I have a two columns of data. X = Model values of NOx concentrations and Y = Observations of NOx concentrations. Now, I want to scatter plot X, Y (markers varying with colors) as well as the colourbar which would show me the counts (i.e. number of data points in that range). X and Y are daily data for a year, i.e. 365 rows.
Please help me. Any help is greatly appreciated.
I have attached a sample image.
If I understand you correctly, the real problem is creating the color information, which is, creating a bivariate histogram. Luckily, MATLAB has a function, hist3, for that in the Statistics & Machine Learning Toolbox. The syntax is
[N,C] = hist3(X,nbins)
where X is a m-by-2 matrix containing the data, and nbins is a 1-by-2 vector containing the number of bins in each dimension. The return value N is a matrix of size nbins(1)-by-nbins(2), and contains the histogram data. C is a 1-by-2 cell array, containing the bin centers in both dimensions.
% Generate sample data
X = randn(10000, 1);
Y = X + rand(10000, 1);
% Generate histogram
[N,C] = hist3([X,Y], [100,100]);
% Plot
imagesc(C{1},C{2},N);
set(gca,'YDir','normal');
colormap(flipud(pink));
colorbar;
Result:
Related
I have two 80*80 matrices. I would like to plot normalized frequency plot. I tried to normalize the 80* 80 grid with the following code:
A = per_monsoon_postmon; % (A is my 80*80 matrix)
A = rand (80,80);
minA = repmat(min(A), [size(A, 1), 1]);
normA = max(A) - min(A);
normA = repmat(normA, [length(normA) 1]);
normalizedA = (A - minA)./normA;
But this code didn't give me the desired result, as grids with nan values also has a number in it. For eg. earlier grid 1*1 is nan now it has a value of 0.8340. Could you please help me how to normalize the 2D matrix and then plot frequency distribution plot in MATLAB? Is there a way to directly plot normalized frequency distribution plot?
If you have nan values in your verctor you might have problems. I would first replace the nans (for example with zeros).
Normalisation between 0 and one works like this:
a=rand(80,80); %generates random 80x80 array
a=a-min(min(a)); %shifts the values from 0 to n, min(min() ) because it is 2x2
a=a./max(max(a));% shifts to 0 to 1
If you want tot plot these values in 3d I would use a surf plot therefore you first generate the sample values and then feed them the z values
[x,y]=meshgrid(1:80);
surf(x,y,z)
I have started to learn Machine Learning, and programming in matlab.
I want to plot a matrix sized m*d where d=3 and m are the number of points.
with y binary vector I'd like to color each point with blue/red.
and plot a plane which is described with the vertical vector to it w.
The problem I trying to solve is to give some kind of visual representation of the data and the linear predictor.
All I know is how to single points with plot3, but no any number of points.
Thanks.
Plot the points using scatter3()
scatter3(X(y,1),X(y,2),X(y,3),'filled','fillcolor','red');
hold on;
scatter3(X(~y,1),X(~y,2),X(~y,3),'filled','fillcolor','blue');
or using plot3()
plot(X(y,1),X(y,2),X(y,3),' o','MarkerEdgeColor','red','MarkerFaceColor','red');
hold on;
plot(X(~y,1),X(~y,2),X(~y,3),' o','MarkerEdgeColor','blue','MarkerFaceColor','blue');
There are a few ways to plot a plane. As long as w(3) isn't very close to 0 then the following will work okay. I'm assuming your plane is defined by x'*w+b=0 where b is a scalar and w and x are column vectors.
x1min = min(X(:,1)); x2min = min(X(:,2));
x1max = max(X(:,1)); x2max = max(X(:,2));
[x1,x2] = meshgrid(linspace(x1min,x1max,20), linspace(x2min, x2max, 20));
x3 = -(w(1)*x1 + w(2)*x2 + b)/w(3);
surf(x1,x2,x3,'FaceColor',[0.6,0.6,0.6],'FaceAlpha',0.7,'EdgeColor',[0.4,0.4,0.4],'EdgeAlpha',0.4);
xlabel('x_1'); ylabel('x_2'); zlabel('x_3'); axis('vis3d');
Resulting plot
I do have generated two 2-D dimensional matrices from my core process. Now, I want to represent them via the bar graphs.
I could manage to get the 3d bar graph for independant matrix as shown in figures attached.
My data matrices are
"xData" - size is : (52 x 46 )
"yData" - size is : (52 x 46)
They'll always have the same size.
