internal: Random KNN Internal Functions
In rknn: Random KNN Classification and Regression

Some internal and under-development functions

   
    rbyb(p, m, eta)
    rbyp(p, m, eta)
    rbyv(p, m, nu)
    rbyz(p, m)
    rbyz.sim(p, m, nsim=1000)
    rbyz.geo(p, m=floor(sqrt(p)), rmax=p)
    rbylambda(p, m, lambda=1)
    knn(train, test, cl, k=1)
    knn.cv (train, cl, k=1)
    knn.reg(train, test = NULL, y, k = 3)
    pressresid(obj)

`m`	Number of elements in a subset to be drawn.
`p`	Total number of available features.
`mtry`	Number of features to be drawn for each KNN.
`eta`	Coverage Probability.
`nu`	mean mutiplicity of a feature
`rmax`	number of series terms for independent geometric approximation
`nsim`	number of simulations for geometric simulation.
`lambda`	mean number of silient features.
`samples`	A vector of indice for a set of observations.
`cl`	A factor for classification labels.
`train`	A data matrix.
`test`	A data matrix.
`y`	A vector of responses.
`k`	Number of nearest neighbors.
`cl`	A vector of class labels.
`K`	Number of folds for cross-validation.
`pk`	A real number between 0 and to indicate the proportion of the feature set to be kept in each step.
`r`	Number of KNN to be generated.
`seed`	An integer seed.
`criterion`	either uses mean_accuracy or mean_support for best.
`obj`	A linear model.