I have a 1 x 10 vector as shown below:
V = [0.500 -5.433 0.543 0.321 1.432 0.543 -0.576 -0.145 -1.322 -0.222]
and want to plot this on MATLAB using plot.
I used plot(v,0,'kx,'marker',10) but it does not seem reasonable for me. Any idea on how to go about with this?
Does anyone have a very good resource for for ISOMAP? Need a very comprehensive step by step easy tutorials on Isomaps. If i can have good videos on it will be very good.
you almost got it, just write:
plot(v,'kx','MarkerSize',10);
note, that I wrote plot and not Plot, Matlab is case sensitive...
when you only have a single vector the plot function assumes that for the x-axis it takes the number of elements of the vector, i.e. plot(1:numel(v),v,...). I recommend you to use the Matlab documentation, if you had read it, you'd see an example that could show you what you did wrong.
bla's solution is okay and produces a scatter plot.
However, you can make a line plot, etc.
Though, notice it is V and not v, as he pointed out that MATLAB is case-sensitive.
Here is how to generate a line plot:
x=0:length(V)
plot(x,V,'r--o',x,V,'r*')
Output:
Related
So I'm trying to plot a graph and this is my code so far
T=10; %set the values given to us
t(1)=0;
delta=0.01;
mu=0.05;
sigma=linspace(0,1,10001); %set the spacing for sigma
v=zeros(length(sigma),T); %set the xeros
k=1;
for sigma=linspace(0.01,1,1000)
t(k)=k*delta;
eta=randn(1); %define eta
S(2)=1+mu*delta+sigma*sqrt(delta)*eta; %set S
S(T+1)=S(10)+mu*delta*S(10)+sigma*sqrt(delta)*eta*S(10);
end
t(10000)=1; %set the rounding error
plot (S,sigma) %plot the graph
xlabel 'S' %label the axis
ylabel 'sigma'
I've tried using . to satisfy inner matrix dimensions (for S) but this hasn't worked. I've been going round in circles for a while now and can't figure it out.
I've tried your code and analyzed the problem, and I have a few doubts about it. It would be so helpful to know what problem are you trying to solve. But, focusing on what we have, one of the things I've seen is about the iteration. It doesn´t make sense to me, you've used a for iteration structure with the sigma condition. That's ok, let's see step by step what's happening:
for sigma=linspace(0.01,1,1000)
t(k)=k*delta;
eta=randn(1); %define eta
S(2)=1+mu*delta+sigma*sqrt(delta)*eta; %set S
S(T+1)=S(10)+mu*delta*S(10)+sigma*sqrt(delta)*eta*S(10);
end
First of all, you can define t(k) of the iteration, as the value doesn´t change with it. Next, you've written S(2) and S(T+1), here, you're doing an iteration, that's correct, but you're overwriting the values over and over again each loop, so you're overwriting some values of a matrix S. About S, it could be helpful to preassign the expected size in memory, you can use:
S=zeros(i,j); % Being i and j, the matrix dimensions.
or simply, as some people like, declaring the variable (not necessary in Matlab):
S=[];
This depends on the problem you're solving. I really want to help you, but I need to know more about the problem. I don't know your working method or how you face your problems, but what I always do is solve it first in a piece of paper, or if it's a numerical problem of some kind and you can't solve it, then just sketch the ideas you have and check things like the coherence in matrix operations (related to matrix sizes), and those kind of things...
Hope I've been helpful, and have a nice day :)
Here is the given system I want to plot and obtain the vector field and the angles they make with the x axis. I want to find the index of a closed curve.
I know how to do this theoretically by choosing convenient points and see how the vector looks like at that point. Also I can always use
to compute the angles. However I am having trouble trying to code it. Please don't mark me down if the question is unclear. I am asking it the way I understand it. I am new to matlab. Can someone point me in the right direction please?
This is a pretty hard challenge for someone new to matlab, I would recommend taking on some smaller challenges first to get you used to matlab's conventions.
That said, Matlab is all about numerical solutions so, unless you want to go down the symbolic maths route (and in that case I would probably opt for Mathematica instead), your first task is to decide on the limits and granularity of your simulated space, then define them so you can apply your system of equations to it.
There are lots of ways of doing this - some more efficient - but for ease of understanding I propose this:
Define the axes individually first
xpts = -10:0.1:10;
ypts = -10:0.1:10;
tpts = 0:0.01:10;
The a:b:c syntax gives you the lower limit (a), the upper limit (c) and the spacing (b), so you'll get 201 points for the x. You could use the linspace notation if that suits you better, look it up by typing doc linspace into the matlab console.
