct S {int a; vector<sometype> b;} S s1; // fill stuff into a and b S* s2 = new S(); *s2 = s1; Could it be that the final copying is illegal in some way? Im really confused right now... Thanks

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  • C++ setting up "flags"

    - by sub
    Example: enum Flags { A, B, C, D }; class MyClass { std::string data; int foo; // Flags theFlags; (???) } How can I achieve that it is possible to set any number of the "flags" A,B,C and D in the enum above in an instance of MyClass? My goal would be something like this: if ( MyClassInst.IsFlagSet( A ) ) // ... MyClassInst.SetFlag( A ); //... Do I have to use some array or vector? If yes, how? Are enums a good idea in this case?

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  • Error occurs while using SPADE method in R

    - by Yuwon Lee
    I'm currently mining sequence patterns using SPADE algorithm in R. SPADE is included in "arulesSequence" package of R. I'm running R on my CentOS 6.3 64bit. For an exercise, I've tried an example presented in http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Sequence_Mining/SPADE When I tried to do "cspade(x, parameter = list(support = 0.4), control = list(verbose = TRUE))" R says: parameter specification: support : 0.4 maxsize : 10 maxlen : 10 algorithmic control: bfstype : FALSE verbose : TRUE summary : FALSE preprocessing ... 1 partition(s), 0 MB [0.096s] mining transactions ... 0 MB [0.066s] reading sequences ...Error in asMethod(object) : 's' is not an integer vector When I try to run SPADE on my Window 7 32bit, it runs well without any error. Does anybody know why such errors occur?

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  • what to use in place of std::map::emplace?

    - by kfmfe04
    For containers such as std::map< std::string, std::unique_ptr< Foo >>, it looks like emplace() has yet to be implemented in stdc++ as of gcc 4.7.2. Unfortunately, I can't store Foo directly by value as it is an abstract super-class. As a simple, but inefficient, place-holder, I've just been using std::map< std::string, Foo* > in conjunction with a std::vector< std::unique_ptr< Foo >> for garbage collection. Do you have a interim solution that is more efficient and more easily replaced once emplace() is available?

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  • C++ template + typedef

    - by MMS
    What is wrong in the following code: Point2D.h template <class T> class Point2D { private: T x; T y; ... }; PointsList.h template <class T> class Point2D; template <class T> struct TPointsList { typedef std::vector <Point2D <T> > Type; }; template <class T> class PointsList { private: TPointsList <T>::Type points; //Compiler error ... }; I would like to create new user type TPointsList without direct type specification...

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  • C++: String and unions

    - by sub
    I'm having a (design) problem: I'm building an interpreter and I need some storage for variables. There are basically two types of content a variable here can have: string or int. I'm using a simple class for the variables, all variables are then stored in a vector. However, as a variable can hold a number or a string, I don't want C++ to allocate both and consume memory for no reason. That's why I wanted to use unions: union { string StringValue; int IntValue; } However, strings don't work with unions. Is there any workaround so no memory gets eaten for no reason?

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  • Do I really need to return Type::size_type?

    - by dehmann
    I often have classes that are mostly just wrappers around some STL container, like this: class Foo { public: typedef std::vector<whatever> Vec; typedef Vec::size_type; const Vec& GetVec() { return vec_; } size_type size() { return vec_.size() } private: Vec vec_; }; I am not so sure about returning size_type. Often, some function will call size() and pass that value on to another function and that one will use it and maybe pass it on. Now everyone has to include that Foo header, although I'm really just passing some size value around, which should just be unsigned int anyway ...? What is the right thing to do here? Is it best practice to really use size_type everywhere?

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  • What is most efficient way of setting row to zeros for a sparce scipy matrix?

    - by Alex Reinking
    I'm trying to convert the following MATLAB code to Python and am having trouble finding a solution that works in any reasonable amount of time. M = diag(sum(a)) - a; where = vertcat(in, out); M(where,:) = 0; M(where,where) = 1; Here, a is a sparse matrix and where is a vector (as are in/out). The solution I have using Python is: M = scipy.sparse.diags([degs], [0]) - A where = numpy.hstack((inVs, outVs)).astype(int) M = scipy.sparse.lil_matrix(M) M[where, :] = 0 # This is the slowest line M[where, where] = 1 M = scipy.sparse.csc_matrix(M) But since A is 334863x334863, this takes like three minutes. If anyone has any suggestions on how to make this faster, please contribute them! For comparison, MATLAB does this same step imperceptibly fast. Thanks!

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  • What is wrong with my loop?

