I'm running a script calling a function and within the function it takes a value from the matrix. Matlab seems to be thinking that the value is still a matrix and is asking for a . when its squared. I should be getting a single value out of my matrix. Any help would be appreciated!
Output
Error using ^
Inputs must be a scalar and a square matrix.
To compute elementwise POWER, use POWER (.^) instead.
Error in ls_error (line 11)
partialSum = (vi - yi)^2;
This is the script i'm running
Exp1H1 = 35.6;
Exp1H2 = 24.7;
Exp2H1 = 46.8;
Exp2H2 = 37.8;
Exp3H1 = 45.7;
Exp3H2 = 36.4;
Exp4H1 = 47.7;
Exp4H2 = 39.2;
Radius = 3.75;
L = 10;
ArrayOfHeightDiff = [(Exp1H1-Exp1H2),(Exp2H1-Exp2H2),(Exp3H1-Exp3H2), (Exp4H1-Exp4H2)];
dhdl = ArrayOfHeightDiff./L
ArrayOfDarcys = [0.29,0.25,0.26,0.23];
v_meas = ((ArrayOfDarcys.*1000)./60)./(pi*Radius^2)
K = [-0.3 : 0.1 : 0.5];
for ii = 1 : 1 : length(K)
ExportSum = ls_error(dhdl, v_meas, K)
ExportSum(1,ii) = ExportSum
end
This is the function
function [ExportSum] = ls_error(dhdl, v_meas, K)
total = 0;
L = length(dhdl);
for ii = 1 : 1 : L
dhdl1 = dhdl(1,ii);
vi = v_meas(1,ii);
yi = 1*K* dhdl1;
partialSum = (vi - yi)^2;
total = total + partialSum;
end
ExportSum = total;
end
In the following code, you create a 1xN vector K, and pass it into ls_error:
K = [-0.3 : 0.1 : 0.5];
for ii = 1 : 1 : length(K)
ExportSum = ls_error(dhdl, v_meas, K)
ExportSum(1,ii) = ExportSum
end
You then use this vector Kand multiply it by two scalars, which will produce a 1xN vector:
yi = 1*K* dhdl1;
partialSum = (vi - yi)^2;
The partialSum calculation is then giving you the error, as you can't perform a scalar square on a vector.
From your code, what I think you meant to do was this:
for ii = 1 : 1 : length(K)
ExportSum = ls_error(dhdl, v_meas, K(ii))
ExportSum(1,ii) = ExportSum
end
Where instead of passing in the entire vector K, you just want to pass in the iith element to use within the calculation of ExportSum which you return.
As a further note, once this bug is solved, it may be worth looking at vectorising your Matlab function (i.e. deliberately passing it the entire vector of K and computing all of the ExportSums at once using vector arithmetic instead of inside a loop), which will hugely speed up your code. It can be complicated, but will likely give you a huge decrease in execution time.
In the calling script you define K as a vector. Therefore, in the function yi is a vector too. Hence the error in (vi - yi)^2.
Related
I am trying to calculate on MatLab the Expectation for the function e^X where X ~ N(mu,sigma^2). In my example, mu = 0 and sigma = 1.
function [E] = expectedval(m,s)
%find E(e^x) with X~N(mu,sigma)
% mu = 0 , sigma = 1
X = normrnd(m,s);
f=exp(X);
mean(f)
So by the end I can run expectedval(0,1) to get my solution. The code runs with no issue, but I would like to compile a histogram to analyse the data with several runs. How may I collect this data with some sort of loop?
I'd appreciate any improvement ideas or suggestions!
Actually, your function just creates one random value for the given mean and variance... so there's actually no point in calculating a mean. On the top of that, the output E value is not assigned before the function finishes. The function can therefore be rewritten as follows:
function E = expectedval(m,s)
X = normrnd(m,s);
E = exp(X);
end
If you want to draw a sample from the normal distribution, and use the mean to compute the correct expected value, you can rewrite it as follows instead:
function E = expectedval(m,s,n)
X = normrnd(m,s,[n 1]);
f = exp(X);
E = mean(f);
end
And here is an example on how to run your function many times with different parameters, collecting the output on each iteration:
m = 0:4;
m_len = numel(m);
s = 1:0.25:2;
s_len = numel(s);
n = 100;
data = NaN(m_len*s_len,1);
data_off = 1;
for s = 1:s_len
for m = 1:m_len
data(data_off) = expectedval(m,s,n);
data_off = data_off + 1;
end
end
I am using MATLAB to calculate the numerical integral of a complex function including natural exponent.
