/** Expands the cluster order while adding new neighbor points to the order.
* @param db All data points that are to be considered by the algorithm. Changes their values.
* @param p The point to be examined.
* @param eps The epsilon representing the radius of the epsilon-neighborhood.
* @param min_pts The minimum number of points to be found within an epsilon-neigborhood.
* @param[out] o_ordered_vector The ordered vector of data points. Elements will be added to this vector.
*/
void expand_cluster_order( DataVector& db, DataPoint* p, const real eps, const unsigned int min_pts, DataVector& o_ordered_vector) {
assert( eps >= 0 && "eps must not be negative");
assert( min_pts > 0 && "min_pts must be greater than 0");
DataVector N_eps = get_neighbors( p, eps, db);
p->reachability_distance( OPTICS::UNDEFINED);
const real core_dist_p = squared_core_distance( p, min_pts, N_eps);
p->processed( true);
o_ordered_vector.push_back( p);
if( core_dist_p == OPTICS::UNDEFINED)
return;
DataSet seeds;
update_seeds( N_eps, p, core_dist_p, seeds);
while( !seeds.empty()) {
DataPoint* q = *seeds.begin();
seeds.erase( seeds.begin()); // remove first element from seeds
DataVector N_q = get_neighbors( q, eps, db);
const real core_dist_q = squared_core_distance( q, min_pts, N_q);
q->processed( true);
o_ordered_vector.push_back( q);
if( core_dist_q != OPTICS::UNDEFINED) {
// *** q is a core-object ***
update_seeds( N_q, q, core_dist_q, seeds);
}
}
}
void clique::expand_cluster(clique_block & p_block) {
p_block.touch();
if (p_block.get_points().size() <= m_density_threshold) {
if (!p_block.get_points().empty()) {
m_result_ptr->noise().insert(m_result_ptr->noise().end(), p_block.get_points().begin(), p_block.get_points().end());
}
return;
}
m_result_ptr->clusters().push_back({ });
cluster & cur_cluster = m_result_ptr->clusters().back();
cur_cluster.insert(cur_cluster.end(), p_block.get_points().begin(), p_block.get_points().end());
std::list<clique_block *> neighbors;
get_neighbors(p_block, neighbors);
for (clique_block * neighbor : neighbors) {
if (neighbor->get_points().size() > m_density_threshold) {
cur_cluster.insert(cur_cluster.end(), neighbor->get_points().begin(), neighbor->get_points().end());
get_neighbors(*neighbor, neighbors);
}
else if (!neighbor->get_points().empty()) {
m_result_ptr->noise().insert(m_result_ptr->noise().end(), neighbor->get_points().begin(), neighbor->get_points().end());
}
}
}
/* Split a mask into a mask with just the cycles, and another mask with the remaining dots. */
void separate_cycles(mask_t mask, mask_t *cycles, mask_t *no_cycles) {
int i, j;
*cycles = mask;
*no_cycles = 0;
for (i = j = 0; i < NUM_DOTS; j = ++i) {
if (MASK_CONTAINS(*cycles, j)) {
/* Repeatedly remove dots that only have less than two neighbors and add
* them to the mask that has no cycles.
