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  • 3D plotting in Matlab

    - by Jill
    I'm using the subplot and then surf functions to generate images in 3D in Matlab. How do I get rid of the axes and axis' gridlines and change the color to all yellow or all green or something like that? Thanks.

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  • MATLAB : frequency distribution

    - by Arkapravo
    I have raw observations of 500 numeric values (ranging from 1 to 25000) in a text file, I wish to make a frequency distribution in MATLAB. I did try the histogram (hist), however I would prefer a frequency distribution curve than blocks and bars. Any help is appreciated !

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  • matlab get color

    - by iteratorr
    I am using matlab for cluster visualization. I want to somehow get the color of my current cluster center fill in the plot and draw line of same color to cluster members. How can I get the color?

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  • MATLAB query about for loop, reading in data and plotting

    - by mp7
    Hi there, I am a complete novice at using matlab and am trying to work out if there is a way of optimising my code. Essentially I have data from model outputs and I need to plot them using matlab. In addition I have reference data (with 95% confidence intervals) which I plot on the same graph to get a visual idea on how close the model outputs and reference data is. In terms of the model outputs I have several thousand files (number sequentially) which I open in a loop and plot. The problem/question I have is whether I can preprocess the data and then plot later - to save time. The issue I seem to be having when I try this is that I have a legend which either does not appear or is inaccurate. My code (apolgies if it not elegant): fn= xlsread(['tbobserved' '.xls']); time= fn(:,1); totalreference=fn(:,4); totalreferencelowerci=fn(:,6); totalreferenceupperci=fn(:,7); figure plot(time,totalrefrence,'-', time, totalreferencelowerci,'--', time, totalreferenceupperci,'--'); xlabel('Year'); ylabel('Reference incidence per 100,000 population'); title ('Total'); clickableLegend('Observed reference data', 'Totalreferencelowerci', 'Totalreferenceupperci','Location','BestOutside'); xlim([1910 1970]); hold on start_sim=10000; end_sim=10005; h = zeros (1,1000); for i=start_sim:end_sim %is there any way of doing this earlier to save time? a=int2str(i); incidenceFile =strcat('result_', 'Sim', '_', a, 'I_byCal_total.xls'); est_tot=importdata(incidenceFile, '\t', 1); cal_tot=est_tot.data; magnitude=1; t1=cal_tot(:,1)+1750; totalmodel=cal_tot(:,3)+cal_tot(:,5); h(a)=plot(t1,totalmodel); xlim([1910 1970]); ylim([0 500]); hold all clickableLegend(h(a),a,'Location','BestOutside') end Essentially I was hoping to have a way of reading in the data and then plot later - ie. optimise the code. I hope you might be able to help. Thanks. mp

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  • Evaluating and graphing functions in Matlab

    - by thiol3
    New to programming, I am trying to graph the following Gaussian function in Matlab (should graph in 3 dimensions) but am making some mistakes somewhere. What is wrong? sigma = 1 for i = 1:20 for j = 1:20 z(i,j) = (1/(2*pi*sigma^2))*exp(-(i^2+j^2)/(2*sigma^2)); end end surf(z)

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  • Matlab code works with one version but not the other