    - by user3966541
    I have the following loop and don't understand why it only runs once: std::vector<sf::RectangleShape> shapes; const int res_width = 640; const int res_height = 480; for (int x = 0; x < res_width / 50; x += 50) { for (int y = 0; y < res_height / 50; y += 50) { sf::RectangleShape shape(sf::Vector2f(50, 50)); shape.setPosition(x * 50, y * 50); sf::Color color = (x % 2 == 0) ? sf::Color::Green : sf::Color::Red; shape.setFillColor(sf::Color::Green); shapes.push_back(shape); } }

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  • Subtle C++ mistake, can you spot it?

    - by aaa
    I ran into a subtle C++ gotcha, took me while to resolve it. Can you spot it? class synchronized_container { boost::mutex mutex_; std::vector <T> container_; void push_back(const T &value) { boost::scoped_lock(mutex_); // raii mutex lock container_.push_back(value); } ... }; scoped lock is a raii mutex lock, obtains lock on constructor, release lock in destructor. The program will work as expected in serial, but will may occasionally produce weird stuff with more than one thread.

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  • Pointers and collection of pointers in C++. How to properly delete.

    - by Julen
    Hello, This is a newbe question but I have alwasy doubts with pointers in C++. This is the situation. I have a class A which as a collection (a vector actually) of pointers of class B. This same class A has another collection of pointers to class C. Finally the objects of class B have also a collection to pointers to class C which point to the same instances the class A points to. My question is, if I delete a member of class-C-type pointer in class B, what happens to the pointer in class A that points to the deleted instance of class C? How this situation has to be treated? Thanks a lot in advance! Julen.

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  • Clojure: find repetition

    - by demi
    Let we have a list of integers: 1, 2, 5, 13, 6, 5, 7 and I want to find the first number has a duplicate before it and return a vector of two indices, In my sample, it's 5 at [2, 5]. What I did so far is loop, but can I do it more elegant, short way? (defn get-cycle [xs] (loop [[x & xs_rest] xs, indices {}, i 0] (if (nil? x) [0 i] ; Sequence is over before we found a duplicate. (if-let [x_index (indices x)] [x_index i] (recur xs_rest (assoc indices x i) (inc i)))))) No need to return number itself, because I can get it by index and, second, it may be not always there.

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  • partial string matching - R

    - by DonDyck
    I need to write a query in R to match partial string in column names. I am looking for something similar to LIKE operator in SQL. For e.g, if I know beginning, middle or end part of the string I would write the query in format: LIKE 'beginning%middle%' in SQL and it would return matching strings. In pmatch or grep it seems I can only specify 'beginning' , 'end' and not the order. Is there any similar function in R that I am looking for? For example, say I am looking in the vector: y<- c("I am looking for a dog", "looking for a new dog", "a dog", "I am just looking") Lets say I want to write a query which picks "looking for a new dog" and I know start of the string is "looking" and end of string is "dog". If I do a grep("dog",y) it will return 1,2,3. Is there any way I can specify beginning and end in grep?

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  • OpenGL Fast-Object Instancing Error

