cond_mutual_inf {knnmi}

Conditional mutual information estimation

Description

Conditional mutual information estimation CMI(X;Y|Z) where X is a continuous vector. The input Y and conditional input Z can be vectors or matrices. If Y and Z are discrete then they must be numeric or integer valued.

Usage

cond_mutual_inf(X, Y, Z, k = 3L)

Arguments

X

input vector.

Y

input vector or matrix.

Z

conditional input vector or matrix.

k

number of nearest neighbors.

Details

Argument Y is a vector of the same size as vector X, or a matrix whose column dimension matches the size of X. Argument Z is also a vector of the same size as vector X, or a matrix whose column dimension matches the size of X. If Y and Z are both matrices they must additionally have the same number of rows. If Y and/or Z are discrete values they must have a numeric or integer type.

Value

Returns the estimated conditional mutual information. The return value is a vector of size 1 if both Y and Z are vectors. If either Y or Z are matrices the return value is a vector whose size is the number of rows in the matrix.

References

Alkiviadis Tsimpiris, Ioannis Vlachos, Dimitris Kugiumtzis, Nearest neighbor estimate of conditional mutual information in feature selection, Expert Systems with Applications, Volume 39, Issue 16, 2012, Pages 12697-12708 doi:10.1016/j.eswa.2012.05.014

Examples

data(mutual_info_df)
set.seed(654321)
cond_mutual_inf(mutual_info_df$Zc_XcYc,
                       mutual_info_df$Xc, t(mutual_info_df$Yc))

M <- cbind(mutual_info_df$Xc, mutual_info_df$Yc)
ZM <- cbind(mutual_info_df$Yc, mutual_info_df$Wc)
cond_mutual_inf(mutual_info_df$Zc_XcYcWc, t(M), t(ZM))