I'm pretty confused on how I would go about summing an infinite amount of matrices in MATLAB. Lets say I have this function (a gaussian):
%Set up grid/coordinate system
Ngrid=400;
w=Ngrid;
h=Ngrid;
%Create Gaussian Distribution
G = zeros ([w, h]);
Sig = 7.3; %I want the end/resultant G to be a summation of Sign from 7.3 to 10 with dx
for x = 1 : w
for y = 1 : h
G (x, y) = exp (-((Sig^-2)*((x-w/2+1)^2 + (y-h/2+1)^2)) / (2));
end
end
I essentially want the end/resultant function G to be a summation of Sign from 7.3 to 10 with dx (which is infinitesimally) small ie integration. How would I go about doing this? I am pretty confused. Can it even be done?
You don't appear to actually be summing G over a range of Sig values. You never change the value of Sig. In any case, assuming that dx isn't too small and that you have the memory this can be done without any loops, let alone two.
Ngrid = 400;
w = Ngrid;
h = Ngrid;
% Create range for Sig
dx = 0.1;
Sig = 7.3:dx:10;
% Build mesh of x and y points
x = 1:w;
y = 1:h;
[X,Y] = meshgrid(x,y);
% Evaluate columnized mesh points at each value of Sig, sum up, reshape to matrix
G = reshape(sum(exp(bsxfun(#rdivide,-((X(:)-w/2+1).^2+(Y(:)-h/2+1).^2),2*Sig.^2)),2),[h w]);
figure
imagesc(G)
axis equal
This results in a figure like this
The long complicated line above can be replaced by this (uses less memory, but may be slower):
G = exp(-((X-w/2+1).^2+(Y-h/2+1).^2)/(2*Sig(1)^2));
for i = 2:length(Sig)
G = G+exp(-((X-w/2+1).^2+(Y-h/2+1).^2)/(2*Sig(i)^2));
end
Related
I've been trying to evaluate a function in matlab. I want my x vector to go from 0 to 1000 and my y vector to go from 0 to 125. They should both have a length of 101.
The equation to be evaluated is z(x,y) = ay + bx, with a=10 and b=20.
a = 10;
b = 20;
n = 101;
dx = 10; % Interval length
dy = 1.25;
x = zeros(1,n);
y = zeros(1,n);
z = zeros(n,n);
for i = 1:n;
x(i) = dx*(i-1);
y(i) = dy*(i-1);
for j = 1:n;
z(i,j) = a*dy*(j-1) + b*dx*(j-1);
end
end
I get an answer, but I don't know if I did it correctly with the indices in the nested for loop?
See MATLAB's linspace function.
a=10;
b=20;
n=101;
x=linspace(0,1000,n);
y=linspace(0,125,n);
z=a*y+b*x;
This is easier and takes care of the interval spacing for you. From the linspace documentation,
y = linspace(x1,x2,n) generates n points. The spacing between the points is (x2-x1)/(n-1).
Edit:
As others have pointed out, my solution above makes a vector, not a matrix which the OP seems to want. As #obchardon pointed out, you can use meshgrid to make that 2D grid of x and y points to generate a matrix of z. Updated approached would be:
a=10;
b=20;
n=101;
x=linspace(0,1000,n);
y=linspace(0,125,n);
[X,Y] = meshgrid(x,y);
z=a*Y+b*X;
(you may swap the order of x and y depending on if you want each variable along the row or column of z.)
I want to integrate
f(x) = exp(-x^2/2)
from x=-infinity to x=+infinity
by using the Monte Carlo method. I use the function randn() to generate all x_i for the function f(x_i) = exp(-x_i^2/2) I want to integrate to calculate afterwards the mean value of f([x_1,..x_n]). My problem is, that the result depends on what values I choose for my borders x1 and x2 (see below). My result is going far away from the real value by increasing the value of x1 and x2. Actually the result should be better and better by increasing x1 and x2.
Does someone see my mistake?
Here is my Matlab code
clear all;
b=10; % border
x1 = -b; % left border
x2 = b; % right border
n = 10^6; % number of random numbers
x = randn(n,1);
f = ones(n,1);
g = exp(-(x.^2)/2);
F = ((x2-x1)/n)*f'*g;
The right value should be ~2.5066.
Thanks
Try this:
clear all;
b=10; % border
x1 = -b; % left border
x2 = b; % right border
n = 10^6; % number of random numbers
x = sort(abs(x1 - x2) * rand(n,1) + x1);
f = exp(-x.^2/2);
F = trapz(x,f)
F =
2.5066
Ok, lets start with writing of general case of MC integration:
I = S f(x) * p(x) dx, x in [a...b]
S here is integral sign.