*/
int num_neighbors;
neighbors_t neighbors;
get_neighbors(*cycles, j, &num_neighbors, neighbors);
while (num_neighbors <= 1) {
*cycles = REMOVE_FROM_MASK(*cycles, j);
*no_cycles = ADD_TO_MASK(*no_cycles, j);
if (num_neighbors == 0) {
break;
}
j = neighbors[0];
get_neighbors(*cycles, j, &num_neighbors, neighbors);
}
}
}
}
std::vector<int> breadth_first_search(int root,
const std::map<int,std::set<int>>& edges) {
// Initialize data structures
std::unordered_map<int,bool> is_visited;
std::vector<int> sorted_nodes;
std::queue<int> search_queue;
search_queue.push(root);
// Visit nodes until search queue is exhausted
while (!search_queue.empty()) {
const auto& node = search_queue.front();
search_queue.pop();
for (const auto& neighbor : get_neighbors(node, edges)) {
if (!is_visited[neighbor]) {
is_visited[neighbor] = true;
sorted_nodes.push_back(neighbor);
search_queue.push(neighbor);
}
}
}
// Return list of sorted nodes
return sorted_nodes;
}
std::vector<int> depth_first_search(int root,
const std::map<int,std::set<int>>& edges) {
// Initialize data structures
std::unordered_map<int,bool> is_visited, is_sorted;
std::vector<int> sorted_nodes;
std::stack<int> search_stack;
search_stack.push(root);
// Visit nodes until search stack is exhausted
while (!search_stack.empty()) {
const auto& node = search_stack.top();
search_stack.pop();
if (!is_sorted[node]) {
if (is_visited[node]) {
// Add node to sorted list if we have already visited
is_sorted[node] = true;
sorted_nodes.push_back(node);
} else {
// Visit node and add neighbors to search stack
is_visited[node] = true;
search_stack.push(node);
for (const auto& neighbor : get_neighbors(node, edges)) {
if (!is_visited[neighbor] && !is_sorted[neighbor]) {
search_stack.push(neighbor);
}
}
}
}
}
// Return list of sorted nodes
return sorted_nodes;
}
dev_info_t *
i_ddi_create_branch(dev_info_t *pdip, int nid)
{
char *buf;
dev_info_t *dip = NULL;
if (pdip == NULL || nid == OBP_NONODE || nid == OBP_BADNODE)
return (NULL);
buf = kmem_alloc(OBP_MAXPROPNAME, KM_SLEEP);
if (getlongprop_buf(nid, OBP_NAME, buf, OBP_MAXPROPNAME) > 0) {
if (check_status(nid, buf, pdip) == DDI_SUCCESS)
dip = ddi_add_child(pdip, buf, nid, -1);
}
kmem_free(buf, OBP_MAXPROPNAME);
if (dip == NULL)
return (NULL);
/*
* Don't create any siblings of the branch root, just
* children.
*/
(void) get_neighbors(dip, DDI_WALK_PRUNESIB);
di_dfs(ddi_get_child(dip), get_neighbors, 0);
return (dip);
}
void legion_network::neuron_states(const double t, const differ_state<double> & inputs, const differ_extra<void *> & argv, differ_state<double> & outputs) {
unsigned int index = *(unsigned int *) argv[1];
const double x = inputs[0];
const double y = inputs[1];
const double p = inputs[2];
double potential_influence = heaviside(p + std::exp(-m_params.alpha * t) - m_params.teta);
double stumulus = 0.0;
if ((*m_stimulus)[index] > 0) {
stumulus = m_params.I;
}
double dx = 3.0 * x - std::pow(x, 3) + 2.0 - y + stumulus * potential_influence + m_oscillators[index].m_coupling_term + m_oscillators[index].m_noise;
double dy = m_params.eps * (m_params.gamma * (1.0 + std::tanh(x / m_params.betta)) - y);
std::vector<unsigned int> * neighbors = get_neighbors(index);
double potential = 0.0;
for (std::vector<unsigned int>::const_iterator index_iterator = neighbors->begin(); index_iterator != neighbors->end(); index_iterator++) {
unsigned int index_neighbor = *index_iterator;
potential += m_params.T * heaviside(m_oscillators[index_neighbor].m_excitatory - m_params.teta_x);
}
delete neighbors;
double dp = m_params.lamda * (1 - p) * heaviside(potential - m_params.teta_p) - m_params.mu * p;
outputs.clear();
outputs.push_back(dx);
outputs.push_back(dy);
outputs.push_back(dp);
}
void legion_network::create_dynamic_connections(const legion_stimulus & stimulus) {
for (size_t i = 0; i < size(); i++) {
/* re-initialization by 0.0 */
std::fill(m_dynamic_connections[i].begin(), m_dynamic_connections[i].end(), 0.0);
std::vector<unsigned int> * neighbors = get_neighbors(i);
if (neighbors->size() > 0 && stimulus[i] > 0) {
int number_stimulated_neighbors = 0;
for (std::vector<unsigned int>::iterator index_iterator = neighbors->begin(); index_iterator != neighbors->end(); index_iterator++) {
if (stimulus[*index_iterator] > 0) {
number_stimulated_neighbors++;
}
}
if (number_stimulated_neighbors > 0) {
double dynamic_weight = m_params.Wt / (double) number_stimulated_neighbors;
for (std::vector<unsigned int>::iterator index_iterator = neighbors->begin(); index_iterator != neighbors->end(); index_iterator++) {
m_dynamic_connections[i][*index_iterator] = dynamic_weight;
}
}
}
if (neighbors != NULL) {
delete neighbors;
}
}
}
// The strategy here is, though this is n^2, it's a very small n, just
// finely-spaced adjacent cells.
