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  • C++ vs Matlab vs Python as a main language for Computer Vision Research

    - by Hough
    Hi all, Firstly, sorry for a somewhat long question but I think that many people are in the same situation as me and hopefully they can also gain some benefit from this. I'll be starting my PhD very soon which involves the fields of computer vision, pattern recognition and machine learning. Currently, I'm using opencv (2.1) C++ interface and I especially like its powerful Mat class and the overloaded operations available for matrix and image operations and seamless transformations. I've also tried (and implemented many small vision projects) using opencv python interface (new bindings; opencv 2.1) and I really enjoy python's ability to integrate opencv, numpy, scipy and matplotlib. But recently, I went back to opencv C++ interface because I felt that the official python new bindings were not stable enough and no overloaded operations are available for matrices and images, not to mention the lack of machine learning modules and slow speeds in certain operations. I've also used Matlab extensively in the past and although I've used mex files and other means to speed up the program, I just felt that Matlab's performance was inadequate for real-time vision tasks, be it for fast prototyping or not. When the project becomes larger and larger, many tasks have to be re-written in C and compiled into Mex files increasingly and Matlab becomes nothing more than a glue language. Here comes the sub-questions: For carrying out research in these fields (machine learning, vision, pattern recognition), what is your main or ideal programming language for rapid prototyping of ideas and testing algorithms contained in papers? For computer vision research work, can you list down the pros and cons of using the following languages? C++ (with opencv + gsl + svmlib + other libraries) vs Matlab (with all its toolboxes) vs python (with the imcomplete opencv bindings + numpy + scipy + matplotlib). Are there computer vision PhD/postgrad students here who are using only C++ (with all its availabe libraries including opencv) without even needing to resort to Matlab or python? In other words, given the current existing computer vision or machine learning libraries, is C++ alone sufficient for fast prototyping of ideas? If you're currently using Java or C# for your research, can you list down the reasons why they should be used and how they compare to other languages in terms of available libraries? What is the de facto vision/machine learning programming language and its associated libraries used in your research group? Thanks in advance. Edit: As suggested, I've opened the question to both academic and non-academic computer vision/machine learning/pattern recognition researchers and groups.

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  • Prolog: Sentence Parser Problem

    - by Devon
    Hey guys, Been sat here for hours now just staring at this code and have no idea what I'm doing wrong. I know what's happening from tracing the code through (it is going on an eternal loop when it hits verbPhrase). Any tips are more then welcome. Thank you. % Knowledge-base det(the). det(a). adjective(quick). adjective(brown). adjective(orange). adjective(sweet). noun(cat). noun(mat). noun(fox). noun(cucumber). noun(saw). noun(mother). noun(father). noun(family). noun(depression). prep(on). prep(with). verb(sat). verb(nibbled). verb(ran). verb(looked). verb(is). verb(has). % Sentece Structures sentence(Phrase) :- append(NounPhrase, VerbPhrase, Phrase), nounPhrase(NounPhrase), verbPhrase(VerbPhrase). sentence(Phrase) :- verbPhrase(Phrase). nounPhrase([]). nounPhrase([Head | Tail]) :- det(Head), nounPhrase2(Tail). nounPhrase(Phrase) :- nounPhrase2(Phrase). nounPhrase(Phrase) :- append(NP, PP, Phrase), nounPhrase(NP), prepPhrase(PP). nounPhrase2([]). nounPhrase2(Word) :- noun(Word). nounPhrase2([Head | Tail]) :- adjective(Head), nounPhrase2(Tail). prepPhrase([]). prepPhrase([Head | Tail]) :- prep(Head), nounPhrase(Tail). verbPhrase([]). verbPhrase(Word) :- verb(Word). verbPhrase([Head | Tail]) :- verb(Head), nounPhrase(Tail). verbPhrase(Phrase) :- append(VP, PP, Phrase), verbPhrase(VP), prepPhrase(PP).

