![netlogo random float netlogo random float](http://ccl.northwestern.edu/netlogo/models/models/Code%20Examples/Random%20Network%20Example.png)
#Netlogo random float Patch
This will open the agent monitor with all the patch variables, with a value for elevation and patch quality. To check if the statement works, click on the setup button in the interface then right-click on any patch in the view and click inspect patch.
#Netlogo random float code
The latter two are fairly simple procedures which we will do straight in the setup procedure.Īdd the following code to the ask patches command in the setup procedure:Ĭheck the random-float primitive in the dictionary and think about the distribution set statements implemented above will give (i.e. 11.3) NetLogo Features And Other Resourcesīesides the artificial landscape we need to calculate insolation from the elevation, initialise patch quality and set PV to false.6.1) Purpose, Entities, State Variables And Scales.5.6) Conceptualizing Agent Based Models (cont'd).5.5) Conceptualizing Agent Based Models.5.3) Purpose, Entities, State Variables And Scales.3.2) Declaration Of Variables, Agents And Collectives.2.2) Implementation Of Agent Based Models (cont'd).
![netlogo random float netlogo random float](https://pic2.zhimg.com/v2-9b19797c8cd8529cba25fe427f428e61_b.jpg)
![netlogo random float netlogo random float](https://pic1.zhimg.com/v2-8dfc11a0cc3278408b46da64c9a13708_1440w.jpg)
![netlogo random float netlogo random float](https://pic1.zhimg.com/v2-84537f75a138ce882a854760a8c71580_b.jpg)
Also worth looking at is the RNetLogo package for running NetLogo inside R. The complete NetLogo file can be found at found at Modeling Commons. The implementation of bacterial chemotaxis is straightforward, using the previously defined reporter calculate-slope: to go single simulation step for both demos Set pcolor (30 + (9.9 * (value - min-value) / (max-value - min-value))) Returning to our example, most of the setup code calculates a chemical gradient, but some bacteria are also created: code for SETUP button - Bacteria Related uses of R for agent decision-making include implementations of neural networks, clustering, pattern-recognition, and the like. R:eval "valu.lm <- lm(valu.hist ~ valu.index)"Ī slightly more efficient option would have been to use r:eval to define an R function in setup, and then to call it here with valu.hist as an argument. R:eval "valu.index <- 1:length(valu.hist)" The r:get reporter is used to return the value of an R expression (in this case, the slope): to-report calculate-slope R interface - calculate slope for values in lst r:put "valu.hist" lst We make the decision using the reporter below, which calculates the slope of the best-fit line through the datapoints (this is a very simple example of using R code to implement an agent’s decision-making). Bacteria move up a chemical gradient by moving in a straight line if the concentration is increasing, and by randomly changing direction if it is not. The second demo (see screenshot above) simulates bacterial chemotaxis. Set history (lput (total / cell-count) history) Record current density Ifelse (counter = 3 or (counter = 2 and alive?)) The step code is also straightforward, using two phases (one to count live neighbours, and one to compute the future state): to go single simulation step for both demosĪsk patches ) ] To birth this procedure applies to a specific patch To death this procedure applies to a specific patch Ifelse (random-float 1 < average-life-density) The setup code for the “Game of Life” is fairly straightforward, using two utility functions to make a patch alive or dead: code for SETUP button - Life Window number of steps over which to analyseĬounter used to pass number of live neighbours between Phase 1 and Phase 2 for BACTERIA Is-life? distinguish between the two demos for LIFE The implementation combines variables for both examples in this tutorial: globals [ More sophisticated statistical analysis in R includes the use of spatial techniques.