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  • Comparing two images by corr2 function

    - by user3696860
    I'm trying to compare two images by corr2 function on Matlab, it's not necess getting 1 end of function so I am using a treshold to find best images among template images. But sometimes it evaluate wrong image. How can I process them to find best match? ` temp=[]; for i=1:10 res=sprintf('%d.png',i) yol=fullfile('cember\taslak_cember\',res); a=imread(yol); b=imread('30_1.png'); a=rgb2gray(a); b=rgb2gray(b); a=im2bw(a,0.4); b=im2bw(b,0.4); c=corr2(a,b); temp=[temp c]; end max(temp) `

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  • I want to make a 2D color plot showing stress magnitude (S) at very loctaion (x, y) based on continuous color change using limited data sets

    - by Alex Liu
    friends, I have to trouble you as I couldn't find a solution after trying for a long time. I have 3 columns of data. x, y, and the stress value (S) at every point (x, y). I want to generate a 2D color plot displaying continuous color change with the magnitude of the stress (S). The stress values increase from -3*10^4 Pa to 4*10^4 Pa. I only have hundreds of data sets for an area, but I want to see the stress magnitude (read from the color) at every location (x, y). What Matlab command should I use? Thank you very much! I want to make a 2D color plot showing stress magnitude (S) at very loctaion (x, y) based on continuous color change using limited

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  • How can you distribute the color intensity of two images using its gradients?

    - by Jeppy-man
    Hello everyone... I am working on an automatic image stitching algorithm using MATLAB. So far, I have downloaded a source code much like the one that I had in mind and so, I'm currently studying how the code work. The problem is, when stitching two or more images together, their color intensity will most probably be different from each other so the stitched seams will be visible to the eye... So, right now, I'm trying to find out how to redistribute their color intensity using the images gradients so that the whole stitched image will have the same color intensity. I hope someone can help me out there and if so, thank you very much...

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  • Writing a script for reading many .csv files with similar filenames

    - by wahalulu
    I have several .csv files with similar filenames except a numeric month (i.e. 03_data.csv, 04_data.csv, 05_data.csv, etc.) that I'd like to read into R. I have two questions: Is there a function in R similar to MATLAB's varname and assignin that will let me create/declare a variable name within a function or loop that will allow me to read the respective .csv file - i.e. 03_data.csv into 03_data data.frame, etc.? I want to write a quick loop to do this because the filenames are similar. As an alternative, is it better to create one dataframe with the first file and then append the rest using a for loop? How would I do that?

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  • Programmatically get valid switch/case values

    - by craigim
    When MATLAB scans through cases in a switch/case block, does it remember the values that it skips, and is it possible to access that list? I have a few functions with long switch\case block and I would like to have them return a list of valid case values if they make it down to otherwise. For instance, I have a function that returns a set of optical constants for a material. It currently has about 20 different materials and it is growing as I consider new ones. I realize I can brute-force it and just re-type all of the valid cases into a cell array under otherwise and have the function throw an error and return the list of valid responses, but maintaining both lists without errors or laziness creeping in over time is challenging.

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  • Generating data on unlevel background

    - by bluejay93
    I want to make an unlevel background and then generate some test data on that using Matlab. I was not clear when I asked this question earlier. So for this simple example for i = 1:10 for j = 1:10 f(i,j)=X.^2 + Y.^2 end end where X and Y have been already defined, it plots it on a flat surface. I don't want to distort the function itself, but I want the surface that it goes onto to be unlevel, changed by some degree or something. I hope that's a little clearer.

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  • Need help with artificial neural network

    - by deckard cain
    I have an input data for neural network that consists of 2 vectors with 200 elements, that i got from some program for generating signals. So it is actually 2x200 input to my nnet. As target data, i have one 1x200 vector that i also got from the same program. That is my training data set. I gather as much of those sets as i want so i transfer them to matlab and save them as, for example, set1, set2,.... When i am creating neural net, using newfit function (backropagation algorithm and everyhting else is set by default because i am kind of unexperianced with neural nets so i will have to experiment) i'm creating it using set1 only for example. Then, when i am to train neural net i train it for set1 then load set2 and train for it and so on. so its like this function net = create_fit_net(inputs,targets) numHiddenNeurons = 20; net = newfit(inputs,targets,numHiddenNeurons); net=train(net,inputs,targets); load set2; net=train(net,inputs,targets); load set3; net=train(net,inputs,targets); load set4; net=train(net,inputs,targets); i am using 4 sets of data here and all sets have variables of same name and size. My quesion is, am i doing this the right way, because, when doing simulation in some other m file, i am getting bad results, and every time i get different results. Does it matter if i create network with one set and then train with others too, and does it matter what set do i use to train network 1st? Also, i am confused about the amount of neurons to use (im using in the example 20 but actually i tried 1, 10, 30, 50, 100 200 and even 300 and i get nothing). If you have any suggestions, i'd be glad to take them into consideration. Any help is welcome. thanks in advance