I get a warning:
Infinite or Not-a-Number value encountered
if I use the function integral, while another error is thrown:
Output of the function must be the same size as the input
if I use the function quadgk.
I think the reason could be that the integrand is infinite when the variable ep is near zero.
Code shown below. Hope you guys can help me figure it out.
close all
clear
clc
%%
N = 10^5;
edot = 10^8;
yita = N/edot;
kB = 8.6173324*10^(-5);
T = 300;
gamainf = 0.115;
dTol = 3;
K0 = 180;
K = K0/160.21766208;
nu = 3*10^12;
i = 1;
data = [];
%% lambda = ec/ef < 1
for ef = 0.01:0.01:0.1
for lambda = 0.01:0.01:0.08
ec = lambda*ef;
f = #(ep) exp(-((32/3)*pi*gamainf^3*(0.5+0.5*sqrt(1+2*dTol*K*(ep-ec)/gamainf)-dTol*K*(ep-ec)/gamainf).^3/(K*(ep-ec)).^2-16*pi*gamainf^3*(0.5+0.5*sqrt(1+2*dTol*K*(ep-ec)/gamainf)-dTol*K*(ep-ec)/gamainf).^2/((1+dTol*K*(ep-ec)/(gamainf*(0.5+0.5*sqrt(1+2*dTol*K*(ep-ec)/gamainf)-dTol*K*(ep-ec)/gamainf)))*(K*(ep-ec)).^2))/(kB*T));
q = integral(f,0,ef,'ArrayValued',true);
% q = quadgk(f,0,ef);
prob = 1-exp(-yita*nu*q);
data(i,1) = ef;
data(i,2) = lambda;
data(i,3) = q;
i = i+1;
end
end
I've rewritten your equations so that a human can actually understand it:
function integration
N = 1e5;
edot = 1e8;
yita = N/edot;
kB = 8.6173324e-5;
T = 300;
gamainf = 0.115;
dTol = 3;
K0 = 180;
K = K0/160.21766208;
nu = 3e12;
i = 1;
data = [];
%% lambda = ec/ef < 1
for ef = 0.01:0.01:0.1
for lambda = 0.01:0.01:0.08
ec = lambda*ef;
q = integral(#f,0,ef,'ArrayValued',true);
% q = quadgk(f,0,ef);
prob = 1 - exp(-yita*nu*q);
data(i,:) = [ef lambda q];
i = i+1;
end
end
function y = f(ep)
G = K*(ep - ec);
r = dTol*G/gamainf;
S = sqrt(1 + 2*r);
x = (1 + S)/2 - r;
Z = 16*pi*gamainf^3;
y = exp( -Z*x.^2.*( 2*x/(3*G.^2) - 1/(G.^2*(1 + r/x))) ) /...
(kB*T));
end
end
Now, for the first iteration, ep = 0.01, the value of the argument of the exp() function inside f is huge. In fact, if I rework the function to return the argument to the exponent (not the value):
function y = f(ep)
% ... all of the above
% NOTE: removed the exp() to return the argument
y = -Z*x.^2.*( 2*x/(3*G.^2) - 1/(G.^2*(1 + r/x))) ) /...
(kB*T);
end
and print its value at some example nodes like so:
for ef = 0.01 : 0.01 : 0.1
for lambda = 0.01 : 0.01 : 0.08
ec = lambda*ef;
zzz(i,:) = [f(0) f(ef/4) f(ef)];
i = i+1;
end
end
zzz
I get this:
% f(0) f(ef/4) f(ef)
zzz =
7.878426438111721e+07 1.093627454284284e+05 3.091140080273912e+03
1.986962280947140e+07 1.201698288371587e+05 3.187767404903769e+03
8.908646053687230e+06 1.325435523124976e+05 3.288027743119838e+03
5.055141696747510e+06 1.467952125661714e+05 3.392088351112798e+03
...
3.601790797707676e+04 2.897200140791236e+02 2.577170427480841e+01
2.869829209254144e+04 3.673888685004256e+02 2.404148067956737e+01
2.381082059148755e+04 4.671147785149462e+02 2.238181495716831e+01
So, integral() has to deal with things like exp(10^7). This may not be a problem per se if the argument would fall off quickly enough, but as shown above, it doesn't.