bool SpatialHash::isWithinDistanceOfAnything(PointType const& physPt,
double distance) const
{
// (save taking a lot of square roots)
double d2 = distance * distance;
Index idx = index_of(physPt);
Cells nbrs;
get_neighbors(idx, nbrs);
// 27 <= we include the center cell itself, too.
assert(nbrs.size() <= 27);
for (Cells::const_iterator itCells = nbrs.begin(), endCells = nbrs.end();
itCells != endCells; ++itCells) {
Pts const* pts = *itCells;
if (pts) {
for (Pts::const_iterator itPts = pts->begin(), endPts = pts->end();
itPts != endPts; ++itPts) {
if (itPts->SquaredEuclideanDistanceTo<double>(physPt) < d2) {
return true;
}
}
}
}
return false;
}
int find_path(AStar_t astar,int x,int y,int x1,int y1,kn_dlist *path){
AStarNode *from = get_node(astar,x,y);
AStarNode *to = get_node(astar,x1,y1);
if(!from || !to || from == to || to->block)
return 0;
minheap_insert(astar->open_list,&from->heap);
AStarNode *current_node = NULL;
while(1){
struct heapele *e = minheap_popmin(astar->open_list);
if(!e){
reset(astar);
return 0;
}
current_node = (AStarNode*)((int8_t*)e-sizeof(kn_dlist_node));
if(current_node == to){
while(current_node)
{
kn_dlist_remove((kn_dlist_node*)current_node);//可能在close_list中，需要删除
if(current_node != from)//当前点无需加入到路径点中
kn_dlist_push_front(path,(kn_dlist_node*)current_node);
AStarNode *t = current_node;
current_node = current_node->parent;
t->parent = NULL;
t->F = t->G = t->H = 0;
t->heap.index = 0;
}
reset(astar);
return 1;
}
//current插入到close表
kn_dlist_push(&astar->close_list,(kn_dlist_node*)current_node);
//获取current的相邻节点
kn_dlist *neighbors = get_neighbors(astar,current_node);
if(neighbors)
{
AStarNode *n;
while((n = (AStarNode*)kn_dlist_pop(neighbors))){
if(n->heap.index)//在openlist中
{
float new_G = current_node->G + cost_2_neighbor(current_node,n);
if(new_G < n->G)
{
//经过当前neighbor路径更佳,更新路径
n->G = new_G;
n->F = n->G + n->H;
n->parent = current_node;
minheap_change(astar->open_list,&n->heap);
}
continue;
}
n->parent = current_node;
n->G = current_node->G + cost_2_neighbor(current_node,n);
n->H = cost_2_goal(n,to);
n->F = n->G + n->H;
minheap_insert(astar->open_list,&n->heap);
}
neighbors = NULL;
}
}
}
int IntAutos::automation_is_constant(int64_t start, int64_t end)
{
Auto *current_auto, *before = 0, *after = 0;
int result;
result = 1; // default to constant
if(!last && !first) return result; // no automation at all
// quickly get autos just outside range
get_neighbors(start, end, &before, &after);
// autos before range
if(before)
current_auto = before; // try first auto
else
current_auto = first;
// test autos in range
for( ; result &&
current_auto &&
current_auto->next &&
current_auto->position < end;
current_auto = current_auto->next)
{
// not constant
if(((IntAuto*)current_auto->next)->value != ((IntAuto*)current_auto)->value)
result = 0;
}
return result;
}
void Triangulation::calculate_boundaries()
{
_VERBOSE("Triangulation::calculate_boundaries");
get_neighbors(); // Ensure _neighbors has been created.
