void bicriteria_as_coreset(
const wplist& src,
wplist bic,
const csize_t dstsize,
wplist& dst) {
typedef wplist::const_iterator citer;
typedef wplist::iterator iter;
bic.resize(dstsize - dst.size());
for (iter it = bic.begin(); it != bic.end(); ++it) {
it->weight = 0;
}
for (iter it = dst.begin(); it != dst.end(); ++it) {
pair<int, double> m = min_dist(*it, bic);
bic[m.first].weight -= it->weight;
}
for (citer it = src.begin(); it != src.end(); ++it) {
pair<int, double> m = min_dist(*it, bic);
bic[m.first].weight += it->weight;
}
std::copy(bic.begin(), bic.begin()+dstsize-dst.size(),
std::back_inserter(dst));
for (iter it = dst.begin(); it != dst.end(); ++it) {
if (it->weight < 0) {
it->weight = 0;
}
}
}
void compressive_storage::forget_weight(wplist& points) {
double factor = std::exp(-config_.forgetting_factor);
typedef wplist::iterator iter;
for (iter it = points.begin(); it != points.end(); ++it) {
it->weight *= factor;
}
}
void kmeans_clustering_method::do_batch_update(wplist& points) {
static jubatus::util::math::random::mtrand r;
bool terminated = false;
if (points.size() < k_) {
return;
}
while (!terminated) {
vector<common::sfv_t> kcenters_new(k_);
vector<double> center_count(k_, 0);
for (wplist::iterator it = points.begin(); it != points.end(); ++it) {
pair<int64_t, double> m = min_dist((*it).data, kcenters_);
scalar_mul_and_add(it->data, it->weight, kcenters_new[m.first]);
center_count[m.first] += it->weight;
}
terminated = true;
for (size_t i = 0; i < k_; ++i) {
if (center_count[i] == 0) {
kcenters_new[i] = kcenters_[i];
continue;
}
kcenters_new[i] = scalar_dot(kcenters_new[i], 1.0 / center_count[i]);
double d = dist(kcenters_new[i], kcenters_[i]);
if (d > 1e-9) {
terminated = false;
}
}
kcenters_ = kcenters_new;
}
}
void compressive_storage::forget_weight(wplist& points) {
double lam = config_.forgetting_threshold;
typedef wplist::iterator iter;
for (iter it = points.begin(); it != points.end(); ++it) {
it->weight *= exp(-lam);
}
}
vector<wplist> kmeans_clustering_method::get_clusters(
const wplist& points) const {
vector<wplist> ret(k_);
for (wplist::const_iterator it = points.begin(); it != points.end(); ++it) {
pair<int64_t, double> m = min_dist(it->data, kcenters_);
ret[m.first].push_back(*it);
}
return ret;
}
void kmeans_compressor::bicriteria_to_coreset(
wplist& src,
wplist& bicriteria,
csize_t dstsize,
wplist& dst) {
if (bicriteria.size() == 0) {
dst = src;
return;
}
double weight_sum = 0;
double squared_min_dist_sum = 0;
for (wplist::iterator it = src.begin(); it != src.end(); ++it) {
std::pair<int, double> m = min_dist(*it, bicriteria);
(*it).free_long = m.first;
(*it).free_double = m.second;
bicriteria.at((*it).free_long).free_double += (*it).weight;
squared_min_dist_sum += pow((*it).free_double, 2) * (*it).weight;
weight_sum += (*it).weight;
}
std::vector<double> weights;
double sumw = 0;
double prob = 0;
for (wplist::iterator it = src.begin(); it != src.end(); ++it) {
weighted_point p = *it;
weighted_point bp = bicriteria.at(p.free_long);
prob = get_probability(p, bp, weight_sum, squared_min_dist_sum);
(*it).free_double = prob;
weights.push_back(prob);
sumw += prob;
}
discrete_distribution d(weights.begin(), weights.end());
std::vector<size_t> ind(dstsize);
std::generate(ind.begin(), ind.end(), d);
for (std::vector<size_t>::iterator it = ind.begin(); it != ind.end(); ++it) {
weighted_point sample = src.at(*it);
sample.weight = 1.0 / dstsize * sumw / sample.free_double * sample.weight;
sample.free_double = 0;
sample.free_long = 0;
dst.push_back(sample);
}
}
std::pair<size_t, double> min_dist(const weighted_point& d1, const wplist& P) {
double md = DBL_MAX;
size_t midx = 0;
for (wplist::const_iterator it = P.begin(); it != P.end(); ++it) {
double d = dist((*it), d1);
if (md > d) {
midx = it - P.begin();
md = d;
}
}
return std::make_pair(midx, md);
}
void kmeans_clustering_method::initialize_centers(wplist& points) {
if (points.size() < k_) {
kcenters_.clear();
for (wplist::iterator it = points.begin(); it != points.end(); ++it) {
kcenters_.push_back(it->data);
}
return;
}
kcenters_.clear();
kcenters_.push_back(points[0].data);
vector<double> weights;
while (kcenters_.size() < k_) {
weights.clear();
for (wplist::iterator it = points.begin(); it != points.end(); ++it) {
pair<int64_t, double> m = min_dist((*it).data, kcenters_);
weights.push_back(m.second * it->weight);
}
discrete_distribution d(weights.begin(), weights.end());
kcenters_.push_back(points[d()].data);
}
}
vector<wplist> kmeans_clustering_method::get_clusters(
const wplist& points) const {
if (kcenters_.empty()) {
throw JUBATUS_EXCEPTION(not_performed());
}
vector<wplist> ret(k_);
for (wplist::const_iterator it = points.begin(); it != points.end(); ++it) {
pair<int64_t, double> m = min_dist(it->data, kcenters_);
ret[m.first].push_back(*it);
}
return ret;
}
eigen_wsvec_list_t eigen_feature_mapper::convert(
const wplist& src,
bool update_map) {
eigen_wsvec_list_t ret(src.size());
eigen_wsvec_list_t::iterator ob = ret.begin();
wplist::const_iterator ib = src.begin();
while (ib != src.end()) {
*ob = convert(*ib, update_map);
++ob;
++ib;
}
for (ob = ret.begin(); ob != ret.end(); ++ob) {
eigen_svec_t v(d_);
for (eigen_svec_t::InnerIterator it(ob->data); it; ++it) {
v.coeffRef(it.index()) = it.value();
}
ob->data = v;
}
return ret;
}
void concat(const wplist& src, wplist& dst) {
dst.insert(dst.end(), src.begin(), src.end());
}