diff options
Diffstat (limited to 'vowpalwabbit/mf.cc')
| -rw-r--r-- | vowpalwabbit/mf.cc | 82 |
1 files changed, 41 insertions, 41 deletions
diff --git a/vowpalwabbit/mf.cc b/vowpalwabbit/mf.cc index ab6b8988..d0236ed0 100644 --- a/vowpalwabbit/mf.cc +++ b/vowpalwabbit/mf.cc @@ -53,53 +53,53 @@ struct mf { }; template <bool cache_sub_predictions> -void predict(mf *data, learner& base, example* ec) { - vw* all = data->all; +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); + 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; + data.sub_predictions[0] = ec.partial_prediction; + prediction += ec.partial_prediction; // store namespace indices - copy_array(data->indices, ec->indices); + copy_array(data.indices, ec.indices); // erase indices - ec->indices.erase(); - ec->indices.push_back(0); + 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++) { + 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) { + 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; + ec.indices[0] = left_ns; // compute l^k * x_l using base learner base.predict(ec, k); - float x_dot_l = ec->partial_prediction; + float x_dot_l = ec.partial_prediction; if (cache_sub_predictions) - data->sub_predictions[2*k-1] = x_dot_l; + data.sub_predictions[2*k-1] = x_dot_l; // set example to right namespace only - ec->indices[0] = right_ns; + 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; + float x_dot_r = ec.partial_prediction; if (cache_sub_predictions) - data->sub_predictions[2*k] = x_dot_r; + data.sub_predictions[2*k] = x_dot_r; // accumulate prediction prediction += (x_dot_l * x_dot_r); @@ -107,15 +107,15 @@ void predict(mf *data, learner& base, example* ec) { } } // restore namespace indices and label - copy_array(ec->indices, data->indices); + copy_array(ec.indices, data.indices); // finalize prediction - ec->partial_prediction = prediction; - ec->final_prediction = GD::finalize_prediction(*(data->all), ec->partial_prediction); + ec.partial_prediction = prediction; + ec.final_prediction = GD::finalize_prediction(*(data.all), ec.partial_prediction); } -void learn(mf* data, learner& base, example* ec) { - vw* all = data->all; +void learn(mf& data, learner& base, example& ec) { + vw* all = data.all; // predict with current weights predict<true>(data, base, ec); @@ -124,71 +124,71 @@ void learn(mf* data, learner& base, example* ec) { base.update(ec); // store namespace indices - copy_array(data->indices, ec->indices); + copy_array(data.indices, ec.indices); // erase indices - ec->indices.erase(); - ec->indices.push_back(0); + 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++) { + 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) { + if (ec.atomics[left_ns].size() > 0 && ec.atomics[right_ns].size() > 0) { // set example to left namespace only - ec->indices[0] = left_ns; + ec.indices[0] = left_ns; // store feature values in left namespace - copy_array(data->temp_features, ec->atomics[left_ns]); + 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]; + 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); + copy_array(ec.atomics[left_ns], data.temp_features); } // set example to right namespace only - ec->indices[0] = right_ns; + ec.indices[0] = right_ns; // store feature values for right namespace - copy_array(data->temp_features, ec->atomics[right_ns]); + 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]; + 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); + copy_array(ec.atomics[right_ns], data.temp_features); } } } // restore namespace indices - copy_array(ec->indices, data->indices); + copy_array(ec.indices, data.indices); } -void finish(mf* o) { +void finish(mf& o) { // restore global pairs - o->all->pairs = o->pairs; + o.all->pairs = o.pairs; // clean up local v_arrays - o->indices.delete_v(); - o->sub_predictions.delete_v(); + o.indices.delete_v(); + o.sub_predictions.delete_v(); } |