So basically you're asking for the integral of a function that ranges in value between exp(~10^7) and exp(~10^3). Needless to say, The d(ef) in the order of 0.01 isn't going to compensate for that, and it'll be non-finite in floating point arithmetic.
I suspect you have a scaling problem. Judging from the names of your variables as well as the equations, I would think that this has something to do with thermodynamics; a reworked form of Planck's law? In that case, I'd check if you're working in nanometers; a few factors of 10^(-9) will creep in, rescaling your integrand to the compfortably computable range.
In any case, it'll be wise to check all your units, because it's something like that that's messing up the numbers.
NB: the maximum exp() you can compute is around exp(709.7827128933840)
I actually vectorizing one of my code and I have some issues.
This is my initial code:
CoordVorBd = random(N+1,3)
CoordCP = random(N,3)
v = random(1,3)
for i = 1 : N
for j = 1 : N
ri1j = (-CoordVorBd (i,:) + CoordCP(j,:));
vij(i,j,:) = cross(v,ri1j))/(norm(ri1j)
end
end
I have start to vectorize that creating some matrix that contains 3*1 Vectors. My size of matrix is N*N*3.
CoordVorBd1(1:N,:) = CoordVorBd(2:N+1,:);
CoordCP_x= CoordCP(:,1);
CoordCP_y= CoordCP(:,2);
CoordCP_z= CoordCP(:,3);
CoordVorBd_x = CoordVorBd([1:N],1);
CoordVorBd_y = CoordVorBd([1:N],2);
CoordVorBd_z = CoordVorBd([1:N],3);
CoordVorBd1_x = CoordVorBd1(:,1);
CoordVorBd1_y = CoordVorBd1(:,2);
CoordVorBd1_z = CoordVorBd1(:,3);
[X,Y] = meshgrid (1:N);
ri1j_x = (-CoordVorBd_x(X) + CoordCP_x(Y));
ri1j_y = (-CoordVorBd_y(X) + CoordCP_y(Y));
ri1j_z = (-CoordVorBd_z(X) + CoordCP_z(Y));
ri1jmat(:,:,1) = ri1j_x(:,:);
ri1jmat(:,:,2) = ri1j_y(:,:);
ri1jmat(:,:,3) = ri1j_z(:,:);
vmat(:,:,1) = ones(N)*v(1);
vmat(:,:,2) = ones(N)*v(2);
vmat(:,:,3) = ones(N)*v(3);
This code works but is heavy in terms of variable creation. I did'nt achieve to apply the vectorization to all the matrix in one time.
The formule like
ri1jmat(X,Y,1:3) = (-CoordVorBd (X,:) + CoordCP(Y,:));
doesn't work...
If someone have some ideas to have something cleaner.
At this point I have a N*N*3 matrix ri1jmat with all my vectors.
I want to compute the N*N rij1norm matrix that is the norm of the vectors
rij1norm(i,j) = norm(ri1jmat(i,j,1:3))
to be able to vectorize the vij matrix.
vij(:,:,1:3) = (cross(vmat(:,:,1:3),ri1jmat(:,:,1:3))/(ri1jmatnorm(:,:));
The cross product works.
I tried numbers of method without achieve to have this rij1norm matrix without doing a double loop.
If someone have some tricks, thanks by advance.
Here's a vectorized version. Note your original loop didn't include the last column of CoordVorBd, so if that was intentional you need to remove it from the below code as well. I assumed it was a mistake.
