/**
\brief in the setup of this object, make a new system randomizer.
\param solve_options the current state of the solver.
\param W input witness set, witn numbers of linears, patches, and variables.
*/
void make_randomizer(const SolverConfiguration & solve_options, const WitnessSet & W)
{
randomizer_ = std::make_shared<SystemRandomizer>(*(new SystemRandomizer));
randomizer_->setup(W.num_variables()-W.num_linears()-W.num_patches(), solve_options.PPD.num_funcs);
}
void create_sliced_system(boost::filesystem::path input_file, boost::filesystem::path output_file,
vec_mp * linears, int num_to_add,
const WitnessSet & W)
{
#ifdef functionentry_output
std::cout << "create_sliced_system" << std::endl;
#endif
if (W.num_var_names()==0) {
std::cout << "trying to create a sliced system, but witness set does not have the variable names." << std::endl;
deliberate_segfault();
}
int *declarations = NULL;
partition_parse(&declarations, input_file, "func_input", "config", 0); // the 0 means not self conjugate.
free(declarations);
FILE *OUT = safe_fopen_write(output_file);
FILE *IN = safe_fopen_read("func_input");
fprintf(OUT,"INPUT\n\n");
copyfile(IN,OUT);
fclose(IN);
std::vector< int > indicators;
for (int ii=0; ii<num_to_add; ii++) {
indicators.push_back(rand());
}
for (int ii=0; ii<num_to_add; ii++) {
std::stringstream linname;
linname << "supp_lin_" << indicators[ii];
write_vector_as_constants(linears[ii], linname.str(), OUT);
linname.clear(); linname.str("");
}
for (int ii=0; ii<num_to_add; ii++) {
fprintf(OUT,"function supp_lin_%d;\n",indicators[ii]);
}
for (int ii=0; ii<num_to_add; ii++) {
fprintf(OUT,"supp_lin_%d = supp_lin_%d_1",indicators[ii],indicators[ii]);
for (int jj=1; jj<W.num_variables(); jj++) {
fprintf(OUT," + %s*supp_lin_%d_%d",W.name(jj).c_str(), indicators[ii],jj+1);
}
fprintf(OUT, ";\n\n");
}
fprintf(OUT,"END;\n\n\n\n");
for (unsigned int ii=0; ii<W.num_patches(); ii++) {
std::stringstream linname;
linname << "patch_" << ii;
write_vector_as_constants(W.patch(ii), linname.str(), OUT);
linname.clear(); linname.str("");
}
fclose(OUT);
}
void WitnessSet::merge(const WitnessSet & W_in, tracker_config_t * T)
{
//error checking first
if ( (num_vars_==0) && (W_in.num_vars_!=0) && (num_points()==0)) {
num_vars_ = W_in.num_vars_;
num_natty_vars_ = W_in.num_natty_vars_;
}
if (W_in.num_natty_vars_ != this->num_natty_vars_) {
std::stringstream ss;
ss << "merging two witness sets with differing numbers of natural variables. "<< W_in.num_natural_variables() <<" merging set, "<< this->num_natural_variables() << " existing\n",
throw std::logic_error(ss.str());
}
//just mindlessly add the linears. up to user to ensure linears get merged correctly. no way to know what they want...
for (unsigned int ii = 0; ii<W_in.num_linears(); ii++) {
WitnessSet::add_linear(W_in.linear(ii));
}
for (unsigned int ii = 0; ii<W_in.num_points(); ii++) {
int is_new = 1;
vec_mp & in_point = W_in.point(ii);
for (unsigned int jj = 0; jj<num_points(); jj++){
vec_mp & curr_point = this->point(jj);
//cache the sizes
int in_size = in_point->size;
int curr_size = curr_point->size;
in_point->size = this->num_natural_variables();
curr_point->size = this->num_natural_variables();
if (isSamePoint_homogeneous_input(curr_point, in_point, T->final_tol_times_mult)) {
is_new = 0;
in_point->size = in_size;
curr_point->size = curr_size;
break;
}
// restore the sizes
in_point->size = in_size;
curr_point->size = curr_size;
}
if (is_new==1)
WitnessSet::add_point( (in_point) );
}
for (unsigned int ii = 0; ii<W_in.num_patches(); ii++) {
int is_new = 1;
for (unsigned int jj = 0; jj<this->num_patches(); jj++){
if ( this->patch(jj)->size == W_in.patch(ii)->size) {
if (isSamePoint_inhomogeneous_input(patch(jj), W_in.patch(ii), T->final_tol_times_mult)) {
is_new = 0;
break;
}
}
}
if (is_new==1)
WitnessSet::add_patch(W_in.patch(ii));
}
return;
}//re: merge_witness_sets
void nullspace_config_setup_right(NullspaceConfiguration *ns_config,
vec_mp *pi, // an array of projections, the number of which is the target dimensions
int ambient_dim,
int *max_degree, // a pointer to the value
std::shared_ptr<SystemRandomizer> randomizer,
const WitnessSet & W,
SolverConfiguration & solve_options)
{
ns_config->set_side(nullspace_handedness::RIGHT);
int toss;
parse_input_file(W.input_filename(), &toss); // re-create the parsed files for the stuffs (namely the SLP).
ns_config->set_randomizer(randomizer); // set the pointer. this randomizer is for the underlying system.