    - by HJ Media Studios
    I have some code that loops through a set of objects and renders instances of those objects. The list of objects that needs to be rendered is stored as a std::map, where an object of class MeshResource contains the vertices and indices with the actual data, and an object of classMeshRenderer defines the point in space the mesh is to be rendered at. My rendering code is as follows: glDisable(GL_BLEND); glEnable(GL_CULL_FACE); glDepthMask(GL_TRUE); glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); glEnable(GL_DEPTH_TEST); for (std::map<MeshResource*, std::vector<MeshRenderer*> >::iterator it = renderables.begin(); it != renderables.end(); it++) { it->first->setupBeforeRendering(); cout << "<"; for (unsigned long i =0; i < it->second.size(); i++) { //Pass in an identity matrix to the vertex shader- used here only for debugging purposes; the real code correctly inputs any matrix. uniformizeModelMatrix(Matrix4::IDENTITY); /** * StartHere fix rendering problem. * Ruled out: * Vertex buffers correctly. * Index buffers correctly. * Matrices correct? */ it->first->render(); } it->first->cleanupAfterRendering(); } geometryPassShader->disable(); glDepthMask(GL_FALSE); glDisable(GL_CULL_FACE); glDisable(GL_DEPTH_TEST); The function in MeshResource that handles setting up the uniforms is as follows: void MeshResource::setupBeforeRendering() { glEnableVertexAttribArray(0); glEnableVertexAttribArray(1); glEnableVertexAttribArray(2); glEnableVertexAttribArray(3); glEnableVertexAttribArray(4); glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, iboID); glBindBuffer(GL_ARRAY_BUFFER, vboID); glVertexAttribPointer(0, 3, GL_FLOAT, GL_FALSE, sizeof(Vertex), 0); // Vertex position glVertexAttribPointer(1, 3, GL_FLOAT, GL_FALSE, sizeof(Vertex), (const GLvoid*) 12); // Vertex normal glVertexAttribPointer(2, 2, GL_FLOAT, GL_FALSE, sizeof(Vertex), (const GLvoid*) 24); // UV layer 0 glVertexAttribPointer(3, 3, GL_FLOAT, GL_FALSE, sizeof(Vertex), (const GLvoid*) 32); // Vertex color glVertexAttribPointer(4, 1, GL_UNSIGNED_SHORT, GL_FALSE, sizeof(Vertex), (const GLvoid*) 44); //Material index } The code that renders the object is this: void MeshResource::render() { glDrawElements(GL_TRIANGLES, geometry->numIndices, GL_UNSIGNED_SHORT, 0); } And the code that cleans up is this: void MeshResource::cleanupAfterRendering() { glDisableVertexAttribArray(0); glDisableVertexAttribArray(1); glDisableVertexAttribArray(2); glDisableVertexAttribArray(3); glDisableVertexAttribArray(4); } The end result of this is that I get a black screen, although the end of my rendering pipeline after the rendering code (essentially just drawing axes and lines on the screen) works properly, so I'm fairly sure it's not an issue with the passing of uniforms. If, however, I change the code slightly so that the rendering code calls the setup immediately before rendering, like so: void MeshResource::render() { setupBeforeRendering(); glDrawElements(GL_TRIANGLES, geometry->numIndices, GL_UNSIGNED_SHORT, 0); } The program works as desired. I don't want to have to do this, though, as my aim is to set up vertex, material, etc. data once per object type and then render each instance updating only the transformation information. The uniformizeModelMatrix works as follows: void RenderManager::uniformizeModelMatrix(Matrix4 matrix) { glBindBuffer(GL_UNIFORM_BUFFER, globalMatrixUBOID); glBufferSubData(GL_UNIFORM_BUFFER, 0, sizeof(Matrix4), matrix.ptr()); glBindBuffer(GL_UNIFORM_BUFFER, 0); }

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  • A Taxonomy of Numerical Methods v1

    - by JoshReuben
    Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

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  • Help with Collision Resolution?