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  • Neural Network with softmax activation

    - by Cambium
    This is more or less a research project for a course, and my understanding of NN is very/fairly limited, so please be patient :) ============== I am currently in the process of building a neural network that attempts to examine an input dataset and output the probability/likelihood of each classification (there are 5 different classifications). Naturally, the sum of all output nodes should add up to 1. Currently, I have two layers, and I set the hidden layer to contain 10 nodes. I came up with two different types of implementations 1) Logistic sigmoid for hidden layer activation, softmax for output activation 2) Softmax for both hidden layer and output activation I am using gradient descent to find local maximums in order to adjust the hidden nodes' weights and the output nodes' weights. I am certain in that I have this correct for sigmoid. I am less certain with softmax (or whether I can use gradient descent at all), after a bit of researching, I couldn't find the answer and decided to compute the derivative myself and obtained softmax'(x) = softmax(x) - softmax(x)^2 (this returns an column vector of size n). I have also looked into the MATLAB NN toolkit, the derivative of softmax provided by the toolkit returned a square matrix of size nxn, where the diagonal coincides with the softmax'(x) that I calculated by hand; and I am not sure how to interpret the output matrix. I ran each implementation with a learning rate of 0.001 and 1000 iterations of back propagation. However, my NN returns 0.2 (an even distribution) for all five output nodes, for any subset of the input dataset. My conclusions: o I am fairly certain that my gradient of descent is incorrectly done, but I have no idea how to fix this. o Perhaps I am not using enough hidden nodes o Perhaps I should increase the number of layers Any help would be greatly appreciated! The dataset I am working with can be found here (processed Cleveland): http://archive.ics.uci.edu/ml/datasets/Heart+Disease

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  • Generating lognormally distributed random number from mean, coeff of variation

    - by Richie Cotton
    Most functions for generating lognormally distributed random numbers take the mean and standard deviation of the associated normal distribution as parameters. My problem is that I only know the mean and the coefficient of variation of the lognormal distribution. It is reasonably straight forward to derive the parameters I need for the standard functions from what I have: If mu and sigma are the mean and standard deviation of the associated normal distribution, we know that coeffOfVar^2 = variance / mean^2 = (exp(sigma^2) - 1) * exp(2*mu + sigma^2) / exp(mu + sigma^2/2)^2 = exp(sigma^2) - 1 We can rearrange this to sigma = sqrt(log(coeffOfVar^2 + 1)) We also know that mean = exp(mu + sigma^2/2) This rearranges to mu = log(mean) - sigma^2/2 Here's my R implementation rlnorm0 <- function(mean, coeffOfVar, n = 1e6) { sigma <- sqrt(log(coeffOfVar^2 + 1)) mu <- log(mean) - sigma^2 / 2 rlnorm(n, mu, sigma) } It works okay for small coefficients of variation r1 <- rlnorm0(2, 0.5) mean(r1) # 2.000095 sd(r1) / mean(r1) # 0.4998437 But not for larger values r2 <- rlnorm0(2, 50) mean(r2) # 2.048509 sd(r2) / mean(r2) # 68.55871 To check that it wasn't an R-specific issue, I reimplemented it in MATLAB. (Uses stats toolbox.) function y = lognrnd0(mean, coeffOfVar, sizeOut) if nargin < 3 || isempty(sizeOut) sizeOut = [1e6 1]; end sigma = sqrt(log(coeffOfVar.^2 + 1)); mu = log(mean) - sigma.^2 ./ 2; y = lognrnd(mu, sigma, sizeOut); end r1 = lognrnd0(2, 0.5); mean(r1) % 2.0013 std(r1) ./ mean(r1) % 0.5008 r2 = lognrnd0(2, 50); mean(r2) % 1.9611 std(r2) ./ mean(r2) % 22.61 Same problem. The question is, why is this happening? Is it just that the standard deviation is not robust when the variation is that wide? Or have a screwed up somewhere?

