Machine Learning Method Based on Isolation Kernel Mean Embedding
Function to restrict values of the data according with the range for e...
Function to check DATA.FRAME
Check the installation of packages and attach them with corresponding ...
Check the installation of a package for some functions
The function to get inverse Gram matrix
The function to calculate Maxima weighted kernel mean mapping for Isol...
The function to get subset of points based on feature mapping
The function to get subset with size psi for Voronoi diagram
The function to get feature representation in RKHS based on Voronoi di...
The function to get feature representation in RKHS based on Voronoi di...
Function to get Approximate Bayesian Computation based on Maxima Weigh...
Function returns the value of similarity or Isolation KERNEL for TWO p...
Function to copy the templates from extdata folder in the library to /...
MaxWiK: Machine Learning Method Based on Isolation Kernel Mean Embeddi...
Density plot
The function to get the mean square error values for statistics of sim...
The norm function for vector
Function to read file
Function to read hyperparameters and their values from the file
Function to restrict data in the size to accelerate the calculations
Function to generate parameters and simulate a model based on MaxWiK a...
The function to get the best tracer bullets related to kernel mean emb...
Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or 'MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023) <doi:10.1007/978-3-031-29168-5_18>.