    - by Milo
    I'm trying to learn about physics by trying to make a simplified GTA 2 clone. My only problem is collision resolution. Everything else works great. I have a rigid body class and from there cars and a wheel class: class RigidBody extends Entity { //linear private Vector2D velocity = new Vector2D(); private Vector2D forces = new Vector2D(); private OBB2D predictionRect = new OBB2D(new Vector2D(), 1.0f, 1.0f, 0.0f); private float mass; private Vector2D deltaVec = new Vector2D(); private Vector2D v = new Vector2D(); //angular private float angularVelocity; private float torque; private float inertia; //graphical private Vector2D halfSize = new Vector2D(); private Bitmap image; private Matrix mat = new Matrix(); private float[] Vector2Ds = new float[2]; private Vector2D tangent = new Vector2D(); private static Vector2D worldRelVec = new Vector2D(); private static Vector2D relWorldVec = new Vector2D(); private static Vector2D pointVelVec = new Vector2D(); public RigidBody() { //set these defaults so we don't get divide by zeros mass = 1.0f; inertia = 1.0f; setLayer(LAYER_OBJECTS); } protected void rectChanged() { if(getWorld() != null) { getWorld().updateDynamic(this); } } //intialize out parameters public void initialize(Vector2D halfSize, float mass, Bitmap bitmap) { //store physical parameters this.halfSize = halfSize; this.mass = mass; image = bitmap; inertia = (1.0f / 20.0f) * (halfSize.x * halfSize.x) * (halfSize.y * halfSize.y) * mass; RectF rect = new RectF(); float scalar = 10.0f; rect.left = (int)-halfSize.x * scalar; rect.top = (int)-halfSize.y * scalar; rect.right = rect.left + (int)(halfSize.x * 2.0f * scalar); rect.bottom = rect.top + (int)(halfSize.y * 2.0f * scalar); setRect(rect); predictionRect.set(rect); } public void setLocation(Vector2D position, float angle) { getRect().set(position, getWidth(), getHeight(), angle); rectChanged(); } public void setPredictionLocation(Vector2D position, float angle) { getPredictionRect().set(position, getWidth(), getHeight(), angle); } public void setPredictionCenter(Vector2D center) { getPredictionRect().moveTo(center); } public void setPredictionAngle(float angle) { predictionRect.setAngle(angle); } public Vector2D getPosition() { return getRect().getCenter(); } public OBB2D getPredictionRect() { return predictionRect; } @Override public void update(float timeStep) { doUpdate(false,timeStep); } public void doUpdate(boolean prediction, float timeStep) { //integrate physics //linear Vector2D acceleration = Vector2D.scalarDivide(forces, mass); if(prediction) { Vector2D velocity = Vector2D.add(this.velocity, Vector2D.scalarMultiply(acceleration, timeStep)); Vector2D c = getRect().getCenter(); c = Vector2D.add(getRect().getCenter(), Vector2D.scalarMultiply(velocity , timeStep)); setPredictionCenter(c); //forces = new Vector2D(0,0); //clear forces } else { velocity.x += (acceleration.x * timeStep); velocity.y += (acceleration.y * timeStep); //velocity = Vector2D.add(velocity, Vector2D.scalarMultiply(acceleration, timeStep)); Vector2D c = getRect().getCenter(); v.x = getRect().getCenter().getX() + (velocity.x * timeStep); v.y = getRect().getCenter().getY() + (velocity.y * timeStep); deltaVec.x = v.x - c.x; deltaVec.y = v.y - c.y; deltaVec.normalize(); setCenter(v.x, v.y); forces.x = 0; //clear forces forces.y = 0; } //angular float angAcc = torque / inertia; if(prediction) { float angularVelocity = this.angularVelocity + angAcc * timeStep; setPredictionAngle(getAngle() + angularVelocity * timeStep); //torque = 0; //clear torque } else { angularVelocity += angAcc * timeStep; setAngle(getAngle() + angularVelocity * timeStep); torque = 0; //clear torque } } public void updatePrediction(float timeStep) { doUpdate(true, timeStep); } //take a relative Vector2D and make it a world Vector2D public Vector2D relativeToWorld(Vector2D relative) { mat.reset(); Vector2Ds[0] = relative.x; Vector2Ds[1] = relative.y; mat.postRotate(JMath.radToDeg(getAngle())); mat.mapVectors(Vector2Ds); relWorldVec.x = Vector2Ds[0]; relWorldVec.y = Vector2Ds[1]; return new Vector2D(Vector2Ds[0], Vector2Ds[1]); } //take a world Vector2D and make it a relative Vector2D public Vector2D worldToRelative(Vector2D world) { mat.reset(); Vector2Ds[0] = world.x; Vector2Ds[1] = world.y; mat.postRotate(JMath.radToDeg(-getAngle())); mat.mapVectors(Vector2Ds); return new Vector2D(Vector2Ds[0], Vector2Ds[1]); } //velocity of a point on body public Vector2D pointVelocity(Vector2D worldOffset) { tangent.x = -worldOffset.y; tangent.y = worldOffset.x; return Vector2D.add( Vector2D.scalarMultiply(tangent, angularVelocity) , velocity); } public void applyForce(Vector2D worldForce, Vector2D worldOffset) { //add linear force forces.x += worldForce.x; forces.y += worldForce.y; //add associated torque torque += Vector2D.cross(worldOffset, worldForce); } @Override public void draw( GraphicsContext c) { c.drawRotatedScaledBitmap(image, getPosition().x, getPosition().y, getWidth(), getHeight(), getAngle()); } public Vector2D getVelocity() { return velocity; } public void setVelocity(Vector2D velocity) { this.velocity = velocity; } public Vector2D getDeltaVec() { return deltaVec; } } Vehicle public class Wheel { private Vector2D forwardVec; private Vector2D sideVec; private float wheelTorque; private float wheelSpeed; private float wheelInertia; private float wheelRadius; private Vector2D position = new Vector2D(); public Wheel(Vector2D position, float radius) { this.position = position; setSteeringAngle(0); wheelSpeed = 0; wheelRadius = radius; wheelInertia = (radius * radius) * 1.1f; } public void setSteeringAngle(float newAngle) { Matrix mat = new Matrix(); float []vecArray = new float[4]; //forward Vector vecArray[0] = 0; vecArray[1] = 1; //side Vector vecArray[2] = -1; vecArray[3] = 0; mat.postRotate(newAngle / (float)Math.PI * 180.0f); mat.mapVectors(vecArray); forwardVec = new Vector2D(vecArray[0], vecArray[1]); sideVec = new Vector2D(vecArray[2], vecArray[3]); } public void addTransmissionTorque(float newValue) { wheelTorque += newValue; } public float getWheelSpeed() { return wheelSpeed; } public Vector2D getAnchorPoint() { return position; } public Vector2D calculateForce(Vector2D relativeGroundSpeed, float timeStep, boolean prediction) { //calculate speed of tire patch at ground Vector2D patchSpeed = Vector2D.scalarMultiply(Vector2D.scalarMultiply( Vector2D.negative(forwardVec), wheelSpeed), wheelRadius); //get velocity difference between ground and patch Vector2D velDifference = Vector2D.add(relativeGroundSpeed , patchSpeed); //project ground speed onto side axis Float forwardMag = new Float(0.0f); Vector2D sideVel = velDifference.project(sideVec); Vector2D forwardVel = velDifference.project(forwardVec, forwardMag); //calculate super fake friction forces //calculate response force Vector2D responseForce = Vector2D.scalarMultiply(Vector2D.negative(sideVel), 2.0f); responseForce = Vector2D.subtract(responseForce, forwardVel); float topSpeed = 500.0f; //calculate torque on wheel wheelTorque += forwardMag * wheelRadius; //integrate total torque into wheel wheelSpeed += wheelTorque / wheelInertia * timeStep; //top speed limit (kind of a hack) if(wheelSpeed > topSpeed) { wheelSpeed = topSpeed; } //clear our transmission torque accumulator wheelTorque = 0; //return force acting on body return responseForce; } public void setTransmissionTorque(float newValue) { wheelTorque = newValue; } public float getTransmissionTourque() { return wheelTorque; } public void setWheelSpeed(float speed) { wheelSpeed = speed; } } //our vehicle object public class Vehicle extends RigidBody { private Wheel [] wheels = new Wheel[4]; private boolean throttled = false; public void initialize(Vector2D halfSize, float mass, Bitmap bitmap) { //front wheels wheels[0] = new Wheel(new Vector2D(halfSize.x, halfSize.y), 0.45f); wheels[1] = new Wheel(new Vector2D(-halfSize.x, halfSize.y), 0.45f); //rear wheels wheels[2] = new Wheel(new Vector2D(halfSize.x, -halfSize.y), 0.75f); wheels[3] = new Wheel(new Vector2D(-halfSize.x, -halfSize.y), 0.75f); super.initialize(halfSize, mass, bitmap); } public void setSteering(float steering) { float steeringLock = 0.13f; //apply steering angle to front wheels wheels[0].setSteeringAngle(steering * steeringLock); wheels[1].setSteeringAngle(steering * steeringLock); } public void setThrottle(float throttle, boolean allWheel) { float torque = 85.0f; throttled = true; //apply transmission torque to back wheels if (allWheel) { wheels[0].addTransmissionTorque(throttle * torque); wheels[1].addTransmissionTorque(throttle * torque); } wheels[2].addTransmissionTorque(throttle * torque); wheels[3].addTransmissionTorque(throttle * torque); } public void setBrakes(float brakes) { float brakeTorque = 15.0f; //apply brake torque opposing wheel vel for (Wheel wheel : wheels) { float wheelVel = wheel.getWheelSpeed(); wheel.addTransmissionTorque(-wheelVel * brakeTorque * brakes); } } public void doUpdate(float timeStep, boolean prediction) { for (Wheel wheel : wheels) { float wheelVel = wheel.getWheelSpeed(); //apply negative force to naturally slow down car if(!throttled && !prediction) wheel.addTransmissionTorque(-wheelVel * 0.11f); Vector2D worldWheelOffset = relativeToWorld(wheel.getAnchorPoint()); Vector2D worldGroundVel = pointVelocity(worldWheelOffset); Vector2D relativeGroundSpeed = worldToRelative(worldGroundVel); Vector2D relativeResponseForce = wheel.calculateForce(relativeGroundSpeed, timeStep,prediction); Vector2D worldResponseForce = relativeToWorld(relativeResponseForce); applyForce(worldResponseForce, worldWheelOffset); } //no throttling yet this frame throttled = false; if(prediction) { super.updatePrediction(timeStep); } else { super.update(timeStep); } } @Override public void update(float timeStep) { doUpdate(timeStep,false); } public void updatePrediction(float timeStep) { doUpdate(timeStep,true); } public void inverseThrottle() { float scalar = 0.2f; for(Wheel wheel : wheels) { wheel.setTransmissionTorque(-wheel.getTransmissionTourque() * scalar); wheel.setWheelSpeed(-wheel.getWheelSpeed() * 0.1f); } } } And my big hack collision resolution: private void update() { camera.setPosition((vehicle.getPosition().x * camera.getScale()) - ((getWidth() ) / 2.0f), (vehicle.getPosition().y * camera.getScale()) - ((getHeight() ) / 2.0f)); //camera.move(input.getAnalogStick().getStickValueX() * 15.0f, input.getAnalogStick().getStickValueY() * 15.0f); if(input.isPressed(ControlButton.BUTTON_GAS)) { vehicle.setThrottle(1.0f, false); } if(input.isPressed(ControlButton.BUTTON_STEAL_CAR)) { vehicle.setThrottle(-1.0f, false); } if(input.isPressed(ControlButton.BUTTON_BRAKE)) { vehicle.setBrakes(1.0f); } vehicle.setSteering(input.getAnalogStick().getStickValueX()); //vehicle.update(16.6666666f / 1000.0f); boolean colided = false; vehicle.updatePrediction(16.66666f / 1000.0f); List<Entity> buildings = world.queryStaticSolid(vehicle,vehicle.getPredictionRect()); if(buildings.size() > 0) { colided = true; } if(!colided) { vehicle.update(16.66f / 1000.0f); } else { Vector2D delta = vehicle.getDeltaVec(); vehicle.setVelocity(Vector2D.negative(vehicle.getVelocity().multiply(0.2f)). add(delta.multiply(-1.0f))); vehicle.inverseThrottle(); } } Here is OBB public class OBB2D { // Corners of the box, where 0 is the lower left. private Vector2D corner[] = new Vector2D[4]; private Vector2D center = new Vector2D(); private Vector2D extents = new Vector2D(); private RectF boundingRect = new RectF(); private float angle; //Two edges of the box extended away from corner[0]. private Vector2D axis[] = new Vector2D[2]; private double origin[] = new double[2]; public OBB2D(Vector2D center, float w, float h, float angle) { set(center,w,h,angle); } public OBB2D(float left, float top, float width, float height) { set(new Vector2D(left + (width / 2), top + (height / 2)),width,height,0.0f); } public void set(Vector2D center,float w, float h,float angle) { Vector2D X = new Vector2D( (float)Math.cos(angle), (float)Math.sin(angle)); Vector2D Y = new Vector2D((float)-Math.sin(angle), (float)Math.cos(angle)); X = X.multiply( w / 2); Y = Y.multiply( h / 2); corner[0] = center.subtract(X).subtract(Y); corner[1] = center.add(X).subtract(Y); corner[2] = center.add(X).add(Y); corner[3] = center.subtract(X).add(Y); computeAxes(); extents.x = w / 2; extents.y = h / 2; computeDimensions(center,angle); } private void computeDimensions(Vector2D center,float angle) { this.center.x = center.x; this.center.y = center.y; this.angle = angle; boundingRect.left = Math.min(Math.min(corner[0].x, corner[3].x), Math.min(corner[1].x, corner[2].x)); boundingRect.top = Math.min(Math.min(corner[0].y, corner[1].y),Math.min(corner[2].y, corner[3].y)); boundingRect.right = Math.max(Math.max(corner[1].x, corner[2].x), Math.max(corner[0].x, corner[3].x)); boundingRect.bottom = Math.max(Math.max(corner[2].y, corner[3].y),Math.max(corner[0].y, corner[1].y)); } public void set(RectF rect) { set(new Vector2D(rect.centerX(),rect.centerY()),rect.width(),rect.height(),0.0f); } // Returns true if other overlaps one dimension of this. private boolean overlaps1Way(OBB2D other) { for (int a = 0; a < axis.length; ++a) { double t = other.corner[0].dot(axis[a]); // Find the extent of box 2 on axis a double tMin = t; double tMax = t; for (int c = 1; c < corner.length; ++c) { t = other.corner[c].dot(axis[a]); if (t < tMin) { tMin = t; } else if (t > tMax) { tMax = t; } } // We have to subtract off the origin // See if [tMin, tMax] intersects [0, 1] if ((tMin > 1 + origin[a]) || (tMax < origin[a])) { // There was no intersection along this dimension; // the boxes cannot possibly overlap. return false; } } // There was no dimension along which there is no intersection. // Therefore the boxes overlap. return true; } //Updates the axes after the corners move. Assumes the //corners actually form a rectangle. private void computeAxes() { axis[0] = corner[1].subtract(corner[0]); axis[1] = corner[3].subtract(corner[0]); // Make the length of each axis 1/edge length so we know any // dot product must be less than 1 to fall within the edge. for (int a = 0; a < axis.length; ++a) { axis[a] = axis[a].divide((axis[a].length() * axis[a].length())); origin[a] = corner[0].dot(axis[a]); } } public void moveTo(Vector2D center) { Vector2D centroid = (corner[0].add(corner[1]).add(corner[2]).add(corner[3])).divide(4.0f); Vector2D translation = center.subtract(centroid); for (int c = 0; c < 4; ++c) { corner[c] = corner[c].add(translation); } computeAxes(); computeDimensions(center,angle); } // Returns true if the intersection of the boxes is non-empty. public boolean overlaps(OBB2D other) { if(right() < other.left()) { return false; } if(bottom() < other.top()) { return false; } if(left() > other.right()) { return false; } if(top() > other.bottom()) { return false; } if(other.getAngle() == 0.0f && getAngle() == 0.0f) { return true; } return overlaps1Way(other) && other.overlaps1Way(this); } public Vector2D getCenter() { return center; } public float getWidth() { return extents.x * 2; } public float getHeight() { return extents.y * 2; } public void setAngle(float angle) { set(center,getWidth(),getHeight(),angle); } public float getAngle() { return angle; } public void setSize(float w,float h) { set(center,w,h,angle); } public float left() { return boundingRect.left; } public float right() { return boundingRect.right; } public float bottom() { return boundingRect.bottom; } public float top() { return boundingRect.top; } public RectF getBoundingRect() { return boundingRect; } public boolean overlaps(float left, float top, float right, float bottom) { if(right() < left) { return false; } if(bottom() < top) { return false; } if(left() > right) { return false; } if(top() > bottom) { return false; } return true; } }; What I do is when I predict a hit on the car, I force it back. It does not work that well and seems like a bad idea. What could I do to have more proper collision resolution. Such that if I hit a wall I will never get stuck in it and if I hit the side of a wall I can steer my way out of it. Thanks I found this nice ppt. It talks about pulling objects apart and calculating new velocities. How could I calc new velocities in my case? http://www.google.ca/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CC8QFjAB&url=http%3A%2F%2Fcoitweb.uncc.edu%2F~tbarnes2%2FGameDesignFall05%2FSlides%2FCh4.2-CollDet.ppt&ei=x4ucULy5M6-N0QGRy4D4Cg&usg=AFQjCNG7FVDXWRdLv8_-T5qnFyYld53cTQ&cad=rja

