int approxBehaviorLongitudinalData(const BehaviorLongitudinalData& r) {
int size = sizeof(r);
// lvalues, lvaluesLessMissings, lvaluesLessMissingStarts
size += 3 * r.observationCount() * r.n() * sizeof(int);
// lmissing, lstructural
size += 2 * r.observationCount() * r.n() * sizeof(bool);
// lobservedDistributions
LOGF(Priority::DEBUG, "BehaviorLongitudinalData\t%s\t%d b",
r.name().c_str(), size);
return size;
}
/**
* Generates a chain connecting the original and simulated initial
* observations of the given data object for the given period.
* The chain is simple in the sense that no two ministeps cancel each other out.
*/
void Chain::createInitialStateDifferences()
{
deallocateVector(this->linitialStateDifferences);
//Rprintf("******** %d create initial\n",this->linitialStateDifferences.size() );
Data * pData = this->lpData;
State * initialState = this->lpInitialState;
int period = this->lperiod;
// Create the required ministeps
for (unsigned variableIndex = 0;
variableIndex < pData->rDependentVariableData().size();
variableIndex++)
{
LongitudinalData * pVariableData =
pData->rDependentVariableData()[variableIndex];
NetworkLongitudinalData * pNetworkData =
dynamic_cast<NetworkLongitudinalData *>(pVariableData);
BehaviorLongitudinalData * pBehaviorData =
dynamic_cast<BehaviorLongitudinalData *>(pVariableData);
if (pNetworkData)
{
const Network * pNetwork1 = pNetworkData->pNetwork(period);
const Network * pNetwork2 =
initialState->pNetwork(pNetworkData->name());
for (int i = 0; i < pNetwork1->n(); i++)
{
IncidentTieIterator iter1 = pNetwork1->outTies(i);
IncidentTieIterator iter2 = pNetwork2->outTies(i);
while (iter1.valid() || iter2.valid())
{
if (iter1.valid() &&
(!iter2.valid() || iter1.actor() < iter2.actor()))
{
if (!pNetworkData->structural(i, iter1.actor(),
period)
// || !pNetworkData->structural(i, iter1.actor(),
// period + 1)
)
{
NetworkChange * pMiniStep =
new NetworkChange(pNetworkData,
i,
iter1.actor(), false);
// PrintValue(getMiniStepDF(*pMiniStep));
this->linitialStateDifferences.
push_back(pMiniStep);
iter1.next();
// PrintValue(getMiniStepDF(
// *this->linitialStateDifferences.back()));
}
else
{
// create step in structural subchain?
}
}
else if (iter2.valid() &&
(!iter1.valid() || iter2.actor() < iter1.actor()))
{
if (!pNetworkData->structural(i, iter2.actor(),
period)
// || !pNetworkData->structural(i, iter2.actor(),
// period + 1)
)
{
this->linitialStateDifferences.push_back(
new NetworkChange(pNetworkData,
i,
iter2.actor(), false));
iter2.next();
//PrintValue(getMiniStepDF(
// *this->linitialStateDifferences.back()));
}
else
{
// create step in structural subchain?
}
}
else
{
iter1.next();
iter2.next();
}
}
}
}
else if (pBehaviorData)
{
for (int i = 0; i < pBehaviorData->n(); i++)
{
int delta = initialState->
behaviorValues(pBehaviorData->name())[i]
- pBehaviorData->value(period, i);
int singleChange = 1;
if (delta < 0)
{
delta = -delta;
singleChange = -1;
}
for (int j = 0; j < delta; j++)
{
if (!pBehaviorData->structural(period, j)
//|| !pBehaviorData->structural(period, j + 1)
)
{
this->linitialStateDifferences.push_back(
new BehaviorChange(pBehaviorData,
i,
singleChange));
// Rprintf(" %d %d in beh\n", i, singleChange);
}
else
{
// create step in structural subchain?
}
}
}
}
}
// Rprintf("xx ********%d %d end create initial diff\n", this->linitialStateDifferences.size(), period);
}