    - by user1325655
    I have a code that works in Matlab version R2010a but shows errors in matlab R2008a. I am trying to implement a self organizing fuzzy neural network with extended kalman filter. I have the code running but it only works in matlab version R2010a. It doesn't work with other versions. Any help? Code attach function [ c, sigma , W_output ] = SOFNN( X, d, Kd ) %SOFNN Self-Organizing Fuzzy Neural Networks %Input Parameters % X(r,n) - rth traning data from nth observation % d(n) - the desired output of the network (must be a row vector) % Kd(r) - predefined distance threshold for the rth input %Output Parameters % c(IndexInputVariable,IndexNeuron) % sigma(IndexInputVariable,IndexNeuron) % W_output is a vector %Setting up Parameters for SOFNN SigmaZero=4; delta=0.12; threshold=0.1354; k_sigma=1.12; %For more accurate results uncomment the following %format long; %Implementation of a SOFNN model [size_R,size_N]=size(X); %size_R - the number of input variables c=[]; sigma=[]; W_output=[]; u=0; % the number of neurons in the structure Q=[]; O=[]; Psi=[]; for n=1:size_N x=X(:,n); if u==0 % No neuron in the structure? c=x; sigma=SigmaZero*ones(size_R,1); u=1; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); pT_n=GetMeGreatPsi(x,Psi(n,:))'; else [Q,O,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n); end; KeepSpinning=true; while KeepSpinning %Calculate the error and if-part criteria ae=abs(d(n)-pT_n*O); %approximation error [phi,~]=GetMePhi(x,c,sigma); [maxphi,maxindex]=max(phi); % maxindex refers to the neuron's index if ae>delta if maxphi<threshold %enlarge width [minsigma,minindex]=min(sigma(:,maxindex)); sigma(minindex,maxindex)=k_sigma*minsigma; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); pT_n=GetMeGreatPsi(x,Psi(n,:))'; else %Add a new neuron and update structure ctemp=[]; sigmatemp=[]; dist=0; for r=1:size_R dist=abs(x(r)-c(r,1)); distIndex=1; for j=2:u if abs(x(r)-c(r,j))<dist distIndex=j; dist=abs(x(r)-c(r,j)); end; end; if dist<=Kd(r) ctemp=[ctemp; c(r,distIndex)]; sigmatemp=[sigmatemp ; sigma(r,distIndex)]; else ctemp=[ctemp; x(r)]; sigmatemp=[sigmatemp ; dist]; end; end; c=[c ctemp]; sigma=[sigma sigmatemp]; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); KeepSpinning=false; u=u+1; end; else if maxphi<threshold %enlarge width [minsigma,minindex]=min(sigma(:,maxindex)); sigma(minindex,maxindex)=k_sigma*minsigma; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); pT_n=GetMeGreatPsi(x,Psi(n,:))'; else %Do nothing and exit the while KeepSpinning=false; end; end; end; end; W_output=O; end function [Q_next, O_next,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n) %O=O(t-1) O_next=O(t) p_n=GetMeGreatPsi(X(:,n),Psi(n,:)); pT_n=p_n'; ee=abs(d(n)-pT_n*O); %|e(t)| temp=1+pT_n*Q*p_n; ae=abs(ee/temp); if ee>=ae L=Q*p_n*(temp)^(-1); Q_next=(eye(length(Q))-L*pT_n)*Q; O_next=O + L*ee; else Q_next=eye(length(Q))*Q; O_next=O; end; end function [ Q , O ] = UpdateStructure(X,Psi,d) GreatPsiBig = GetMeGreatPsi(X,Psi); %M=u*(r+1) %n - the number of observations [M,~]=size(GreatPsiBig); %Others Ways of getting Q=[P^T(t)*P(t)]^-1 %************************************************************************** %opts.SYM = true; %Q = linsolve(GreatPsiBig*GreatPsiBig',eye(M),opts); % %Q = inv(GreatPsiBig*GreatPsiBig'); %Q = pinv(GreatPsiBig*GreatPsiBig'); %************************************************************************** Y=GreatPsiBig\eye(M); Q=GreatPsiBig'\Y; O=Q*GreatPsiBig*d'; end %This function works too with x % (X=X and Psi is a Matrix) - Gets you the whole GreatPsi % (X=x and Psi is the row related to x) - Gets you just the column related with the observation function [GreatPsi] = GetMeGreatPsi(X,Psi) %Psi - In a row you go through the neurons and in a column you go through number of %observations **** Psi(#obs,IndexNeuron) **** GreatPsi=[]; [N,U]=size(Psi); for n=1:N x=X(:,n); GreatPsiCol=[]; for u=1:U GreatPsiCol=[ GreatPsiCol ; Psi(n,u)*[1; x] ]; end; GreatPsi=[GreatPsi GreatPsiCol]; end; end function [phi, SumPhi]=GetMePhi(x,c,sigma) [r,u]=size(c); %u - the number of neurons in the structure %r - the number of input variables phi=[]; SumPhi=0; for j=1:u % moving through the neurons S=0; for i=1:r % moving through the input variables S = S + ((x(i) - c(i,j))^2) / (2*sigma(i,j)^2); end; phi = [phi exp(-S)]; SumPhi = SumPhi + phi(j); %phi(u)=exp(-S) end; end %This function works too with x, it will give you the row related to x function [Psi] = GetMePsi(X,c,sigma) [~,u]=size(c); [~,size_N]=size(X); %u - the number of neurons in the structure %size_N - the number of observations Psi=[]; for n=1:size_N [phi, SumPhi]=GetMePhi(X(:,n),c,sigma); PsiTemp=[]; for j=1:u %PsiTemp is a row vector ex: [1 2 3] PsiTemp(j)=phi(j)/SumPhi; end; Psi=[Psi; PsiTemp]; %Psi - In a row you go through the neurons and in a column you go through number of %observations **** Psi(#obs,IndexNeuron) **** end; end

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  • Matlab regex if statement

    - by Dan
    I want to have matlab take user input but accept both cases of a letter. For example I have: function nothing = checkGC(gcfile) if exist(gcfile) reply = input('file exists, would you like to overwrite? [Y/N]: ', 's'); if (reply == [Yy]) display('You have chosen to overwrite!') else $ Do nothing end end The if statement obviously doesn't work, but basically I want to accept a lowercase or uppcase Y. Whats the best way to do this?