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  • function taking in an input image and different kernel size

    - by drifterOcean19
    I have this filtering function that takes an input image, performs convolution using a given kernel, and returns the resulting image. However, I can't seem to work it out how to make it takes different kernel sizes.For example instead of pre-defined 3x3 kernel as below in the code, it could instead take 5x5 or 7x7. and then the user could input the type of kernel/filter they want(Depending on the intended effect). I can't seem to put my head around it. i'm quite new to matlab. function [newImg] = kernelFunc(imgB) img=imread(imgB); figure,imshow(img); img2=zeros(size(img)+2); newImg=zeros(size(img)); for rgb=1:3 for x=1:size(img,1) for y=1:size(img,2) img2(x+1,y+1,rgb)=img(x,y,rgb); end end end for rgb=1:3 for i= 1:size(img2,1)-2 for j=1:size(img2,2)-2 window=zeros(9,1); inc=1; for x=1:3 for y=1:3 window(inc)=img2(i+x-1,j+y-1,rgb); inc=inc+1; end end kernel=[1;2;1;2;4;2;1;2;1]/16; med=window.*kernel; disp(med); med=sum(med); med=floor(med); newImg(i,j,rgb)=med; end end end newImg=uint8(newImg); figure,imshow(newImg); end

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  • Is it possible that we override global variables?

    - by Ram Moj
    I have this function: function example(y) global TICTOC; tic TICTOC=5; toc end and I expect TICTOC=5 change the result of toc, since TICTOC is a global variable in tic and toc functions; but this is not the case; Does anyone know the reason? I like to know the answer, because I 'm worried to declare a global variable, which it's name has been declared global in some other functions, I'm not aware of. I saw this function in matlab 2008b help function tic % TIC Start a stopwatch timer. % TIC; any stuff; TOC % prints the time required. % See also: TOC, CLOCK. global TICTOC TICTOC = clock; function t = toc % TOC Read the stopwatch timer. % TOC prints the elapsed time since TIC was used. % t = TOC; saves elapsed time in t, does not print. % See also: TIC, ETIME. global TICTOC if nargout < 1 elapsed_time = etime(clock, TICTOC) else t = etime(clock, TICTOC); end thanks.

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  • How can I correctly calculate the direction for a moving object?

    - by Jakub Hampl
    I'm solving the following problem: I have an object and I know its position now and its position 300ms ago. I assume the object is moving. I have a point to which I want the object to get. What I need is to get the angle from my current object to the destination point in such a format that I know whether to turn left or right. The idea is to assume the current angle from the last known position and the current position. I'm trying to solve this in MATLAB. I've tried using several variations with atan2 but either I get the wrong angle in some situations (like when my object is going in circles) or I get the wrong angle in all situations. Examples of code that screws up: a = new - old; b = dest - new; alpha = atan2(a(2) - b(2), a(1) - b(1); where new is the current position (eg. x = 40; y = 60; new = [x y];), old is the 300ms old position and dest is the destination point. Edit Here's a picture to demonstrate the problem with a few examples: In the above image there are a few points plotted and annotated. The black line indicates our estimated current facing of the object. If the destination point is dest1 I would expect an angle of about 88°. If the destination point is dest2 I would expect an angle of about 110°. If the destination point is dest3 I would expect an angle of about -80°.

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  • Out-of-memory algorithms for addressing large arrays

    - by reve_etrange
    I am trying to deal with a very large dataset. I have k = ~4200 matrices (varying sizes) which must be compared combinatorially, skipping non-unique and self comparisons. Each of k(k-1)/2 comparisons produces a matrix, which must be indexed against its parents (i.e. can find out where it came from). The convenient way to do this is to (triangularly) fill a k-by-k cell array with the result of each comparison. These are ~100 X ~100 matrices, on average. Using single precision floats, it works out to 400 GB overall. I need to 1) generate the cell array or pieces of it without trying to place the whole thing in memory and 2) access its elements (and their elements) in like fashion. My attempts have been inefficient due to reliance on MATLAB's eval() as well as save and clear occurring in loops. for i=1:k [~,m] = size(data{i}); cur_var = ['H' int2str(i)]; %# if i == 1; save('FileName'); end; %# If using a single MAT file and need to create it. eval([cur_var ' = cell(1,k-i);']); for j=i+1:k [~,n] = size(data{j}); eval([cur_var '{i,j} = zeros(m,n,''single'');']); eval([cur_var '{i,j} = compare(data{i},data{j});']); end save(cur_var,cur_var); %# Add '-append' when using a single MAT file. clear(cur_var); end The other thing I have done is to perform the split when mod((i+j-1)/2,max(factor(k(k-1)/2))) == 0. This divides the result into the largest number of same-size pieces, which seems logical. The indexing is a little more complicated, but not too bad because a linear index could be used. Does anyone know/see a better way?

