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INGOR
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GenDataFilter generates a simulated dataset for the given network structure. The network structure can be generated by the RNDNetworkFilter.
n=n The number of samples to generate.
file=file The generated data set is put in the specified file. The data set is put in GDF format. See the programming document of ytGDF for details.
args={key [ =value ,... ]} Arguments for outputting the dataset. See the programming document of ytGDF for the available key and values.
types=t1:t2: ... Types of variables. If not specified they are automatically determined at random with the ratio specified by the r argument. t i is either d and c where i represents the index of variables.
disc=r1:r2: ... The probabilities of the number of categories for discrete variables. r1 + ... + r N needs to be 1.0, representing that a discrete variable will have i + 1 categories with probability r i. If this is not specified, all the discrete variables have two possible values (categories).
r=x The ratio of discrete variables.
dehybrid Specifies to save generated data as dynamic model represented by a bipartite graph.
categorical Discrete variables are regarded as categorical values.
sd=x (default: x=0.3) Standard deviation of the system noise. The system noise is generated and added to the calculated data using random values of the normal distribution with the specified standard deviation.
osd=x (default: x=0) Standard deviation of the observation noise. The observation noise is generated and added to the generated data using random values of the normal distribtion with the specified standard deviation. If 0 is specified, no observation noise is aadded.
func=f1:f2:... The list of function IDs to be assigned for edges.
use_net_func Use function IDs for edges in node property "model.func" of the input network.
dbn Shrinking time-expanded DBN model data before writing into a file. This assumes mainly time-expanded DBN networks generated by RNDNetworkFilter with "m=dbn" option.
1.9.3