phi = array(0, dim=c(p,m,k,L))
rho = array(0, dim=c(m,m,k,L))
Pi = matrix(0, k, L)
- lvraisemblance = matrix(0, L, 2)
+ llh = matrix(0, L, 2) #log-likelihood
for(lambdaIndex in 1:L){
a = A1[, 1, lambdaIndex]
densite[i,lambdaIndex] = densite[i,lambdaIndex] + Pi[r,lambdaIndex]*det(rho[,,r,lambdaIndex])/(sqrt(2*pi))^m*exp(-tcrossprod(delta)/2.0)
}
}
- lvraisemblance[lambdaIndex,1] = sum(log(densite[,lambdaIndex]))
- lvraisemblance[lambdaIndex,2] = (dimension+m+1)*k-1
+ llh[lambdaIndex,1] = sum(log(densite[,lambdaIndex]))
+ llh[lambdaIndex,2] = (dimension+m+1)*k-1
}
- return(list(phi=phi, rho=rho, Pi=Pi, lvraisemblance = lvraisemblance))
-}
\ No newline at end of file
+ return(list(phi=phi, rho=rho, Pi=Pi, llh = llh))
+}