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  • UpdatePanel Full Postback

    - by Korivo
    Greetings, here is the scenario. I have and .aspx page with and updatepanel like this <asp:UpdatePanel id="uPanelMain" runat="server"> <ContentTemplate> <uc:Calendar id="ucCalendar" runat="server" Visible="true" /> <uc:Scoring id="ucScoring" runat="server" Visible="false" /> </ContentTemplate> The control ucCalendar is loaded first and it contains a grid like this <asp:DataGrid CssClass="grid" ID="gridGames" runat="server" AutoGenerateColumns="False" HeaderStyle-CssClass="gridHeader" ItemStyle-CssClass="gridScoringRow" GridLines="None" ItemStyle-BackColor="#EEEEEE" AlternatingItemStyle-BackColor="#F5F5F5" OnEditCommand="doScoreGame" OnDeleteCommand="doEditGame" OnCancelCommand="printLineup" OnItemDataBound="gridDataBound"> <Columns> <asp:TemplateColumn > <ItemTemplate> <asp:CheckBox ID="chkDelete" runat="server" /> </ItemTemplate> </asp:TemplateColumn> <asp:BoundColumn DataField="idGame" Visible="false" /> <asp:BoundColumn DataField="isClose" Visible="false" /> <asp:TemplateColumn HeaderText="Status"> <ItemTemplate> <asp:Image ID="imgStatus" runat="server" ImageUrl="~/img/icoX.png" alt="icoStatus" /> </ItemTemplate> </asp:TemplateColumn> <asp:TemplateColumn> <ItemTemplate> <asp:LinkButton ID="linkScore" runat="server" CommandName="Edit" Text="Score" /> </ItemTemplate> </asp:TemplateColumn> </Columns> </asp:DataGrid> So when i click the "linkButton", the codebehind of the userControl calls a public method in the .aspx as this: From the userControl protected void doScoreGame(object sender, DataGridCommandEventArgs e) { ((GM)this.Page).showScoring(null, null); } From the .aspx page public void showScoring(object sender, EventArgs e) { removeLastLoadedControl(); ucScoring.Visible = true; } So, here comes the problem: There are two postbacks taking place when I change the visible attribute of the ucScoring control. The first postback is fine, it's handled by the updatePanel. The second postback is a full postback, and i really don't understand why it is happening. I'm really lost here, please help! Thanks Mat

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  • I am getting error when using Attributes in Rcpp and have RcppArmadillo code

