I'm implementing bidirectional A* algorithm in Python 2.7.12 and testing it on the map of Romania from from Russell and Norvig, Chapter 3. The edges have weights and the aim is to find the shortest path between two nodes.
Here is the visualization of the testing graph:
The example where my Bidirectional A* is failing is that where the starting point is 'a' and the goal is 'u'. This is the path that my implementation has found:
The length of ['a', 's', 'f', 'b', 'u'] is 535.
This is the actual shortest path from 'a' to 'u':
The length of ['a', 's', 'r', 'p', 'b', 'u'] is 503.
As we can see, my implementation failed to find the shortest path. I think that the problem may be in my stopping conditions, but I don't know.
This is the python script with my implementation of A* (I used Euclidean distance as a heuristic) and few other help classes and functions:
from __future__ import division
import math
from networkx import *
import random
import pickle
import sys
import heapq
import matplotlib.pyplot as plt
class PriorityQueue():
"""Implementation of a priority queue"""
def __init__(self):
self.queue = []
self.node_finder = dict()
self.current = 0
self.REMOVED_SYMBOL = '<removed>'
def next(self):
if self.current >=len(self.queue):
self.current
raise StopIteration
out = self.queue[self.current]
self.current += 1
return out
def pop(self):
while self.queue:
node = heapq.heappop(self.queue)
nodeId = node[1]
if nodeId is not self.REMOVED_SYMBOL:
try:
del self.node_finder[nodeId]
except KeyError:
dummy=1
return node
def remove(self, nodeId):
node = self.node_finder[nodeId]
node[1] = self.REMOVED_SYMBOL
def __iter__(self):
return self
def __str__(self):
return 'PQ:[%s]'%(', '.join([str(i) for i in self.queue]))
def append(self, node):
nodeId = node[1]
nodePriority = node[0]
node = [nodePriority, nodeId]
self.node_finder[nodeId] = node
heapq.heappush(self.queue, node)
def update(self, node):
nodeId = node[1]
nodePriority = node[0]
node = [nodePriority, nodeId]
self.remove(nodeId)
self.node_finder[nodeId] = node
heapq.heappush(self.queue, node)
def getPriority(self, nodeId):
return self.node_finder[nodeId][0]
def __contains__(self, key):
self.current = 0
return key in [n for v,n in self.queue]
def __eq__(self, other):
return self == other
def size(self):
return len(self.queue)
def clear(self):
self.queue = []
def top(self):
return self.queue[0]
__next__ = next
def bidirectional_a_star(graph, start, goal):
if start == goal:
return []
pq_s = PriorityQueue()
pq_t = PriorityQueue()
closed_s = dict()
closed_t = dict()
g_s = dict()
g_t = dict()
g_s[start] = 0
g_t[goal] = 0
cameFrom1 = dict()
cameFrom2 = dict()
def euclidean_distance(graph, v, goal):
xv, yv = graph.node[v]['pos']
xg, yg = graph.node[goal]['pos']
return ((xv-xg)**2 + (yv-yg)**2)**0.5
def h1(v): # heuristic for forward search (from start to goal)
return euclidean_distance(graph, v, goal)
def h2(v): # heuristic for backward search (from goal to start)
return euclidean_distance(graph, v, start)
cameFrom1[start] = False
cameFrom2[goal] = False
pq_s.append((0+h1(start), start))
pq_t.append((0+h2(goal), goal))
done = False
i = 0
mu = 10**301 # 10**301 plays the role of infinity
connection = None
while pq_s.size() > 0 and pq_t.size() > 0 and done == False:
i = i + 1
if i % 2 == 1: # alternate between forward and backward A*
fu, u = pq_s.pop()
closed_s[u] = True
for v in graph[u]:
weight = graph[u][v]['weight']
if v in g_s:
if g_s[u] + weight < g_s[v]:
g_s[v] = g_s[u] + weight
cameFrom1[v] = u
if v in closed_s:
del closed_s[v]
if v in pq_s:
pq_s.update((g_s[v]+h1(v), v))
else:
pq_s.append((g_s[v]+h1(v), v))
else:
g_s[v] = g_s[u] + weight
cameFrom1[v] = u
pq_s.append((g_s[v]+h1(v), v))
if v in closed_t:
if g_s[u] + weight + g_t[v] < mu:
mu = g_s[u] + weight + g_t[v]
connection = v
done = True
else:
fu, u = pq_t.pop()
closed_t[u] = True
for v in graph[u]:
weight = graph[u][v]['weight']
if v in g_t:
if g_t[u] + weight < g_t[v]:
g_t[v] = g_t[u] + weight
cameFrom2[v] = u
if v in closed_t:
del closed_t[v]
if v in pq_t:
pq_t.update((g_t[v]+h2(v), v))
else:
pq_t.append((g_t[v]+h2(v), v))
else:
g_t[v] = g_t[u] + weight
cameFrom2[v] = u
pq_t.append((g_t[v]+h2(v), v))
if v in closed_s:
if g_t[u] + weight + g_s[v] < mu:
mu = g_t[u] + weight + g_s[v]