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  • Computing, storing, and retrieving values to and from an N-Dimensional matrix

    - by Adam S
    This question is probably quite different from what you are used to reading here - I hope it can provide a fun challenge. Essentially I have an algorithm that uses 5(or more) variables to compute a single value, called outcome. Now I have to implement this algorithm on an embedded device which has no memory limitations, but has very harsh processing constraints. Because of this, I would like to run a calculation engine which computes outcome for, say, 20 different values of each variable and stores this information in a file. You may think of this as a 5(or more)-dimensional matrix or 5(or more)-dimensional array, each dimension being 20 entries long. In any modern language, filling this array is as simple as having 5(or more) nested for loops. The tricky part is that I need to dump these values into a file that can then be placed onto the embedded device so that the device can use it as a lookup table. The questions now, are: What format(s) might be acceptable for storing the data? What programs (MATLAB, C#, etc) might be best suited to compute the data? C# must be used to import the data on the device - is this possible given your answer to #1?

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  • find consecutive nonzero values

    - by thymeandspace
    I am trying to write a simple MATLAB program that will find the first chain (more than 70) of consecutive nonzero values and return the starting value of that consecutive chain. I am working with movement data from a joystick and there are a few thousand rows of data with a mix of zeros and nonzero values before the actual trial begins (coming from subjects slightly moving the joystick before the trial actually started). I need to get rid of these rows before I can start analyzing the movement from the trials. I am sure this is a relatively simple thing to do so I was hoping someone could offer insight. Thank you in advance -Lilly EDIT: Here's what I tried: s = zeros(size(x1)); for i=2:length(x1) if(x1(i-1) ~= 0) s(i) = 1 + s(i-1); end end display(S); for a vector x1 which has a max chain of 72 but I dont know how to find the max chain and return its first value, so I know where to trim. I also really don't think this is the best strategy, since the max chain in my data will be tens of thousands of values. Thanks for helping me edit, Steve. :)

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  • How could I eliminate the meter names with an 'x' after them?

    - by Rose Comete
    Hi I imported some Excel data into MatLab - it is a list of about 200 meter names with about 28 rows each, but the problem is that for each there is a duplicate for the other direction - with the same meter name with an 'x' after it. Does anyone have any ideas as to how I can eliminate these ones with an 'x' after wards? Attached is the part of my code that imports the data, but unfortunately I have not got enough points on this site yet to be allowed to upload data/photos x clear all fid=fopen('sue1.csv'); % Open the file sue1.csv and read it all and put it into an array data = textscan(fid,'%s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s','Delimiter',',','CollectOutput',1); fclose(fid) j = 1; k = 1; % j - turbine number, k - date number for i = 1:length(data{1,1}) % Run through all the data if strcmp(data{1,1}(i),'') == 0 meterold{j}(k,:) = data{1,1}(i,:); % if strcmp(data{1,1}(i),'MeterName') == 0 % nummeter{j}(k,:) = str2num(data{1,1}(i,3:end)); % end k = k + 1; else % These commands are followed in the strings match (empty line) k = 1; % Reset the day counter as we're back to the beginning j = j + 1; % Add one to the meter counter as we're now looking at % a new turbine end end

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  • Parallelize or vectorize all-against-all operation on a large number of matrices?