CoordVorBd = rand(N+1,3);
CoordCP = rand(N,3);
v = rand(1,3);
repCoordVor=kron(CoordVorBd', ones(1,size(CoordCP,1)))'; %based on http://stackoverflow.com/questions/16266804/matlab-repeat-every-column-sequentially-n-times
repCoordCP=repmat(CoordCP, size(CoordVorBd,1),1); %repeat matrix
V2=-repCoordVor + repCoordCP; %your ri1j
nrm123=sqrt(sum(V2.^2,2)); %vectorized norm for each row
vij_unformatted=cat(3,(v(:,2).*V2(:,3) - V2(:,2).*v(:,3))./nrm123,(v(:,3).*V2(:,1) - V2(:,3).*v(:,1))./nrm123,(v(:,1).*V2(:,2) - V2(:,1).*v(:,2))./nrm123); % cross product, expanded, and each term divided by norm, could use bsxfun(#rdivide,cr123,nrm123) instead, if cr123 is same without divisions
vij=permute(reshape( vij_unformatted,N,N+1,3),[2,1,3]); %reformat to match your vij
Here is another way to do it using arrayfun
% Define a meshgrid of indices to run over
[I, J] = meshgrid(1:N, 1:(N+1));
% Calculate ril for each index
rilj = arrayfun(#(x, y) -CoordVorBd (y,:) + CoordCP(x,:), I, J, 'UniformOutput', false);
%Calculate vij for each point
temp_vij1 = arrayfun(#(x, y) cross(v, rilj{x, y}) / norm(rilj{x, y}), J, I, 'UniformOutput', false);
%Reshape the matrix into desired format
temp_vij2 = cell2mat(temp_vij1);
vij = cat(3, temp_vij2(:, 1:3:end), temp_vij2(:, 2:3:end), temp_vij2(:, 3:3:end));
I am trying to sum a function and then attempting to find the root of said function. That is, for example, take:
Consider that I have a matrix,X, and vector,t, of values: X(2*n+1,n+1), t(n+1)
for j = 1:n+1
sum = 0;
for i = 1:2*j+1
f = #(g)exp[-exp[X(i,j)+g]*(t(j+1)-t(j))];
sum = sum + f;
end
fzero(sum,0)
end
That is,
I want to evaluate at
j = 1
f = #(g)exp[-exp[X(1,1)+g]*(t(j+1)-t(j))]
fzero(f,0)
j = 2
f = #(g)exp[-exp[X(1,2)+g]*(t(j+1)-t(j))] + exp[-exp[X(2,2)+g]*(t(j+1)-t(j))] + exp[-exp[X(3,2)+g]*(t(j+1)-t(j))]
fzero(f,0)
j = 3
etc...
However, I have no idea how to actually implement this in practice.
Any help is appreciated!
PS - I do not have the symbolic toolbox in Matlab.
I suggest making use of matlab's array operations:
zerovec = zeros(1,n+1); %preallocate
for k = 1:n+1
f = #(y) sum(exp(-exp(X(1:2*k+1,k)+y)*(t(k+1)-t(k))));
zerovec(k) = fzero(f,0);
end
However, note that the sum of exponentials will never be zero, unless the exponent is complex. Which fzero will never find, so the question is a bit of a moot point.
Another solution is to write a function:
function [ sum ] = func(j,g,t,X)
sum = 0;
for i = 0:2*j
f = exp(-exp(X(i+1,j+1)+g)*(t(j+3)-t(j+2)));
sum = sum + f;
end
end
Then loop your solver
for j=0:n
fun = #(g)func(j,g,t,X);
fzero(fun,0)
end
I want to auto-correlate a random noise vector with out any built-in MATLAB functions.
My auto correlation equation that is given is:
Rxx[L] = ∑ from n = 1 to N-1 [x(n)*x(n+L)]
L = [0:200]
I have written the code below but the plot Rxx vs L plot is not what I am expecting.
I am expecting my plot to start at some maximum at L = 0 or L = 1 since MATLAB starts its index at 1. Then exponentially decrease and saturates at a min of zero.
clc
clear all
randn('seed',2496132);
n = randn(1,1024);
upperbound = numel(n)-1;
for L = 1:200
for j = 1 : upperbound
n1(j) = n(j)+L;
Rxx(j) = (n(j)*n1(j));
end
Rxx_sum(L) = sum(Rxx);
Rxx = 0;
end
plot([1:200], Rxx_sum)
You have error in inner loop: you need to use n1(j) = n(j+L); instead n1(j) = n(j)+L;. E.g. you need add L to index instead value.
Second error is following: if you want to use upperbound = numel(n)-1 than you should use L equal to 0 or 1 only. E.g. you outer loop will be
for L = 0:1
...
Rxx_sum(L+1) = sum(Rxx);
...
Instead of this you can also correct upperbound value:
upperbound = numel(n) - maxL;
There maxL is maximal value of L that will used in next loop.
One more tip: it is possible to increase calculation speed if you replace inner loop with scalar product, e.g.
for L = 1:200
Rxx_sum(L) = n(1:upperbound) * n(1+L:upperbound+L)';
end
I ended up fixing my script with the help of the above code.
clc
clear all
randn('seed',2496132);
z = randn(1,1024);
n = [z zeros(1,200)];
upperbound = numel(z)-1;
for L = 0:200
for j = 1 : upperbound
Rxx(j) = (n(j)*n(j+L));
>end
Rxx_sum(L+1) = sum(Rxx);
Rxx = 0;
end
plot([0:200],Rxx_sum)