    - by howard123
    I am trying to create a package with RcppArmadillo. The code uses the new attributes methodology of Rcpp. The sourceCpp works fine and compiles the code, but when I build a package I get errors when I use RcppArmadillo code. Without the RcppArmadillo code and using regulare C++, I do not get these errors. The C++ code (it is essentially the fastLm sample code) is: // [[Rcpp::depends(RcppArmadillo)]] #include <Rcpp.h> #include <RcppArmadillo.h> using namespace Rcpp; // [[Rcpp::depends(RcppArmadillo)]] #include <RcppArmadillo.h> // [[Rcpp::export]] List fastLm(NumericVector yr, NumericMatrix Xr) { int n = Xr.nrow(), k = Xr.ncol(); arma::mat X(Xr.begin(), n, k, false); arma::colvec y(yr.begin(), yr.size(), false); arma::colvec coef = arma::solve(X, y); arma::colvec resid = y - X*coef; double sig2 = arma::as_scalar(arma::trans(resid)*resid/(n-k)); arma::colvec stderrest = arma::sqrt( sig2 * arma::diagvec( arma::inv(arma::trans(X)*X)) ); return List::create(Named("coefficients") = coef, Named("stderr") = stderrest); } Here is the compilation error, after I execute "R Rcpp::compileAttributes() * Updated src/RcppExports.cpp == Rcmd.exe INSTALL --no-multiarch NewPackage * installing to library 'C:/Users/Howard/Documents/R/win-library/2.15' * installing *source* package 'NewPackage' ... ** libs g++ -m64 -I"C:/R/R-2-15-2/include" -DNDEBUG -I"C:/Users/Howard/Documents/R/win-library/2.15/Rcpp/include" -I"C:/Users/Howard/Documents/R/win-library/2.15/RcppArmadillo/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -mtune=core2 -c RcppExports.cpp -o RcppExports.o g++ -m64 -I"C:/R/R-2-15-2/include" -DNDEBUG -I"C:/Users/Howard/Documents/R/win-library/2.15/Rcpp/include" -I"C:/Users/Howard/Documents/R/win-library/2.15/RcppArmadillo/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -mtune=core2 -c test_arma3.cpp -o test_arma3.o g++ -m64 -shared -s -static-libgcc -o NewPackage.dll tmp.def RcppExports.o test_arma3.o C:/Users/Howard/Documents/R/win-library/2.15/Rcpp/lib/x64/libRcpp.a -Ld:/RCompile/CRANpkg/extralibs64/local/lib/x64 -Ld:/RCompile/CRANpkg/extralibs64/local/lib -LC:/R/R-2-15-2/bin/x64 -lR test_arma3.o:test_arma3.cpp:(.text+0xae4): undefined reference to `dgemm_' test_arma3.o:test_arma3.cpp:(.text+0x19db): undefined reference to `dgemm_' test_arma3.o:test_arma3.cpp:(.text+0x1b0c): undefined reference to `dgemv_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma6auxlib8solve_odIdNS_3MatIdEEEEbRNS2_IT_EES6_RKNS_4BaseIS4_T0_EE[_ZN4arma6auxlib8solve_odIdNS_3MatIdEEEEbRNS2_IT_EES6_RKNS_4BaseIS4_T0_EE]+0x702): undefined reference to `dgels_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma6auxlib8solve_udIdNS_3MatIdEEEEbRNS2_IT_EES6_RKNS_4BaseIS4_T0_EE[_ZN4arma6auxlib8solve_udIdNS_3MatIdEEEEbRNS2_IT_EES6_RKNS_4BaseIS4_T0_EE]+0x51c): undefined reference to `dgels_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma6auxlib10det_lapackIdEET_RKNS_3MatIS2_EEb[_ZN4arma6auxlib10det_lapackIdEET_RKNS_3MatIS2_EEb]+0x14b): undefined reference to `dgetrf_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma6auxlib5solveIdNS_3MatIdEEEEbRNS2_IT_EES6_RKNS_4BaseIS4_T0_EEb[_ZN4arma6auxlib5solveIdNS_3MatIdEEEEbRNS2_IT_EES6_RKNS_4BaseIS4_T0_EEb]+0x375): undefined reference to `dgesv_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma4gemvILb1ELb0ELb0EE15apply_blas_typeIdEEvPT_RKNS_3MatIS3_EEPKS3_S3_S3_[_ZN4arma4gemvILb1ELb0ELb0EE15apply_blas_typeIdEEvPT_RKNS_3MatIS3_EEPKS3_S3_S3_]+0x17d): undefined reference to `dgemv_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma27glue_times_redirect2_helperILb1EE5applyINS_2OpINS_3MatIdEENS_9op_htransEEES5_EEvRNS4_INT_9elem_typeEEERKNS_4GlueIS8_T0_NS_10glue_timesEEE[_ZN4arma27glue_times_redirect2_helperILb1EE5applyINS_2OpINS_3MatIdEENS_9op_htransEEES5_EEvRNS4_INT_9elem_typeEEERKNS_4GlueIS8_T0_NS_10glue_timesEEE]+0x37a): undefined reference to `dgemm_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE[_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE]+0x2c1): undefined reference to `dgetrf_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE[_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE]+0x322): undefined reference to `dgetri_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE[_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE]+0x398): undefined reference to `dgetri_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE[_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE]+0x775): undefined reference to `dgetrf_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE[_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE]+0x7d6): undefined reference to `dgetri_' test_arma3.o:test_arma3.cpp:(.text$_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE[_ZN4arma10op_diagvec5applyINS_2OpINS_4GlueINS2_INS_3MatIdEENS_9op_htransEEES5_NS_10glue_timesEEENS_6op_invEEEEEvRNS4_INT_9elem_typeEEERKNS2_ISC_S0_EE]+0x892): undefined reference to `dgetri_' collect2: ld returned 1 exit status ERROR: compilation failed for package 'NewPackage' * removing 'C:/Users/Howard/Documents/R/win-library/2.15/NewPackage' * restoring previous 'C:/Users/Howard/Documents/R/win-library/2.15/NewPackage' Exited with status 1.