Now, I want to represent them together in 'Grouped Style' As show in here. I got the 3D dimensional matrix by combining them (xData and yData) together i.e. generated 52 x 46 x 2 matrix and then tried to plot with bar3 command; but, I got an error and couldn't plot.
Do you guys have any idea on how to do it ?
I manage to do that using a loop and this SO answer:
% generate random data
sz = [5 4];
xData = rand(sz);
yData = rand(sz);
% plot zeros in the same size to prepare plot
Z = zeros(sz);
h0 = bar3(Z);
set(h0,'EdgeColor','none','FaceColor','none');
axis tight
% plot each data column in a loop
hold on
for kk = 1:size(xData,2)
h1 = bar3([xData(:,kk),yData(:,kk)],'grouped');
% move current bars to their x position
cellfun(#(x) set(h1,'XData', x + (kk - 1)), get(h1,'XData'));
end
I am working on a joint pdf problem in which the random variable
U = sqrt(X^2+Y^2)
X and Y are uniformly distributed over (-2,2). I want to plot joint pdf of X and Y. Then compute pdf of U and plot it as well. I am using matlab R2011a, and so far, I have come up with the following code. On running the code I got an error message
Undefined function or method 'makedist' for input arguement type 'char'.
I found out that makedist is not on 2011 version. So I tried using
a=-2;
b=2;
X=a+(b-a)*rand(-10,10);
Y= a+(b-a)*rand(-10,10).
However, I am not sure how to compute pdfs of X and Y, and then joint pdf of XY from this. Any help, partial or holistic, is appreciated.
Here is the matlab code for the problem
%% Create distribution objects for X~U(-2,2) and Y~U(-2,2)
pdx=makedist('Uniform','lower',-2,'upper',2);
pdy=makedist('Uniform','lower',-2,'upper',2);
%Compute the pfs
x_ref=-10:1:10;
y_ref=-10:1:10;
pdf_x=pdf(pdx,x_ref);
pdf_y=pdf(pdy,y_ref);
% Plot the pdfs
figure 1;
stairs(x_ref,pdf_x,'g','Linewidth',2);
hold on;
stairs(y_ref,pdf_y,'r','Linewidth',2);
ylim([0 1.5]);
hold off;
% Joint pdf of x and Y
pdfXY=pdf_x*pdf_y;
figure 2;
plot(pdfXY);
%CDF and PDF of U
U=sqrt(X^2+Y^2);
Umin=0;
Umax=sqrt(b^2+b^2);
a=lower;
b=upper;
x=sqrt(U^2-Y^2);
xmin=0;
xmax=x;
ymin=0;
ymax=U;
Ucdf=integral2(pdfXY,xmin,xmax,ymin,ymax);
% plot CDF of U
figure 3;
plot(Ucdf)
I am just looking to plot the regions than for any specific sample set. X and Y are continuous independent uniform random variables.
As your x and y are independent at random, the theoretical joint distribution is just a product of the two
P(x,y) = P(x)*P(y)
In terms of MATLAB code, you may think of x and y running along two different dimensions:
N = 10; %// think of a probability mass function over N points
x = linspace(-2,2, N);
y = linspace(-2,2, N)';
Px = ones(N,1)./N;
Py = ones(1,10)./N;
%// Then the joint will be:
Jxy = bsxfun(#times, Px , Py);
figure
pcolor(x,y,Jxy)
You can now plug whatever distribution you like, if they are independent for Px and Py, and it will work
I'm working on MATLAB, where I have a vector which I need to split into two classes and then get a histogram of both resulting vectors (which have different sizes). The values represent height records so the interval is about 140-185.
How can I get a normalized histogram of both resulting vectors in different colors. I was able to get both normalized vectors in the same colour (which is indistiguible) and and also a histogram with different colours but not not normalized...
I hope you understand my question and will be able to help me.
Thanks in advance :)
Maybe this is what you need:
matrix = [155+10*randn(2000,1) 165+10*randn(2000,1)];
matrix(1:1100,1) = NaN;
matrix(1101:2000,2) = NaN; %// example data
[y x] = hist(matrix, 15); %// 15 is desired number of bins
y = bsxfun(#rdivide, y, sum(y)) / (x(2)-x(1)); %// normalize to area 1
bar(x,y) %// plots each column of y vs x. Automatically uses different colors