double Fw(vector<double> &drv)
{
double norm = dot_product(wvec, wvec);
for (unsigned w = 0; w < drv.size(); w++)
drv[w] += 2*wvec[w];
double sum = 0.0;
for (unsigned s = 0; s < subgraphs.size(); s++)
{
Subgraph *sg = subgraphs[s];
Params *params = parameters[s];
PowerMethod *power = powers[s];
params->set_wvec(wvec);
sg->recompute_scores(*params);
params->recalculate_derivs();
power->pers_pagerank();
power->derivatives();
double sum_p = 0.0;
vector<double> sum_d(wvec.size(), 0.0);
candidate_sums(s, sum_p, sum_d);
double loss = 0.0;
for (unsigned d = 0; d < sg->positive.size(); d++)
{
unsigned pos = sg->positive[d];
double p_d = power->get_pagerank(pos);
for (unsigned l = 0; l < sg->negative.size(); l++)
{
unsigned neg = sg->negative[l];
double p_l = power->get_pagerank(neg);
double diff = p_l/sum_p - p_d/sum_p;
loss += hloss(diff);
for (unsigned w = 0; w < wvec.size(); w++)
drv[w] += dhloss(diff) * (
(power->get_derivative(neg, w)*sum_p - p_l*sum_d[w])/(sum_p*sum_p)
-
(power->get_derivative(pos, w)*sum_p - p_d*sum_d[w])/(sum_p*sum_p)
);
}
}
sum += loss;
}
return norm + sum;
}
void run(Graph *graph, Users_data *profile, vector<user_id>& users, const char* outfile)
{
for (unsigned u = 0; u < users.size(); u++)
{
user_id id = users[u];
Subgraph* sub = new Subgraph(graph, id, TIMEPOINT);
Params* prm = new Params(wvec, sub->subgraph, sub->mutual);
sub->recompute_scores(*prm);
PowerMethod* power = new PowerMethod(sub, prm, wvec.size());
power->pers_pagerank();
evaluate(power);
// output after every 5000 users
if (u % 5000 == 0)
report(outfile);
delete power;
delete prm;
delete sub;
}
report(outfile);
}