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  • image processing algorithm in MATLAB

    - by user261002
    I am trying to reconstruct an algorithm belong to this paper: Decomposition of biospeckle images in temporary spectral bands Here is an explanation of the algorithm: We recorded a sequence of N successive speckle images with a sampling frequency fs. In this way it was possible to observe how a pixel evolves through the N images. That evolution can be treated as a time series and can be processed in the following way: Each signal corresponding to the evolution of every pixel was used as input to a bank of filters. The intensity values were previously divided by their temporal mean value to minimize local differences in reflectivity or illumination of the object. The maximum frequency that can be adequately analyzed is determined by the sampling theorem and s half of sampling frequency fs. The latter is set by the CCD camera, the size of the image, and the frame grabber. The bank of filters is outlined in Fig. 1. In our case, ten 5° order Butterworth11 filters were used, but this number can be varied according to the required discrimination. The bank was implemented in a computer using MATLAB software. We chose the Butter-worth filter because, in addition to its simplicity, it is maximally flat. Other filters, an infinite impulse response, or a finite impulse response could be used. By means of this bank of filters, ten corresponding signals of each filter of each temporary pixel evolution were obtained as output. Average energy Eb in each signal was then calculated: where pb(n) is the intensity of the filtered pixel in the nth image for filter b divided by its mean value and N is the total number of images. In this way, en values of energy for each pixel were obtained, each of hem belonging to one of the frequency bands in Fig. 1. With these values it is possible to build ten images of the active object, each one of which shows how much energy of time-varying speckle there is in a certain frequency band. False color assignment to the gray levels in the results would help in discrimination. and here is my MATLAB code base on that : clear all for i=0:39 str = num2str(i); str1 = strcat(str,'.mat'); load(str1); D{i+1}=A; end new_max = max(max(A)); new_min = min(min(A)); for i=20:180 for j=20:140 ts = []; for k=1:40 ts = [ts D{k}(i,j)]; %%% kth image pixel i,j --- ts is time series end ts = double(ts); temp = mean(ts); ts = ts-temp; ts = ts/temp; N = 5; % filter order W = [0.00001 0.05;0.05 0.1;0.1 0.15;0.15 0.20;0.20 0.25;0.25 0.30;0.30 0.35;0.35 0.40;0.40 0.45;0.45 0.50]; N1 = 5; for ind = 1:10 Wn = W(ind,:); [B,A] = butter(N1,Wn); ts_f(ind,:) = filter(B,A,ts); end for ind=1:10 imag_test1{ind}(i,j) =sum((ts_f(ind,:)./mean(ts_f(ind,:))).^2); end end end for i=1:10 temp_imag = imag_test1{i}(:,:); x=isnan(temp_imag); temp_imag(x)=0; temp_imag=medfilt2(temp_imag); t_max = max(max(temp_imag)); t_min = min(min(temp_imag)); temp_imag = (temp_imag-t_min).*(double(new_max-new_min)/double(t_max-t_min))+double(new_min); imag_test2{i}(:,:) = temp_imag; end for i=1:10 A=imag_test2{i}(:,:); B=A/max(max(A)); B=histeq(B); figure,imshow(B) colorbar end but I am not getting the same result as paper. has anybody has aby idea why? or where I have gone wrong? Refrence Link to the paper

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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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  • Sorting linked lists in Pascal

