NAME¶
simulation::montecarlo - Monte Carlo simulations
SYNOPSIS¶
package require
Tcl ?8.4?
package require
simulation::montecarlo 0.1
package require
simulation::random
package require
math::statistics
::simulation::montecarlo::getOption keyword
::simulation::montecarlo::hasOption keyword
::simulation::montecarlo::setOption keyword value
::simulation::montecarlo::setTrialResult values
::simulation::montecarlo::setExpResult values
::simulation::montecarlo::getTrialResults
::simulation::montecarlo::getExpResult
::simulation::montecarlo::transposeData values
::simulation::montecarlo::integral2D ...
::simulation::montecarlo::singleExperiment args
DESCRIPTION¶
The technique of
Monte Carlo simulations is basically simple:
- •
- generate random values for one or more parameters.
- •
- evaluate the model of some system you are interested in and record the
interesting results for each realisation of these parameters.
- •
- after a suitable number of such trials, deduce an overall characteristic
of the model.
You can think of a model of a network of computers, an ecosystem of some kind or
in fact anything that can be quantitatively described and has some stochastic
element in it.
The package
simulation::montecarlo offers a basic framework for such a
modelling technique:
#
# MC experiments:
# Determine the mean and median of a set of points and compare them
#
::simulation::montecarlo::singleExperiment -init {
package require math::statistics
set prng [::simulation::random::prng_Normal 0.0 1.0]
} -loop {
set numbers {}
for { set i 0 } { $i < [getOption samples] } { incr i } {
lappend numbers [$prng]
}
set mean [::math::statistics::mean $numbers]
set median [::math::statistics::median $numbers] ;# ? Exists?
setTrialResult [list $mean $median]
} -final {
set result [getTrialResults]
set means {}
set medians {}
foreach r $result {
foreach {m M} $r break
lappend means $m
lappend medians $M
}
puts [getOption reportfile] "Correlation: [::math::statistics::corr $means $medians]"
} -trials 100 -samples 10 -verbose 1 -columns {Mean Median}
This example attemps to find out how well the median value and the mean value of
a random set of numbers correlate. Sometimes a median value is a more robust
characteristic than a mean value - especially if you have a statistical
distribution with "fat" tails.
PROCEDURES¶
The package defines the following auxiliary procedures:
- ::simulation::montecarlo::getOption keyword
- Get the value of an option given as part of the singeExperiment
command.
- string keyword
- Given keyword (without leading minus)
- ::simulation::montecarlo::hasOption keyword
- Returns 1 if the option is available, 0 if not.
- string keyword
- Given keyword (without leading minus)
- ::simulation::montecarlo::setOption keyword
value
- Set the value of the given option.
- string keyword
- Given keyword (without leading minus)
- string value
- (New) value for the option
- ::simulation::montecarlo::setTrialResult values
- Store the results of the trial for later analysis
- list values
- List of values to be stored
- ::simulation::montecarlo::setExpResult values
- Set the results of the entire experiment (typically used in the final
phase).
- list values
- List of values to be stored
- ::simulation::montecarlo::getTrialResults
- Get the results of all individual trials for analysis (typically used in
the final phase or after completion of the command).
- ::simulation::montecarlo::getExpResult
- Get the results of the entire experiment (typically used in the final
phase or even after completion of the singleExperiment command).
- ::simulation::montecarlo::transposeData values
- Interchange columns and rows of a list of lists and return the
result.
- list values
- List of lists of values
There are two main procedures:
integral2D and
singleExperiment.
- ::simulation::montecarlo::integral2D ...
- Integrate a function over a two-dimensional region using a Monte Carlo
approach.
Arguments PM
- ::simulation::montecarlo::singleExperiment args
- Iterate code over a number of trials and store the results. The iteration
is gouverned by parameters given via a list of keyword-value pairs.
- int n
- List of keyword-value pairs, all of which are available during the
execution via the getOption command.
The
singleExperiment command predefines the following options:
- •
- -init code: code to be run at start up
- •
- -loop body: body of code that defines the computation to be run
time and again. The code should use setTrialResult to store the
results of each trial (typically a list of numbers, but the interpretation
is up to the implementation). Note: Required keyword.
- •
- -final code: code to be run at the end
- •
- -trials n: number of trials in the experiment (required)
- •
- -reportfile file: opened file to send the output to (default:
stdout)
- •
- -verbose: write the intermediate results (1) or not (0) (default:
0)
- •
- -analysis proc: either "none" (no automatic analysis),
standard (basic statistics of the trial results and a correlation matrix)
or the name of a procedure that will take care of the analysis.
- •
- -columns list: list of column names, useful for verbose output and
the analysis
Any other options can be used via the getOption procedure in the body.
TIPS¶
The procedure
singleExperiment works by constructing a temporary
procedure that does the actual work. It loops for the given number of trials.
As it constructs a temporary procedure, local variables defined at the start
continue to exist in the loop.
KEYWORDS¶
math, montecarlo simulation, stochastic modelling
CATEGORY¶
Mathematics
COPYRIGHT¶
Copyright (c) 2008 Arjen Markus <arjenmarkus@users.sourceforge.net>