I want to replicate a figure from this article. More specifically, I want to replicate Figure number 4, which I believe is the representation of Equation 9.
So far I have come up with this code:
% implementing equation 9 and figure 4
step = 0.01; t = 1:step:3600;
d = 3; % dimension
N = 8000; % number of molecules
H = 0.01; % H = [0.01,0.1,1] is in mol/micrometer^3
H = H*6.02214078^5; % hence I scaled the Avogadro's number (right or wrong?)
D = 10; % diffusion coefficient in micrometer^2/sec
u(1) = 1./(1.^(d/2)); % inner function in equation 9; first pulse
for i = 2:numel(t)/1000
u(i) = u(i-1)+(1./(i.^(d/2))); % u-> the pulse number
lmda(i) = (1/(4*pi*D))*((N/(H)).*sum(u)).^(2/d);
end
figure;plot(lmda)
But I am not able to replicate it.
Equation 9
For details on the parameters, refer to the above code. The authors did mention that the summation in equation 9 is a Reimann Zeta series. Wonder if that has anything to do with the result?
Figure 4, which I am trying to replicate:
Could someone kindly tell me the mistake I am making?
P.s: This is not a homework.
Problem 1: You think you are scaling by Avogadro's number on this line
H = H*6.02214078^5;
In fact, you're scaling by approximately 7920=6.022^5. If you wanted to scale by the Avogadro number then you should do:
H = H * 6.02214078e23 % = 6.02214078 * 10^23 : the Avogadro number
Problem 2: You aren't plotting against t, you're plotting against the sample number which doesn't really make sense (unless your t happened to be in integer seconds). Remove the /1000 from your loop
for i = 2:numel(t)
% ...
end
% Then plot
plot(t, lmda)
At this stage we can see something is really wrong. Now that we're scaling by the correct Avo number, the orders of magnitude are way out. I suggest that you trust the H in figure 4 and the H in equation 9 are the same H, it would be very confusing if the author intended anything different!
On that basis, I would suggest you are using the wrong D, N, or time between pulses. I've set up the pulse timing a bit clearer in my code below. I've also streamlined your loop somewhat using vectorisation, and removed the H scaling.
If you tweak it so dtPulses=100 as well as D=100, then the plots are almost identical. You perhaps need to consider how these two numbers affect the result...
% implementing equation 9 and figure 4
d = 3; % dimension
N = 8000; % number of molecules
D = 100; % diffusion coefficient in micrometer^2/sec
dtPulses = 10; % Seconds between pulses
tPulses = 1:dtPulses:3600; % Time array to plot against
nt = numel(tPulses);
i = 1:nt; % pulse numbers
u = 1 ./ (i.^(d/2)); % inner function in equation 9: individual pulse
for k = 2:nt % Running sum
u(k) = u(k-1)+u(k);
end
% Now plot for different H (mol/micrometer^3)
H = [0.01, 0.1, 1];
figure; hold on; linestyles = {':k', '--k', '-k'};
for nH = 1:3
lmda = ((1/(4*pi*D))*(N/H(nH)).*u).^(2/d);
plot(tPulses, lmda, linestyles{nH}, 'linewidth', 2)
end
Related
I am trying to solve the following least squares problem:
b(alpha)=A(alpha,beta)x(beta)
I am trying to use an alternative approach, which is to assume the functional form of x(beta) through the use of tunable parameters, say x(beta, a, c). How can I solve this problem in MATLAB for a least squares solution for those parameters?
I second the comments -this would be much easier if you gave a slightly more verbose description of your problem and most importantly add a minimal working example.
As far as I understand though, you want to solve a linear system of equations with some additional assumptions about the fitted parameters. This can be done by expressing them as an optimisation problem.
Here for example I've fitted a quadratic where the coefficients of x^0 and x^1 are both dependant on some other arbitrary parameter a (for this example a = 6 - that's what we're trying to recover from the data).
There are 2 different approaches plotted here - unconstrained and constrained optimisation. You can see that all of them approximate our data well, but only the constrained optimisation recovers a value of a close to 6 (5.728). Anyway, have a look at the code and I hope this helps with your problem somewhat. If you can, try to use the reduced number of parameters approach. It is always better to reduce your fitting problems to lower dimensional spaces if possible - much less risk of local minima and much faster solutions.
Here is the code:
close all; clear; clc;
%% Generate test data
x = 1:100;
rng(0); % Seed rng
% Polynomial where we know something about the parameters - we know that if
% the coefficient of x^0 is 'a'm then the coefficient of x^1 is (1-a).
a = 6;
y = a + (1-a).*x + 0.1*x.^2;
y = y + 30*randn(size(x)); % Add some noise
%% Fit with mrdivide and Vandermonde matrix
A = vander(x); A = A(:,end-2:end)';
b = y;
k1 = b/A;
%% Fit with an unconstrained optimiser
f = #(k) optimfun1(x,y,k);
k0 = [1 1 1]; % Starting point
k2 = fminsearch(f,k0);
%% Fit with a constrained optimiser
f = #(k) optimfun1(x,y,k);
k0 = [1 1 1];
Aeq = [0 1 1]; beq = 1; % Constrain k2 = 1 - k3 which is equivalent to k2+k3 = 1
k3 = fmincon(f,k0,[],[],Aeq,beq);
%% Fit with a reduced number of parameters
f = #(k) optimfun2(x,y,k);
k0 = [1 1];
k4 = fminsearch(f,k0);
k4 = [k4 1-k4(2)]; % Infer the last coeff.
%% Plot
plot(x,y,'ko');
hold on
plot(x,polyval(k1,x));
plot(x,polyval(k2,x));
plot(x,polyval(k3,x));
plot(x,polyval(k4,x));
legend('k^{dat} = [6.000 -5.000 0.100];',...
sprintf('k^{unc}_1 = [%.3f %.3f %.3f]',flipud(k1(:))),...
sprintf('k^{unc}_2 = [%.3f %.3f %.3f]',flipud(k2(:))),...
sprintf('k^{cns}_1 = [%.3f %.3f %.3f]',flipud(k3(:))),...
sprintf('k^{cns}_2 = [%.3f %.3f %.3f]',flipud(k4(:))),...
'location','northwest');
grid on;
xlabel('x');
ylabel('f(x)');
title(sprintf('f(x) = a + (1-a)x + 0.1x^2; a = %d',a));
function diff = optimfun1(x,y,k)
yfit = polyval(k,x);
dy = yfit-y;
diff = sum(dy.^2); % Sum of squared residuals
end
function diff = optimfun2(x,y,k)
k = [k 1-k(2)]; % Infer the last coeff.
yfit = polyval(k,x);
dy = yfit-y;
diff = sum(dy.^2);
end
Without knowing exactly how does the parameter works, it is difficult to figure out what to do. For example if the parameter is
x(beta, a, c) = a * x(beta) + c
Then your equation becomes
b(alpha)= A(alpha,beta) * (a * x(beta) + c)
b(alpha) - c*A(alpha,beta) = A(alpha,beta) * a * x(beta)
which then perhaps you can solve in the standard way (I'm treating b and A as numbers and x as the only variable here disregarding the alpha and beta). For more non-linear relation, it gets complex.
Given
d²x/dt² + a·dx/dt + 7.9·x³ = 3.2·sin(xt)
with initial conditions
x(0) = +1.2
dx/dt(0) = −3.3
x(2.3) = −0.6
Find numerically all the possible values of a, each accurate to at least 3 significant digits.
Is there any method other than brute force for solving this?
As far as I can see, it is not possible to solve this problem as stated.
Here is what I did. I implemented your problem in a reasonably general way:
%{
Find all 'a' for which
d²x/dt² + a·dx/dt + 7.9·x³ - 3.2·sin(xt) = 0
with initial conditions
x(0) = +1.2
dx/dt(0) = −3.3
x(2.3) = −0.6
%}
function odetest
% See how the function search_a(a) behaves around a = 0:
test_as = 0 : 0.1 : 10;
da = zeros(size(test_as));
for ii = 1:numel(test_as)
da(ii) = search_a(test_as(ii)); end
figure(100), clf, hold on
plot(test_as, da)
axis tight
xlabel('a')
ylabel('|x(2.3) - 0.6|')
% Roughly cherry-pick some positive values, improve the estimate, and
% plot the solutions
opt = optimset('tolfun',1e-14, 'tolx',1e-12);
plot_x(fminsearch(#search_a, 0.0, opt), 1)
plot_x(fminsearch(#search_a, 1.4, opt), 2)
plot_x(fminsearch(#search_a, 3.2, opt), 3)
% Plot single solution
function plot_x(a,N)
[xt, t] = solve_ode(a);
figure(N), clf, hold on
plot(t,xt)
plot(2.3, -0.6, 'rx', 'markersize', 20)
title (['x(t) for a = ' num2str(a)])
xlabel('t')
ylabel('x(t)')
end
end
% Solve the problem for a value a, and return the difference between the
% actual value and desired value (-0.6)
function da = search_a(a)
a_desired = -0.6;
xt = solve_ode(a);
da = abs(xt(end) - a_desired);
end
% Solve the problem for any given value of a
function [xt, t] = solve_ode(a)
y0 = [1.2 -3.3];
tfinal = 2.3;
opt = odeset('AbsTol',1e-12, 'RelTol',1e-6);
[t,yt] = ode45(#(y,t) odefun(y,t,a), [0 tfinal], y0, opt);
xt = yt(:,1); % transform back to x(t)
end
% Most ODE solvers solve first-order systems. This is not a problem for a
% second-order system, because if we make the transformation
%
% y(t) = [ x (t)
% x'(t) ]
%
% Then we can solve for
%
% y'(t) = [ x' (t)
% x''(t) ] <- the second-order homogeneous DE
%
function dydt = odefun(t,y,a)
dydt = [y(2)
-a*y(2) - 7.9*y(1)^3 + 3.2*sin(y(1)*t)];
end
The first part gave me this figure:
Some further investigation suggests that this only grows for larger a.
This figure gave rise to the initial estimates a = [0, 1.4, 3.2], which I improved via fminsearch() and plotted the solutions of:
So, that probably enables you to hand in your homework :)
However, why I say it's impossible to answer the question as stated, is because this is what the first plot looks like for negative a:
The oscillatory behavior seems to continue indefinitely, and the spacing in between the zeros seems to decrease in a non-predictable way.
Now, my university days are long behind me, and I'm not so well-versed in ODE theory anymore. Perhaps there is a pattern to it, that just doesn't show because of numerical problems. Or perhaps the oscillation stops after some value, never to return again. Or perhaps another zero turns up at a = +1053462664212.25.
I can't prove any of these things, I just know how to brute-force it; the rest is up to you.
I have written a matlab function (Version 7.10.0.499 (R2010a)) to evaluate incoming FT signal and calculate the morlet wavelet for the signal. I have a similar program, but I needed to make it more readable and closer to mathematical lingo. The output plot is supposed to be a 2D plot with colour showing the intensity of the frequencies. My plot seems to have all frequencies the same per time. The program does make an fft per row of time for each frequency, so I suppose another way to look at it is that the same line repeats itself per step in my for loop. The issue is I have checked with the original program, which does return the correct plot, and I cannot locate any difference beyond what I named the values and how I organized the code.
function[msg] = mile01_wlt(FT_y, f_mn, f_mx, K, N, F_s)
%{
Fucntion to perform a full wlt of a morlet wavelett.
optimization of the number of frequencies to be included.
FT_y satisfies the FT(x) of 1 envelope and is our ft signal.
f min and max enter into the analysis and are decided from
the f-image for optimal values.
While performing the transformation there are different scalings
on the resulting "intensity".
Plot is made with a 2D array and a colour code for intensity.
version 05.05.2016
%}
%--------------------------------------------------------------%
%{
tableofcontents:
1: determining nr. of analysis f, prints and readies f's to be used.
2: ensuring correct orientation of FT_y
3:defining arrays
4: declaring waveletdiagram and storage of frequencies
5: for-loop over all frequencies:
6: reducing file to manageable size by truncating time.
7: marking plot to highlight ("randproblemer")
8: plotting waveletdiagram
%}
%--------------------------------------------------------------%
%1: determining nr. of analysis f, prints and readies f's to be used.
DF = floor( log(f_mx/f_mn) / log(1+( 1/(8*K) ) ) ) + 1;% f-spectre analysed
nr_f_analysed = DF %output to commandline
f_step = (f_mx/f_mn)^(1/(DF-1)); % multiplicative step for new f_a
f_a = f_mn; %[Hz] frequency of analysis
T = N/F_s; %[s] total time sampled
C = 2.0; % factor to scale Psi
%--------------------------------------------------------------%
%2: ensuring correct orientation of FT_y
siz = size(FT_y);
if (siz(2)>siz(1))
FT_y = transpose(FT_y);
end;
%--------------------------------------------------------------%
%3:defining arrays
t = linspace(0, T*(N-1)/N, N); %[s] timespan
f = linspace(0, F_s*(N-1)/N, N); %[Hz] f-specter
%--------------------------------------------------------------%
%4: declaring waveletdiagram and storage of frequencies
WLd = zeros(DF,N); % matrix of DF rows and N columns for storing our wlt
f_store = zeros(1,DF); % horizontal array for storing DF frequencies
%--------------------------------------------------------------%
%5: for-loop over all frequencies:
for jj = 1:DF
o = (K/f_a)*(K/f_a); %factor sigma
Psi = exp(- 0*(f-f_a).*(f-f_a)); % FT(\psi) for 1 envelope
Psi = Psi - exp(-K*K)*exp(- o*(f.*f)); % correctional element
Psi = C*Psi; %factor. not set in stone
%next step fits 1 row in the WLd (3 alternatives)
%WLd(jj,:) = abs(ifft(Psi.*transpose(FT_y)));
WLd(jj,:) = sqrt(abs(ifft(Psi.*transpose(FT_y))));
%WLd(jj,:) = sqrt(abs(ifft(Psi.*FT_y))); %for different array sizes
%and emphasizes weaker parts.
%prep for next round
f_store (jj) = f_a; % storing used frequencies
f_a = f_a*f_step; % determines the next step
end;
%--------------------------------------------------------------%
%6: reducing file to manageable size by truncating time.
P = floor( (K*F_s) / (24*f_mx) );%24 not set in stone
using_every_P_point = P %printout to cmdline for monitoring
N_P = floor(N/P);
points_in_time = N_P %printout to cmdline for monitoring
% truncating WLd and time
WLd2 = zeros(DF,N_P);
for jj = 1:DF
for ii = 1:N_P
WLd2(jj,ii) = WLd(jj,ii*P);
end
end
t_P = zeros(1,N_P);
for ii = 1:N_P % set outside the initial loop to reduce redundancy
t_P(ii) = t(ii*P);
end
%--------------------------------------------------------------%
%7: marking plot to highlight boundary value problems
maxval = max(WLd2);%setting an intensity
mxv = max(maxval);
% marks in wl matrix
for jj= 1:DF
m = floor( K*F_s / (P*pi*f_store(jj)) ); %finding edges of envelope
WLd2(jj,m) = mxv/2; % lower limit
WLd2(jj,N_P-m) = mxv/2;% upper limit
end
%--------------------------------------------------------------%
%8: plotting waveletdiagram
figure;
imagesc(t_P, log10(f_store), WLd2, 'Ydata', [1 size(WLd2,1)]);
set(gca, 'Ydir', 'normal');
xlabel('Time [s]');
ylabel('log10(frequency [Hz])');
%title('wavelet power spectrum'); % for non-sqrt inensities
title('sqrt(wavelet power spectrum)'); %when calculating using sqrt
colorbar('location', 'southoutside');
msg = 'done.';
There are no error message, so I am uncertain what exactly I am doing wrong.
Hope I followed all the guidelines. Otherwise, I apologize.
edit:
my calling program:
% establishing parameters
N = 2^(16); % | number of points to sample
F_s = 3.2e6; % Hz | samplings frequency
T_t = N/F_s; % s | length in seconds of sample time
f_c = 2.0e5; % Hz | carrying wave frequency
f_m = 8./T_t; % Hz | modulating wave frequency
w_c = 2pif_c; % Hz | angular frequency("omega") of carrying wave
w_m = 2pif_m; % Hz | angular frequency("omega") of modulating wave
% establishing parameter arrays
t = linspace(0, T_t, N);
% function variables
T_h = 2*f_m.*t; % dimless | 1/2 of the period for square signal
% combined carry and modulated wave
% y(t) eq. 1):
y_t = 0.5.*cos(w_c.*t).*(1+cos(w_m.*t));
% y(t) eq. 2):
% y_t = 0.5.*cos(w_c.*t)+0.25*cos((w_c+w_m).*t)+0.25*cos((w_c-w_m).*t);
%square wave
sq_t = cos(w_c.*t).*(1 - mod(floor(t./T_h), 2)); % sq(t)
% the following can be exchanged between sq(t) and y(t)
plot(t, y_t)
% plot(t, sq_t)
xlabel('time [s]');
ylabel('signal amplitude');
title('plot of harmonically modulated signal with carrying wave');
% title('plot of square modulated signal with carrying wave');
figure()
hold on
% Fourier transform and plot of freq-image
FT_y = mile01_fftplot(y_t, N, F_s);
% FT_sq = mile01_fftplot(sq_t, N, F_s);
% Morlet wavelet transform and plot of WLdiagram
%determining K, check t-image
K_h = 57*4; % approximation based on 1/4 of an envelope, harmonious
%determining f min and max, from f-image
f_m = 1.995e5; % minimum frequency. chosen to showcase all relevant f
f_M = 2.005e5; % maximum frequency. chosen to showcase all relevant f
%calling wlt function.
name = 'mile'
msg = mile01_wlt(FT_y, f_m, f_M, K_h, N, F_s)
siz = size(FT_y);
if (siz(2)>siz(1))
FT_y = transpose(FT_y);
end;
name = 'arnt'
msg = arnt_wltransf(FT_y, f_m, f_M, K_h, N, F_s)
The time image has a constant frequency, but the amplitude oscillates resempling a gaussian curve. My code returns a sharply segmented image over time, where each point in time holds only 1 frequency. It should reflect a change in intensity across the spectra over time.
hope that helps and thanks!
I found the error. There is a 0 rather than an o in the first instance of Psi. Thinking I'll maybe rename the value as sig or something. besides this the code works. sorry for the trouble there
I am creating figures in a for loop. The figure is a 2D mesh plot, which is supposed to be updated every iteration. The value to be plotted in a 200x200 array.
My problem is: It seems the calculation is running every iteration, but the plot is always the first one created, no matter I just plot or save to file.
Here is my code:
x = 1:200;
y = x;
for i = 1:100000
c = calculate(stuff, c); % value to be created, nothing to do with x and y
h = figure;
mesh(x,y,c);
saveas(h, sprintf('FIG%d.jpg',i);
drawnow; % did not work with or without this command
close(h);
end
First, thank you for all your inputs and suggestions! I didn't expect to get so many help within such a short time!
Then, I can answer some of the confusions here.
To Daniel: yes the c is changing. The program is calculating c based on its previous value. And there is sufficient step for c to change.
To R.Schifini: I tried pause(.1) but it didn't help unfortunately
To Andrew: thanks for pointing it. The complete program is attached now. And as to Daniel, the program calculate the value of c based on its previous values.
To The-Duck: I tried clf(h, 'reset') but unfortunately it didn't help.
Complete code:
Main program: please refer to wikipedia for the physical equation if you are interested
http://en.wikipedia.org/wiki/Cahn%E2%80%93Hilliard_equation
% Program to calculate composition evolution for nucleation and growth
% by solving Cahn-Hilliard equation - Time dependent non-linear
% differential equation
% Parameter
sig = 0.1; % J/m^2
delta = 10E-9; % m
D = 1E-9; %m^2/s
A = 10*sig/delta; % J/m
K = 3*sig*delta; % J/m^3
M = D/(2*A); % m^2/s
N = 200; % mesh size
dt = 1E-12; %s
h = delta/10;
% Rng control
r = -1+2.*rand(N);
beta = 1E-3;
n = 10000;
% initialization
c0 = zeros(200);
c0 = c0+ 0.1+beta.*r;
c = c0;
x = h.*linspace(-N/2,N/2,N);
y=x;
% Iteration
for i = 1:n
LP_c = laplacian(c,h);
d_f = A*(4*(c.^3)-6*(c.^2)+2*c);
sub = d_f - (2*K)*LP_c;
LP_RHS = laplacian(sub,h);
RHS = M*LP_RHS;
c = c + dt.*RHS;
% Save image every 2000 steps
% if ( i==1000 || i==10000 || i==100000)
% h = mesh(x,y,c);
% pause(.1);
% saveas(h, sprintf('FIG%d.jpg',i));
% clf(h,'reset');
% end
end
%h = figure;
mesh(x,y,c);
Laplacian function:
function LP_c = laplacian(c,h)
v1 = circshift(c,[0 -1]);
v2 = circshift(c,[0 1]);
v3 = circshift(c,[-1 0]);
v4 = circshift(c,[1 0]);
LP_c = (v1+v2+v3+v4-4.*c)./(h^2);
end
Result:
You can see the commented part in main program is for plotting periodically. They all give the same plots for each iteration. I tried the current OR version, also tried if ( mod(i,2000) == 0) to plot more pics. There is no difference. Shown:
However, if I comment out the periodic plotting, just run the program for different values of n, I got different plots, and they obey physical laws (evolving structure), shown in time order
Therefore I excluded the possibility that c might not update itself. It has to be some misuse of the plotting function of matlab. Or maybe some memory issue?
An interesting point I discovered during edition session: If I put the command h = figure in front of the loop and plot after the loop end, like this:
h = figure;
% Iteration
for i = 1:n
LP_c = laplacian(c,h);
d_f = A*(4*(c.^3)-6*(c.^2)+2*c);
sub = d_f - (2*K)*LP_c;
LP_RHS = laplacian(sub,h);
RHS = M*LP_RHS;
c = c + dt.*RHS;
end
mesh(x,y,c);
It seems all value of c calculated during the loop will overlap and give a figure shown below: I guess this indicates some facts about the plotting function of matlab, but I am not sure
Btw, can I answer directly to each comment and high light the new added section in my post? Sorry I am not as familiar with Stack Overlow as I should have :)
I ran your routine and with the following changes it works for me:
% Iteration
for i = 1:n
LP_c = laplacian(c,h);
d_f = A*(4*(c.^3)-6*(c.^2)+2*c);
sub = d_f - (2*K)*LP_c;
LP_RHS = laplacian(sub,h);
RHS = M*LP_RHS;
c = c + dt.*RHS;
% Save image every 2000 steps
if ( mod(i,2000)==0)
h1 = mesh(x,y,c);
drawnow;
saveas(h1, sprintf('FIG%d.jpg',i));
end
end
The main change is the figure handle variable from h to h1. Why? You are already using variable h in your equations.
Regards,
I need to write MATLAB code that will integrate over a R^5 hypercube using Monte Carlo. I have a basic algorithm that works when I have a generic function. But the function I need to integrate is:
∫dA
A is an element of R^5.
If I had ∫f(x)dA then I think my algorithm would work.
Here is the algorithm:
% Writen by Jerome W Lindsey III
clear;
n = 10000;
% Make a matrix of the same dimension
% as the problem. Each row is a dimension
A = rand(5,n);
% Vector to contain the solution
B = zeros(1,n);
for k = 1:n
% insert the integrand here
% I don't know how to enter a function {f(1,n), f(2,n), … f(5n)} that
% will give me the proper solution
% I threw in a function that will spit out 5!
% because that is the correct solution.
B(k) = 1 / (2 * 3 * 4 * 5);
end
mean(B)
In any case, I think I understand what the intent here is, although it does seem like somewhat of a contrived exercise. Consider the problem of trying to find the area of a circle via MC, as discussed here. Here samples are being drawn from a unit square, and the function takes on the value 1 inside the circle and 0 outside. To find the volume of a cube in R^5, we could sample from something else that contains the cube and use an analogous procedure to compute the desired volume. Hopefully this is enough of a hint to make the rest of the implementation straightforward.
I'm guessing here a bit since the numbers you give as "correct" answer don't match to how you state the exercise (volume of unit hypercube is 1).
Given the result should be 1/120 - could it be that you are supposed to integrate the standard simplex in the hypercube?
The your function would be clear. f(x) = 1 if sum(x) < 1; 0 otherwise
%Question 2, problem set 1
% Writen by Jerome W Lindsey III
clear;
n = 10000;
% Make a matrix of the same dimension
% as the problem. Each row is a dimension
A = rand(5,n);
% Vector to contain the solution
B = zeros(1,n);
for k = 1:n
% insert the integrand here
% this bit of code works as the integrand
if sum(A(:,k)) < 1
B(k) = 1;
end
end
clear k;
clear A;
% Begin error estimation calculations
std_mc = std(B);
clear n;
clear B;
% using the error I calculate a new random
% vector of corect length
N_new = round(std_mc ^ 2 * 3.291 ^ 2 * 1000000);
A_new = rand(5, N_new);
B_new = zeros(1,N_new);
clear std_mc;
for k = 1:N_new
if sum(A_new(:,k)) < 1
B_new(k) = 1;
end
end
clear k;
clear A_new;
% collect descriptive statisitics
M_new = mean(B_new);
std_new = std(B_new);
MC_new_error_999 = std_new * 3.921 / sqrt(N_new);
clear N_new;
clear B_new;
clear std_new;
% Display Results
disp('Integral in question #2 is');
disp(M_new);
disp(' ');
disp('Monte Carlo Error');
disp(MC_new_error_999);