    - by user3712174
    I'm doing my final project for Informatics class and I can't get my sorting procedure to work. Have a look at my program, specifically the bolded part (some things are in Croatian. - if you need something translated, let me know): type pokazivac=^slog; slog=record prezime_ime:string[30]; redni_broj:string[2]; fakultet:string[50]; bodovi:integer; sljedeci:pokazivac; end; var pocetni, trenutni, prethodni:pokazivac; i:integer; procedure racunaj; var i,a,c:integer; b,d,e,f,g,h,j:real; begin write('Postotak bodova (u decimalnom zapisu) koje ucenik ostvaruje na temelju prosjeka ocjena - '); readln(e); e:=e*1000/4; write('Prosjek ocjena u prvom razredu : '); readln(f); f:=f/5*e; write('Prosjek ocjena u drugom razredu : '); readln(g); g:=g/5*e; write('Prosjek ocjena u trecem razredu : '); readln(h); h:=h/5*e; write('Prosjek ocjena u cetvrtom razredu : '); readln(j); j:=j/5*e; d:=f+g+h+j; write('Broj predmeta (ne racunajuci hrvatski jezik, strani jezik i matematiku) koju je ucenik/ca polagao na maturi - '); readln(a); write('Postotak rijesnosti ispita iz hrvatskog jezika te zatim maksimum bodova koje je ucenik/ca mogao ostvariti - '); readln(b); readln(c); d:=d+b*c; write('Postotak rijesnosti ispita iz stranog jezika te zatim maksimum bodova koje je ucenik/ca mogao ostvariti - '); readln(b); readln(c); d:=d+(b*c); write('Postotak rijesnosti ispita iz matematike te zatim maksimum bodova koje je ucenik/ca mogao ostvariti - '); readln(b); readln(c); d:=d+(b*c); for i:=1 to a do begin writeln('Postotak rijesnosti dodatnog predmeta te zatim maksimum bodova koje je ucenik/ca mogao ostvariti - '); readln(b); readln(c); d:=d+(b*c); end; d:=round(d); writeln('Vas broj bodova je: ', d:4:2); write('Za nastavak pritisnite ENTER..'); readln; end; procedure unos; begin new(trenutni); write('Redni broj ucenika - ');readln(trenutni^.redni_broj); write('Prezime i ime - ');readln(trenutni^.prezime_ime); write('Naziv fakultet - ');readln(trenutni^.fakultet); write('Bodovi - ');readln(trenutni^.bodovi); trenutni^.sljedeci:=pocetni; pocetni:=trenutni; end; procedure ispis; begin writeln(); writeln('Lista popisanih ucenika:'); writeln(); trenutni:=pocetni; while trenutni<>NIL do begin with trenutni^do begin writeln('IME: ',prezime_ime); writeln('FAKULTET: ',fakultet); writeln('BODOVI: ',bodovi); writeln(); end; trenutni:=trenutni^.sljedeci; end; writeln(); write('Za nastavak pritisnite ENTER..'); readln; end; procedure brisi; var s:string; begin trenutni:= pocetni; prethodni:=pocetni; write('Redni broj ucenika kojeg zelite izbrisati - '); readln(s); while trenutni<>NIL do begin if trenutni^.redni_broj=s then begin prethodni^.sljedeci:=trenutni^.sljedeci; dispose(trenutni); break; end; trenutni:=trenutni^.sljedeci; end; end; procedure izmjeni; var s:string; begin trenutni:=pocetni; write('Redni broj ucenika cije podatke zelite izmijeniti - '); readln(s); while trenutni<> NIL do begin if trenutni^.redni_broj=s then begin write(trenutni^.prezime_ime, ' - '); readln(trenutni^.prezime_ime); write(trenutni^.fakultet, ' - '); readln(trenutni^.fakultet); write(trenutni^.bodovi, ' - '); readln(trenutni^.bodovi); break; end; trenutni:=trenutni^.sljedeci; end; end; **procedure sortiraj; var t1,t2,t:pokazivac; begin t1:=pocetni; while t1 <> NIL do begin t2:=t1^.sljedeci; while t2<>NIL do if t2^.bodovi<t1^.bodovi then begin new(t); t^.redni_broj:=t1^.redni_broj; t^.prezime_ime:=t1^.prezime_ime; t^.fakultet:=t1^.fakultet; t^.bodovi:=t1^.bodovi; t1^.redni_broj:=t2^.redni_broj; t1^.prezime_ime:=t2^.prezime_ime; t1^.fakultet:=t2^.fakultet; t1^.bodovi:=t2^.bodovi; t2^.redni_broj:=t^.redni_broj; t2^.prezime_ime:=t^.prezime_ime; t2^.fakultet:=t^.fakultet; t2^.bodovi:=t^.bodovi; dispose(t); end; t2:=t2^.sljedeci; end; t1:=t1^.sljedeci; write('Za nastavak pritisnite ENTER..'); readln; end;** begin pocetni:=NIL; trenutni:=NIL; writeln('******************************************'); writeln('**********DOBRODOSLI U FAX-O-MAT**********'); writeln('******************************************'); repeat writeln('1 - Racunaj broj bodova'); writeln('2 - Dodaj ucenika'); writeln('3 - Brisi ucenika'); writeln('4 - Ispis liste'); writeln('5 - Izmjeni podatke'); writeln('6 - Sortiraj listu prema broju bodova'); writeln('0 - Kraj'); readln(i); case i of 1:racunaj; 2:unos; 3:brisi; 4:ispis; 5:izmjeni; 6:sortiraj; end; until i=0; end. Either it crashes with a fatal error, or when I press the number 6, nothing happens. The pointer keeps blinking and I can't enter any more numbers.