connection = v
done = True
if u in closed_s and u in closed_t:
if g_s[u] + g_t[u] < mu:
mu = g_s[u] + g_t[u]
connection = u
stopping_distance = min(min([f for (f,x) in pq_s]), min([f for (f,x) in pq_t]))
if mu <= stopping_distance:
done = True
#connection = u
continue
if connection is None:
# start and goal are not connected
return None
#print cameFrom1
#print cameFrom2
path = []
current = connection
#print current
while current != False:
#print predecessor
path = [current] + path
current = cameFrom1[current]
current = connection
successor = cameFrom2[current]
while successor != False:
path = path + [successor]
current = successor
successor = cameFrom2[current]
return path
# This function visualizes paths
def draw_graph(graph, node_positions={}, start=None, goal=None, path=[]):
explored = list(graph.get_explored_nodes())
labels ={}
for node in graph:
labels[node]=node
if not node_positions:
node_positions = networkx.spring_layout(graph)
edge_labels = networkx.get_edge_attributes(graph,'weight')
networkx.draw_networkx_nodes(graph, node_positions)
networkx.draw_networkx_edges(graph, node_positions, style='dashed')
networkx.draw_networkx_edge_labels(graph, node_positions, edge_labels=edge_labels)
networkx.draw_networkx_labels(graph,node_positions, labels)
networkx.draw_networkx_nodes(graph, node_positions, nodelist=explored, node_color='g')
if path:
edges = [(path[i], path[i+1]) for i in range(0, len(path)-1)]
networkx.draw_networkx_edges(graph, node_positions, edgelist=edges, edge_color='b')
if start:
networkx.draw_networkx_nodes(graph, node_positions, nodelist=[start], node_color='b')
if goal:
networkx.draw_networkx_nodes(graph, node_positions, nodelist=[goal], node_color='y')
plt.plot()
plt.show()
# this function calculates the length of the path
def calculate_length(graph, path):
pairs = zip(path, path[1:])
return sum([graph.get_edge_data(a, b)['weight'] for a, b in pairs])
#Romania map data from Russell and Norvig, Chapter 3.
romania = pickle.load(open('romania_graph.pickle', 'rb'))
node_positions = {n: romania.node[n]['pos'] for n in romania.node.keys()}
start = 'a'
goal = 'u'
path = bidirectional_a_star(romania, start, goal)
print "This is the path found by bidirectional A* :", path
print "Its length :", calculate_length(romania, path)
# visualize my path
draw_graph(romania, node_positions=node_positions, start=start, goal=goal, path=path)
# compare to the true shortest path between start and goal
true_path = networkx.shortest_path(romania, start, goal, weight='weight')
print "This is the actual shortest path: ", true_path
print "Its lenght: ", calculate_length(romania, true_path)
#visualize true_path
draw_graph(romania, node_positions=node_positions, start=start, goal=goal, path=true_path)
Pickle data for Romania can be downloaded from here.
I corrected some errors in PriorityQueue and bidirectional_a_star. It's working fine now.
The corrected code for the class and the function is as follows:
class PriorityQueue():
"""Implementation of a priority queue"""
def __init__(self):
self.queue = []
self.node_finder = dict()
self.current = 0
self.REMOVED_SYMBOL = '<removed>'
def next(self):
if self.current >=len(self.queue):
self.current
raise StopIteration
out = self.queue[self.current]
while out == self.REMOVED_SYMBOL:
self.current += 1
out = self.queue[self.current]
self.current += 1
return out
def pop(self):
# TODO: finish this
while self.queue:
node = heapq.heappop(self.queue)
nodeId = node[1]
if nodeId is not self.REMOVED_SYMBOL:
try:
del self.node_finder[nodeId]
except KeyError:
dummy=1
return node
#raise KeyError('pop from an empty priority queue')
def remove(self, nodeId):
node = self.node_finder[nodeId]
node[1] = self.REMOVED_SYMBOL
def __iter__(self):
return self
def __str__(self):
return 'PQ:[%s]'%(', '.join([str(i) for i in self.queue]))
def append(self, node):
# node = (priority, nodeId)
nodeId = node[1]
nodePriority = node[0]
node = [nodePriority, nodeId]
self.node_finder[nodeId] = node
heapq.heappush(self.queue, node)
def update(self, node):
nodeId = node[1]
nodePriority = node[0]
node = [nodePriority, nodeId]
self.remove(nodeId)
self.node_finder[nodeId] = node
heapq.heappush(self.queue, node)
def getPriority(self, nodeId):
return self.node_finder[nodeId][0]
