CGraph* CreateRandomAndSpecificForIDNetGraph(int num_nodes,
int num_indep_nodes, int max_size_family)
{
PNL_CHECK_LEFT_BORDER(num_nodes, 10);
PNL_CHECK_RANGES(num_indep_nodes, 1, num_nodes-1);
PNL_CHECK_RANGES(max_size_family, 2, num_nodes);
int i, j, k;
CGraph *pGraph = CGraph::Create(0, NULL, NULL, NULL);
PNL_CHECK_IF_MEMORY_ALLOCATED(pGraph);
srand((unsigned int)time(NULL));
pGraph->AddNodes(num_nodes);
int num_parents;
int ind_parent;
intVector prev_nodes(0);
for (i = num_indep_nodes; i < num_nodes; i++)
{
prev_nodes.resize(0);
for (j = 0; j < i; j++)
prev_nodes.push_back(j);
num_parents = rand() % (max_size_family - 1);
num_parents += 1;
num_parents = (num_parents > i) ? i : num_parents;
for (j = 0; j < num_parents; j++)
{
ind_parent = rand() % prev_nodes.size();
pGraph->AddEdge(prev_nodes[ind_parent], i, 1);
prev_nodes.erase(prev_nodes.begin() + ind_parent);
}
}
intVector parents(0);
intVector childs(0);
for (i = 0; i < num_nodes; i++)
{
if (pGraph->GetNumberOfChildren(i) == 0)
{
pGraph->GetParents(i, &parents);
for (j = 0; j < parents.size(); j++)
{
pGraph->GetChildren(parents[j], &childs);
for (k = 0; k < childs.size(); k++)
if ((childs[k] != i) &&
(pGraph->GetNumberOfChildren(childs[k]) == 0) &&
(pGraph->GetNumberOfParents(childs[k]) == 1))
{
if (i < childs[k])
{
pGraph->RemoveEdge(parents[j], childs[k]);
pGraph->AddEdge(i, childs[k], 1);
}
else
{
pGraph->AddEdge(childs[k], i, 1);
}
}
}
}
}
return pGraph;
}
CIDNet* CreateRandomIDNet(int num_nodes, int num_indep_nodes,
int max_size_family, int num_decision_nodes, int max_num_states_chance_nodes,
int max_num_states_decision_nodes, int min_utility, int max_utility,
bool is_uniform_start_policy)
{
PNL_CHECK_RANGES(num_decision_nodes, 1, num_nodes-1);
PNL_CHECK_LEFT_BORDER(max_num_states_chance_nodes, 1);
PNL_CHECK_LEFT_BORDER(max_num_states_decision_nodes, 1);
PNL_CHECK_LEFT_BORDER(max_utility, min_utility);
CGraph* pGraph =
CreateRandomAndSpecificForIDNetGraph(num_nodes, num_indep_nodes,
max_size_family);
if (!pGraph->IsDAG())
{
PNL_THROW(CInconsistentType, " the graph should be a DAG ");
}
if (!pGraph->IsTopologicallySorted())
{
PNL_THROW(CInconsistentType,
" the graph should be sorted topologically ");
}
if (pGraph->NumberOfConnectivityComponents() > 1)
{
PNL_THROW(CInconsistentType, " the graph should be linked ");
}
int i, j, k;
CNodeType *nodeTypes = new CNodeType [num_nodes];
intVector nonValueNodes(0);
intVector posibleDecisionNodes(0);
nonValueNodes.resize(0);
posibleDecisionNodes.resize(0);
for (i = 0; i < num_nodes; i++)
{
if (pGraph->GetNumberOfChildren(i) == 0)
{
nodeTypes[i].SetType(1, 1, nsValue);
}
else
{
nonValueNodes.push_back(i);
posibleDecisionNodes.push_back(i);
}
}
int ind_decision_node;
int num_states;
int index;
int node;
intVector neighbors(0);
neighborTypeVector neigh_types(0);
num_decision_nodes = (num_decision_nodes > posibleDecisionNodes.size()) ?