// Create set of all boundary TriEdges, which are those which do not
// have a neighbor triangle.
typedef std::set<TriEdge> BoundaryEdges;
BoundaryEdges boundary_edges;
for (int tri = 0; tri < _ntri; ++tri) {
if (!is_masked(tri)) {
for (int edge = 0; edge < 3; ++edge) {
if (get_neighbor(tri, edge) == -1) {
boundary_edges.insert(TriEdge(tri, edge));
}
}
}
}
// Take any boundary edge and follow the boundary until return to start
// point, removing edges from boundary_edges as they are used. At the same
// time, initialise the _tri_edge_to_boundary_map.
while (!boundary_edges.empty()) {
// Start of new boundary.
BoundaryEdges::iterator it = boundary_edges.begin();
int tri = it->tri;
int edge = it->edge;
_boundaries.push_back(Boundary());
Boundary& boundary = _boundaries.back();
while (true) {
boundary.push_back(TriEdge(tri, edge));
boundary_edges.erase(it);
_tri_edge_to_boundary_map[TriEdge(tri, edge)] =
BoundaryEdge(_boundaries.size()-1, boundary.size()-1);
// Move to next edge of current triangle.
edge = (edge+1) % 3;
// Find start point index of boundary edge.
int point = get_triangle_point(tri, edge);
// Find next TriEdge by traversing neighbors until find one
// without a neighbor.
while (get_neighbor(tri, edge) != -1) {
tri = get_neighbor(tri, edge);
edge = get_edge_in_triangle(tri, point);
}
if (TriEdge(tri,edge) == boundary.front())
break; // Reached beginning of this boundary, so finished it.
else
it = boundary_edges.find(TriEdge(tri, edge));
}
}
}
Pixel soft_dot(const Ink *inky, const SHORT x, const SHORT y)
{
Rgb3 rgb;
Pixel neighbors[9];
average_colors(&rgb, neighbors,
get_neighbors(neighbors,x,y),
vb.pencel->cmap);
return(bclosest_col(&rgb,COLORS,inky->dither));
}
/* Get all unique paths through a color mask with no cycles, starting from an end-point
* (a dot with only one neighbor). This is accomplished by searching for all paths that
* connect two end-points, and then computing all of its sub-paths. This uses a modified
* depth-first search that "fast-forwards" through parts of paths with only one way to go.
*/
void build_paths(
mask_t color_mask, /* The color mask that paths are being found in. */
visited_t visited, /* Matrix of (start,end) pairs representing visited paths. */
int start_index, /* Where to start the path (or where it was started, after recursion). */
int allow_shrinkers, /* Whether to generate paths of length 1, which requires a power-up in-game. */
int *num_moves, /* Number of moves currently stored in moves. */
move_list_t moves, /* Array of moves being generated. */
int current_index, /* Current end position of the path. */
int path_length, /* Current path length. */
path_t path /* Array of indices representing the path. */
) {
int num_neighbors, i;
neighbors_t neighbors;
/* Follow the path until the end or an intersection is reached, removing
* dots as we go to avoid moving backwards.
*/
get_neighbors(color_mask, current_index, &num_neighbors, neighbors);
color_mask = REMOVE_FROM_MASK(color_mask, current_index);
path[path_length++] = current_index;
while (num_neighbors == 1) {
current_index = neighbors[0];
get_neighbors(color_mask, current_index, &num_neighbors, neighbors);
color_mask = REMOVE_FROM_MASK(color_mask, current_index);
path[path_length++] = current_index;
}
/* If there are no neighbors left (i.e. the end of the path has been reached),
* then yield the resulting path if it hasn't been visited yet.
