#' Kernel function
#'
#' @param d Distance between center and point
#' @param kernel_width Width of kernel
kernel = function(d, kernel_width){
sqrt(exp(-(d^2) / kernel_width^2))
}
#' Get euclidean distances of samples to instances to be explained
#' @param point_explain Vector of scaled features
#' @param points_sample data.frame of scaled features for the sample points
#' @return Vector with distances of samples to instance to be explained
get_distances = function(point_explain, points_sample){
# euclidean distance
apply(points_sample, 1, function(x){
sum((point_explain - x)^2)
})
}
# Function for creating y values
get_y = function(x1, x2, noise_prob = 0){
y = sign(sign(x2-1+abs(x1*2))/3 - sign(x2-.5+abs(x1*3))/3) + 1
y = y * (1 - rbinom(length(x1), 1, prob = noise_prob))
# flip classes
y = 1 - y
y
}
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