posibleDecisionNodes.size() : num_decision_nodes;
for (i = 0; (i < num_decision_nodes) && (posibleDecisionNodes.size()>0); i++)
{
ind_decision_node = rand() % posibleDecisionNodes.size();
node = posibleDecisionNodes[ind_decision_node];
num_states = GetRandomNumberOfStates(max_num_states_decision_nodes);
nodeTypes[node].SetType(1, num_states, nsDecision);
index = -1;
for (j = 0; j < nonValueNodes.size(); j++)
{
if (nonValueNodes[j] == node)
{
index = j;
break;
}
}
if (index != -1)
nonValueNodes.erase(nonValueNodes.begin() + index);
posibleDecisionNodes.erase(posibleDecisionNodes.begin() +
ind_decision_node);
pGraph->GetNeighbors(node, &neighbors, &neigh_types);
for (j = 0; j < neighbors.size(); j++)
{
index = -1;
for (k = 0; k < posibleDecisionNodes.size(); k++)
{
if (neighbors[j] == posibleDecisionNodes[k])
{
index = k;
break;
}
}
if (index != -1)
posibleDecisionNodes.erase(posibleDecisionNodes.begin() + index);
}
}
for (i = 0; i < nonValueNodes.size(); i++)
{
num_states = GetRandomNumberOfStates(max_num_states_chance_nodes);
nodeTypes[nonValueNodes[i]].SetType(1, num_states, nsChance);
}
int *nodeAssociation = new int[num_nodes];
for (i = 0; i < num_nodes; i++)
{
nodeAssociation[i] = i;
}
CIDNet *pIDNet = CIDNet::Create(num_nodes, num_nodes, nodeTypes,
nodeAssociation, pGraph);
pGraph = pIDNet->GetGraph();
CModelDomain* pMD = pIDNet->GetModelDomain();
CFactor **myParams = new CFactor*[num_nodes];
int *nodeNumbers = new int[num_nodes];
int **domains = new int*[num_nodes];
intVector parents(0);
for (i = 0; i < num_nodes; i++)
{
nodeNumbers[i] = pGraph->GetNumberOfParents(i) + 1;
domains[i] = new int[nodeNumbers[i]];
pGraph->GetParents(i, &parents);
for (j = 0; j < parents.size(); j++)
{
domains[i][j] = parents[j];
}
domains[i][nodeNumbers[i]-1] = i;
}
pIDNet->AllocFactors();
for (i = 0; i < num_nodes; i++)
{
myParams[i] = CTabularCPD::Create(domains[i], nodeNumbers[i], pMD);
}
float **data = new float*[num_nodes];
int size_data;
int num_states_node;
int num_blocks;
intVector size_nodes(0);
float belief, sum_beliefs;
for (i = 0; i < num_nodes; i++)
{
size_data = 1;
size_nodes.resize(0);
for (j = 0; j < nodeNumbers[i]; j++)
{
size_nodes.push_back(pIDNet->GetNodeType(domains[i][j])->GetNodeSize());
size_data *= size_nodes[j];
}
num_states_node = size_nodes[size_nodes.size() - 1];
num_blocks = size_data / num_states_node;
data[i] = new float[size_data];
switch (pIDNet->GetNodeType(i)->GetNodeState())
{
case nsChance:
{
for (j = 0; j < num_blocks; j++)
{
sum_beliefs = 0.0;
for (k = 0; k < num_states_node - 1; k++)
{
belief = GetBelief(1.0f - sum_beliefs);
data[i][j * num_states_node + k] = belief;
sum_beliefs += belief;
}
belief = 1.0f - sum_beliefs;
data[i][j * num_states_node + num_states_node - 1] = belief;
}
break;
}
case nsDecision:
{
if (is_uniform_start_policy)
{
belief = 1.0f / float(num_states_node);
for (j = 0; j < num_blocks; j++)
{
sum_beliefs = 0.0;
for (k = 0; k < num_states_node - 1; k++)
{
data[i][j * num_states_node + k] = belief;
sum_beliefs += belief;
}
data[i][j * num_states_node + num_states_node - 1] =
1.0f - sum_beliefs;
}
}
else
{
for (j = 0; j < num_blocks; j++)
{
sum_beliefs = 0.0;
for (k = 0; k < num_states_node - 1; k++)
{
belief = GetBelief(1.0f - sum_beliefs);
data[i][j * num_states_node + k] = belief;
sum_beliefs += belief;
}
belief = 1.0f - sum_beliefs;
data[i][j * num_states_node + num_states_node - 1] = belief;
}
}
break;
}
case nsValue:
{
for (j = 0; j < num_blocks; j++)
{
data[i][j] = float(GetUtility(min_utility, max_utility));
}
break;
}
}
}
for (i = 0; i < num_nodes; i++)
{
myParams[i]->AllocMatrix(data[i], matTable);
pIDNet->AttachFactor(myParams[i]);
}
delete [] nodeTypes;
delete [] nodeAssociation;
return pIDNet;
}
int testRandomFactors()
{
int ret = TRS_OK;
int nnodes = 0;
int i;
while(nnodes <= 0)
{
trsiRead( &nnodes, "5", "Number of nodes in Model" );
}
//create node types
int seed1 = pnlTestRandSeed();
//create string to display the value
char *value = new char[20];
#if 0
value = _itoa(seed1, value, 10);
#else
sprintf( value, "%d", seed1 );
#endif
trsiRead(&seed1, value, "Seed for srand to define NodeTypes etc.");
delete []value;
trsWrite(TW_CON|TW_RUN|TW_DEBUG|TW_LST, "seed for rand = %d\n", seed1);