Usually, p(x) is normalized probability density function, f(x) you want to integrate, and algorithm is very simple one:
set accumulator s to zero
start loop of N events
sample x randomly from p(x)
given x, compute f(x) and add to accumulator
back to start loop if not done
if done, divide accumulator by N and return it
In simplest textbook case you have
I = S f(x) dx, x in [a...b]
where it means PDF is equal to uniformly distributed one
p(x) = 1/(b-a)
but what you have to sum is actually (b-a)*f(x), because your integral now looks like
I = S (b-a)*f(x) 1/(b-a) dx, x in [a...b]
In general, if both f(x) and p(x) could serve as PDF, then it is matter of choice whether you integrate f(x) over p(x), or p(x) over f(x). No difference! (Well, except maybe computation time)
So, back to particular integral (which is equal to \sqrt{2\pi}, i believe)
I = S exp(-x^2/2) dx, x in [-infinity...infinity]
You could use more traditional approach like #Agriculturist and write it
I = S exp(-x^2/2)*(2a) 1/(2a) dx, x in [-a...a]
and sample x from U(0,1) in [-a...a] interval, and for each x compute exp() and average it and get the result
From what I understand, you want to use exp() as PDF, so your integral looks like
I = S D * exp(-x^2/2)/D dx, x in [-infinity...infinity]
PDF to be normalized so it shall include normalization factor D, which is exactly equal to \sqrt{2 \pi} from gaussian integral.
Now f(x) is just a constant equal to D. It doesn't depend on x. It means that you for each sampled x should add to accumulator a CONSTANT value of D. After running N samples,
in accumulator you'll have exactly N*D. To find mean you'll divide by N and as a result you'll get perfect D, which is \sqrt{2 \pi}, which, in turn, is
2.5066.
Too rusty to write any matlab, and Happy New Year anyway
We were asked to define our own differential operators on MATLAB, and I did it following a series of steps, and then we should use the differential operators to solve a boundary value problem:
-y'' + 2y' - y = x, y(0) = y(1) =0
my code was as follows, it was used to compute this (first and second derivative)
h = 2;
x = 2:h:50;
y = x.^2 ;
n=length(x);
uppershift = 1;
U = diag(ones(n-abs(uppershift),1),uppershift);
lowershift = -1;
L= diag(ones(n-abs(lowershift),1),lowershift);
% the code above creates the upper and lower shift matrix
D = ((U-L))/(2*h); %first differential operator
D2 = (full (gallery('tridiag',n)))/ -(h^2); %second differential operator
d1= D*y.'
d2= ((D2)*y.')
then I changed it to this after posting it here and getting one response that encouraged the usage of Identity Matrix, however I still seem to be getting no where.
h = 2;
n=10;
uppershift = 1;
U = diag(ones(n-abs(uppershift),1),uppershift);
lowershift = -1;
L= diag(ones(n-abs(lowershift),1),lowershift);
D = ((U-L))/(2*h); %first differential operator
D2 = (full (gallery('tridiag',n)))/ -(h^2); %second differential operator
I= eye(n);
eqn=(-D2 + 2*D - I)*y == x
solve(eqn,y)
I am not sure how to proceed with this, like should I define y and x, or what exactly? I am clueless!
Because this is a numerical approximation to the solution of the ODE, you are seeking to find a numerical vector that is representative of the solution to this ODE from time x=0 to x=1. This means that your boundary conditions make it so that the solution is only valid between 0 and 1.
Also this is now the reverse problem. In the previous post we did together, you know what the input vector was, and doing a matrix-vector multiplication produced the output derivative operation on that input vector. Now, you are given the output of the derivative and you are now seeking what the original input was. This now involves solving a linear system of equations.
Essentially, your problem is now this:
YX = F
Y are the coefficients from the matrix derivative operators that you derived, which is a n x n matrix, X would be the solution to the ODE, which is a n x 1 vector and F would be the function you are associating the ODE with, also a n x 1 vector. In our case, that would be x. To find Y, you've pretty much done that already in your code. You simply take each matrix operator (first and second derivative) and you add them together with the proper signs and scales to respect the left-hand side of the ODE. BTW, your first derivative and second derivative matrices are correct. What's left is adding the -y term to the mix, and that is accomplished by -eye(n) as you have found out in your code.
Once you formulate your Y and F, you can use the mldivide or \ operator and solve for X and get the solution to this linear system via:
X = Y \ F;
The above essentially solves the linear system of equations formed by Y and F and will be stored in X.