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  • Database Tutorial: The method open() is undefined for the type MainActivity.DBAdapter

    - by user2203633
    I am trying to do this database tutorial on SQLite Eclipse: https://www.youtube.com/watch?v=j-IV87qQ00M But I get a few errors at the end.. at db.ppen(); i get error: The method open() is undefined for the type MainActivity.DBAdapter and similar for insert record and close. MainActivity: package com.example.studentdatabase; import java.io.File; import java.io.FileNotFoundException; import java.io.FileOutputStream; import java.io.IOException; import java.io.InputStream; import java.io.OutputStream; import android.app.Activity; import android.app.ListActivity; import android.content.Intent; import android.database.Cursor; import android.os.Bundle; import android.util.Log; import android.view.LayoutInflater; import android.view.View; import android.view.ViewGroup; import android.widget.BaseAdapter; import android.widget.Button; import android.widget.Toast; public class MainActivity extends Activity { /** Called when the activity is first created. */ //DBAdapter db = new DBAdapter(this); @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); Button addBtn = (Button)findViewById(R.id.add); addBtn.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { Intent i = new Intent(MainActivity.this, addassignment.class); startActivity(i); } }); try { String destPath = "/data/data/" + getPackageName() + "/databases/AssignmentDB"; File f = new File(destPath); if (!f.exists()) { CopyDB( getBaseContext().getAssets().open("mydb"), new FileOutputStream(destPath)); } } catch (FileNotFoundException e) { e.printStackTrace(); } catch (IOException e) { e.printStackTrace(); } DBAdapter db = new DBAdapter(); //---add an assignment--- db.open(); long id = db.insertRecord("Hello World", "2/18/2012", "DPR 224", "First Android Project"); id = db.insertRecord("Workbook Exercises", "3/1/2012", "MAT 100", "Do odd numbers"); db.close(); //---get all Records--- /* db.open(); Cursor c = db.getAllRecords(); if (c.moveToFirst()) { do { DisplayRecord(c); } while (c.moveToNext()); } db.close(); */ /* //---get a Record--- db.open(); Cursor c = db.getRecord(2); if (c.moveToFirst()) DisplayRecord(c); else Toast.makeText(this, "No Assignments found", Toast.LENGTH_LONG).show(); db.close(); */ //---update Record--- /* db.open(); if (db.updateRecord(1, "Hello Android", "2/19/2012", "DPR 224", "First Android Project")) Toast.makeText(this, "Update successful.", Toast.LENGTH_LONG).show(); else Toast.makeText(this, "Update failed.", Toast.LENGTH_LONG).show(); db.close(); */ /* //---delete a Record--- db.open(); if (db.deleteRecord(1)) Toast.makeText(this, "Delete successful.", Toast.LENGTH_LONG).show(); else Toast.makeText(this, "Delete failed.", Toast.LENGTH_LONG).show(); db.close(); */ } private class DBAdapter extends BaseAdapter { private LayoutInflater mInflater; //private ArrayList<> @Override public int getCount() { return 0; } @Override public Object getItem(int arg0) { return null; } @Override public long getItemId(int arg0) { return 0; } @Override public View getView(int arg0, View arg1, ViewGroup arg2) { return null; } } public void CopyDB(InputStream inputStream, OutputStream outputStream) throws IOException { //---copy 1K bytes at a time--- byte[] buffer = new byte[1024]; int length; while ((length = inputStream.read(buffer)) > 0) { outputStream.write(buffer, 0, length); } inputStream.close(); outputStream.close(); } public void DisplayRecord(Cursor c) { Toast.makeText(this, "id: " + c.getString(0) + "\n" + "Title: " + c.getString(1) + "\n" + "Due Date: " + c.getString(2), Toast.LENGTH_SHORT).show(); } public void addAssignment(View view) { Intent i = new Intent("com.pinchtapzoom.addassignment"); startActivity(i); Log.d("TAG", "Clicked"); } } DBAdapter code: package com.example.studentdatabase; public class DBAdapter { public static final String KEY_ROWID = "id"; public static final String KEY_TITLE = "title"; public