*max_degree = randomizer->max_degree()-1; // minus one for differentiated degree
ns_config->max_degree = *max_degree;
// set some integers
ns_config->num_projections = ambient_dim;
ns_config->num_v_vars = W.num_natural_variables()-1;
ns_config->num_synth_vars = W.num_synth_vars(); // this may get a little crazy if we chain into this more than once. this code is written to be called into only one time beyond the first.
ns_config->num_natural_vars = W.num_natural_variables();
ns_config->ambient_dim = ambient_dim;
ns_config->target_projection = (vec_mp *) br_malloc(ns_config->num_projections * sizeof(vec_mp));
for (int ii=0; ii<ns_config->num_projections; ii++) {
init_vec_mp2(ns_config->target_projection[ii], W.num_variables(),solve_options.T.AMP_max_prec);
ns_config->target_projection[ii]->size = W.num_variables();
vec_cp_mp(ns_config->target_projection[ii], pi[ii]);
}
ns_config->num_jac_equations = (ns_config->num_natural_vars - 1);// N-1; the subtraction of 1 is for the 1 hom-var.
// me must omit any previously added synthetic vars.
ns_config->num_additional_linears = ambient_dim-1;
ns_config->num_v_linears = ns_config->num_jac_equations;
// this check is correct.
int check_num_func = randomizer->num_rand_funcs() + ns_config->num_jac_equations + ns_config->num_additional_linears + W.num_patches() + 1; // +1 for v patch from this incoming computation
int check_num_vars = ns_config->num_natural_vars + ns_config->num_synth_vars + ns_config->num_v_vars;
if (check_num_func != check_num_vars) {
std::cout << color::red();
std::cout << "mismatch in number of equations...\n" << std::endl;
std::cout << "left: " << check_num_func << " right " << check_num_vars << std::endl;
std::cout << color::console_default();
throw std::logic_error("logic error in nullspace_left");
}
// set up the linears in $v$ ( the M_i linears)
ns_config->v_linears = (vec_mp *)br_malloc(ns_config->num_v_linears*sizeof(vec_mp));
for (int ii=0; ii<ns_config->num_v_linears; ii++) {
init_vec_mp2(ns_config->v_linears[ii],ns_config->num_v_vars,solve_options.T.AMP_max_prec);
ns_config->v_linears[ii]->size = ns_config->num_v_vars;
for (int jj=0; jj<ns_config->num_v_vars; jj++){
get_comp_rand_mp(&ns_config->v_linears[ii]->coord[jj]); // should this be real? no.
}
}
// the last of the linears will be used for the slicing, and passed on to later routines
int offset = 1;
ns_config->additional_linears_terminal = (vec_mp *)br_malloc((ns_config->num_additional_linears)*sizeof(vec_mp));
ns_config->additional_linears_starting = (vec_mp *)br_malloc((ns_config->num_additional_linears)*sizeof(vec_mp));
for (int ii=0; ii<ns_config->num_additional_linears; ii++) {
init_vec_mp2(ns_config->additional_linears_terminal[ii],W.num_variables(),solve_options.T.AMP_max_prec);
ns_config->additional_linears_terminal[ii]->size = W.num_variables();
for (int jj=0; jj<W.num_natural_variables(); jj++){
get_comp_rand_mp(&ns_config->additional_linears_terminal[ii]->coord[jj]); // should this be real? no.
}
for (int jj=W.num_natural_variables(); jj<W.num_variables(); jj++) {
set_zero_mp(&ns_config->additional_linears_terminal[ii]->coord[jj]);
}
init_vec_mp2(ns_config->additional_linears_starting[ii],W.num_variables(),solve_options.T.AMP_max_prec);
ns_config->additional_linears_starting[ii]->size = W.num_variables();
vec_cp_mp(ns_config->additional_linears_starting[ii], W.linear(ii+offset));
}
// set up the patch in $v$. we will include this in an inversion matrix to get the starting $v$ values.
init_vec_mp2(ns_config->v_patch,ns_config->num_v_vars,solve_options.T.AMP_max_prec);
ns_config->v_patch->size = ns_config->num_v_vars;
for (int ii=0; ii<ns_config->num_v_vars; ii++) {
get_comp_rand_mp(&ns_config->v_patch->coord[ii]);
}
mat_mp temp_getter; init_mat_mp2(temp_getter,0, 0,solve_options.T.AMP_max_prec);
temp_getter->rows = 0; temp_getter->cols = 0;
//the 'ns_config->starting_linears' will be used for the x variables. we will homotope to these 1 at a time
ns_config->starting_linears = (vec_mp **)br_malloc( randomizer->num_rand_funcs()*sizeof(vec_mp *));
for (int ii=0; ii<randomizer->num_rand_funcs(); ii++) {
int curr_degree = std::max(0,randomizer->randomized_degree(ii)-1);
ns_config->starting_linears[ii] = (vec_mp *) br_malloc(curr_degree*sizeof(vec_mp));
make_matrix_random_mp(temp_getter,curr_degree, W.num_natural_variables(), solve_options.T.AMP_max_prec); // this matrix is nearly orthogonal
for (unsigned int jj=0; jj<curr_degree; jj++) {
init_vec_mp2(ns_config->starting_linears[ii][jj],W.num_variables(),solve_options.T.AMP_max_prec);
ns_config->starting_linears[ii][jj]->size = W.num_variables();
for (int kk=0; kk<W.num_natural_variables(); kk++) {
set_mp(&ns_config->starting_linears[ii][jj]->coord[kk], &temp_getter->entry[jj][kk]);
}
for (int kk=W.num_natural_variables(); kk<W.num_variables(); kk++) {
set_zero_mp(&ns_config->starting_linears[ii][jj]->coord[kk]);
}
}
}
clear_mat_mp(temp_getter);
return;
}
