ivaBSS (version 1.0.0)

joint_ISI: Joint Intersymbol Inference

Description

Calculates the joint intersymbol inference for two sets of matrices.

Usage

joint_ISI(W, A)

Arguments

W

Array of unmixing matrices with dimension [P, P, D].

A

Array of true mixing matrices with dimension [P, P, D].

Value

Numeric value between 0 and 1, where 0 is the optimal result indicating that the sources are separated perfectly and aligned in same order in each dataset.

Details

The function returns the joint intersymbol inference for the set of estimated unmixing matrices and the set of true mixing matrices. The joint ISI gets the value between 0 and 1, where 0 is the optimal result. The joint ISI calculates the average intersymbol inference over each dataset as well as penalizes if the sources are not aligned in same order for each dataset.

References

Anderson, M. (2013). Independent vector analysis: Theory, algorithms, and applications. PhD dissertation, University of Maryland, Baltimore County.

See Also

avg_ISI, jbss_achieved

Examples

# NOT RUN {
# Mixing matrices and unmixing matrices generated
# from standard normal distribution
P <- 4; D <- 4;
W <- array(rnorm(P * P * D), c(P, P, D))
A <- array(rnorm(P * P * D), c(P, P, D))

joint_ISI(W, A)

if (require("LaplacesDemon")) {
  # Generate sources from multivariate Laplace distribution
  P <- 4; N <- 1000; D <- 4;
  S <- array(NA, c(P, N, D))

  for (i in 1:P) {
    U <- array(rnorm(D * D), c(D, D))
    Sigma <- crossprod(U)
    S[i, , ] <- rmvl(N, rep(0, D), Sigma)
  }

  # Generate mixing matrices from standard normal distribution
  A <- array(rnorm(P * P * D), c(P, P, D))

  # Generate mixtures
  X <- array(NaN, c(P, N, D))
  for (d in 1:D) {
    X[, , d] <- A[, , d] %*% S[, , d]
  }

  # Estimate sources and unmixing matrices
  res_G <- NewtonIVA(X, source_density = "gaussian")
  joint_ISI(coef(res_G), A)
}
# }