Now you can create a grid of your coordinate points. You actually end up with three 3d matrices, one holding the x-coords of your space and the others holding the y and t. They look redundant, but it's worth it because you can use matrix operations on them.
[XX, YY, TT] = meshgrid(xpts, ypts, tpts);
From here on you can perform whatever operations you like on those matrices. So to compute x^2.y you could do
x2y = XX.^2 .* YY;
remembering that you'll get a 3d matrix out of it and all the slices in the third dimension (corresponding to t) will be the same.
Some notes
Matlab has a good builtin help system. You can type 'help functionname' to get a quick reminder in the console or 'doc functionname' to open the help browser for details and examples. They really are very good, they'll help enormously.
I used XX and YY because that's just my preference, but I avoid single-letter variable names as a general rule. You don't have to.
Matrix multiplication is the default so if you try to do XX*YY you won't get the answer you expect! To do element-wise multiplication use the .* operator instead. This will do a11 = b11*c11, a12 = b12*c12, ...
To raise each element of the matrix to a given power use .^rather than ^ for similar reasons. Likewise division.
You have to make sure your matrices are the correct size for your operations. To do elementwise operations on matrices they have to be the same size. To do matrix operations they have to follow the matrix rules on sizing, as will the output. You will find the size() function handy for debugging.
Plotting vector fields can be done with quiver. To plot the components separately you have more options: surf, contour and others. Look up the help docs and they will link to similar types. The plot family are mainly about lines so they aren't much help for fields without creative use of the markers, colours and alpha.
To plot the curve, or any other contour, you don't have to test the values of a matrix - it won't work well anyway because of the granularity - you can use the contour plot with specific contour values.
Solving systems of dynamic equations is completely possible, but you will be doing a numeric simulation and your results will again be subject to the granularity of your grid. If you have closed form solutions, like your phi expression, they may be easier to work with conceptually but harder to get working in matlab.
This kind of problem is tractable in matlab but it involves some non-basic uses which are pretty hard to follow until you've got your head round Matlab's syntax. I would advise to start with a 2d grid instead
[XX, YY] = meshgrid(xpts, ypts);
and compute some functions of that like x^2.y or x^2 - y^2. Get used to plotting them using quiver or plotting the coordinates separately in intensity maps or surfaces.
Say for example I have data which forms the parabolic curve y=x^2, and I want to read off the x value for a given y value. How do I go about doing this in MATLAB?
If it were a straight line, I could just use the equation of the line of best fit to calculate easily, however I can't do this with a curved line. If I can't find a solution, I'll solve for roots
Thanks in advance.
If all data are arrays (not analytical expressions), I usually do that finding minimal absolute error
x=some_array;
[~,ind]=min(abs(x.^2-y0))
Here y0 is a given y value
If your data are represented by a function, you can use fsolve:
function y = myfun(x)
y=x^2-y0
[x,fval] = fsolve(#myfun,x0,options)
For symbolic computations, one can use solve
syms x
solve(x^2 - y0)
Assuming your two curves are just two vectors of data, I would suggest you use Fast and Robust Curve Intersections from the File Exchange. See also these two similar questions: how to find intersection points when lines are created from an array and Finding where plots may cross with octave / matlab.
I know there are many plotting documents for Matlab online and I am pretty sure that it has been asked many times. I aplogize in advance for any inconvenience.
I am dealing with a new distribution and I need to draw 3D plot for different values of parameters (I can do it with Excel or any other programs, however, since my other graphs is drawn with MATLAB, and I need to put this 3D in Matlab, too, to publish it as an article). I calculated the result using MATLAB loops, however, plotting gives me the hardest time. I had no other choice but to ask for your assistance. I have these equations for different alphas and betas with a constant sigma and calculate Galton's Skewness and Moor's Kurtosis given with the last two equations.
median=sqrt(2*(sigma^2)*beta*gammaincinv(0.5,alpha));
q1=sqrt(2*(sigma^2)*beta*gammaincinv((6/8),alpha));
q3=sqrt(2*(sigma^2)*beta*gammaincinv((2/8),alpha));
q4=sqrt(2*(sigma^2)*beta*gammaincinv((7/8),alpha));
q5=sqrt(2*(sigma^2)*beta*gammaincinv((5/8),alpha));
q6=sqrt(2*(sigma^2)*beta*gammaincinv((3/8),alpha));
q7=sqrt(2*(sigma^2)*beta*gammaincinv((1/8),alpha));
galtonskewness=(q1-2*median+q3)/(q1-q3);
moorskurtosis=(q4-q5+q6-q7)/(q1-q3);
Let's assume that,
sigma=1
beta=[0.1 0.2 0.5 1 2 5];
alpha=[0.1 0.2 0.5 1 2 5];
I have used mesh(X,Y,Z) for the same range of alphas and betas with the same increment but I take the error "these values cannot be complex". I just want to draw something like the one below.
