sudoku: The function to get the best tracer bullets related to kernel...

In MaxWiK: Machine Learning Method Based on Isolation Kernel Mean Embedding

The function to get the best tracer bullets related to kernel mean embedding

Description

The function sudoku() allows to get the best tracer bullets related to kernel mean embedding. The calculation performs ONLY for parameters dataset DT = par.sim. This function performs a heuristic algorithm to seek a space/area related to the feature mapping in Hilbert space for the dataset of the parameters.
The main idea of the algorithm is just:

1. Generate points between the centers of Voronoi diagrams related to the Maxima weighted feature mapping based on Isolation Kernel
   

2. Following strategy to puzzle out of SUDOKU: delete all points that do not match feature mapping
   

3. Output: The remaining points should be corresponding to the feature mapping.

The function get_pairs_of_data_frame() is used to get pairs of points from the Data Frame that is the most distant each other. In other words, the algorithm seeks the most distant coupled point to each point from the data frame

The function generate_points_between_two_points() is used to generate points between two given points

The function get_tracer_bullets() is used to to get 'tracer bullets' or tracer points generated between all the pairs of the most distant points

Usage

sudoku(DT, iKernelABC, n_bullets = 20, n_best = 10, halfwidth = 0.5)

get_pairs_of_data_frame(DF)

generate_points_between_two_points(pair, n = 10)

get_tracer_bullets(DF, n_bullets = 20)

Arguments

DT	Whole dataset of parameters
iKernelABC	Result of calculations based on Isolation Kernel ABC that can be gotten by the function get.MaxWiK()
n_bullets	Integer number of tracer points between each pair of points from DF
n_best	Integer number of the best tracer bullets / points to consider them at the next algorithmic step
halfwidth	Criterion to choose the best tracer points like: if similarity_of_point >= halfwidth then it is the point to be included to the pool of the best points
DF	Data frame of oints that is used for generation of tracer points, so it is usually a subset of points corresponding to Voronoi sites/seeds
pair	Data frame of two points
n	Integer number of points that should be located between two input points

Value

The function sudoku() returns the list of next objects:

• tracer_bullets that is all the points generated during the run of the algorithm,

• criterion that is a value of the similarity that is used to choose the best tracer points,

• best_tracer_bullets that is the best tracer points that have similarity more or equal than criterion value,

• surroundings_best_points that is the best tracer points that have similarity more or equal than halfwidth value,

• feature_tracers that is results of the function get_voronoi_feature_PART_dataset() applied to the new tracer points,

• similarity_to_mean that is numeric vector of similarities of all the tracers points.

The function get_pairs_of_data_frame() returns the list of the pairs of points

The function generate_points_between_two_points() returns data frame of generated points between two given points, including given points as the first and the last rows

The function get_tracer_bullets() returns data frame of generated tracer points

Functions

• get_pairs_of_data_frame(): The function to get pairs from Data Frame

• generate_points_between_two_points(): The function to generate points between the pair of given points

• get_tracer_bullets(): The function to get 'tracer bullets' or tracer points

Examples

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MaxWiK documentation built on April 3, 2025, 8:47 p.m.