Auto Topic: heap
auto_heap | topic
Coverage Score
1
Mentioned Chunks
9
Mentioned Docs
2
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
heap
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: def | CO_OCCURS | Auto Topic: heap | 4 |
| Auto Topic: goal_pos | CO_OCCURS | Auto Topic: heap | 4 |
| Auto Topic: abs | CO_OCCURS | Auto Topic: heap | 4 |
| Auto Topic: cols | CO_OCCURS | Auto Topic: heap | 3 |
| Auto Topic: cur | CO_OCCURS | Auto Topic: heap | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
assignments CIS5210-Assignments/M3/homework3.py | 0.61 | 4 | al_pos start_h = self._manhattan(start_state) heap = [(start_h, 0, 0, start_state, empty_idx, start_h)] best_g = {start_state: 0} parent = {start_state: (None, None)} closed = set() counter = 0 while heap: _, g, _, state, empty, h = heapq.heappop(heap) if state in closed: continu ... |
assignments CIS5210-Assignments/M3/homework3.py | 0.61 | 4 | ... -1, 1.0), (0, 1, 1.0), (-1, -1, rt2), (-1, 1, rt2), (1, -1, rt2), (1, 1, rt2), ) def h(r, c): return math.hypot(gr - r, gc - c) heap = [(h(sr, sc), 0, start_idx)] counter = 0 while heap: _, _, idx = heapq.heappop(heap) if closed[idx]: continue if idx == goal_idx: path = [] cur = ... |
assignments CIS5210-Assignments/M3/homework3.py | 0.61 | 4 | tate[i + 2] == 0 and state[i + 1] != 0: yield i, i + 2 start_h = h(start) heap = [(start_h, 0, 0, start, start_h)] best_g = {start: 0} parent = {start: (None, None)} closed = set() counter = 0 while heap: _, g, _, state, cur_h = heapq.heappop(heap) if state in closed: continue if ... |
assignments CIS5210-Assignments/M3/homework3.py | 0.57 | 2 | ld_d = abs(tr - gr) + abs(tc - gc) new_d = abs(er - gr) + abs(ec - gc) h2 = h - old_d + new_d counter += 1 heapq.heappush( heap, (g2 + h2, g2, counter, nxt_state, nxt_empty, h2), ) return None ############################################################ # Section 2: Grid Navigati ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... uch agents are fragile. Consider the lowly dung beetle. After digging its nest and laying its eggs, it fetches a ball of dung from a nearby heap to plug the entrance. If the ball of dung is removed from its grasp en route, the beetle continues its task and |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | After digging its nest and laying its eggs, it fetches a ball of dung from a nearby heap to plug the entrance. If the ball of dung is removed from its grasp en route, the beetle continues its task and pantomimes plugging the nest with the nonexistent dung ball, never noticing tha ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... s (except for the wumpus, which is too big to fall in). The only redeeming feature of this bleak environment is the possibility of finding a heap of gold. Although the wumpus world is rather tame by modern computer game standards, it illustrates some important points about intelli ... |
assignments CIS5210-Assignments/M3/homework3.py | 0.55 | 1 | ... elf._goal_state idx_to_rc = self._idx_to_rc goal_pos = self._goal_pos start_h = self._manhattan(start_state) heap = [(start_h, 0, 0, start_state, empty_idx, start_h)] best_g = {start_state: 0} parent = {start_state: (None, None)} closed = set() counter = 0 |
assignments CIS5210-Assignments/M3/homework3.py | 0.55 | 1 | ... f i + 2 < length and state[i + 2] == 0 and state[i + 1] != 0: yield i, i + 2 start_h = h(start) heap = [(start_h, 0, 0, start, start_h)] best_g = {start: 0} parent = {start: (None, None)} closed = set() counter = 0 |