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  • Share Your Top 30 Visited Domains with Visitation Cloud for Firefox

    - by Asian Angel
    Curious about the domains that you visit most or perhaps you want a way to share that information on a social website? Now you can see and share the 30 most visited domains in your browser’s history with the Visitation Cloud extension. Accessing Visitation Cloud As soon as you install the extension you can get started using it. Depending on how your browser’s UI is set up there are three methods for accessing Visitation Cloud: a “Visitation Cloud Button” inserted at the end of your “Bookmarks Toolbar”, a menu listing in the “Tools Menu”, and a “Toolbar Button” (not shown here). Visitation Cloud in Action As soon as you activate Visitation Cloud a new window will appear with your top domains displayed in a cloud format. Keep in mind that this is more than just a static image…each listing is actually a clickable link. Clicking on any of the listings will open that domain in a new tab or window depending on your particular browser settings. If you feel that you have a great set of links and want to share it with your friends then that is easy to do. Right click anywhere within the Visitation Cloud Window and select “Save as…”. The “cloud image” can be saved in “.png, .jpg, or Scalable Vector Graphics (.svg)” format. For our example we chose the “.svg format”. Perhaps you love the set of links but not the layout…right click and select “Randomize” to change how the cloud looks. Here is our cloud after being “Randomized”. Things definitely got moved around… Accessing the Visitation Cloud Image in other Browsers Once you have your “cloud image” saved you can share it with friends or save it for your own future use in other browsers. Here is our “cloud image” open in Opera Browser with link opening in progress. The same “cloud image” open in Google Chrome. Very nice… Conclusion While this may not be something that everyone will use Visitation Cloud does make for a rather unique, interesting, & fun way to access and share your most visited domains. Links Download the Visitation Cloud extension (Mozilla Add-ons) Similar Articles Productive Geek Tips Fix "Security Error: Domain Name Mismatch" Warning in FirefoxAdd Variety to Your Searches with Search CloudletRestore Your Missing/Deleted Smart Bookmarks Folder in Firefox 3Blocking Spam from International Senders in Windows Vista MailSee Where a Package is Installed on Ubuntu TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Share High Res Photos using Divvyshot Draw Online using Harmony How to Browse Privately in Firefox Kill Processes Quickly with Process Assassin Need to Come Up with a Good Name? Try Wordoid StockFox puts a Lightweight Stock Ticker in your Statusbar

