ML IALGO LINREG: Difference between revisions

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*{{TAG|ML_IALGO_LINREG}}=4: Singular value decomposition with Tikhonov regularization. Usable with {{TAG|NSW}}=0,1.
*{{TAG|ML_IALGO_LINREG}}=4: Singular value decomposition with Tikhonov regularization. Usable with {{TAG|NSW}}=0,1.


In the current implementation the on-the-fly learning algorithm requires to have a probability model included within the regression. So only the Bayesian linear regression method ({{TAG|ML_IALGO_LINREG}}=1) is usable with this option. All other methods should be used only a single step calculation ({{TAG|NSW}}=0) to refine the force-field after the force field was trained with {TAG|ML_IALGO_LINREG}}=1. {{TAG|ML_IALGO_LINREG}}=3  is the most tested for this purpose up to now. It should be also noted that this method is also computationally more demanding that the Bayesian linear regression.
In the current implementation the on-the-fly learning algorithm requires to have a probability model included within the regression. So only the Bayesian linear regression method ({{TAG|ML_IALGO_LINREG}}=1) is usable with this option. All other methods should be used only in a single step calculation ({{TAG|NSW}}=0) to refine the force-field after the force field was trained with {{TAG|ML_IALGO_LINREG}}=1. {{TAG|ML_IALGO_LINREG}}=3  is the most tested for this purpose up to now. It should be also noted that this method is also computationally more demanding that the Bayesian linear regression.


== Related Tags and Sections ==
== Related Tags and Sections ==

Revision as of 18:45, 12 October 2021

ML_IALGO_LINREG = [integer]
Default: ML_IALGO_LINREG = 1 

Description: This tag determines which algorithm is employed to solve the system of linear equations in the ridge regression method for machine learning.


In the ridge regression method for machine learning one needs to solve for the unknown weights within

For more details please see here.

The following options are available to solve for :

In the current implementation the on-the-fly learning algorithm requires to have a probability model included within the regression. So only the Bayesian linear regression method (ML_IALGO_LINREG=1) is usable with this option. All other methods should be used only in a single step calculation (NSW=0) to refine the force-field after the force field was trained with ML_IALGO_LINREG=1. ML_IALGO_LINREG=3 is the most tested for this purpose up to now. It should be also noted that this method is also computationally more demanding that the Bayesian linear regression.

Related Tags and Sections

ML_LMLFF, ML_W1, ML_WTOTEN, ML_WTIFOR, ML_WTSIF, ML_ISTART

Examples that use this tag