It must be something easy that I am missing out, but I do not understand where the mistake is. I appreciate any help. Thank you!
I ran the above code for a 2D mesh of points for alpha and beta between 0.1 and 5 for both dimensions and I got results for both.
I suspect it's due to your alpha and beta declaration. You are only providing a few points, and if you try to use mesh, it won't get good results. Therefore, define a meshgrid of points for both alpha and beta, then vectorize your MATLAB code to produce the kurotsis and skewness curves. Only under certain situations should you use for loops. In general, you should avoid using them whenever possible.
How meshgrid works is that given a range of X and Y values, it will produce two (or three if you want 3D co-ordinates) arrays where each location in each array gives you the spatial co-ordinate at that particular location. Therefore, if we did something like:
[X,Y] = meshgrid(1:3, 1:3);
This is what we get:
X =
1 2 3
1 2 3
1 2 3
Y =
1 1 1
2 2 2
3 3 3
Notice that in a 2D grid, for the top-left corner, (x,y) = (1,1), and so for the corresponding location in X, we get 1 and Y we get 1. If you do the same logic for any other position in the 2D grid, you simply look at the X and Y values in each array and it will tell you what the component is for each dimension.
As such, instead of looping through all possible points in your grid, generate them all using meshgrid, then vectorize the computation by calculating your values all at once rather than individually. Once you do this, you have the right structure to be able to put this into mesh.
Therefore, try doing this instead:
%// Define meshgrid of points
[alpha,beta] = meshgrid(0.1:0.1:5, 0.1:0.1:5);
%// From your code
sigma = 1;
%// Calculate quantities - Notice that this is all vectorized
med=sqrt(2*(sigma^2)*beta.*gammaincinv(0.5,alpha));
q1=sqrt(2*(sigma^2)*beta.*gammaincinv((6/8),alpha));
q3=sqrt(2*(sigma^2)*beta.*gammaincinv((2/8),alpha));
q4=sqrt(2*(sigma^2)*beta.*gammaincinv((7/8),alpha));
q5=sqrt(2*(sigma^2)*beta.*gammaincinv((5/8),alpha));
q6=sqrt(2*(sigma^2)*beta.*gammaincinv((3/8),alpha));
q7=sqrt(2*(sigma^2)*beta.*gammaincinv((1/8),alpha));
galtonskewness=(q1-2*med+q3)./(q1-q3);
moorskurtosis=(q4-q5+q6-q7)./(q1-q3);
%// Show our meshes
figure;
mesh(alpha, beta, galtonskewness);
figure;
mesh(alpha, beta, moorskurtosis);
Also take note that I renamed your median variable to med. MATLAB has a function called median and so you don't want to unintentionally shadow over this function with a variable of the same name.
This is what I get:
Take note that I'm not getting the plots that you have placed in your post. It may be because I'm choosing the wrong variables to define the mesh, or perhaps your equations may be incorrect. Double check what you know in theory to what you have here in code and try again.
This should hopefully give you enough to start with though!
I have a matrix which is 1*1*10000, the slightly odd dimensions are the result of the matrix algebra used to calculate it.
I simply want to be able to plot the 10000 data points contained in it, but matlab seems unable to do it?
Can someone please tell me how I can plot the data?
Seems simple but I really can't figure out how to do it!
Baz
yes you need to reduce the dimensions to a vector:
A = zeros(1,1,100)
vector = squeeze(A(1,1,:))
as when you'd access the third dimension this would only return a 3D-Matrix again:
z = A(1,1,:)
would NOT work. So use squeeze() ;-) Then plot as usual.
Doc-Link: http://www.mathworks.de/de/help/matlab/ref/squeeze.html
And as Ander pointed out in comments, no need to give any dimensions, as it removes singleton-dimensions by itself. So just use vector = squeeze(A). MATLAB recognizes the way to go itself.