def huffman(C):
n = len(C)
Q = PriorityQueue(C, MinHeap)
for i in range(n - 1):
x = Q.extract_min()
y = Q.extract_min()
z = Node(x.key() + y.key())
z.left = x
z.right = y
Q.insert(z)
return Q.extract_min()
def test_node_heaping():
'''
Test priority queues with nodes as elements.
'''
a = Node(label='a', msg="boom!", priority=1)
b = Node(label='b', msg="hi", priority=2)
c = Node(label='c', msg="ok", priority=3)
d = Node(label='d', msg="oh", priority=4)
q = PriorityQueue([b, c, d])
assert q.min == b
assert q.min.msg == 'hi'
assert q.min.label == 'b'
assert q == [b, c, d]
q.insert(a)
assert q.min == a
assert q.min.msg is 'boom!'
assert q.min.label == 'a'
assert q == [a, b, d, c]
assert q.delete('c') == c
assert q.sort() == [a, b, d]
assert q.min == a
assert q.min.label == 'a'
min = q.shift()
assert min == a
assert min.label == 'a'
assert q.sort() == [b, d]
assert q.min == b
assert q.min.label == 'b'
q = PriorityQueue([d, c, b, a])
assert [a, b, c, d] == q.sort()
assert [a, b, c, d] == [q.shift() for x in range(q.size)]
assert q.size == 0
assert q == []
from itertools import permutations
nodes = [a, b, c, d]
for perm in permutations(nodes):
q = PriorityQueue(perm)
assert [a, b, c, d] == q.sort()
assert [a, b, c, d] == [q.shift() for x in range(q.size)]
assert q.size == 0
assert q == []
def test_node_heaping():
'''
Test priority queues with nodes as elements.
'''
a = Node(label='a', msg="boom!", priority=1)
b = Node(label='b', msg="hi", priority=2)
c = Node(label='c', msg="ok", priority=3)
d = Node(label='d', msg="oh", priority=4)
q = PriorityQueue([b, c, d])
assert q.min == b
assert q.min.msg == 'hi'
assert q.min.label == 'b'
assert q == [b, c, d]
q.insert(a)
assert q.min == a
assert q.min.msg is 'boom!'
assert q.min.label == 'a'
assert q == [a, b, d, c]
assert q.delete('c') == c
assert q.sort() == [a, b, d]
assert q.min == a
assert q.min.label == 'a'
min = q.shift()
assert min == a
assert min.label == 'a'
assert q.sort() == [b, d]
assert q.min == b
assert q.min.label == 'b'
q = PriorityQueue([d, c, b, a])
assert [a, b, c, d] == q.sort()
assert [a, b, c, d] == [q.shift() for x in range(q.size)]
assert q.size == 0
assert q == []
from itertools import permutations
nodes = [a, b, c, d]
for perm in permutations(nodes):
q = PriorityQueue(perm)
assert [a, b, c, d] == q.sort()
assert [a, b, c, d] == [q.shift() for x in range(q.size)]
assert q.size == 0
assert q == []
class DijkstrasMazeSolver(object):
"""Gets shortest maze exit path with help of Dijkstras algorithm.
It converts 0/1 matrix maze to edge weighted graph representation
(due to MazeToGraphConverter) and then finds shortest path.
Also it uses Heap data structure to quickly get minimums.
"""
infinity = 10**10
def __init__(self, input_maze=None):
is_text = type(input_maze) == str
if is_text:
converter = TextToMatrixMazeConverter(input_maze)
converter = MazeToGraphConverter(converter.maze,
converter.start_cell)
else:
converter = MazeToGraphConverter(input_maze)
self.vertices = converter.graph_vertices
self.edge_weights = converter.graph_edge_weights
self.adjacency_list = converter.graph_adjacency_list
self.edge_weights = converter.graph_edge_weights
self.start_v = converter.start_cell
self.exit_v = converter.exit_cell
self.dist = dict()
self.prev = dict()
self.queue = PriorityQueue()
self.shortest_path = self.shortest_exit_path()
def shortest_exit_path(self):
self.count_shortest_paths(self.start_v, self.exit_v)
length = self.dist[self.exit_v]
path = self.get_path(self.exit_v)
return path, length
def get_path(self, v, path=()):
path = (v, ) + path
if self.prev[v] and v != self.start_v:
return self.get_path(self.prev[v], path)
else:
return path
def count_shortest_paths(self, source, dest):
self.dist[source] = 0
self.prev[source] = None
for v in self.vertices:
if v != source:
self.dist[v] = self.infinity
self.prev[v] = None
else:
self.queue.insert((v, self.dist[v]))
while self.queue:
u = self.queue.pop_min()[0]
if u == dest:
break
for v in self.adjacency_list[u]:
edge = frozenset((u, v))
alt = self.dist[u] + self.edge_weights[edge]
if alt < self.dist[v]:
self.dist[v] = alt
self.prev[v] = u
if v not in self.queue:
self.queue.insert((v, alt))
class DijkstrasMazeSolver(object):
"""Gets shortest maze exit path with help of Dijkstras algorithm.
It converts 0/1 matrix maze to edge weighted graph representation
(due to MazeToGraphConverter) and then finds shortest path.
Also it uses Heap data structure to quickly get minimums.
"""
infinity = 10 ** 10
def __init__(self, input_maze=None):
is_text = type(input_maze) == str
if is_text:
converter = TextToMatrixMazeConverter(input_maze)
converter = MazeToGraphConverter(converter.maze, converter.start_cell)
else:
converter = MazeToGraphConverter(input_maze)
self.vertices = converter.graph_vertices
self.edge_weights = converter.graph_edge_weights
self.adjacency_list = converter.graph_adjacency_list
self.edge_weights = converter.graph_edge_weights
self.start_v = converter.start_cell
self.exit_v = converter.exit_cell
self.dist = dict()
self.prev = dict()
self.queue = PriorityQueue()
self.shortest_path = self.shortest_exit_path()
def shortest_exit_path(self):
self.count_shortest_paths(self.start_v, self.exit_v)
length = self.dist[self.exit_v]
path = self.get_path(self.exit_v)
return path, length
def get_path(self, v, path=()):
path = (v,) + path
if self.prev[v] and v != self.start_v:
return self.get_path(self.prev[v], path)
else:
return path
def count_shortest_paths(self, source, dest):
self.dist[source] = 0
self.prev[source] = None
for v in self.vertices:
if v != source:
self.dist[v] = self.infinity
self.prev[v] = None
else:
self.queue.insert((v, self.dist[v]))
while self.queue:
u = self.queue.pop_min()[0]
if u == dest:
break
for v in self.adjacency_list[u]:
edge = frozenset((u, v))
alt = self.dist[u] + self.edge_weights[edge]
if alt < self.dist[v]:
self.dist[v] = alt
self.prev[v] = u
if v not in self.queue:
self.queue.insert((v, alt))
