Home >> All >> org >> mrd

•	org.mrd.analysis.* (7)	•	org.mrd.control.* (1)	•	org.mrd.event.* (2)
•	org.mrd.examples.* (3)	•	org.mrd.jelly.* (51)	•	org.mrd.model.* (4)
•	org.mrd.model2.* (1)	•	org.mrd.models.* (7)	•	org.mrd.random.* (4)
•	org.mrd.repast.* (9)	•	org.mrd.util.* (3)

Package Samples:

• org.mrd.analysis
• org.mrd.control
• org.mrd.examples.simple
• org.mrd.jelly.distribution
• org.mrd.jelly.repast.schedule
• org.mrd.jelly.repast
• org.mrd.jelly
• org.mrd.model
• org.mrd.model2
• org.mrd.random
• org.mrd.util
• org.mrd.event
• org.mrd.repast.control
• org.mrd.repast.landscape
• org.mrd.repast.util
• org.mrd.models.cagn
• org.mrd.models.example
• org.mrd.models.simple
• org.mrd.jelly.io
• org.mrd.jelly.random

Classes:

• Landscape: Landscape Encapsulates much of the Internal Datastructure management of a spatial simulation. It takes care of traking agents that are dead and need to be added or removed from the simulation landscape. It is composed of Three primary data structures: 1.) Agent Grid - used to track nieghorhood relationships 2.) Resource Grid - represents available resources in an area 3.) Agent List - A randomizable or sortable list that is iterated over to update the agents. There are 2 secondary datastructures, a reaper queue and a birth queue, these store dead/born agents that need to be removed/added to the ...
• Landscape: Landscape Encapsulates much of the Internal Datastructure management of a spatial simulation. It takes care of traking agents that are dead and need to be added or removed from the simulation landscape. It is composed of Three primary data structures: 1.) LandscapeAgent Grid - used to track nieghorhood relationships 2.) Resource Grid - represents available resources in an area 3.) LandscapeAgent List - A randomizable or sortable list that is iterated over to update the agents. There is one secondary datastructure, a birth queue, this store new agents that need to be added to the model at the end ...
• Landscape: Landscape Encapsulates much of the Internal Datastructure management of a spatial simulation. It takes care of traking agents that are dead and need to be added or removed from the simulation landscape. It is composed of Three primary data structures: 1.) Agent Grid - used to track nieghorhood relationships 2.) Resource Grid - represents available resources in an area 3.) Agent List - A randomizable or sortable list that is iterated over to update the agents. There is one secondary datastructure, a birth queue, this store new agents that need to be added to the model at the end of a iteration.
• Beta: This is a random number generation class that produces pseudo-random doubles acording to a configured Beta Distribution. The Distribution is supported by the cern.jet.random.Beta Distribution Class. The distribution can be configured by two strategies 1.) Alpha and Beta parameters can be provided for the distribution. 2.) The mean and standard devation can be provided to configure the distribution. With this strategy alpha and beta are estimated through the method of matching moments to be alpha = mean{[mean(1-mean)/s^2] - 1} beta = (1-mean) mean{[mean(1-mean)/s^2] - 1}
• Beta: This is a random number generation class that produces pseudo-random doubles acording to a configured Beta Distribution. The Distribution is supported by the cern.jet.random.Beta Distribution Class. The distribution can be configured by two strategies 1.) Alpha and Beta parameters can be provided for the distribution. 2.) The mean and standard devation can be provided to configure the distribution. With this strategy alpha and beta are estimated through the equations: alpha = mean{[mean(1-mean)/s^2] - 1} beta = (1-mean) mean{[mean(1-mean)/s^2] - 1}
• TabDataFileHeader: Given a model and this will create the appropriate header for any data file. For batch runs, constant parameters will be in one header (the true header), and dynamic parameters will be in a block header and reflect the parameter value at that time. Note : the actual parameter values in the file header are created via the call to getFileHeader(). For DataRecorder's this call is made in the DataRecorder's constructor. Consequently, any changes to model parameter's made after this call are not reflected in the file header.
• ExampleModel: This is a simple example layout for a RePast Model, All the basic requirements are available plus 3 added features. 1.) The Stepable interface provides one method "step" to place code which will happen every interation of the model. 2.) The MaxIterations property provides a means to have the model stop after a specific set of iterations have past. 3.) the "log" property is added to allow easly dumping of messages to the console window.
• Gamma: This is a random number generation class that produces pseudo-random doubles acording to a configured Lambda Distribution. The Distribution is supported by the cern.jet.random.Gamma Distribution Class. The distribution can be configured by two strategies 1.) Alpha and Lambda parameters can be provided for the distribution.
• Gamma: This is a random number generation class that produces pseudo-random doubles acording to a configured Lambda Distribution. The Distribution is supported by the cern.jet.random.Gamma Distribution Class. The distribution can be configured by two strategies 1.) Alpha and Lambda parameters can be provided for the distribution.
• Pair: Represents a Pair in the CAGN model. A pair is an abstraction in the model where only females are tracked. This makes an assumption that sex ratios in the model are always equal. This class encapsulates the behavior of pairs, they need to get tested for survival and reproduction during each iteration of the model.
• Pair: Represents a Pair in the CAGN model. A pair is an abstraction in the model where only females are tracked. This makes an assumption that sex ratios in the model are always equal. This class encapsulates the behavior of pairs, they need to get tested for survival and reproduction during each iteration of the model.
• Pair: Represents a Pair in the CAGN model. A pair is an abstraction in the model where only females are tracked. This makes an assumption that sex ratios in the model are always equal. This class encapsulates the behavior of pairs, they need to get tested for survival and reproduction during each iteration of the model.
• Normal: This is a random number generation class that produces pseudo-random doubles acording to a configured Normal Distribution. It uses the cern.jet.random.Normal Class to support this distribution. The distribution can be configured by supplying a mean and a standard devation. A seed can also be provided.
• Normal: This is a random number generation class that produces pseudo-random doubles acording to a configured Normal Distribution. It uses the cern.jet.random.Normal Class to support this distribution. The distribution can be configured by supplying a mean and a standard devation. A seed can also be provided.
• Uniform: This is a random number generation class that produces pseudo-random doubles acording to a configured Normal Distribution. It uses the cern.jet.random.Normal Class to support this distribution. The distribution can be configured by supplying a mean and a standard devation. A seed can also be provided.
• Uniform: This is a random number generation class that produces pseudo-random doubles acording to a configured Normal Distribution. It uses the cern.jet.random.Normal Class to support this distribution. The distribution can be configured by supplying a mean and a standard devation. A seed can also be provided.
• RunningBasicStatistics: For each iteration of the model scan the list of agents, and determine the following number of agents/number survived. keep calculated running average and std
• Bernoulli: Constructs a Bernoulli Trial with the given successProbability, using a cern.jet.random.engine.MersenneTwister seeded with the given seed.
• Agent: Used to supply "standard" methods to Agents that use the Landscape object.
• LandscapeAgent: Used to supply "standard" methods to Agents that use the Landscape object.
• CagnModel: The CAGN Model.
• Model: The CAGN Model.
• AppendingDataRecorder

Home | Contact Us | Privacy Policy | Terms of Service