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%For C1
lambda1 = [60.21, 41.58, 9.11, 8.71, 3.83, 3.74, 18.06]
r1 = poissrnd(lambda1)
%For C2
lambda2 = [41.58, 60.21, 8.71, 9.11, 3.74, 3.83, 18.06]
r2 = poissrnd(lambda2)
%Designed training sequences x1 and x2
x1 = [1,1,1,0,0,0,0,1,0,1,0,1,1,0,0,1] ;
x2 = [1,1,1,0,1,0,0,0,1,1,1,0,0,0,0,1] ;
%X[3] to X[16]
X3 = [1 1 1 1 1 1 1]' ;
X4 = [0 0 1 1 1 1 1]' ;
X5 = [0 1 0 0 1 1 1]' ;
X6 = [0 0 0 1 0 0 1]' ;
X7 = [0 0 0 0 0 1 1]' ;
X8 = [1 0 0 0 0 0 1]' ;
X9 = [0 1 1 0 0 0 1]' ;
X10 = [1 1 0 1 1 0 1]' ;
X11 = [0 1 1 1 0 1 1]' ;
X12 = [1 0 0 1 1 1 1]' ;
X13 = [1 0 1 0 0 1 1]' ;
X14 = [0 0 1 0 1 0 1]' ;
X15 = [0 0 0 0 1 0 1]' ;
X16 = [1 1 0 0 0 0 1]' ;
%X,a 7x14 matrix
X = [X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16];
%C, a 7x2 matrix
C = [r1; r2]' ;
%Y, a 14x2 matrix
Y = X'*C ;
%Yd = Poiss(Y) (at equation (8))
Yd = poissrnd(Y)
%y1, a 14x1 matrix ; y2, a 14x1 matrix
y1 = Y(:,1)
y2 = Y(:,2)
%Least Square Estimate of C
Cls = (inv(X*X'))*(X*Yd)
% To set to zero all the negative entries of C
Cls1 = max(Cls,0)
%Mean square error of LS C and C
MSE = mean((C - Cls1).^2)
4 comentarios
Eric Chua
el 27 de Mayo de 2020
Subhadeep Koley
el 27 de Mayo de 2020
@Eric Chua Which parameter will be changed in every iteration?
Eric Chua
el 27 de Mayo de 2020
Eric Chua
el 27 de Mayo de 2020
Respuesta aceptada
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