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samplings documentation tutorials demo download community explore with samplings suggest edits documentation explore content 1 execute a single run 2 design of experiment 3 the directsampling method 4 model replication execute a single run before exploring you model your model you might want to run it for an single set of inputs to achieve it the syntax is the following val input_i val int val input_j val double singlerun evaluation my_own_model input seq input_i 10 input_j 10 0 hook display this run the model my_own_model once and display the result design of experiment design of experiment doe is the art of setting up an experimentation in a model simulation context it boils down to declaring the inputs under study most of the time they re parameters and the values they will take for a batch of several simulations with the idea of revealing a property of the model e g sensitivity your model inputs can be sampled in the traditional way by using a grid or regular sampling or by sampling uniformly inside their respective domains for higher dimension input space specific statistics techniques ensuring low discrepancy like latin hypercube sampling and sobolsequence are available you can also use your own doe in openmole by providing a csv file containing your samples to openmole the directsampling method in openmole a doe is set up through the directsampling constructor this constructor will generate a workflow which is illustrated below you may recognize the map reduce design pattern provided that an aggregation operator is defined otherwise it would just be a map sampling over several inputs samplings can be performed over several inputs domains as well as on several input types using the cartesian product operator x as follow val i val int val j val double val k val string val l val long val m val file val b val boolean directsampling evaluation mymodel sampling i in 0 to 10 by 2 x j in 0 0 to 5 0 by 0 5 x k in list leonardo donatello raphaël michelangelo x l in randomsequence long size 10 x m in workdirectory dir files filter f f getname startswith exp f getname endswith csv x b in truefalse hook workdirectory path of a file the directsampling task executes the model mymodel for every possible combination of the 5 inputs provided in the sampling parameter the hook provided after the task will save the results of your sampling in a file see the next section for more details about this hook the arguments of the directsampling task are the following evaluation is the task or a composition of tasks that uses your inputs typically your model task sampling is where you define your doe i e the inputs you want varied aggregation optional is an aggregation operation to be performed on the outputs of your evaluation task the l parameter is a uniform sampling of 10 numbers of the long type taken in the long min_value long max_value domain of the long native type more details can be found here the m parameter is a sampling over different files that have been uploaded to the b workdirectory the files are explored as items of a list gathered by the files function and applied on the dir directory optionally this list of files can be filtered with any string boolean functions such as contains startswith endswith see the java class string documentation for more details more information on this sampling type here hook the code hook keyword is used to save or display results generated during the execution of a workflow the generic way to use it is to write either code hook workdirectory path of a file to save the results in a file or code hook display to display the results in the standard output br br there are some arguments specific to the directsampling method which can be added to the hook output is to choose what to do with the results as shown above either a file path or the word code display values seq i j specifies which variables from the data flow should be saved or displayed by default all variables from the dataflow are used here is a use example val i val int val j val double directsampling evaluation mymodel sampling i in 0 to 10 by 2 x j in 0 0 to 5 0 by 0 5 hook output display values seq i for more details about hooks check the corresponding language page model replication if your model is stochastic you may want to define a replication task to run several replications of the model for the same parameter values this is similar to using a uniform distribution sampling on the seed of the model and openmole provides a specific constructor for this the replication task the replication sampling is used as follow val myseed val int val i val int val o val double val mymodel scalatask val rng random myseed val o i 2 0 1 rng nextdouble set inputs i myseed outputs i o replication evaluation mymodel seed myseed sample 100 aggregation seq o evaluate median hook display the arguments for replication are the following evaluation is the task or a composition of tasks that uses your inputs typically your model task and a hook seed is the prototype for the seed which will be sampled with an uniform distribution in its domain it must ba a val int or a val long this prototype will be provided as an input to the model sample int is the number of replications index optional val int is an optional variable val that can provide you with a replication index for each replication distributionseed optional long is an optional seed to be given to he uniform distribution of the seed meta seed providing this value will fix the pseudo random sequence generated for the prototype seed aggregation optional is a list of aggregations to be performed on the outputs of your evaluation task hook the hook keyword is used to save or display results generated during the execution of a workflow the generic way to use it is to write either hook workdirectory path of a file csv to save the results in a csv file or hook display to display the results in the standard output there are some arguments specific to the replication task which can be added to the hook output is to choose what to do with the results as shown above either a file path or the word display values seq i j specifies which variables from the data flow should be saved or displayed by default all variables from the dataflow are used header col1 col2 colz customises the header of the csv file to be created with the string it receives as a parameter please note that this only happens if the file doesn t exist when the hook is executed arrayonrow true forces the flattening of input lists such that all list variables are written to a single row line of the csv file it defaults to false includeseed is a boolean to specify whether you want the seed from the replication task to be saved with the workflow variables false by default here is a use example val myseed val int val i val int val o val double val mymodel scalatask import scala util random val rng new random myseed val o i 2 0 1 rng nextdouble set inputs i myseed outputs i o replication evaluation mymodel seed myseed sample 100 hook output display values seq i includeseed true for more details about hooks check the corresponding language page sampling methods elementary samplings high dimension samplings uniform sampling sampling over files sampling from a file operations on samplings aggregate sampling results see also in the doc plug explore scale up language genetic algorithms developers gui community forum chat faq development changes sources join us about us papers team partners communication school twitter contact