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  • compilation of image stitching code in matlab

    - by chee
    i am facing lots of problems while running code for image stitching given at this link http://se.cs.ait.ac.th/cvwiki/matlab:tutorial:image_stitching_from_high-view_images_using_homography may i get help regarding this type of problems here. EDIT: Image stitching code fails with the following message: ??? Undefined function or variable 'x2'. Error in ==compute_direct_homography at 26 amplified_x2=x2.*repmat([diagonal_ratio(x1,x2) diagonal_ratio(x1,x2) 1]',1,size(x2,2)); %assumption 1degree of lat and long =110,000 meters refer wiki Error in == project at 3 compute_direct_homography;

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  • Funny plots in MATLAB

    - by Arkapravo
    I recently learned the ezplot function in MATLAB. Recently I typed in ezplot('x^y - y^x', [-100 100 -100 100]); and this is what I got; Can anyone please tell me whatever is happening ? for lower scaling of x and y ( [ -10 10 -10 10]) there are more patterns in the 2nd 3rd and 4th quadrants. I was not very sure of the shape of curve, but I did not expect this !

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  • How to parse the file name and rename in Matlab

    - by Paul
    I am reading a .xls file and then procesing it inside and rewriting it in the end of my program. I was wondering if someone can help me to parse the dates as my input file name is like file_1_2010_03_03.csv and i want my outputfile to be newfile_2010_03_03.xls is there a way to incorporate in matlab program so i do not have to manually write the command xlswrite('newfile_2010_03_03.xls', M); everytime and change the dates as i input files with diff dates like file_2_2010_03_04.csv. Thanks

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  • showing .tif images in matlab

    - by sepideh
    I am trying to show a .tif image in matlab and I use these two line of codes a = imread('C:\Users\sepideh\Desktop\21_15.tif'); imshow(a) that encounters this warning Warning: Image is too big to fit on screen; displaying at 3% In imuitools\private\initSize at 73 In imshow at 262 what is the cause of this warning and what can I do to fix that? the main trouble is it sometimes doesn't show the image and of course even if it shows the image CPU usage gets high that I can't zoom properly

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  • Numpy/Python performing terribly vs. Matlab

    - by Nissl
    Novice programmer here. I'm writing a program that analyzes the relative spatial locations of points (cells). The program gets boundaries and cell type off an array with the x coordinate in column 1, y coordinate in column 2, and cell type in column 3. It then checks each cell for cell type and appropriate distance from the bounds. If it passes, it then calculates its distance from each other cell in the array and if the distance is within a specified analysis range it adds it to an output array at that distance. My cell marking program is in wxpython so I was hoping to develop this program in python as well and eventually stick it into the GUI. Unfortunately right now python takes ~20 seconds to run the core loop on my machine while MATLAB can do ~15 loops/second. Since I'm planning on doing 1000 loops (with a randomized comparison condition) on ~30 cases times several exploratory analysis types this is not a trivial difference. I tried running a profiler and array calls are 1/4 of the time, almost all of the rest is unspecified loop time. Here is the python code for the main loop: for basecell in range (0, cellnumber-1): if firstcelltype == np.array((cellrecord[basecell,2])): xloc=np.array((cellrecord[basecell,0])) yloc=np.array((cellrecord[basecell,1])) xedgedist=(xbound-xloc) yedgedist=(ybound-yloc) if xloc>excludedist and xedgedist>excludedist and yloc>excludedist and yedgedist>excludedist: for comparecell in range (0, cellnumber-1): if secondcelltype==np.array((cellrecord[comparecell,2])): xcomploc=np.array((cellrecord[comparecell,0])) ycomploc=np.array((cellrecord[comparecell,1])) dist=math.sqrt((xcomploc-xloc)**2+(ycomploc-yloc)**2) dist=round(dist) if dist>=1 and dist<=analysisdist: arraytarget=round(dist*analysisdist/intervalnumber) addone=np.array((spatialraw[arraytarget-1])) addone=addone+1 targetcell=arraytarget-1 np.put(spatialraw,[targetcell,targetcell],addone) Here is the matlab code for the main loop: for basecell = 1:cellnumber; if firstcelltype==cellrecord(basecell,3); xloc=cellrecord(basecell,1); yloc=cellrecord(basecell,2); xedgedist=(xbound-xloc); yedgedist=(ybound-yloc); if (xloc>excludedist) && (yloc>excludedist) && (xedgedist>excludedist) && (yedgedist>excludedist); for comparecell = 1:cellnumber; if secondcelltype==cellrecord(comparecell,3); xcomploc=cellrecord(comparecell,1); ycomploc=cellrecord(comparecell,2); dist=sqrt((xcomploc-xloc)^2+(ycomploc-yloc)^2); if (dist>=1) && (dist<=100.4999); arraytarget=round(dist*analysisdist/intervalnumber); spatialsum(1,arraytarget)=spatialsum(1,arraytarget)+1; end end end end end end Thanks!

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