The first thing you need to do is define a vector of points going from x=0 to x=1. linspace is probably the most suitable where you can specify how many points we want. Let's assume 100 points for now:
x = linspace(0,1,100);
Therefore, h in our case is just 1/100. In general, if you want to solve from the starting point x = a up to the end point x = b, the step size h is defined as h = (b - a)/n where n is the total number of points you want to solve for in the ODE.
Now, we have to include the boundary conditions. This simply means that we know the beginning and ending of the solution of the ODE. This means that y(0) = y(1) = 0. As such, we make sure that the first row of Y has only the first column set to 1 and the last row of Y has only the last column set to 1, and we'll set the output position in F to both be 0. This symbolizes that we already know the solution at these points.
Therefore, your final code to solve is just:
%// Setup
a = 0; b = 1; n = 100;
x = linspace(a,b,n);
h = (b-a)/n;
%// Your code
uppershift = 1;
U = diag(ones(n-abs(uppershift),1),uppershift);
lowershift = -1;
L= diag(ones(n-abs(lowershift),1),lowershift);
D = ((U-L))/(2*h); %first differential operator
D2 = (full (gallery('tridiag',n)))/ -(h^2);
%// New code - Create differential equation matrix
Y = (-D2 + 2*D - eye(n));
%// Set boundary conditions on system
Y(1,:) = 0; Y(1,1) = 1;
Y(end,:) = 0; Y(end,end) = 1;
%// New code - Create F vector and set boundary conditions
F = x.';
F(1) = 0; F(end) = 0;
%// Solve system
X = Y \ F;
X should now contain your numerical approximation to the ODE in steps of h = 1/100 starting from x=0 up to x=1.
Now let's see what this looks like:
figure;
plot(x, X);
title('Solution to ODE');
xlabel('x'); ylabel('y');
You can see that y(0) = y(1) = 0 as per the boundary conditions.
Hope this helps, and good luck!
here's my code:
function test(n)
r = (0:1000)/n;
Phi = 2*pi*r;
[x,y] = pol2cart(Phi,r);
plot(x,y)
end
here's the output for n = 100,10,1 in that order:
what's happening with the last graph? Why is it freaking out? >.> ...
more fun shapes after n = 1:
here n = 0.9, 0.8, 0.7, 0.6
Mathematically, the right most one should connect the points (n,0) where n is a natural number between 0 and 1000. Due to floating point precision, it is slightly off the 0 and get's very small values close to zero for the y-value. Please notice the 10^-x (can't read the x because it is to small) which indicates the scale for the y-axes. These are very small numbers.
For any values <4 it is nearly impossible to recognize the original spiral, here is a plot which shows the inner 20 revolutions for four different n values.
For any small value of n you will see an output similar to n=0.01 but for any large n it's just some aliasing which is either a line (which looks rough because of precision errors if you zoom in enough) or some spiral.
Code used to plot:
r = 0:.01:20;
Phi = 2*pi*r;
[x,y] = pol2cart(Phi,r);
plot(x,y,'r')
hold on
r = 0:.9:20;
Phi = 2*pi*r;
[x,y] = pol2cart(Phi,r);
plot(x,y,'g')
r = 0:1:20;
Phi = 2*pi*r;
[x,y] = pol2cart(Phi,r);
plot(x,y,'b')
r = 0:1.1:20;
Phi = 2*pi*r;
[x,y] = pol2cart(Phi,r);
plot(x,y,'black')
legend({'0.01','.9','1','1.1'})
I am actually attempting to write code for the cubic spline interpolation. Cubic spline boils down to a series of n-1 segments where n is the number of original coordinates given initially and the segments are each represented by some cubic function.
I have figured out how to get all the coefficients and values for each segment, stored in vectors a,b,c,d, but I don't know how to plot the function as a piecewise function on different intervals. Here is my code so far. The very last for loop is where I have attempted to plot each segment.