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  • View HTML Tags and Webpage Combined in Firefox

    - by Asian Angel
    Do you want an easier way to see a webpage’s html tags without viewing the source code in a separate window? Now you can view the webpage and tags combined in the same window using the X-Ray extension for Firefox. Before Usually if you want to see the source code behind a webpage you have to view it in a separate window. If you are only interested in a specific section then you have to search through the entire set of code just to find what you are looking for. After The X-Ray extension will let you see the document’s tags (including class and ID names) “side by side” with the webpage in the same tab. You can use either the context menu or the tools menu to access the X-Ray command. Here is the same webpage section shown in the first screenshot above. It may look a little odd at first until you get used to seeing both together. Note: You can return the webpage to its’ normal view by either clicking on the X-Ray command again or refreshing the page. The code for part of the sidebar on the same webpage… Followed by one of the sets of links at the end. Looking at another example suppose you are interested in how part of the main feed is set up. Being able to see how a particular element is set up directly in the webpage is certainly better than searching through the entire page of code. Conclusion If you design webpages and want an easy way to see how someone else’s website is coded then you may want to give this extension a try. Links Download the X-Ray extension (Mozilla Add-ons) Similar Articles Productive Geek Tips View Webpage Source Code in Tabs in FirefoxCreate Pre-Formatted Links in FirefoxRemove Webpage Formatting or View the HTML Code When Copying in FirefoxInsert Special Characters & Coding in Online Forms in FirefoxCombine the Address Bar and Progress Bar Together in Firefox TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips HippoRemote Pro 2.2 Xobni Plus for Outlook All My Movies 5.9 CloudBerry Online Backup 1.5 for Windows Home Server Convert BMP, TIFF, PCX to Vector files with RasterVect Free Identify Fonts using WhatFontis.com Windows 7’s WordPad is Actually Good Greate Image Viewing and Management with Zoner Photo Studio Free Windows Media Player Plus! – Cool WMP Enhancer Get Your Team’s World Cup Schedule In Google Calendar

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  • How do you stop OgreBullet Capsule from falling over?

    - by Nathan Baggs
    I've just started implementing bullet into my Ogre project. I followed the install instructions here: http://www.ogre3d.org/tikiwiki/OgreBullet+Tutorial+1 And the rest if the tutorial here: http://www.ogre3d.org/tikiwiki/OgreBullet+Tutorial+2 I got that to work fine however now I wanted to extend it to a handle a first person camera. I created a CapsuleShape and a Rigid Body (like the tutorial did for the boxes) however when I run the game the capsule falls over and rolls around on the floor, causing the camera swing wildly around. I need a way to fix the capsule to always stay upright, but I have no idea how Below is the code I'm using. (part of) Header File OgreBulletDynamics::DynamicsWorld *mWorld; // OgreBullet World OgreBulletCollisions::DebugDrawer *debugDrawer; std::deque<OgreBulletDynamics::RigidBody *> mBodies; std::deque<OgreBulletCollisions::CollisionShape *> mShapes; OgreBulletCollisions::CollisionShape *character; OgreBulletDynamics::RigidBody *characterBody; Ogre::SceneNode *charNode; Ogre::Camera* mCamera; Ogre::SceneManager* mSceneMgr; Ogre::RenderWindow* mWindow; main file bool MinimalOgre::go(void) { ... mCamera = mSceneMgr->createCamera("PlayerCam"); mCamera->setPosition(Vector3(0,0,0)); mCamera->lookAt(Vector3(0,0,300)); mCamera->setNearClipDistance(5); mCameraMan = new OgreBites::SdkCameraMan(mCamera); OgreBulletCollisions::CollisionShape *Shape; Shape = new OgreBulletCollisions::StaticPlaneCollisionShape(Vector3(0,1,0), 0); // (normal vector, distance) OgreBulletDynamics::RigidBody *defaultPlaneBody = new OgreBulletDynamics::RigidBody( "BasePlane", mWorld); defaultPlaneBody->setStaticShape(Shape, 0.1, 0.8); // (shape, restitution, friction) // push the created objects to the deques mShapes.push_back(Shape); mBodies.push_back(defaultPlaneBody); character = new OgreBulletCollisions::CapsuleCollisionShape(1.0f, 1.0f, Vector3(0, 1, 0)); charNode = mSceneMgr->getRootSceneNode()->createChildSceneNode(); charNode->attachObject(mCamera); charNode->setPosition(mCamera->getPosition()); characterBody = new OgreBulletDynamics::RigidBody("character", mWorld); characterBody->setShape( charNode, character, 0.0f, // dynamic body restitution 10.0f, // dynamic body friction 10.0f, // dynamic bodymass Vector3(0,0,0), Quaternion(0, 0, 1, 0)); mShapes.push_back(character); mBodies.push_back(characterBody); ... }

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