*/
if (num_neighbors == 0) {
if (!visited[start_index][current_index]) {
get_subpaths(num_moves, moves, visited, allow_shrinkers, path_length, path);
}
return;
}
/* Recurse on the remaining branches of the intersection. */
for (i = 0; i < num_neighbors; i++) {
build_paths(color_mask, visited, start_index, allow_shrinkers, num_moves, moves, neighbors[i], path_length, path);
}
}
arma::Mat<double> RobotChasing::calculate_probabilities()
{
calculate_states();
arma::Mat<double> all_probabilities(state_to_ind.size() * m_possible_actions.size(),state_to_ind.size(), arma::fill::zeros);
int it = 0;
for(unsigned int i = 0; i <= m_max_x; i++)
{
for(unsigned int j = 0; j <= m_max_y; j++)
{
for(unsigned int k = 0; k <= m_max_x; k++)
{
for(unsigned int l = 0; l <= m_max_y; l++)
{
for(unsigned int a = 0; a < m_possible_actions.size(); a++)
{
vector<double> s;
s.push_back(i);
s.push_back(j);
s.push_back(k);
s.push_back(l);
if(i == k && j == l)
{
int ind = state_to_ind[s];
all_probabilities(it, ind) += 1.0;
}
else
{
vector<vector<double> > neighbors = get_neighbors(s,a);
for(unsigned int n = 0; n < neighbors.size(); n++)
{
vector<double> neighbor = neighbors[n];
// for(unsigned int z = 0; z < neighbor.size(); z++)
// {
// cout << neighbor[z] << ",";
// }
// cout << endl;
int ind = state_to_ind[neighbor];
all_probabilities(it, ind) += 1.0/neighbors.size();
}
}
it++;
}
}
}
}
}
//all_probabilities.print();
//exit(1);
return all_probabilities;
}
bool is_closure(const std::set<int>& nodes,
const std::map<int,std::set<int>>& edges) {
for (const auto& node : nodes) {
for (const auto& neighbor : get_neighbors(node, edges)) {
if (nodes.count(neighbor) == 0) {
return false;
}
}
}
return true;
}
void print(const std::set<int>& nodes,
const std::map<int,std::set<int>>& edges,
std::ostream& os) {
for (const auto& node : nodes) {
os << "node " << node << " neighbors :";
for (const auto& neighbor : get_neighbors(node, edges)) {
os << " " << neighbor;
}
os << "\n";
}
}
std::map<int,std::set<int>> induce_subgraph(const std::set<int>& nodes,
const std::map<int,std::set<int>>& edges) {
std::map<int,std::set<int>> induced_edges;
for (const auto& node : nodes) {
for (const auto& neighbor : get_neighbors(node, edges)) {
if (nodes.count(neighbor) > 0) {
induced_edges[node].insert(neighbor);
}
}
}
return induced_edges;
}
void pcnn::calculate_states(const pcnn_stimulus & stimulus) {
std::vector<double> feeding(size(), 0.0);
std::vector<double> linking(size(), 0.0);
std::vector<double> outputs(size(), 0.0);
for (unsigned int index = 0; index < size(); index++) {
pcnn_oscillator & current_oscillator = m_oscillators[index];
std::vector<unsigned int> neighbors;
get_neighbors(index, neighbors);
double feeding_influence = 0.0;
double linking_influence = 0.0;
for (std::vector<unsigned int>::const_iterator iter = neighbors.begin(); iter != neighbors.end(); iter++) {
const double output_neighbor = m_oscillators[(*iter)].output;
feeding_influence += output_neighbor * m_params.M;
linking_influence += output_neighbor * m_params.W;
}
feeding_influence *= m_params.VF;
linking_influence *= m_params.VL;
feeding[index] = m_params.AF * current_oscillator.feeding + stimulus[index] + feeding_influence;
linking[index] = m_params.AL * current_oscillator.linking + linking_influence;
/* calculate internal activity */
double internal_activity = feeding[index] * (1.0 + m_params.B * linking[index]);
/* calculate output of the oscillator */
if (internal_activity > current_oscillator.threshold) {
outputs[index] = OUTPUT_ACTIVE_STATE;
}
else {
outputs[index] = OUTPUT_INACTIVE_STATE;
}
}
/* fast linking */
if (m_params.FAST_LINKING) {
fast_linking(feeding, linking, outputs);
}
/* update states of oscillators */
for (unsigned int index = 0; index < size(); index++) {
pcnn_oscillator & oscillator = m_oscillators[index];
oscillator.feeding = feeding[index];
