static void search(char *query, struct list *list)
{
DIR *dir;
struct dirent *dp;
static char path[PATH_MAX];
char lowercase_query[NAME_LENGTH];
assert(strlen(query) < NAME_LENGTH);
if (snprintf(path, PATH_MAX, "%s/players", config.root) >= PATH_MAX) {
fprintf(stderr, "Path to teerank database too long\n");
exit(EXIT_FAILURE);
}
if (!(dir = opendir(path))) {
fprintf(stderr, "opendir(%s): %s\n", path, strerror(errno));
exit(EXIT_FAILURE);
}
to_lowercase(query, lowercase_query);
init_list(list);
while ((dp = readdir(dir))) {
unsigned relevance;
if (!strcmp(dp->d_name, ".") || !strcmp(dp->d_name, ".."))
continue;
relevance = get_relevance(dp->d_name, lowercase_query);
try_add_result(list, relevance, dp->d_name);
}
closedir(dir);
}
/** The actual LambdaRank implementation. */
virtual void compute_gradients(
graphchi_vertex<TypeVertex, FeatureEdge> &query, Gradient* umodel) {
std::vector<double> lambdas(query.num_outedges());
std::vector<double> s_is(query.num_outedges());
/* First, we compute all the outputs... */
for (int i = 0; i < query.num_outedges(); i++) {
s_is[i] = get_score(query.outedge(i));
// std::cout << "s[" << i << "] == " << s_is[i] << std::endl;
}
/* ...and the retrieval measure scores. */
opt.compute(query);
/* Now, we compute the errors (lambdas). */
for (int i = 0; i < query.num_outedges() - 1; i++) {
int rel_i = get_relevance(query.outedge(i));
for (int j = i + 1; j < query.num_outedges(); j++) {
int rel_j = get_relevance(query.outedge(j));
if (rel_i != rel_j) {
double S_ij = rel_i > rel_j ? 1 : -1;
double lambda_ij = dC_per_ds_i(S_ij, s_is[i], s_is[j]) *
fabs(opt.delta(query, i, j));
/* lambda_ij = -lambda_ji */
lambdas[i] += lambda_ij;
lambdas[j] -= lambda_ij;
}
}
}
/* Finally, the model update. */
for (int i = 0; i < query.num_outedges(); i++) {
// -lambdas[i], as C is a utility function in this case
umodel->update(query.outedge(i)->get_vector()->get_data(), s_is[i], lambdas[i]);
}
}
static PotentialRow *
potential_row_new (const char *title, const char *location,
const char *keywords, int visit_count,
gboolean is_bookmark)
{
PotentialRow *row = g_slice_new0 (PotentialRow);
row->title = g_strdup (title);
row->location = g_strdup (location);
row->keywords = g_strdup (keywords);
row->relevance = get_relevance (location, visit_count, is_bookmark);
row->is_bookmark = is_bookmark;
return row;
}