def __contains__(self, key):
self.current = 0
return key in [n for v,n in self.queue]
def __eq__(self, other):
return self == other
def size(self):
return len([1 for priority, node in self.queue if node!=self.REMOVED_SYMBOL])
def clear(self):
self.queue = []
def top(self):
return self.queue[0]
__next__ = next
def bidirectional_a_star(graph, start, goal):
if start == goal:
return []
pq_s = PriorityQueue()
pq_t = PriorityQueue()
closed_s = dict()
closed_t = dict()
g_s = dict()
g_t = dict()
g_s[start] = 0
g_t[goal] = 0
cameFrom1 = dict()
cameFrom2 = dict()
def euclidean_distance(graph, v, goal):
xv, yv = graph.node[v]['pos']
xg, yg = graph.node[goal]['pos']
return ((xv-xg)**2 + (yv-yg)**2)**0.5
def h1(v): # heuristic for forward search (from start to goal)
return euclidean_distance(graph, v, goal)
def h2(v): # heuristic for backward search (from goal to start)
return euclidean_distance(graph, v, start)
cameFrom1[start] = False
cameFrom2[goal] = False
pq_s.append((0+h1(start), start))
pq_t.append((0+h2(goal), goal))
done = False
i = 0
mu = 10**301 # 10**301 plays the role of infinity
connection = None
while pq_s.size() > 0 and pq_t.size() > 0 and done == False:
i = i + 1
if i % 2 == 1: # alternate between forward and backward A*
fu, u = pq_s.pop()
closed_s[u] = True
for v in graph[u]:
weight = graph[u][v]['weight']
if v in g_s:
if g_s[u] + weight < g_s[v]:
g_s[v] = g_s[u] + weight
cameFrom1[v] = u
if v in closed_s:
del closed_s[v]
if v in pq_s:
pq_s.update((g_s[v]+h1(v), v))
else:
pq_s.append((g_s[v]+h1(v), v))
else:
g_s[v] = g_s[u] + weight
cameFrom1[v] = u
pq_s.append((g_s[v]+h1(v), v))
else:
fu, u = pq_t.pop()
closed_t[u] = True
for v in graph[u]:
weight = graph[u][v]['weight']
if v in g_t:
if g_t[u] + weight < g_t[v]:
g_t[v] = g_t[u] + weight
cameFrom2[v] = u
if v in closed_t:
del closed_t[v]
if v in pq_t:
pq_t.update((g_t[v]+h2(v), v))
else:
pq_t.append((g_t[v]+h2(v), v))
else:
g_t[v] = g_t[u] + weight
cameFrom2[v] = u
pq_t.append((g_t[v]+h2(v), v))
if u in closed_s and u in closed_t:
if g_s[u] + g_t[u] < mu:
mu = g_s[u] + g_t[u]
connection = u
try:
stopping_distance = max(min([f for (f,x) in pq_s]), min([f for (f,x) in pq_t]))
except ValueError:
continue
if mu <= stopping_distance:
done = True
connection = u
continue
if connection is None:
# start and goal are not connected
return None
#print cameFrom1
#print cameFrom2
path = []
current = connection
#print current
while current != False:
#print predecessor
path = [current] + path
current = cameFrom1[current]
current = connection
successor = cameFrom2[current]
while successor != False:
path = path + [successor]
current = successor
successor = cameFrom2[current]
return path
Related
I am writing a function that recursively finds the minimum and maximum values in a binary tree and returns a tuple (min, max). My code is returning the correct min and max for my test case but separately. Any advice on how to get it to return a tuple is appreciated! (As a note, I am not allowed to use LinkedBinaryTree functions that iterate over the tree for me but I attached the class I am currently using under my code)
My code
from LinkedBinaryTree import LinkedBinaryTree
def min_and_max(bin_tree):
if bin_tree is None:
raise Exception("Tree is empty")
def subtree_min_and_max(root):
minval = root.data
maxval = root.data
if (root.left is None and root.right is None):
temp = (minval, maxval)
return temp
elif (root.left is None):
ltemp = subtree_min_and_max(root.right)
if (ltemp[0] < minval):
minval= ltemp[0]
if (ltemp[1] > maxval):
maxval = ltemp[1]
temp = (minval, maxval)
return temp
elif (root.right is None):
subtree_min_and_max(root.left)
rtemp = subtree_min_and_max(root.right)
if (rtemp[0] < minval):
minval = rtemp[0]
if (rtemp[1] > maxval):
maxval = rtemp[1]
temp = (minval, maxval)
return temp
else:
ltemp = subtree_min_and_max(root.left)
rtemp = subtree_min_and_max(root.right)
print((min(root.data, ltemp[0], rtemp[0])))
print(max(root.data, ltemp[1], rtemp[1]))
temp = (min(root.data, ltemp[0], rtemp[0]), max(root.data, ltemp[1], rtemp[1]))
return temp
return subtree_min_and_max(bin_tree.root)
LinkedBinaryTree class
from ArrayQueue import ArrayQueue
class LinkedBinaryTree:
class Node:
def __init__(self, data, left=None, right=None):
