I´m trying to generalize a regressor vector in my NARX-model function.
The regressor vector looks like this:
[-y(i-1) -w(i)*y(i-1) u(i-1) w(i)*u(i-1) u(i-2) w(i)*u(i-2)]
I want to be able to tell my function how many y- and u-values it should consider. So for example if I want to have a regressor vector with 2 y-values and 1 u-value it should generate this:
[-y(i-1) -w(i)*y(i-1) -y(i-2) -w(i)*y(i-2) u(i-1) w(i)*u(i-1)]
And so on for any amount of y- and u-values back in time.
Any help is much obliged!
Related
I am trying trying to graph the polynomial fit of a 2D dataset in Matlab.
This is what I tried:
rawTable = readtable('Test_data.xlsx','Sheet','Sheet1');
x = rawTable.A;
y = rawTable.B;
figure(1)
scatter(x,y)
c = polyfit(x,y,2);
y_fitted = polyval(c,x);
hold on
plot(x,y_fitted,'r','LineWidth',2)
rawTable.A and rawTable.A are randomly generated numbers. (i.e. the x dataset cannot be represented in the following form : x=0:0.1:100)
The result:
second-order polynomial
But the result I expect looks like this (generated in Excel):
enter image description here
How can I graph the second-order polynomial fit in MATLAB?
I sense some confusion regarding what the output of each of those Matlab function mean. So I'll clarify. And I think we need some details as well. So expect some verbosity. A quick answer, however, is available at the end.
c = polyfit(x,y,2) gives the coefficient vectors of the polynomial fit. You can get the fit information such as error estimate following the documentation.
Name this polynomial as P. P in Matlab is actually the function P=#(x)c(1)*x.^2+c(2)*x+c(3).
Suppose you have a single point X, then polyval(c,X) outputs the value of P(X). And if x is a vector, polyval(c,x) is a vector corresponding to [P(x(1)), P(x(2)),...].
Now that does not represent what the fit is. Just as a quick hack to see something visually, you can try plot(sort(x),polyval(c,sort(x)),'r','LineWidth',2), ie. you can first sort your data and try plotting on those x-values.
However, it is only a hack because a) your data set may be so irregularly spaced that the spline doesn't represent function or b) evaluating on the whole of your data set is unnecessary and inefficient.
The robust and 'standard' way to plot a 2D function of known analytical form in Matlab is as follows:
Define some evenly-spaced x-values over the interval you want to plot the function. For example, x=1:0.1:10. For example, x=linspace(0,1,100).
Evaluate the function on these x-values
Put the above two components into plot(). plot() can either plot the function as sampled points, or connect the points with automatic spline, which is the default.
(For step 1, quadrature is ambiguous but specific enough of a term to describe this process if you wish to communicate with a single word.)
So, instead of using the x in your original data set, you should do something like:
t=linspace(min(x),max(x),100);
plot(t,polyval(c,t),'r','LineWidth',2)
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 modeled my 1D data (1000*1 matrix) into 3 Gaussians, using
gmdistribution.fit(X,3)
How can I plot something Like this?
It shows the probability of a given point belonging to each class.
Write a function plotGaussian, which takes the mean, the variance, and a range of values. The function should generate the points to plot, and call the plot function. Then do hold on, and call plotGaussian 3 times.
I have multiple vectors of varying lengths that I would like to plot next to each other in 3D space in Matlab.
As an example:
Say I have three vectors:
X is a 5x2 vector,
Y is a 10x2 vector and
Z is a 15x2 vector.
Each element of every vector has the format:
x value, y value
but the x values of the various vectors do not match.
I would like to plot these vectors in 3D space, next to each other. The reason why I don't want to plot them using "hold" is because most of the data have the same values, but I would like to see how many of the plots have the same value at a specific time.
I hope my questions makes sense. Please just ask if anyone is unsure.
I think you are looking for the function ribbon.
Documentation: http://www.mathworks.fr/help/techdoc/ref/ribbon.html
EDIT:
if your x's do not have the same length, you can combine it with interp1 as follow:
x1=0:0.1:1;
x2=0:0.02:1.5;
y1=x1.^2;
y2=sqrt(x2);
y2=interp1(x2,y2,x1);
ribbon(x1',[y1;y2]')
I'm trying to find a way to plot a truss in MATLAB, I can do it by using an adjacency matrix and the gplot function, but its very long winded approach especially if there are a lot of nodes connected to one another. Is there a faster way to do this?
I think gplot is a good function to plot a truss. However, it might be possible to simplify the creation of the adjacency matrix.
For example, if your coordinates are stored in a n-by-2 array, and there is a strut for every pair of nodes that is separated by less than dMax, you can create the adjacency matrix like this:
%# create a distance matrix
distMatSquared = coordinates * coordinates'; %' #SO formatting
%# create an adjacency matrix that has a 1 wherever
%# distMatSquared is smaller than dMax^2, and that has 0 everywhere else
adjacencyMatrix = distMatSquared < dMax^2;
%# plot the truss with circles at the nodes
figure,gplot(adjacencyMatrix,coordinates,'-o');
If this is a truss in the Mechanics of Materials sense:
http://www.mathworks.com/matlabcentral/fileexchange/2170-mastering-mechanics-1-using-matlab-5
and the supporting book
http://www.amazon.com/Mastering-Mechanics-Using-MATLAB-Materials/dp/0138640343
I wrote some truss visualization and just general strength of material stuff into this.