/**
* Vertex update function - computes the least square step
*/
void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
vertex_data & vdata = latent_factors_inmem[vertex.id()];
mat XtX = mat::Zero(D, D);
vec Xty = vec::Zero(D);
bool compute_rmse = (vertex.num_outedges() > 0);
// Compute XtX and Xty (NOTE: unweighted)
for(int e=0; e < vertex.num_edges(); e++) {
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 += nbr_latent.pvec * nbr_latent.pvec.transpose();
if (compute_rmse) {
double prediction;
rmse_vec[omp_get_thread_num()] += sparse_als_predict(vdata, nbr_latent, observation, prediction);
}
}
double regularization = lambda;
if (regnormal)
lambda *= vertex.num_edges();
for(int i=0; i < D; i++) XtX(i,i) += regularization;
bool isuser = vertex.id() < (uint)M;
if (algorithm == SPARSE_BOTH_FACTORS || (algorithm == SPARSE_USR_FACTOR && isuser) ||
(algorithm == SPARSE_ITM_FACTOR && !isuser)){
double sparsity_level = 1.0;
if (isuser)
sparsity_level -= user_sparsity;
else sparsity_level -= movie_sparsity;
vdata.pvec = CoSaMP(XtX, Xty, (int)ceil(sparsity_level*(double)D), 10, 1e-4, D);
}
else vdata.pvec = XtX.selfadjointView<Eigen::Upper>().ldlt().solve(Xty);
}
void test_cosamp(){
mat A= init_mat("0.9528 0.5982 0.8368 ; 0.7041 0.8407 0.5187; 0.9539 0.4428 0.0222", 3, 3);
vec b= init_vec(" 0.3759 0.8986 0.4290",3);
int K=1;
double epsilon =1e-3;
vec ret = CoSaMP(A,b,K,10, epsilon,3);
vec right = init_vec("0 1.2032 0", 3);
double diff = norm(ret - right);
assert(diff <1e-4);
}