%initializations
x = [1 1.3 1.9 2.1 2.6 3.0 3.9 4.4 4.7 5.0 6 7 8 9.2 10.5 11.3 11.6 12 12.6 13 13.3].';
y = [1.3 1.5 1.85 2.1 2.6 2.7 2.4 2.15 2.05 2.1 2.25 2.3 2.25 1.95 1.4 0.9 0.7 0.6 0.5 0.4 0.25].';
%n is the amount of coordinates
n = length(x);
%solving for a-d for all n-1 segments
a = zeros(n,1);
b = zeros(n,1);
d = zeros(n,1);
%%%%%%%%%%%%%% SOLVE FOR a's %%%%%%%%%%%%%
%Condition (b) in Definition 3.10 on pg 146
%Sj(xj) = f(xj) aka yj
for j = 1: n
a(j) = y(j);
end
%initialize hj
h = zeros(n-1,1);
for j = 1: n-1
h(j) = x(j+1) - x(j);
end
A = zeros(n,n);
bv = zeros(n,1); %bv = b vector
%initialize corners to 1
A(1,1) = 1;
A(n,n) = 1;
%set main diagonal
for k = 2: n-1
A(k,k) = 2*(h(k-1) + h(k));
end
%set upper and then lower diagonals
for k = 2 : n-1
A(k,k+1) = h(k); %h2, h3, h4...hn-1
A(k,k-1) = h(k-1); %h1, h2, h3...hn
end
%fill up the b vector using equation in notes
%first and last spots are 0
for j = 2 : n-1
bv(j) = 3*(((a(j+1)-a(j)) / h(j)) - ((a(j) - a(j-1)) / h(j-1)));
end
%augmented matrix
A = [A bv];
%%%%%%%%%%%% BEGIN GAUSSIAN ELIMINATION %%%%%%%%%%%%%%%
offset = 1;
%will only need n-1 iterations since "first" pivot row is unchanged
for k = 1: n-1
%Searching from row p to row n for non-zero pivot
for p = k : n
if A(p,k) ~= 0;
break;
end
end
%row swapping using temp variable
if p ~= k
temp = A(p,:);
A(p,:) = A(k,:);
A(k,:) = temp;
end
%Eliminations to create Upper Triangular Form
for j = k+1:n
A(j,offset:n+1) = A(j,offset:n+1) - ((A(k, offset:n+1) * A(j,k)) / A(k,k));
end
offset = offset + 1;
end
c = zeros(n,1); %initializes vector of data of n rows, 1 column
%Backward Subsitution
%First, solve the nth equation
c(n) = A(n,n+1) / A(n,n);
%%%%%%%%%%%%%%%%% SOLVE FOR C's %%%%%%%%%%%%%%%%%%
%now solve the n-1 : 1 equations (the rest of them going backwards
for j = n-1:-1:1 %-1 means decrement
c(j) = A(j,n+1);
for k = j+1:n
c(j) = c(j) - A(j,k)*c(k);
end
c(j) = c(j)/A(j,j);
end
%%%%%%%%%%%%% SOLVE FOR B's and D's %%%%%%%%%%%%%%%%%%%%
for j = n-1 : -1 : 1
b(j) = ((a(j+1)-a(j)) / h(j)) - (h(j)*(2*c(j) + c(j+1)) / 3);
d(j) = (c(j+1) - c(j)) / 3*h(j);
end
%series of equation segments
for j = 1 : n-1
f = #(x) a(j) + b(j)*(x-x(j)) + c(j)*(x-x(j))^2 + d(j)*(x-x(j))^3;
end
plot(x,y,'o');
Let's assume that I have calculated vectors a,b,c,d correctly for each segment. How do I plot each cubic segment such that they all appear graphed on a single plot?
I appreciate the help.
That's pretty easy. You've already done half of the work by defining an anonymous function that is for the cubic spline in between each interval. However, you need to make sure that the operations in the function are element-wise. You currently have it operating on scalars, or assuming that we are using matrix operations. Don't do that. Use .* instead of * and .^ instead of ^. The reason why you need to do this is to make generating the points on the spline a lot easier, where my next point follows.
All you have to do next is define a bunch of x points within the interval defined by the neighbouring x key points and substitute them into your function, then plot the result.... so something like this:
figure;
hold on;
for j = 1 : n-1
f = #(x) a(j) + b(j).*(x-x(j)) + c(j).*(x-x(j)).^2 + d(j)*(x-x(j)).^3; %// Change function to element-wise operations - be careful
x0 = linspace(x(j), x(j+1)); %// Define set of points
y0 = f(x0); %// Find output points
plot(x0, y0, 'r'); %// Plot the line in between the key points
end
plot(x, y, 'bo');
We spawn a new figure, then use hold on so that when we call plot multiple times, we append the results to the same figure. Next, for each set of cubic spline coefficients we have, define a spline function, then generate a bunch of x values with linspace that are between the current x key point and the one beside it. By default, linspace generates 100 points between a start point (i.e. x(j)) and end point (i.e. x(j+1)). You can control how many points you want to generate by specifying a third parameter (so something like linspace(x(j), x(j+1), 25); to generate 25 points). We use these x values and substitute them into our spline equation to get our y values. We then plot this result on the figure using a red line. Once we're done, we plot the key points as blue open circles on top of the curve.
As a bonus, I ran your code with the above plotting mechanism, and this is what I get: