I run the following code and expect to get a vector IB, I do get a vector but all of whose elements are same I don't know what the problem is with this code?
function IB = ibtest(VCC)
RL = [1000, 10000, 200000, 400000, 600000, 800000, 1000000];
RB = 22000;
RP = 50;
R = 470;
B = 300;
i = 0;
for t = 1:length(RL)
i = i+1;
IB(i) = ((VCC - 2.1)*(RL(i) + RP)) / ( (RL(i) * RP) + (RB*(RL(i) + RP)) + (301 * 470 * (RL(i) + RP)) );
end
IB
end
There's nothing wrong with your code. You should get equal numbers as you increase numerator and denominator by the same fraction every iteration. Try running this code:
function IB = ibtest(VCC)
RL = [1000, 10000, 200000, 400000, 600000, 800000, 1000000];
RB = 22000;
RP = 50;
R = 470;
B = 300;
for t = 1:length(RL)
num = ((VCC - 2.1)*(RL(t) + RP))
denom = ( (RL(t) * RP) + (RB*(RL(t) + RP)) + (301 * 470 * (RL(t) + RP)) )
IB(t) = num / denom
end
end
I also don't know what you want to compute so if you don't get what you expect there must be something wrong with the formula.
Related
Consider the following code.
Wx = zeros(N, N);
for ii = 1 : 1 : N
x_ref = X(ii); y_ref = Y(ii);
nghlst_Local = nghlst(ii, find(nghlst(ii, :))); Nl = length(nghlst_Local);
x_Local = X(nghlst_Local, 1); y_Local = Y(nghlst_Local, 1);
PhiU = ones(Nl+1, Nl+1); PhiU(end, end) = 0;
Phi = ones(Nl+1, Nl+1); Phi(end, end) = 0;
Bx = zeros(Nl+1,1);
for jj = 1 : 1 : Nl
for kk = 1 : 1 : Nl
rx = x_Local(jj,1) - x_Local(kk,1);
ry = y_Local(jj,1) - y_Local(kk,1);
PhiU(jj, kk) = (1 - U(1,1))) / sqrt(rx^2 + ry^2 + c^2);
end
rx = x_ref - x_Local(jj);
ry = y_ref - y_Local(jj);
Bx(jj, 1) = ( (Beta * pi * U(1,1)/(2*r_0*norm(U))) * cos( (pi/2) * (-rx * U(1,1) - ry * U(2,1)) / (r_0 * norm(U)) ) ) / sqrt(rx^2 + ry^2 + c^2) - rx * (1 - Beta * sin( (pi/2) * (-rx * U(1,1) - ry * U(2,1)) / (r_0 * norm(U)) ))/ (rx^2 + ry^2 + c^2)^(3/2);
end
invPhiU = inv(PhiU);
CX = Bx' * invPhiU; CX = CX (1, 1:end-1); Wx (ii, nghlst_Local) = CX;
end
I want to convert the first for loop into parfor loop. The rest of the code works fine, but the following assignment statement does not work when I change for to parfor.
Wx (ii, nghlst_Local) = CX;
I want to know what is this is wrong and how to remove such errors. Thank you.
I am right now stuck on a problem in matlab. What I have done is that I have an equation that is passed on into another function which works by the bisection-method.
But I have a multiplier that I am trying to implement which somehow leads to the function crashing.
Before I introduced the multiplier it all worked, I tried breaking it down by entering the multiplier value manually and it didn't work
P_{1} = 0.6;
P_{2} = 0.2;
P_{3} = 0.2;
a_1 = 4/3;
a_2 = -7/3;
b_1 = -1/3;
b_2 = 4/3;
persistent multiplier
multiplier = exp(a_1 * 44 + a_2 * 14 + 0);
eqn = #(x) ((a_1 * x + b_1)^a_1) * ((a_2 * x + b_2)^a_2) * x ...
-(P_{1}^a_1) * (P_{2}^a_2) * P_{3} * multiplier;
Q_{3} = Bisectionmethod(a_1, a_2, b_1, b_2, eqn);
Here is the calculating part of the bisection method.
x_lower = max(0, -b_1 / a_1);
x_upper = -b_2 / a_2;
x_mid = (x_lower + x_upper)/2;
Conditional statement encompassing the method of bisection
while abs(eqn(x_mid)) > 10^(-10)
if (eqn(x_mid) * eqn(x_upper)) < 0
x_lower = x_mid;
else
x_upper = x_mid;
end
x_mid = (x_lower + x_upper)/2;
end
Based on the information you provided this is what I came up with
function Q = Stackoverflow
persistent multiplier
P{1} = 0.6;
P{2} = 0.2;
P{3} = 0.2;
a1 = 4/3;
a2 = -7/3;
b1 = -1/3;
b2 = 4/3;
multiplier = exp(a1 * 44 + a2 * 14 + 0);
eqn = #(x) ((a1 .* x + b1).^a1) .* ((a2 .* x + b2).^a2) .* x -(P{1}.^a1) .* (P{2}.^a2) .* P{3} .* multiplier;
Q{3} = Bisectionmethod(eqn, max([0, -b1/a1]), -b2/a2, 1E-10);
end
function XOut = Bisectionmethod(f, xL, xH, EPS)
if sign(f(xL)) == sign(f(xH))
XOut = [];
error('Cannot bisect interval because can''t ensure the function crosses 0.')
end
x = [xL, xH];
while abs(diff(x)) > EPS
x(sign(f(mean(x))) == sign(f(x))) = mean(x);
end
XOut = mean(x);
end
I am currently trying to run experiments in parallel using MATLAB 2011b that are very time-consuming.
I am wondering if someone could help me 'translate' the following block of generic (non-working) parfor code into something that will work in the spmd code.
amountOfOptions = 8;
startStockPrice = 60 + 40 * rand(1,amountOfOptions);
strike = 70 + 20 * rand(1,amountOfOptions);
v = 0.35 + 0.3 * rand(1,amountOfOptions);
IV = 0.25 + 0.1 * rand(1,amountOfOptions);
sigma = 0.15 + 0.65 * rand(1,amountOfOptions);
riskFreeRate = 0.05 + 0.1 * rand(1,amountOfOptions);
tn = fix(1 + 3 * rand(1,amountOfOptions));
tic;
for g=1:amountOfOptions
for i=1:10
N = i*5;
Cti = zeros(1,N);
Sti = zeros(1,N);
B = zeros(1,N);
d1_ti = zeros(1,N);
delta_t = zeros(1,N);
ctn = 0;
cmtn = 0;
result = 0;
t = (1:N)/N;
dt = 1/N;
c_mt0 = 0;
for j=1:10
B = sigma(g)*randn(1,N);
part1 = startStockPrice(g)*normcdf((log(startStockPrice(g)/strike(g))+(riskFreeRate(g)+(0.5*(IV(g))^2))*(tn))/(v(g)*sqrt(tn)),0,sigma(g));
part2 = exp(-riskFreeRate(g)*tn)*strike(g)*normcdf((log(startStockPrice(g)/strike(g))+(riskFreeRate(g)-(0.5*(IV(g))^2))*(tn))/(IV(g)*sqrt(tn)));
c_mt0 = part1 - part2;
Sti(1) = startStockPrice(g);
for j = 2:N-1
Sti(j)=Sti(j-1)*exp( (riskFreeRate(g)-dt*0.5*sigma(g)^2) * t(j)*dt + sigma(g)*B(j));
end
Sti(N) = Sti(N-1)*exp( (riskFreeRate(g)-dt*0.5*sigma(g)^2) * t(N)*dt + sigma(g)*B(N));
parfor i = 1:N-1
d1ti(i) = (log(Sti(i)/strike(g)) + (riskFreeRate(g) + v(g).^2/2) * (tn - t(i))) / (v(g) * sqrt(tn - t(i)));
end
parfor i = 1:N-1
Cti(i) = Sti(i).*normcdf((d1ti(i)),0,sigma(g)) - exp(-riskFreeRate(g).*(tn(g) - t(i))).*strike(g).*normcdf(((d1ti(i) - v(g)*sqrt(tn(g) - t(i)))) , 0 ,sigma(g));
end
if((Sti(N) - strike(g)) > 0)
ctn = Sti(N) - strike(g);
else
ctn = 0;
end
parfor i = 1:N-1
delta_t(i) = normcdf((d1ti(i)),0,sigma(g));
end
cmtn = ctn - c_mt0*exp(riskFreeRate(g)*tn(g));
result= cmtn + result;
end
result= result/10;
end
end
time = toc;
I've always used parfor over spmd because it's more logical for me. Since parfor requires that each iteration within the loop be independent of all other iterations. It's as easy as encapsulating it using the following method.
% Initial Variables
amountOfOptions = 8;
startStockPrice = 60 + 40 * rand(1,amountOfOptions);
strike = 70 + 20 * rand(1,amountOfOptions);
v = 0.35 + 0.3 * rand(1,amountOfOptions);
IV = 0.25 + 0.1 * rand(1,amountOfOptions);
sigma = 0.15 + 0.65 * rand(1,amountOfOptions);
riskFreeRate = 0.05 + 0.1 * rand(1,amountOfOptions);
tn = fix(1 + 3 * rand(1,amountOfOptions));
% Open Parpool
try
parpool;
catch
end
% Use parfor
parfor i = 1:amountOfOptions
[startStockPrice(i),strike(i),v(i),IV(i),sigma(i),riskFreeRate(i),tn(i)] = fun( startStockPrice(i),strike(i),v(i),IV(i),sigma(i),riskFreeRate(i),tn(i) );
end
Then you can create the encapsulating function fun that will accept all the parameters and process/reoutput them. It will have the following definition/header:
function [startStockPrice,strike,v,IV,sigma,riskFreeRate,tn] = fun( startStockPrice,strike,v,IV,sigma,riskFreeRate,tn );
I've been trying to port a set of differential equations from Matlab R2014b to python 3.4.
I've used both odeint and ode, with no satisfactory results. The expected results are the ones I get from Matlab where xi = xl and xj = xk with an offset of 180 in phase for each group, as you can see from the image below.
The code I'm running in Matlab is the following:
function Fv = cpg(t, ini_i);
freq = 2;
%fixed parameters
alpha = 5;
beta = 50;
miu = 1;
b = 1;
%initial conditions
xi = ini_i(1);
yi = ini_i(2);
xj = ini_i(3);
yj = ini_i(4);
xk = ini_i(5);
yk = ini_i(6);
xl = ini_i(7);
yl = ini_i(8);
ri = sqrt(xi^2 + yi^2);
rj = sqrt(xj^2 + yj^2);
rk = sqrt(xk^2 + yk^2);
rl = sqrt(xl^2 + yl^2);
%frequency for all oscillators
w_swing = 1;
w_stance = freq*w_swing;
%Coupling matrix, that determines a walking gate for
%a quadruped robot.
k = [ 0, -1, -1, 1;
-1, 0, 1, -1;
-1, 1, 0, -1;
1, -1, -1, 0];
%Hopf oscillator 1
omegai = w_stance/(exp(-b*yi)+1) + w_swing/(exp(b*yi)+1);
xi_dot = alpha*(miu - ri^2)*xi - omegai*yi;
yi_dot = beta*(miu - ri^2)*yi + omegai*xi + k(1,2)*yj + k(1,3)*yk + k(1,4)*yl;
%Hopf oscillator 2
omegaj = w_stance/(exp(-b*yj)+1) + w_swing/(exp(b*yj)+1);
xj_dot = alpha*(miu - rj^2)*xj - omegaj*yj;
yj_dot = beta*(miu - rj^2)*yj + omegaj*xj + k(2,1)*yi + k(2,3)*yk + k(2,4)*yl;
%Hopf oscillator 3
omegak = w_stance/(exp(-b*yk)+1) + w_swing/(exp(b*yj)+1);
xk_dot = alpha*(miu - rk^2)*xk - omegak*yk;
yk_dot = beta *(miu - rk^2)*yk + omegak*xk + k(3,4)*yl + k(3,2)*yj + k(3,1)*yi;
%Hopf oscillator 4
omegal = w_stance/(exp(-b*yl)+1) + w_swing/(exp(b*yl)+1);
xl_dot = alpha*(miu - rl^2)*xl - omegal*yl;
yl_dot = beta *(miu - rl^2)*yl + omegal*xl + k(4,3)*yk + k(4,2)*yj + k(4,1)*yi;
%Outputs
Fv(1,1) = xi_dot;
Fv(2,1) = yi_dot;
Fv(3,1) = xj_dot;
Fv(4,1) = yj_dot;
Fv(5,1) = xk_dot;
Fv(6,1) = yk_dot;
Fv(7,1) = xl_dot;
Fv(8,1) = yl_dot;
However, when I moved the code to python I get an output like this.
In python I ran the ODE solver using the same time step as in Matlab and using solver 'dopri5', which is supposed to be equivalent to the one I'm using in Matlab, ode45. I use the same initial conditions in both cases. I've used both odeint and ode with similar results.
I just started programming in Python and it is my first implementation using Scipy and Numpy so maybe I'm misinterpreting something?
def cpg(t, ini, k, freq):
alpha = 5
beta = 50
miu = 1
b = 1
assert freq == 2
'Initial conditions'
xi = ini[0]
yi = ini[1]
xj = ini[2]
yj = ini[3]
xk = ini[4]
yk = ini[5]
xl = ini[6]
yl = ini[7]
ri = sqrt(xi**2 + yi**2)
rj = sqrt(xj**2 + yj**2)
rk = sqrt(xk**2 + yk**2)
rl = sqrt(xl**2 + yl**2)
'Frequencies for each oscillator'
w_swing = 1
w_stance = freq * w_swing
'First Oscillator'
omegai = w_stance/(exp(-b*yi)+1) + w_swing/(exp(b*yi)+1)
xi_dot = alpha*(miu - ri**2)*xi - omegai*yi
yi_dot = beta*(miu - ri**2)*yi + omegai*xi + k[1]*yj + k[2]*yk + k[3]*yl
'Second Oscillator'
omegaj = w_stance/(exp(-b*yj)+1) + w_swing/(exp(b*yj)+1)
xj_dot = alpha*(miu - rj**2)*xj - omegaj*yj
yj_dot = beta*(miu - rj**2)*yj + omegaj*xj + k[4]*yi + k[6]*yk + k[7]*yl
'Third Oscillator'
omegak = w_stance/(exp(-b*yk)+1) + w_swing/(exp(b*yk)+1)
xk_dot = alpha*(miu - rk**2)*xk - omegak*yk
yk_dot = beta*(miu - rk**2)*yk + omegak*xk + k[8]*yi + k[9]*yj + k[11]*yl
'Fourth Oscillator'
omegal = w_stance/(exp(-b*yl)+1) + w_swing/(exp(b*yl)+1)
xl_dot = alpha*(miu - rl**2)*xl - omegal*yl
yl_dot = beta*(miu - rl**2)*yl + omegal*xl + k[12]*yi + k[13]*yj + k[14]*yk
return [xi_dot, yi_dot, xj_dot, yj_dot, xk_dot, yk_dot, xl_dot, yl_dot]
The way that I'm calling the ODE is as follows:
X0 = [1,-1, 0,-1, 1,1, 0,1]
r = ode(cpg).set_integrator('dopri5')
r.set_initial_value(X0).set_f_params(k_trot, 2)
t1 = 30.
dt = .012
while r.successful() and r.t < (t1-dt):
r.integrate(r.t+dt)
I hope I was clear enough.
Any suggestions?
can someone help me how to show gabor filter in matlab, i can show it but its not what i want. this is my code :
[Gf,gabout] = gaborfilter1(B,sx,sy,f,theta(j));
G{m,n,i,j} = Gf;
and this is gabor filter class:
function [Gf,gabout] = gaborfilter(I,Sx,Sy,f,theta);
if isa(I,'double')~=1
I = double(I);
end
for x = -fix(Sx):fix(Sx)
for y = -fix(Sy):fix(Sy)
xPrime = x * cos(theta) + y * sin(theta);
yPrime = y * cos(theta) - x * sin(theta);
Gf(fix(Sx)+x+1,fix(Sy)+y+1) = exp(-.5*((xPrime/Sx)^2+(yPrime/Sy)^2))*cos(2*pi*f*xPrime);
end
end
Imgabout = conv2(I,double(imag(Gf)),'same');
Regabout = conv2(I,double(real(Gf)),'same');
gabout = sqrt(Imgabout.*Imgabout + Regabout.*Regabout);
Then, I imshow with this code:
imshow(G{m,n,i,j},[]);
and the results :
But i want this result, can someone help me how to slove this?
Use the following function. I hope this is useful.
----------------------------------------------------------------
gfs = GaborFilter(51,0.45,0.05,6,4);
n=0;
for s=1:6
for d=1:4
n=n+1;
subplot(6,4,n)
imshow(real(squeeze(gfs(s,d,:,:))),[])
end
end
Sample Image
----------------------------------------------------------------
function gfs = GaborFilter(winLen,uh,ul,S,D)
% gfs(SCALE, DIRECTION, :, :)
winLen = winLen + mod(winLen, 2) -1;
x0 = (winLen + 1)/2;
y0 = x0;
if S==1
a = 1;
su = uh/sqrt(log(4));
sv = su;
else
a = (uh/ul)^(1/(S-1));
su = (a-1)*uh/((a+1)*sqrt(log(4)));
if D==1
tang = 1;
else
tang = tan(pi/(2*D));
end
sv = tang * (uh - log(4)*su^2/uh)/sqrt(log(4) - (log(4)*su/uh)^2);
end
sx = 1/(2*pi*su);
sy = 1/(2*pi*sv);
coef = 1/(2*pi*sx*sy);
gfs = zeros(S, D, winLen, winLen);
for d = 1:D
theta = (d-1)*pi/D;
for s = 1:S
scale = a^(-(s-1));
gab = zeros(winLen);
for x = 1:winLen
for y = 1:winLen
X = scale * ((x-x0)*cos(theta) + (y-y0)*sin(theta));
Y = scale * (-(x-x0)*sin(theta) + (y-y0)*cos(theta));
gab(x, y) = -0.5 * ( (X/sx).^2 + (Y/sy).^2 ) + (2*pi*1j*uh)*X ;
end
end
gfs(s, d, :, :) = scale * coef * exp(gab);
end
end
Replace the "cos" component by complex part->complex(0, (2*pi*f*xprime)) ans also multiply equation by scaling factor of (1/sqrt(2*Sy*Sx)).