static final String KEY_DUEDATE = "duedate"; public static final String KEY_COURSE = "course"; public static final String KEY_NOTES = "notes"; private static final String TAG = "DBAdapter"; private static final String DATABASE_NAME = "AssignmentsDB"; private static final String DATABASE_TABLE = "assignments"; private static final int DATABASE_VERSION = 2; private static final String DATABASE_CREATE = "create table if not exists assignments (id integer primary key autoincrement, " + "title VARCHAR not null, duedate date, course VARCHAR, notes VARCHAR );"; private final Context context; private DatabaseHelper DBHelper; private SQLiteDatabase db; public DBAdapter(Context ctx) { this.context = ctx; DBHelper = new DatabaseHelper(context); } private static class DatabaseHelper extends SQLiteOpenHelper { DatabaseHelper(Context context) { super(context, DATABASE_NAME, null, DATABASE_VERSION); } @Override public void onCreate(SQLiteDatabase db) { try { db.execSQL(DATABASE_CREATE); } catch (SQLException e) { e.printStackTrace(); } } @Override public void onUpgrade(SQLiteDatabase db, int oldVersion, int newVersion) { Log.w(TAG, "Upgrading database from version " + oldVersion + " to " + newVersion + ", which will destroy all old data"); db.execSQL("DROP TABLE IF EXISTS contacts"); onCreate(db); } } //---opens the database--- public DBAdapter open() throws SQLException { db = DBHelper.getWritableDatabase(); return this; } //---closes the database--- public void close() { DBHelper.close(); } //---insert a record into the database--- public long insertRecord(String title, String duedate, String course, String notes) { ContentValues initialValues = new ContentValues(); initialValues.put(KEY_TITLE, title); initialValues.put(KEY_DUEDATE, duedate); initialValues.put(KEY_COURSE, course); initialValues.put(KEY_NOTES, notes); return db.insert(DATABASE_TABLE, null, initialValues); } //---deletes a particular record--- public boolean deleteContact(long rowId) { return db.delete(DATABASE_TABLE, KEY_ROWID + "=" + rowId, null) > 0; } //---retrieves all the records--- public Cursor getAllRecords() { return db.query(DATABASE_TABLE, new String[] {KEY_ROWID, KEY_TITLE, KEY_DUEDATE, KEY_COURSE, KEY_NOTES}, null, null, null, null, null); } //---retrieves a particular record--- public Cursor getRecord(long rowId) throws SQLException { Cursor mCursor = db.query(true, DATABASE_TABLE, new String[] {KEY_ROWID, KEY_TITLE, KEY_DUEDATE, KEY_COURSE, KEY_NOTES}, KEY_ROWID + "=" + rowId, null, null, null, null, null); if (mCursor != null) { mCursor.moveToFirst(); } return mCursor; } //---updates a record--- public boolean updateRecord(long rowId, String title, String duedate, String course, String notes) { ContentValues args = new ContentValues(); args.put(KEY_TITLE, title); args.put(KEY_DUEDATE, duedate); args.put(KEY_COURSE, course); args.put(KEY_NOTES, notes); return db.update(DATABASE_TABLE, args, KEY_ROWID + "=" + rowId, null) > 0; } } addassignment code: package com.example.studentdatabase; public class addassignment extends Activity { DBAdapter db = new DBAdapter(this); @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.add); } public void addAssignment(View v) { Log.d("test", "adding"); //get data from form EditText nameTxt = (EditText)findViewById(R.id.editTitle); EditText dateTxt = (EditText)findViewById(R.id.editDuedate); EditText courseTxt = (EditText)findViewById(R.id.editCourse); EditText notesTxt = (EditText)findViewById(R.id.editNotes); db.open(); long id = db.insertRecord(nameTxt.getText().toString(), dateTxt.getText().toString(), courseTxt.getText().toString(), notesTxt.getText().toString()); db.close(); nameTxt.setText(""); dateTxt.setText(""); courseTxt.setText(""); notesTxt.setText(""); Toast.makeText(addassignment.this,"Assignment Added", Toast.LENGTH_LONG).show(); } public void viewAssignments(View v) { Intent i = new Intent(this, MainActivity.class); startActivity(i); } } What is wrong here? Thanks in advance.

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