    - by reve_etrange
    I have approximately 5,000 matrices with the same number of rows and varying numbers of columns (20 x ~200). Each of these matrices must be compared against every other in a dynamic programming algorithm. In this question, I asked how to perform the comparison quickly and was given an excellent answer involving a 2D convolution. Serially, iteratively applying that method, like so list = who('data_matrix_prefix*') H = cell(numel(list),numel(list)); for i=1:numel(list) for j=1:numel(list) if i ~= j eval([ 'H{i,j} = compare(' char(list(i)) ',' char(list(j)) ');']); end end end is fast for small subsets of the data (e.g. for 9 matrices, 9*9 - 9 = 72 calls are made in ~1 s). However, operating on all the data requires almost 25 million calls. I have also tried using deal() to make a cell array composed entirely of the next element in data, so I could use cellfun() in a single loop: # who(), load() and struct2cell() calls place k data matrices in a 1D cell array called data. nextData = cell(k,1); for i=1:k [nextData{:}] = deal(data{i}); H{:,i} = cellfun(@compare,data,nextData,'UniformOutput',false); end Unfortunately, this is not really any faster, because all the time is in compare(). Both of these code examples seem ill-suited for parallelization. I'm having trouble figuring out how to make my variables sliced. compare() is totally vectorized; it uses matrix multiplication and conv2() exclusively (I am under the impression that all of these operations, including the cellfun(), should be multithreaded in MATLAB?). Does anyone see a (explicitly) parallelized solution or better vectorization of the problem?

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  • Filter design for audio signal.

    - by beanyblue
    What I am trying to do is simple. I have a few .wav files. I want to remove noise and filter out specific frequencies. I don't have matlab and I intend to write my own code for all the filters. Right now, I have a way to read the .wav file and dump out the structure into a text file. My questions are the following: Can I directly apply the digital filters on this sampled data?{ ie, can I directly do a convolution between my input samples and h(n) for the filter function that i choose?). How do I choose the number of coefficients for the Window function? I have octave, so if someone can point me to anything that gives me some idea on how to process the .wav file using octave, that would be great too. I want to be able to filter out the frequency and then listen to the sound again. Is this possible with octave? I'm just a beginner with these kinds of things, so please bear with me if my questions are too naive. Any help will be great.

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  • Have a trouble with the function roots

    - by user3707462
    Hey guys I have multiple problems with using function 'roots'. I Have to find zeros of 's^1000 + 1'. I made Y = zeros(1,1000) then manually changed the 1000th matrice to '1'. but then 'root' function does not work with it ! Another problem is that I am having trouble with matrix multiplication. The question is finding zeros(roots) of (s^6 + 6*s^5 + 15*s^4 + 20*s^3 + 15*s^2 + 6*s +1)*(s^6 + 6s^5 + 15*s^4 +15*s^2 +6*s +1) so i did: a = [1 6 15 20 15 6 1] b = [1 6 15 0 15 6 1] y = a.*b; roots(y) but this gives me -27.9355 + 0.0000i -8.2158 + 0.0000i 0.1544 + 0.9880i 0.1544 - 0.9880i -0.1217 + 0.0000i -0.0358 + 0.0000i where I calculate the original equation with wolfram then I have made matrix as : p = [1 12 66 200 375 492 524 492 375 200 66 12 1] roots(p) and this gives me : -3.1629 + 2.5046i -3.1629 - 2.5046i 0.3572 + 0.9340i 0.3572 - 0.9340i -1.0051 + 0.0000i -1.0025 + 0.0044i -1.0025 - 0.0044i -0.9975 + 0.0044i -0.9975 - 0.0044i -0.9949 + 0.0000i -0.1943 + 0.1539i -0.1943 - 0.1539i and I think the second solution is right (that is what wolfram alpha gave me) How would you answer these two questions through matlab guys?

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  • Rotation Matrix calculates by column not by row

    - by pinnacler
    I have a class called forest and a property called fixedPositions that stores 100 points (x,y) and they are stored 250x2 (rows x columns) in MatLab. When I select 'fixedPositions', I can click scatter and it will plot the points. Now, I want to rotate the plotted points and I have a rotation matrix that will allow me to do that. The below code should work: theta = obj.heading * pi/180; apparent = [cos(theta) -sin(theta) ; sin(theta) cos(theta)] * obj.fixedPositions; But it wont. I get this error. ??? Error using == mtimes Inner matrix dimensions must agree. Error in == landmarkslandmarks.get.apparentPositions at 22 apparent = [cos(theta) -sin(theta) ; sin(theta) cos(theta)] * obj.fixedPositions; When I alter forest.fixedPositions to store the variables 2x250 instead of 250x2, the above code will work, but it wont plot. I'm going to be plotting fixedPositions constantly in a simulation, so I'd prefer to leave it as it, and make the rotation work instead. Any ideas? Also, fixed positions, is the position of the xy points as if you were looking straight ahead. i.e. heading = 0. heading is set to 45, meaning I want to rotate points clockwise 45 degrees. Here is my code: classdef landmarks properties fixedPositions %# positions in a fixed coordinate system. [x, y] heading = 45; %# direction in which the robot is facing end properties (Dependent) apparentPositions end methods function obj = landmarks(numberOfTrees) %# randomly generates numberOfTrees amount of x,y coordinates and set %the array or matrix (not sure which) to fixedPositions obj.fixedPositions = 100 * rand([numberOfTrees,2]) .* sign(rand([numberOfTrees,2]) - 0.5); end function obj = set.apparentPositions(obj,~) theta = obj.heading * pi/180; [cos(theta) -sin(theta) ; sin(theta) cos(theta)] * obj.fixedPositions; end function apparent = get.apparentPositions(obj) %# rotate obj.positions using obj.facing to generate the output theta = obj.heading * pi/180; apparent = [cos(theta) -sin(theta) ; sin(theta) cos(theta)] * obj.fixedPositions; end end end P.S. If you change one line to this: obj.fixedPositions = 100 * rand([2,numberOfTrees]) .* sign(rand([2,numberOfTrees]) - 0.5); Everything will work fine... it just wont plot.

