/**
* For each j and the given i, this method calculates the sum
* sum_h w_{ih} x_{hj}.
*/
void WXXClosureEffect::calculateSums(int i,
const Network * pNetwork,
double * sums) const
{
int n = pNetwork->n();
// Initialize
for (int j = 0; j < n; j++)
{
sums[j] = 0;
}
// Iterate over all h with non-zero non-missing w_{ih}
for (DyadicCovariateValueIterator iterH = this->rowValues(i);
iterH.valid();
iterH.next())
{
int h = iterH.actor();
// Iterate over all j with a tie from h
for (IncidentTieIterator iterJ = pNetwork->outTies(h);
iterJ.valid();
iterJ.next())
{
int j = iterJ.actor();
// Add the term w_{ih} x_{hj} (= w_{ih})
sums[j] += iterH.value();
}
}
}
/**
* For each j and the given i, this method calculates the sum
* sum_h w_{ih} w_{hj}, or other directionalities depending on out1 and out2.
*/
void WWXClosureEffect::calculateSums(int i,
const Network * pNetwork,
double * sums) const
{
int n = pNetwork->n();
// Initialize
for (int j = 0; j < n; j++)
{
sums[j] = 0;
}
if (this->lout1)
// Iterate over all h with non-zero non-missing w_{ih}
{
for (DyadicCovariateValueIterator iterH = this->rowValues(i);
iterH.valid();
iterH.next())
{
int h = iterH.actor();
if (this->lout2)
{
// Iterate over all j with non-zero non-missing w_{hj}
for (DyadicCovariateValueIterator iterJ = this->rowValues(h);
iterJ.valid();
iterJ.next())
{
int j = iterJ.actor();
// Add the term w_{ih} w_{hj}
sums[j] += iterH.value() * iterJ.value();
}
}
else
{
// Iterate over all j with non-zero non-missing w_{jh}
for (DyadicCovariateValueIterator iterJ = this->columnValues(h);
iterJ.valid();
iterJ.next())
{
int j = iterJ.actor();
// Add the term w_{ih} w_{jh}
sums[j] += iterH.value() * iterJ.value();
}
}
}
}
else
// Iterate over all h with non-zero non-missing w_{hi}
{
for (DyadicCovariateValueIterator iterH = this->columnValues(i);
iterH.valid();
iterH.next())
{
int h = iterH.actor();
if (this->lout2)
{
// Iterate over all j with non-zero non-missing w_{hj}
for (DyadicCovariateValueIterator iterJ = this->rowValues(h);
iterJ.valid();
iterJ.next())
{
int j = iterJ.actor();
// Add the term w_{hi} w_{hj}
sums[j] += iterH.value() * iterJ.value();
}
}
else
{
// Iterate over all j with non-zero non-missing w_{jh}
for (DyadicCovariateValueIterator iterJ = this->columnValues(h);
iterJ.valid();
iterJ.next())
{
int j = iterJ.actor();
// Add the term w_{hi} w_{jh}
sums[j] += iterH.value() * iterJ.value();
}
}
}
}
}