double State::transition_row_partition_assignments(const MatrixD& data,
vector<int> which_rows) {
vector<int> global_column_indices = create_sequence(data.size2());
double score_delta = 0;
//
int num_rows = which_rows.size();
if (num_rows == 0) {
num_rows = data.size1();
which_rows = create_sequence(num_rows);
//FIXME: use own shuffle so seed control is in effect
std::random_shuffle(which_rows.begin(), which_rows.end());
}
set<View*>::iterator svp_it;
for (svp_it = views.begin(); svp_it != views.end(); svp_it++) {
// for each view
View& v = **svp_it;
vector<int> view_cols = get_indices_to_reorder(global_column_indices,
v.global_to_local);
const MatrixD data_subset = extract_columns(data, view_cols);
map<int, vector<double> > row_data_map = construct_data_map(data_subset);
vector<int>::iterator vi_it;
for (vi_it = which_rows.begin(); vi_it != which_rows.end(); vi_it++) {
// for each SPECIFIED row
int row_idx = *vi_it;
vector<double> vd = row_data_map[row_idx];
score_delta += v.transition_z(vd, row_idx);
}
}
data_score += score_delta;
return score_delta;
}
// helper for cython
double State::transition_view_i(int which_view, const MatrixD& data) {
vector<int> global_column_indices = create_sequence(data.size2());
View& v = get_view(which_view);
vector<int> view_cols = get_indices_to_reorder(global_column_indices,
v.global_to_local);
const MatrixD data_subset = extract_columns(data, view_cols);
map<int, vector<double> > data_subset_map = construct_data_map(data_subset);
return v.transition(data_subset_map);
}
double State::transition_views(const MatrixD& data) {
vector<int> global_column_indices = create_sequence(data.size2());
//
double score_delta = 0;
// ordering doesn't matter, don't need to shuffle
for (int view_idx = 0; view_idx < get_num_views(); view_idx++) {
View& v = get_view(view_idx);
vector<int> view_cols = get_indices_to_reorder(global_column_indices,
v.global_to_local);
const MatrixD data_subset = extract_columns(data, view_cols);
map<int, vector<double> > data_subset_map = construct_data_map(data_subset);
score_delta += v.transition(data_subset_map);
}
return score_delta;
}
void State::construct_base_hyper_grids(const MatrixD& data, int N_GRID,
vector<double> ROW_CRP_ALPHA_GRID,
vector<double> COLUMN_CRP_ALPHA_GRID) {
int num_rows = data.size1();
int num_cols = data.size2();
if (ROW_CRP_ALPHA_GRID.size() == 0) {
ROW_CRP_ALPHA_GRID = create_crp_alpha_grid(num_rows, N_GRID);
}
if (COLUMN_CRP_ALPHA_GRID.size() == 0) {
COLUMN_CRP_ALPHA_GRID = create_crp_alpha_grid(num_cols, N_GRID);
}
row_crp_alpha_grid = ROW_CRP_ALPHA_GRID;
column_crp_alpha_grid = COLUMN_CRP_ALPHA_GRID;
construct_cyclic_base_hyper_grids(N_GRID, num_rows, vm_b_grid);
construct_continuous_base_hyper_grids(N_GRID, num_rows, r_grid, nu_grid);
construct_multinomial_base_hyper_grids(N_GRID, num_rows,
multinomial_alpha_grid);
}
double State::transition_features(const MatrixD &data, vector<int> which_features) {
double score_delta = 0;
int num_features = which_features.size();
if(num_features==0) {
which_features = create_sequence(data.size2());
// FIXME: use seed to shuffle
std::random_shuffle(which_features.begin(), which_features.end());
}
vector<int>::iterator it;
for(it=which_features.begin(); it!=which_features.end(); it++) {
int feature_idx = *it;
vector<double> feature_data = extract_col(data, feature_idx);
// kernel selection
if(ct_kernel == 0) {
score_delta += transition_feature_gibbs(feature_idx, feature_data);
} else if(ct_kernel == 1) {
score_delta += transition_feature_mh(feature_idx, feature_data);
} else {
printf("Invalid CT_KERNEL");
assert(0==1);
}
}
return score_delta;
}
