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CF toolkit: Use rankUpdate() to calculate XtX in ALS and WALS #24

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CF toolkit: Use rankUpdate() to calculate XtX in ALS and WALS #24

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16967fe
Fixed typo in regnormal code
Apr 29, 2014
8e49446
Merge branch 'master' of https://github.com/GraphChi/graphchi-cpp
May 5, 2014
162a6ed
LibFM: fixed typo in load last_item_bias code
May 5, 2014
5c403ad
Merge branch 'master' of https://github.com/GraphChi/graphchi-cpp
May 14, 2014
07e530e
ALS/WALS: use rankUpdate() to update XtX
May 14, 2014
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2 changes: 1 addition & 1 deletion toolkits/collaborative_filtering/als.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ struct ALSVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeData
float observation = vertex.edge(e)->get_data();
vertex_data & nbr_latent = latent_factors_inmem[vertex.edge(e)->vertex_id()];
Xty += nbr_latent.pvec * observation;
XtX.triangularView<Eigen::Upper>() += nbr_latent.pvec * nbr_latent.pvec.transpose();
XtX.selfadjointView<Eigen::Upper>().rankUpdate(nbr_latent.pvec);
if (compute_rmse) {
double prediction;
rmse_vec[omp_get_thread_num()] += als_predict(vdata, nbr_latent, observation, prediction);
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2 changes: 1 addition & 1 deletion toolkits/collaborative_filtering/libfm.cpp
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Expand Up @@ -314,7 +314,7 @@ int main(int argc, const char ** argv) {
vec user_bias = load_matrix_market_vector(training +"_U_bias.mm", false, true);
vec item_bias = load_matrix_market_vector(training +"_V_bias.mm", false, true);
vec time_bias = load_matrix_market_vector(training+ "_T_bias.mm", false, true);
vec last_item_bias = load_matrix_market_vector(training+"_L_bias.m", false, true);
vec last_item_bias = load_matrix_market_vector(training+"_L_bias.mm", false, true);
for (uint i=0; i<M+N+K+M; i++){
if (i < M)
latent_factors_inmem[i].bias = user_bias[i];
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2 changes: 1 addition & 1 deletion toolkits/collaborative_filtering/wals.cpp
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Expand Up @@ -119,7 +119,7 @@ struct WALSVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeDat
const edge_data & edge = vertex.edge(e)->get_data();
vertex_data & nbr_latent = latent_factors_inmem[vertex.edge(e)->vertex_id()];
Xty += nbr_latent.pvec * edge.weight * edge.time;
XtX.triangularView<Eigen::Upper>() += nbr_latent.pvec * nbr_latent.pvec.transpose() * edge.time;
XtX.selfadjointView<Eigen::Upper>().rankUpdate(nbr_latent.pvec, edge.time);
if (compute_rmse) {
double prediction;
rmse_vec[omp_get_thread_num()] += wals_predict(vdata, nbr_latent, edge.weight, prediction) * edge.time;
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