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  • Why is this class re-initialized every time?

    - by pinnacler
    I have 4 files and the code 'works' as expected. I try to clean everything up, place code into functions, etc... and everything looks fine... and it doesn't work. Can somebody please explain why MatLab is so quirky... or am I just stupid? Normally, I type terminator = simulation(100,20,0,0,0,1); terminator.animate(); and it should produce a map of trees with the terminator walking around in a forest. Everything rotates to his perspective. When I break it into functions... everything ceases to work. I really only changed a few lines of code, shown in comments. Code that works: classdef simulation properties landmarks robot end methods function obj = simulation(mapSize, trees, x,y,heading,velocity) obj.landmarks = landmarks(mapSize, trees); obj.robot = robot(x,y,heading,velocity); end function animate(obj) %Setup Plots fig=figure; xlabel('meters'), ylabel('meters') set(fig, 'name', 'Phil''s AWESOME 80''s Robot Simulator') xymax = obj.landmarks.mapSize*3; xymin = -(obj.landmarks.mapSize*3); l=scatter([0],[0],'bo'); axis([xymin xymax xymin xymax]); obj.landmarks.apparentPositions %Simulation Loop THIS WAS ORGANIZED for n = 1:720, %Calculate and Set Heading/Location obj.robot.headingChange = navigate(n); %Update Position obj.robot.heading = obj.robot.heading + obj.robot.headingChange; obj.landmarks.heading = obj.robot.heading; y = cosd(obj.robot.heading); x = sind(obj.robot.heading); obj.robot.x = obj.robot.x + (x*obj.robot.velocity); obj.robot.y = obj.robot.y + (y*obj.robot.velocity); obj.landmarks.x = obj.robot.x; obj.landmarks.y = obj.robot.y; %Animate set(l,'XData',obj.landmarks.apparentPositions(:,1),'YData',obj.landmarks.apparentPositions(:,2)); rectangle('Position',[-2,-2,4,4]); drawnow end end end end ----------- classdef landmarks properties fixedPositions %# positions in a fixed coordinate system. [ x, y ] mapSize = 10; %Map Size. Value is side of square x=0; y=0; heading=0; headingChange=0; end properties (Dependent) apparentPositions end methods function obj = landmarks(mapSize, numberOfTrees) obj.mapSize = mapSize; obj.fixedPositions = obj.mapSize * rand([numberOfTrees, 2]) .* sign(rand([numberOfTrees, 2]) - 0.5); end function apparent = get.apparentPositions(obj) %-STILL ROTATES AROUND ORIGINAL ORIGIN currentPosition = [obj.x ; obj.y]; apparent = bsxfun(@minus,(obj.fixedPositions)',currentPosition)'; apparent = ([cosd(obj.heading) -sind(obj.heading) ; sind(obj.heading) cosd(obj.heading)] * (apparent)')'; end end end ---------- classdef robot properties x y heading velocity headingChange end methods function obj = robot(x,y,heading,velocity) obj.x = x; obj.y = y; obj.heading = heading; obj.velocity = velocity; end end end ---------- function headingChange = navigate(n) %steeringChange = 5 * rand(1) * sign(rand(1) - 0.5); Most chaotic shit %Draw an S if n <270 headingChange=1; elseif n<540 headingChange=-1; elseif n<720 headingChange=1; else headingChange=1; end end Code that does not work... classdef simulation properties landmarks robot end methods function obj = simulation(mapSize, trees, x,y,heading,velocity) obj.landmarks = landmarks(mapSize, trees); obj.robot = robot(x,y,heading,velocity); end function animate(obj) %Setup Plots fig=figure; xlabel('meters'), ylabel('meters') set(fig, 'name', 'Phil''s AWESOME 80''s Robot Simulator') xymax = obj.landmarks.mapSize*3; xymin = -(obj.landmarks.mapSize*3); l=scatter([0],[0],'bo'); axis([xymin xymax xymin xymax]); obj.landmarks.apparentPositions %Simulation Loop for n = 1:720, %Calculate and Set Heading/Location %Update Position headingChange = navigate(n); obj.robot.updatePosition(headingChange); obj.landmarks.updatePerspective(obj.robot.heading, obj.robot.x, obj.robot.y); %Animate set(l,'XData',obj.landmarks.apparentPositions(:,1),'YData',obj.landmarks.apparentPositions(:,2)); rectangle('Position',[-2,-2,4,4]); drawnow end end end end ----------------- classdef landmarks properties fixedPositions; %# positions in a fixed coordinate system. [ x, y ] mapSize; %Map Size. Value is side of square x; y; heading; headingChange; end properties (Dependent) apparentPositions end methods function obj = createLandmarks(mapSize, numberOfTrees) obj.mapSize = mapSize; obj.fixedPositions = obj.mapSize * rand([numberOfTrees, 2]) .* sign(rand([numberOfTrees, 2]) - 0.5); end function apparent = get.apparentPositions(obj) %-STILL ROTATES AROUND ORIGINAL ORIGIN currentPosition = [obj.x ; obj.y]; apparent = bsxfun(@minus,(obj.fixedPositions)',currentPosition)'; apparent = ([cosd(obj.heading) -sind(obj.heading) ; sind(obj.heading) cosd(obj.heading)] * (apparent)')'; end function updatePerspective(obj,tempHeading,tempX,tempY) obj.heading = tempHeading; obj.x = tempX; obj.y = tempY; end end end ----------------- classdef robot properties x y heading velocity end methods function obj = robot(x,y,heading,velocity) obj.x = x; obj.y = y; obj.heading = heading; obj.velocity = velocity; end function updatePosition(obj,headingChange) obj.heading = obj.heading + headingChange; tempy = cosd(obj.heading); tempx = sind(obj.heading); obj.x = obj.x + (tempx*obj.velocity); obj.y = obj.y + (tempy*obj.velocity); end end end The navigate function is the same... I would appreciate any help as to why things aren't working. All I did was take the code from the first section from under comment: %Simulation Loop THIS WAS ORGANIZED and break it into 2 functions. One in robot and one in landmarks. Is a new instance created every time because it's constantly printing the same heading for this line int he robot class obj.heading = obj.heading + headingChange;