oscillator.linking = linking[index];
oscillator.output = outputs[index];
oscillator.threshold = m_params.AT * oscillator.threshold + m_params.VT * outputs[index];
}
}
double sync_network::phase_kuramoto(const double t, const double teta, const std::vector<void *> & argv) const {
unsigned int index = *(unsigned int *) argv[1];
double phase = 0.0;
std::vector<size_t> neighbors;
get_neighbors(index, neighbors);
for (std::vector<size_t>::const_iterator index_iterator = neighbors.cbegin(); index_iterator != neighbors.cend(); index_iterator++) {
unsigned int index_neighbor = (*index_iterator);
phase += std::sin(m_oscillators[index_neighbor].phase - teta);
}
phase = m_oscillators[index].frequency + (phase * weight / size());
return phase;
}
void LifeGame::update_cell(int cell, std::vector<bool> &from, std::vector<bool> &to) {
int neighbors = get_neighbors(cell%size, cell / size);
if (from[cell]) {
if ((neighbors < 2) || (neighbors > 3))
to[cell] = false;
else
to[cell] = true;
}
else {
if (neighbors == 3)
to[cell] = true;
else
to[cell] = false;
}
}
bool is_topologically_sorted(const std::set<int>& nodes,
const std::map<int,std::set<int>>& edges) {
if (!is_closure(nodes, edges)) {
std::stringstream err;
err << __FILE__ << " " << __LINE__ << " :: " << "graph is not a closure";
throw lbann_exception(err.str());
}
for (const auto& node : nodes) {
const auto& neighbors = get_neighbors(node, edges);
if (neighbors.size() > 0 && *neighbors.begin() <= node) {
return false;
}
}
return true;
}
std::map<int,std::set<int>> transpose(const std::set<int>& nodes,
const std::map<int,std::set<int>>& edges) {
if (!is_closure(nodes, edges)) {
std::stringstream err;
err << __FILE__ << " " << __LINE__ << " :: " << "graph is not a closure";
throw lbann_exception(err.str());
}
std::map<int,std::set<int>> transpose_edges;
for (const auto& node : nodes) {
for (const auto& neighbor : get_neighbors(node, edges)) {
transpose_edges[neighbor].insert(node);
}
}
return transpose_edges;
}
/* Get all possible paths in a color mask without cycles. */
void get_paths(mask_t color_mask, int allow_shrinkers, int *num_moves, move_list_t moves) {
int i, num_neighbors;
neighbors_t neighbors;
path_t path;
visited_t visited = {{0}};
/* Repeatedly call build_paths() from each end-point of the color mask */
for (i = 0; i < NUM_DOTS; i++) {
if (MASK_CONTAINS(color_mask, i)) {
get_neighbors(color_mask, i, &num_neighbors, neighbors);
if (num_neighbors <= 1) {
build_paths(color_mask, visited, i, allow_shrinkers, num_moves, moves, i, 0, path);
}
}
}
}
void register_pe (pos_desc *mypos, int pe_number)
{
mypos->xpos = pe_number % x_size;
mypos->ypos = pe_number / x_size;
mypos->neighbor_count = get_neighbors(mypos->xpos, mypos->ypos);
memset(mypos->neighbors, -1, MAX_NEIGHBORS * sizeof(int));
if (have_north_neighbor(mypos->ypos))
mypos->neighbors[NORTH] = pe_number - x_size;
if (have_south_neighbor(mypos->ypos))
mypos->neighbors[SOUTH] = pe_number + x_size;
if (have_east_neighbor(mypos->xpos))
mypos->neighbors[EAST] = pe_number + 1;
if (have_west_neighbor(mypos->xpos))
mypos->neighbors[WEST] = pe_number - 1;
return;
}
double FloatAutos::get_automation_constant(int64_t start, int64_t end)
{
Auto *current_auto, *before = 0, *after = 0;
// quickly get autos just outside range
get_neighbors(start, end, &before, &after);
// no auto before range so use first
if(before)
current_auto = before;
else
current_auto = first;
// no autos at all so use default value
if(!current_auto) current_auto = default_auto;
return ((FloatAuto*)current_auto)->value;
}
void pcnn::fast_linking(const std::vector<double> & feeding, std::vector<double> & linking, std::vector<double> & output) {
std::vector<double> previous_outputs(output.cbegin(), output.cend());
bool previous_output_change = true;
bool current_output_change = false;
while (previous_output_change) {
