iKernel() function returns value of similarity or Isolation KERNEL
for TWO points that is number in the range [0,1]
iKernel_point_dataset() function returns vector of values of similarity based on Isolation Kernel between a new point and all the points of dataset
get_weights_iKernel() function returns list of two objects:
the first object is numeric vector of weights for RKHS space, and
the second object is numeric vector of weights of similarity for iFeature_point
corresponding observation point
GRAM_iKernel() is the function to calculate Gram matrix for Isolation Kernel method based on Voronoi diagrams
iKernel(Matrix_iKernel, pnt_1, pnt_2, t)iKernel_point_dataset(Matrix_iKernel, t, nr, iFeature_point)
get_weights_iKernel(GI, Matrix_iKernel, t, nr, iFeature_point)
GRAM_iKernel(Matrix_iKernel, check_pos_def = FALSE)
The function iKernel() returns a value of similarity or Isolation KERNEL for TWO points
The function iKernel_point_dataset() returns a value of Isolation Kernel between a new point and dataset represented via Matrix_iKernel
The function get_weights_iKernel() returns the
list of weights for RKHS space and weights of similarity for iFeature_point
The function GRAM_iKernel() returns Gram matrix of Isolation Kernel
Matrix of indexes of Voronoi cells for each point and each tree based on Isolation Kernel calculation
The first point of dataset
The second point of dataset
is a number of columns of Matrix_iKernel or dimension of Matrix_iKernel (corresponding to the number of trees t)
is number of rows in Matrix_iKernel or size of dataset
Feature mapping in RKHS for a new point, that can be gotten via add_new_point_iKernel() function
The inverse Gram matrix
Logical parameter to check the Gram matrix is positive definite or do not check
iKernel_point_dataset(): The function to get Isolation Kernel between a new point and dataset
get_weights_iKernel(): The function to get weights from Feature mapping
GRAM_iKernel(): The function to calculate Gram matrix for Isolation Kernel method