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  • Problems with real-valued input deep belief networks (of RBMs)

    - by Junier
    I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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  • Problems with real-valued deep belief networks (of RBMs)

    - by Junier
    I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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  • How would i down-sample a .wav file then reconstruct it using nyquist? - in matlab [closed]

    - by martin
    This is all done in MatLab 2010 My objective is to show the results of: undersampling, nyquist rate/ oversampling First i need to downsample the .wav file to get an incomplete/ or impartial data stream that i can then reconstuct. Heres the flow chart of what im going to be doing So the flow is analog signal - sampling analog filter - ADC - resample down - resample up - DAC - reconstruction analog filter what needs to be achieved: F= Frequency F(Hz=1/s) E.x. 100Hz = 1000 (Cyc/sec) F(s)= 1/(2f) Example problem: 1000 hz = Highest frequency 1/2(1000hz) = 1/2000 = 5x10(-3) sec/cyc or a sampling rate of 5ms This is my first signal processing project using matlab. what i have so far. % Fs = frequency sampled (44100hz or the sampling frequency of a cd) [test,fs]=wavread('test.wav'); % loads the .wav file left=test(:,1); % Plot of the .wav signal time vs. strength time=(1/44100)*length(left); t=linspace(0,time,length(left)); plot(t,left) xlabel('time (sec)'); ylabel('relative signal strength') **%this is were i would need to sample it at the different frequecys (both above and below and at) nyquist frequency.*I think.*** soundsc(left,fs) % shows the resaultant audio file , which is the same as original ( only at or above nyquist frequency however) Can anyone tell me how to make it better, and how to do the various sampling at different frequencies?

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