def find_shortest_path(self, start_vetex, end_vertex):
"""
Return a list of vertices creating a path from the start to end vertices.
Keyword arguments:
start_vetex -- the starting vertex.
end_vertex -- the ending vertex.
Time complexity: O(V)
Space complexity: O(V)
"""
if end_vertex.distance == float('inf'):
return []
else:
reverse = Stack()
current_vertex = end_vertex
reverse.push(current_vertex)
while current_vertex != start_vetex:
current_vertex = current_vertex.previous_vertex
reverse.push(current_vertex)
path = []
while not reverse.is_empty():
path.append(reverse.pop())
return path
class SetOfStacks:
LIMIT_PER_STACK = 2
def __init__(self):
self.main_stack = Stack()
def pop(self):
if self.is_empty():
return None
elif self._top_stack().is_empty():
self.main_stack.pop()
self.pop()
return self._top_stack().pop()
def push(self, item):
if self.is_empty():
self.main_stack.push(Stack())
self._top_stack().push(item)
def is_empty(self):
return self.main_stack.is_empty()
def peek(self):
if self.is_empty():
return None
return self._top_stack().peek().value
def _top_stack(self):
return self.main_stack.peek()
def ids(problem):
"""
Implement iterative deepening search.
"""
start = problem.get_start_state()
depth = 1
while True:
s = Stack()
visited = {}
d_map = {}
cur = start
s.push(cur)
visited[start] = None
d_map[start] = 1
while not s.is_empty():
cur = s.pop()
if problem.is_goal_state(cur):
path = []
while cur is not None:
path.append(cur)
cur = visited[cur]
path.reverse()
print len(visited)
return path
if d_map[cur] != depth:
for state in problem.get_successors(cur):
if state not in visited or d_map[cur] + 1 < d_map[state]:
s.push(state)
visited[state] = cur
d_map[state] = d_map[cur] + 1
depth += 1
def dfs(problem):
"""
Implement depth-first search.
"""
start = problem.get_start_state()
s = Stack()
visited = {}
cur = start
s.push(start)
visited[start] = None
while not s.is_empty():
cur = s.pop()
if problem.is_goal_state(cur):
break
for state in problem.get_successors(cur):
if state not in visited:
s.push(state)
visited[state] = cur
path = []
while cur is not None:
path.append(cur)
cur = visited[cur]
path.reverse()
print len(visited)
return path
def my_pow(base, exp, modulus):
from datastructures import Stack
powers = Stack()
var_exp = exp
while var_exp > 0:
powers.push(var_exp % 2)
var_exp = var_exp / 2
rem = 1
while not powers.is_empty():
p = powers.pop()
rem = ((base ** p) * ((rem ** 2) % modulus)) % modulus
return rem
def sort_stack(stack):
temp_stack = Stack()
threshold = stack.pop()
while not stack.is_empty():
if stack.peek() >= threshold:
stack_node = stack.pop()
temp_stack.push(threshold)
threshold = stack_node
elif stack.peek() < threshold:
temp_stack.push(threshold)
threshold = stack.pop()
while not temp_stack.is_empty() and threshold < temp_stack.peek():
stack.push(temp_stack.pop())
temp_stack.push(threshold)
if not stack.is_empty():
threshold = stack.pop()
temp_stack.push(threshold)
while not temp_stack.is_empty():
stack.push(temp_stack.pop())
return stack
stack.push(temp_stack.pop())
temp_stack.push(threshold)
if not stack.is_empty():
threshold = stack.pop()
temp_stack.push(threshold)
while not temp_stack.is_empty():
stack.push(temp_stack.pop())
return stack
if __name__ == '__main__': # tests
stack = Stack()
original_values = [8, 3, 5, 9, 7, 2]
for v in original_values:
stack.push(v)
sorted_stack = Stack()
sorted_values = sorted(original_values, reverse=True)
for v in sorted_values:
sorted_stack.push(v)
expected_sorted_stack = sort_stack(stack)
while not expected_sorted_stack.is_empty() and not sorted_stack.is_empty():
assert expected_sorted_stack.pop() == sorted_stack.pop()