nosof94exalcove_opt: Parameter optimization of ex-ALCOVE model with nosof94 CIRP
In catlearn: Formal Psychological Models of Categorization and Learning
View source: R/nosof94exalcove_opt.R
nosof94exalcove_opt R Documentation
Parameter optimization of ex-ALCOVE model with nosof94 CIRP
Description
Uses nosof94exalcove to find best-fitting parameters for
the ex-ALCOVE model for the nosof94 CIRP.
Usage
nosof94exalcove_opt(recompute = FALSE, xtdo = FALSE)
Arguments
recompute
When set to TRUE, the function re-runs the
optimization. When set to FALSE, the function returns a stored copy
of the results of the optimization.
xtdo
eXTenDed Output; where set to TRUE, some further details
of the optimization procedure are printed to the console.
Details
This function is an archive of the optimization procedure used to
derive the best-fitting parameters for the nosof94exalcove
simulation; see Spicer et al. (2017) for a tutorial introduction to
the concept of simulation archives.
Optimization used the L-BFGS-B method from the optim
function of the standard R stats package. The objective
function was sum of squared errors. Please inspect the source code for
further details (e.g. type nosof94exalcove_opt). The
optimization was repeated for 15 different sets of starting values.
Where recompute = TRUE, the function can take many hours to
run, depending on your system, and there is no progress bar. You can
use Task Manager (Windows) or equivalent if you want some kind of
visual feedback that the code is working hard. The code uses all the
processor cores on the local machine, so speed of execution is a
simple function of clock speed times processor cores. So, for example,
a 4 GHz i7 processor (8 virutal cores) will take a quarter of the time
to run this compared to a 2 GHz i5 processor (4 virtual cores).
Value
A vector containing the best-fitting values for c, phi, la, and lw, in
that order. See slpALCOVE for an explanation of these
parameters.
Author(s)
Andy Wills
References
Spicer, S., Jones, P.M., Inkster, A.B., Edmunds, C.E.R. & Wills,
A.J. (2017). Progress in learning theory through distributed
collaboration: Concepts, tools, and examples. Manuscript in
preparation.
catlearn documentation built on April 4, 2023, 5:12 p.m.
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View source: R/nosof94exalcove_opt.R
| nosof94exalcove_opt | R Documentation |
Parameter optimization of ex-ALCOVE model with nosof94 CIRP
Description
Uses nosof94exalcove to find best-fitting parameters for
the ex-ALCOVE model for the nosof94 CIRP.
Usage
nosof94exalcove_opt(recompute = FALSE, xtdo = FALSE)
Arguments
recompute |
When set to TRUE, the function re-runs the optimization. When set to FALSE, the function returns a stored copy of the results of the optimization. |
xtdo |
eXTenDed Output; where set to TRUE, some further details of the optimization procedure are printed to the console. |
Details
This function is an archive of the optimization procedure used to
derive the best-fitting parameters for the nosof94exalcove
simulation; see Spicer et al. (2017) for a tutorial introduction to
the concept of simulation archives.
Optimization used the L-BFGS-B method from the optim
function of the standard R stats package. The objective
function was sum of squared errors. Please inspect the source code for
further details (e.g. type nosof94exalcove_opt). The
optimization was repeated for 15 different sets of starting values.
Where recompute = TRUE, the function can take many hours to
run, depending on your system, and there is no progress bar. You can
use Task Manager (Windows) or equivalent if you want some kind of
visual feedback that the code is working hard. The code uses all the
processor cores on the local machine, so speed of execution is a
simple function of clock speed times processor cores. So, for example,
a 4 GHz i7 processor (8 virutal cores) will take a quarter of the time
to run this compared to a 2 GHz i5 processor (4 virtual cores).
Value
A vector containing the best-fitting values for c, phi, la, and lw, in
that order. See slpALCOVE for an explanation of these
parameters.
Author(s)
Andy Wills
References
Spicer, S., Jones, P.M., Inkster, A.B., Edmunds, C.E.R. & Wills, A.J. (2017). Progress in learning theory through distributed collaboration: Concepts, tools, and examples. Manuscript in preparation.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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