RBNinference
Class PFNetwork
java.lang.Object
RBNinference.PFNetwork
public class PFNetwork
- extends java.lang.Object
Method Summary |
int |
allnodesSize()
|
double[] |
allsampleweight()
|
Atom |
atomAt(int i)
|
int |
instValAt(int i)
|
void |
prepareForSampling(int sampleordmode,
int adaptivemode,
boolean[] samplelogmode,
int numpar,
AtomList queryatoms,
int num_subsamples_minmax,
int num_subsamples_adapt,
java.io.BufferedWriter logwriter)
|
void |
propagateDeterministic(java.util.Vector instnodes,
RelStruc A,
boolean usesampleinst)
|
Rel |
relAt(int i)
|
double[] |
sampleInst(int subsind,
boolean verbose)
Sample one instantiation; set
instantiated field of the nodes
accordingly |
int |
sampleValAt(int i)
|
void |
setSampleProbs(SampleProbs sps,
int num_subsamples,
java.io.BufferedWriter logwriter)
|
void |
showNodes()
|
void |
showSampleOrd(RelStruc A,
java.io.BufferedWriter lwr)
|
double[] |
trueSampleWeightAt(int i)
|
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
PFNetwork
public PFNetwork()
PFNetwork
public PFNetwork(Primula pr,
java.util.Vector an,
RelStruc A,
Instantiation inst)
allnodesSize
public int allnodesSize()
allsampleweight
public double[] allsampleweight()
atomAt
public Atom atomAt(int i)
instValAt
public int instValAt(int i)
prepareForSampling
public void prepareForSampling(int sampleordmode,
int adaptivemode,
boolean[] samplelogmode,
int numpar,
AtomList queryatoms,
int num_subsamples_minmax,
int num_subsamples_adapt,
java.io.BufferedWriter logwriter)
throws RBNCompatibilityException,
RBNInconsistentEvidenceException,
java.io.IOException
- Throws:
RBNCompatibilityException
RBNInconsistentEvidenceException
java.io.IOException
propagateDeterministic
public void propagateDeterministic(java.util.Vector instnodes,
RelStruc A,
boolean usesampleinst)
throws RBNCompatibilityException,
RBNInconsistentEvidenceException
- Throws:
RBNCompatibilityException
RBNInconsistentEvidenceException
relAt
public Rel relAt(int i)
sampleValAt
public int sampleValAt(int i)
trueSampleWeightAt
public double[] trueSampleWeightAt(int i)
showNodes
public void showNodes()
showSampleOrd
public void showSampleOrd(RelStruc A,
java.io.BufferedWriter lwr)
throws java.io.IOException
- Throws:
java.io.IOException
sampleInst
public double[] sampleInst(int subsind,
boolean verbose)
throws RBNCompatibilityException,
RBNInconsistentEvidenceException
- Sample one instantiation; set
instantiated field of the nodes
accordingly
If inst is nonempty, then do importance sampling
of the conditional distribution given inst, and
return the weight of the sample
Instantiated nodes that become isolated prob zero nodes
will not contribute to the weight -- this is
no problem, because their weight is constant for
all samples
subsind is the index of the subsample for this instantiation
- Throws:
RBNCompatibilityException
RBNInconsistentEvidenceException
setSampleProbs
public void setSampleProbs(SampleProbs sps,
int num_subsamples,
java.io.BufferedWriter logwriter)
throws java.io.IOException
- Throws:
java.io.IOException