initialize,simulatedDAG-method | R Documentation |
creation of a "simulatedDAG" containing a list of DAGs and associated observations
## S4 method for signature 'simulatedDAG'
initialize(.Object, NDAG = 1,
noNodes = sample(10:20, size = 1), functionType = "linear",
quantize = FALSE, verbose = TRUE, N = sample(100:500, size = 1),
seed = 1234, sdn = 0.5, goParallel = FALSE, additive = FALSE)
.Object |
: simulatedDAG object |
NDAG |
: number of DAGs to be created and simulated |
noNodes |
: number of Nodes of the DAGs. If it is a two-valued vector , the value of Nodes is randomly sampled in the interval |
functionType |
: type of the dependency. It is of class "character" and is one of ("linear", "quadratic","sigmoid","kernel") @param quantize : if TRUE it discretize the observations into two bins. If it is a two-valued vector [a,b], the value of quantize is randomly sampled in the interval [a,b] @param goParallel : if TRUE it uses parallelism @param additive : if TRUE the output is the sum of the H transformation of the inputs, othervise it is the H transformation of the sum of the inputs. |
verbose |
: if TRUE it prints out the state of progress |
N |
: number of sampled observations for each DAG. If it is a two-valued vector [a,b], the value of N is randomly sampled in the interval [a,b] |
seed |
: random seed |
sdn |
: standard deviation of aditive noise. If it is a two-valued vector, the value of N is randomly sampled in the interval |
Gianluca Bontempi, Maxime Flauder (2015) From dependency to causality: a machine learning approach. JMLR, 2015, http://jmlr.org/papers/v16/bontempi15a.html
require(RBGL)
require(gRbase)
require(foreach)
descr=new("D2C.descriptor")
descr.example<-new("D2C.descriptor",bivariate=FALSE,ns=3,acc=TRUE)
trainDAG<-new("simulatedDAG",NDAG=10, N=c(50,100),noNodes=c(15,40),
functionType = "linear", seed=0,sdn=c(0.45,0.75))
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