for (unsigned int index = 0; index < size(); index++) {
pcnn_oscillator & current_oscillator = m_oscillators[index];
std::vector<unsigned int> neighbors;
get_neighbors(index, neighbors);
double linking_influence = 0.0;
for (std::vector<unsigned int>::const_iterator iter = neighbors.begin(); iter != neighbors.end(); iter++) {
linking_influence += previous_outputs[(*iter)] * m_params.W;
}
linking_influence *= m_params.VL;
linking[index] = linking_influence;
double internal_activity = feeding[index] * (1.0 + m_params.B * linking[index]);
if (internal_activity > current_oscillator.threshold) {
output[index] = OUTPUT_ACTIVE_STATE;
}
else {
output[index] = OUTPUT_INACTIVE_STATE;
}
if (output[index] != previous_outputs[index]) {
current_output_change = true;
}
}
/* check for changes for avoiding useless operation copy */
if (current_output_change) {
std::copy(output.begin(), output.end(), previous_outputs.begin());
}
previous_output_change = current_output_change;
current_output_change = false;
}
}
/* Find a path through a mask. Returns the length of the path stored in `path`. The length is 0 if no path was found. */
int _mask_to_path(mask_t mask, mask_t visited, int index, int edges[NUM_DOTS][NUM_DOTS], int path_length, path_t path) {
int j, num_neighbors, nowhere_to_go;
neighbors_t neighbors;
/* All of the branches of this DFS share the same buffer for path, but that's okay because it
* quits as soon as it finds a path that uses all of the dots.
*/
path[path_length++] = index;
visited = ADD_TO_MASK(visited, index);
nowhere_to_go = 1;
get_neighbors(mask, index, &num_neighbors, neighbors);
for (j = 0; j < num_neighbors; j++) {
int new_index = neighbors[j];
/* Don't follow the same edge twice. */
if (!edges[index][new_index]) {
int final_path_length, new_edges[NUM_DOTS][NUM_DOTS];
nowhere_to_go = 0;
/* Make a copy of the edges and mark the edge about to be traversed. */
memcpy(new_edges, edges, sizeof(new_edges));
new_edges[index][new_index] = 1;
new_edges[new_index][index] = 1;
final_path_length= _mask_to_path(mask, visited, new_index, new_edges, path_length, path);
if (final_path_length > 0) {
return final_path_length;
}
}
}
/* End condition: all dots have been visited and there is nowhere left to go.
* This works even for cycles where it might visit the same dot twice. */
if (mask == visited && nowhere_to_go) {
return path_length;
}
/* No path was found. */
return 0;
}
bool is_cyclic(const std::set<int>& nodes,
const std::map<int,std::set<int>>& edges) {
// Check that graph is valid
if (!is_closure(nodes, edges)) {
std::stringstream err;
err << __FILE__ << " " << __LINE__ << " :: " << "graph is not a closure";
throw lbann_exception(err.str());
}
// Topologically sorted graphs are not cyclic
if (is_topologically_sorted(nodes, edges)) {
return false;
}
// Perform depth-first searches to detect cycles
std::unordered_map<int,bool> is_visited, is_sorted;
std::stack<int> search_stack;
for (auto&& it = nodes.rbegin(); it != nodes.rend(); ++it) {
search_stack.push(*it);
}
while (!search_stack.empty()) {
const auto& node = search_stack.top();
search_stack.pop();
if (!is_sorted[node]) {
if (is_visited[node]) {
is_sorted[node] = true;
} else {
is_visited[node] = true;
search_stack.push(node);
for (const auto& neighbor : get_neighbors(node, edges)) {
if (is_visited[neighbor] && !is_sorted[neighbor]) {
return true;
}
search_stack.push(neighbor);
}
}
}
}
return false;
}
SCM get_living_neighbors (SCM board_smob, SCM cell_smob) {
SCM list; struct cell *cell;
int i;
int count;
scm_assert_smob_type(board_tag, board_smob);
scm_assert_smob_type(cell_tag, cell_smob);
list = get_neighbors(board_smob, cell_smob);
count = 0;
for (i = 0; i < scm_to_int(scm_length(list)); i++) {
cell = (struct cell *) SCM_SMOB_DATA(scm_list_ref(list, scm_from_int(i)));
if (cell->status > 0) {
count++;
}
}
return scm_from_int(count);
}
