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/*
Copyright (c) by respective owners including Yahoo!, Microsoft, and
individual contributors. All rights reserved. Released under a BSD (revised)
license as described in the file LICENSE.
*/
#ifdef _WIN32
#include <winsock2.h>
#else
#include <netdb.h>
#endif
#include "reductions.h"
#include "simple_label.h"
#include "gd.h"
#include "rand48.h"
using namespace std;
using namespace LEARNER;
namespace MF {
struct mf {
vector<string> pairs;
uint32_t rank;
uint32_t increment;
// array to cache w*x, (l^k * x_l) and (r^k * x_r)
// [ w*(1,x_l,x_r) , l^1*x_l, r^1*x_r, l^2*x_l, r^2*x_2, ... ]
v_array<float> sub_predictions;
// array for temp storage of indices
v_array<unsigned char> indices;
// array for temp storage of features
v_array<feature> temp_features;
vw* all;
};
template <bool cache_sub_predictions>
void predict(mf& data, learner& base, example& ec) {
vw* all = data.all;
float prediction = 0;
if (cache_sub_predictions)
data.sub_predictions.resize(2*all->rank+1, true);
// predict from linear terms
base.predict(ec);
// store linear prediction
if (cache_sub_predictions)
data.sub_predictions[0] = ec.partial_prediction;
prediction += ec.partial_prediction;
// store namespace indices
copy_array(data.indices, ec.indices);
// erase indices
ec.indices.erase();
ec.indices.push_back(0);
// add interaction terms to prediction
for (vector<string>::iterator i = data.pairs.begin(); i != data.pairs.end(); i++) {
int left_ns = (int) (*i)[0];
int right_ns = (int) (*i)[1];
if (ec.atomics[left_ns].size() > 0 && ec.atomics[right_ns].size() > 0) {
for (size_t k = 1; k <= all->rank; k++) {
ec.indices[0] = left_ns;
// compute l^k * x_l using base learner
base.predict(ec, k);
float x_dot_l = ec.partial_prediction;
if (cache_sub_predictions)
data.sub_predictions[2*k-1] = x_dot_l;
// set example to right namespace only
ec.indices[0] = right_ns;
// compute r^k * x_r using base learner
base.predict(ec, k + all->rank);
float x_dot_r = ec.partial_prediction;
if (cache_sub_predictions)
data.sub_predictions[2*k] = x_dot_r;
// accumulate prediction
prediction += (x_dot_l * x_dot_r);
}
}
}
// restore namespace indices and label
copy_array(ec.indices, data.indices);
// finalize prediction
ec.partial_prediction = prediction;
((label_data*)ec.ld)->prediction = GD::finalize_prediction(*(data.all), ec.partial_prediction);
}
void learn(mf& data, learner& base, example& ec) {
vw* all = data.all;
// predict with current weights
predict<true>(data, base, ec);
// update linear weights
base.update(ec);
// store namespace indices
copy_array(data.indices, ec.indices);
// erase indices
ec.indices.erase();
ec.indices.push_back(0);
// update interaction terms
// looping over all pairs of non-empty namespaces
for (vector<string>::iterator i = data.pairs.begin(); i != data.pairs.end(); i++) {
int left_ns = (int) (*i)[0];
int right_ns = (int) (*i)[1];
if (ec.atomics[left_ns].size() > 0 && ec.atomics[right_ns].size() > 0) {
// set example to left namespace only
ec.indices[0] = left_ns;
// store feature values in left namespace
copy_array(data.temp_features, ec.atomics[left_ns]);
for (size_t k = 1; k <= all->rank; k++) {
// multiply features in left namespace by r^k * x_r
for (feature* f = ec.atomics[left_ns].begin; f != ec.atomics[left_ns].end; f++)
f->x *= data.sub_predictions[2*k];
// update l^k using base learner
base.update(ec, k);
// restore left namespace features (undoing multiply)
copy_array(ec.atomics[left_ns], data.temp_features);
}
// set example to right namespace only
ec.indices[0] = right_ns;
// store feature values for right namespace
copy_array(data.temp_features, ec.atomics[right_ns]);
for (size_t k = 1; k <= all->rank; k++) {
// multiply features in right namespace by l^k * x_l
for (feature* f = ec.atomics[right_ns].begin; f != ec.atomics[right_ns].end; f++)
f->x *= data.sub_predictions[2*k-1];
// update r^k using base learner
base.update(ec, k + all->rank);
// restore right namespace features
copy_array(ec.atomics[right_ns], data.temp_features);
}
}
}
// restore namespace indices
copy_array(ec.indices, data.indices);
}
void finish(mf& o) {
// restore global pairs
o.all->pairs = o.pairs;
// clean up local v_arrays
o.indices.delete_v();
o.sub_predictions.delete_v();
}
learner* setup(vw& all, po::variables_map& vm) {
mf* data = new mf;
// copy global data locally
data->all = &all;
data->rank = all.rank;
// store global pairs in local data structure and clear global pairs
// for eventual calls to base learner
data->pairs = all.pairs;
all.pairs.clear();
// initialize weights randomly
if(!vm.count("initial_regressor"))
{
for (size_t j = 0; j < (all.reg.weight_mask + 1) >> all.reg.stride_shift; j++)
all.reg.weight_vector[j << all.reg.stride_shift] = (float) (0.1 * frand48());
}
learner* l = new learner(data, all.l, 2*data->rank+1);
l->set_learn<mf, learn>();
l->set_predict<mf, predict<false> >();
l->set_finish<mf,finish>();
return l;
}
}
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