The new version of the spsann package includes some bug fixes and a few modifications. Users now can
choose how optimCLHS
computes objective function values: as in the original paper or as in the FORTRAN
implementation. Users now also must inform the weights
passed to optimCLHS
as to guarantee that s/he is
aware of what s/he is doing. The same apples to other functions that deal with multi-objective optimization
problems: optimACDC
and optimSPAM
. Another important modification in the current version of spsann is
the possibility to use a finite set of candidate locations by setting cellsize = 0
. This is useful when
optimizing sample points only in the feature space and should reduce the computation time needed to find the
solution.
plot.OptimizedSampleConfiguration
related to the selection of the information to be
displayed.weights
of optimCLHS
, optimACDC
and optimSPAM
now is mandatory. The user is now required to
set the weights as to guarantee that s/he is aware of what s/he is doing.data.frame
s that store objective function
values.cellsize = 0
. When
this is done, spsann now checks for neighbouring candidate locations already included in the sample as to
avoid duplicated sampling points.optimCLHS
computes objective function values.optimCLHS
computes objective function values.optimCLHS
computes objective function values.Now spsann can be used to augment an existing sample configuration, that is, add new sampling points
to a spatial sample configuration generated using spsann or any other means. To do so, when using one
of the functions from the family of optim...()
functions, the user must pass to the function argument
points
an object of class list
containing two named sub-arguments: fixed
, a matrix with the
coordinates of the existing sample configuration -- kept fixed during the optimization --, and free
,
the number of sample points that should be added to the existing sample configuration -- free to move around
during the optimization.
This is a major release of package spsann that includes several conceptual changes. Despite our efforts,
it was not possible to guarantee the compatibility with previous versions. We have decided not to deprecate
functions and function arguments because (1) this would require deprecating a lot of code and (2) you should
first read the updated package documentation to understand the conceptual changes that we have made before you
start using it. This is a summary of the changes:
A completely new annealing schedule was implemented. The reason for this modification is that the former
annealing schedule showed to be inefficient during our tests. The new annealing schedule is the very simple
and most-used schedule proposed by Kirkpatrick et al. (1983). We have also replaced the acceptance criterion
with the well-known Metropolis criterion. This new implementation showed to be more efficient in our tests
than our early implementation. Setting up this new annealing schedule is done using the new function
scheduleSPSANN
.
A more elegant solution to jitter the sample points was implemented. It consists of using a finite set of
candidate locations that are seen by the algorithm as the centre of grid cells. In the first stage, we
select a grid cell with replacement. In the second stage, we select a location within that grid cell using
simple random sampling. This guarantees that any location in the sampling region is a candidate location
for the jittered sample point.
Solving multi-objective combinatorial optimization problems (MOCOP) has become easier with the creation of
the new function minmaxPareto()
. This function computes the Pareto maximum and minimum values of the
objective functions that compose the MOCOP needed to scale the objective functions to the same approximate
range of values.
The user can now chose to follow the progress of the optimization using a text progress bar in the R console
or a Tk progress bar widget. A Tk progress bar widget is useful when running spsann in parallel
processors.
The output of the optimization is now stored in an object of class OptimizedSampleConfiguration
. This
object contains three slots. The first (points
) holds the coordinates of the optimized sample
configuration. The second, spsann
, stores information about the settings used with the spatial simulated
annealing algorithm. The third, objective
, holds the settings used with the chosen objective function.
Methods were implemented to retrieve information from the new class, as well as producing plots of the
optimized sample configuration.
Package documentation was expanded and adapted to cope with the conceptual changes that were made. It also
includes a vignette that gives a short description of the package and its structure, as well as presents
a few examples on how to use the package. It is strongly recommended to read the new package documentation
and the accompanying vignette before you start using the package.
* Finally, bugs were fixed, warning messages were improved, and a faster code was implemented whenever
possible.
devel
branch was merged into master
branch. knitr
is
the engine used to produce the package vignette.OptimizedSampleConfiguration
.OptimizedSampleConfiguration
is no longer exported.optimPPL
was incorrect because it neglected the fact that, in a full
distance matrix, two points a and b form two pairs, i.e. ab and ba.
The mistake is due to the fact that we use SpatialTools::dist1
to compute
the distance matrix instead of stats::dist
.optimPPL
.autofun
to check the number of accepted jitters in the first
chain. If the number of accepted jitters is superior to the value passed to
schedule$initial.acceptance
, the process continues and a message is printed
informing the proportion of jitters that have been accepted.optimCLHS()
following the original Fortran code of Budiman Minasny."txt"
, for a text progress bar in the R console, "tk"
, to put
up a Tk progress bar widget, and NULL
to omit the progress bar. A Tk
progress bar widget is useful when running spsann in parallel
processors. The tcltk-package is now a suggested package.base
, and due to examples that take more than 5
seconds to run.plotOSC()
),
with options to display the evolution of the energy state and/or the
optimized sample configuration.minmaxPareto()
) was optimized to be
used with both ACDC and SPAN.OptimizedSampleConfiguration
) to store the output of
optim
functions.optimMKV()
-function to avoid errors due to
the LDLfactor error of the gstat-package had to be reformulated. We are
now using try()
with a default value which is returned in case of error.scheduleSPSANN()
.optimMKV()
-function to avoid errors due to
the LDLfactor error of the gstat-package.minmaxPareto()
.x.max
and y.max
are, by default, set to half of the maximum distance
in the x- and y-coordinates of candi
, respectively. In the same manner, the
argument cutoff
of optimPPL()
is set, by default, to half of the diagonal
of the rectangle of sides x.max
and y.max
.optimCORR()
that was causing the following error: Error
in if (new_energy <= old_energy) { : missing value where TRUE/FALSE needed.
This bug used to affect optimACDC()
and optimSPAN()
.optimCLHS
).objSPSANN()
) was created to retrieve the energy state
of an optimized sample configuration (OSC) at a given point of the
optimization.R CMD check
.utils::globalVariables
to avoid the R CMD check
note
no visible binding for global variable [variable name]
. Source of the
solution: http://stackoverflow.com/a/12429344/3365410.plotit
,
track
, verbose
, and iteration
. The first three were set to FALSE
,
while the last was set to 100
.optimSPAN()
and objSPAN()
are now full operational..optimPPLcheck()
was
renamed as .checkPPL()
, and .getLagBreaks()
was renamed as .lagsPPL()
.
Note that the first part of the function name indicates what it does, while
the second indicates the objective function to which it applies. This
standardization is important to ease the construction of multi-objective
optimization problems.covars.type
.autofun
was created to set-up the covariates (covar
).boundary
of the spatial domain can now be estimated internally. The user
should use the rgeos package if a more precise boundary
is needed.obj...()
and optim...()
functions are included in individual files. These R code chunks are used to
automatically build internal functions. Currently, R code chunks are
used to check the arguments of the family of optim...()
functions, prepare
points
and candi
, set plotting options, estimate the boundary
,
prepare for jittering, plot and jitter, and prepare the output.obj...()
functions may not return the same criterion
value of the optimized sample configuration returned by the family of
optim...()
functions if the number of iterations used in the optimization
is equal to 100. The problem seems to disappear if a larger number of
iterations is used.spJitterFinite()
now tries to find an alternative point if the new point
already is included in the sample. The number of tries is equal to the total
number of points included in the sample. Because the more points we have, the
more likely it is that the candidate point already is included in the sample.spJitterFinite()
now returns the old point if the new point already is in
the sample. This is to avoid an infinite loop at the end of the optimization
when the objective function creates a cluster of points.optimACDC()
, including new argument definitions;optimACDC()
: optimDIST()
and optimCORR()
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