//create 2 node types and model domain for them
nodeTypeVector modelNodeType;
modelNodeType.resize(2);
modelNodeType[0] = CNodeType( 1, 4 );
modelNodeType[1] = CNodeType( 1, 3 );
intVector NodeAssociat;
NodeAssociat.assign(nnodes, 0);
for( i = 0; i < nnodes; i++ )
{
float rand = pnlRand( 0.0f, 1.0f );
if( rand < 0.5f )
{
NodeAssociat[i] = 1;
}
}
CModelDomain* pMDDiscr = CModelDomain::Create( modelNodeType,
NodeAssociat );
//create random graph - number of nodes for every node is rand too
int lowBorder = nnodes - 1;
int upperBorder = int((nnodes * (nnodes - 1))/2);
int numEdges = pnlRand( lowBorder, upperBorder );
mark:
CGraph* pGraph = tCreateRandomDAG( nnodes, numEdges, 1 );
if ( pGraph->NumberOfConnectivityComponents() != 1 )
{
delete pGraph;
goto mark;
}
CBNet* pDiscrBNet = CBNet::CreateWithRandomMatrices( pGraph, pMDDiscr );
//start jtree inference just for checking
//the model is valid for inference and all operations can be made
CEvidence* pDiscrEmptyEvid = CEvidence::Create( pMDDiscr, 0, NULL, valueVector() );
CJtreeInfEngine* pDiscrInf = CJtreeInfEngine::Create( pDiscrBNet );
pDiscrInf->EnterEvidence( pDiscrEmptyEvid );
const CPotential* pot = NULL;
for( i = 0; i < nnodes; i++ )
{
intVector domain;
pDiscrBNet->GetFactor(i)->GetDomain( &domain );
pDiscrInf->MarginalNodes( &domain.front(), domain.size() );
pot = pDiscrInf->GetQueryJPD();
}
//make copy of Graph for using with other models
pGraph = CGraph::Copy( pDiscrBNet->GetGraph() );
delete pDiscrInf;
delete pDiscrBNet;
delete pDiscrEmptyEvid;
delete pMDDiscr;
//create gaussian model domain
modelNodeType[0] = CNodeType( 0, 4 );
modelNodeType[1] = CNodeType( 0, 2 );
CModelDomain* pMDCont = CModelDomain::Create( modelNodeType,
NodeAssociat );
CBNet* pContBNet = CBNet::CreateWithRandomMatrices( pGraph, pMDCont );
CEvidence* pContEmptyEvid = CEvidence::Create( pMDCont, 0, NULL, valueVector() );
CNaiveInfEngine* pContInf = CNaiveInfEngine::Create( pContBNet );
pContInf->EnterEvidence( pContEmptyEvid );
for( i = 0; i < nnodes; i++ )
{
intVector domain;
pContBNet->GetFactor(i)->GetDomain( &domain );
pContInf->MarginalNodes( &domain.front(), domain.size() );
pot = pContInf->GetQueryJPD();
}
pGraph = CGraph::Copy(pContBNet->GetGraph());
delete pContInf;
delete pContBNet;
delete pContEmptyEvid;
delete pMDCont;
//find the node that haven't any parents
//and change its node type for it to create Conditional Gaussian CPD
int numOfNodeWithoutParents = -1;
intVector parents;
parents.reserve(nnodes);
for( i = 0; i < nnodes; i++ )
{
pGraph->GetParents( i, &parents );
if( parents.size() == 0 )
{
numOfNodeWithoutParents = i;
break;
}
}
//change node type of this node, make it discrete
CNodeType ntTab = CNodeType( 1,4 );
modelNodeType.push_back( ntTab );
NodeAssociat[numOfNodeWithoutParents] = 2;
//need to change this model domain
CModelDomain* pMDCondGau = CModelDomain::Create( modelNodeType, NodeAssociat );
CBNet* pCondGauBNet = CBNet::CreateWithRandomMatrices( pGraph, pMDCondGau );
//need to create evidence for all gaussian nodes
intVector obsNodes;
obsNodes.reserve(nnodes);
int numGauVals = 0;
for( i = 0; i < numOfNodeWithoutParents; i++ )
{
int GauSize = pMDCondGau->GetVariableType(i)->GetNodeSize();
numGauVals += GauSize;
obsNodes.push_back( i );
}
for( i = numOfNodeWithoutParents + 1; i < nnodes; i++ )
{
int GauSize = pMDCondGau->GetVariableType(i)->GetNodeSize();
numGauVals += GauSize;
obsNodes.push_back( i );
}
valueVector obsGauVals;
obsGauVals.resize( numGauVals );
floatVector obsGauValsFl;
obsGauValsFl.resize( numGauVals);
pnlRand( numGauVals, &obsGauValsFl.front(), -3.0f, 3.0f);
//fill the valueVector
for( i = 0; i < numGauVals; i++ )
{
obsGauVals[i].SetFlt(obsGauValsFl[i]);
}
CEvidence* pCondGauEvid = CEvidence::Create( pMDCondGau, obsNodes, obsGauVals );
CJtreeInfEngine* pCondGauInf = CJtreeInfEngine::Create( pCondGauBNet );
pCondGauInf->EnterEvidence( pCondGauEvid );
pCondGauInf->MarginalNodes( &numOfNodeWithoutParents, 1 );
pot = pCondGauInf->GetQueryJPD();
pot->Dump();
delete pCondGauInf;
delete pCondGauBNet;
delete pCondGauEvid;
delete pMDCondGau;
return trsResult( ret, ret == TRS_OK ? "No errors" :
"Bad test on RandomFactors");
}