self.data = data
self.parent = None
self.left = left
if (self.left is not None):
self.left.parent = self
self.right = right
if (self.right is not None):
self.right.parent = self
def __init__(self, root=None):
self.root = root
self.size = self.count_nodes()
def __len__(self):
return self.size
def is_empty(self):
return len(self) == 0
def count_nodes(self):
def subtree_count(root):
if (root is None):
return 0
else:
left_count = subtree_count(root.left)
right_count = subtree_count(root.right)
return 1 + left_count + right_count
return subtree_count(self.root)
def sum(self):
def subtree_sum(root):
if (root is None):
return 0
else:
left_sum = subtree_sum(root.left)
right_sum = subtree_sum(root.right)
return root.data + left_sum + right_sum
return subtree_sum(self.root)
def height(self):
def subtree_height(root):
if (root.left is None and root.right is None):
return 0
elif (root.left is None):
return 1 + subtree_height(root.right)
elif (root.right is None):
return 1 + subtree_height(root.left)
else:
left_height = subtree_height(root.left)
right_height = subtree_height(root.right)
return 1 + max(left_height, right_height)
if(self.is_empty()):
raise Exception("Tree is empty")
return subtree_height(self.root)
def preorder(self):
def subtree_preorder(root):
if (root is None):
pass
else:
yield root
yield from subtree_preorder(root.left)
yield from subtree_preorder(root.right)
yield from subtree_preorder(self.root)
def postorder(self):
def subtree_postorder(root):
if (root is None):
pass
else:
yield from subtree_postorder(root.left)
yield from subtree_postorder(root.right)
yield root
yield from subtree_postorder(self.root)
def inorder(self):
def subtree_inorder(root):
if (root is None):
pass
else:
yield from subtree_inorder(root.left)
yield root
yield from subtree_inorder(root.right)
yield from subtree_inorder(self.root)
def breadth_first(self):
if (self.is_empty()):
return
line = ArrayQueue()
line.enqueue(self.root)
while (line.is_empty() == False):
curr_node = line.dequeue()
yield curr_node
if (curr_node.left is not None):
line.enqueue(curr_node.left)
if (curr_node.right is not None):
line.enqueue(curr_node.right)
def __iter__(self):
for node in self.breadth_first():
yield node.data
My tester code
root = LinkedBinaryTree.Node(3)
T = LinkedBinaryTree(root)
a = LinkedBinaryTree.Node(2)
a.parent = root
root.left = a
b = LinkedBinaryTree.Node(7)
b.parent = root
root.right = b
c = LinkedBinaryTree.Node(9)
c.parent = a
a.left = c
d = LinkedBinaryTree.Node(5)
d.parent = c
c.left = d
e = LinkedBinaryTree.Node(1)
e.parent = c
c.right = e
f = LinkedBinaryTree.Node(8)
f.parent = b
b.left = f
g = LinkedBinaryTree.Node(4)
g.parent = b
b.right = g
print(min_and_max(T))
The tester code makes a tree that looks like
In subtree_min_and_max, when the case root.right is None, your code do:
subtree_min_and_max(root.left)
rtemp = subtree_min_and_max(root.right)
Which should be a single
rtemp = subtree_min_and_max(root.left)
as at this time, the sub-tree has empty right children, and the procedure should search for min and max in the left children.
Here is my code for Dijkstra's Algorithm.
I have declared a "Vertex" class and a "Graph" class.
I am using heapq module and heapifying the list "unvisitedQueue" of tuples. But even then an error shows up saying " TypeError: '<' not supported between instances of 'Vertex' and 'Vertex' " even when "v.getDistance()" returns either 0 or float('inf').
import heapq
class Vertex:
def __init__(self, node):
self.id = node
self.adjacent = {}
self.previous = None
self.distance = float('inf')
def addNeighbor(self, neighbor, weight = 0):
self.adjacent[neighbor] = weight
def getConnections(self):
return self.adjacent.keys()
def getVertex_ID(self):
return self.id
def getWeight(self, neighbor):
return self.adjacent[neighbor]
def setDistance(self, dist):
self.distance = dist
def getDistance(self):
return self.distance
def setPrevious(self, prev):
self.previous = prev
def __str__(self):
return str(self.id) + "adjacent : " + str([x.id for x in self.adjacent])
class Graph:
def __init__(self):
self.vertDictionary = {}
self.numVertices = 0
def __iter__(self):
return iter(self.vertDictionary.values())
def addVertex(self, node):
self.numVertices += 1
newVertex = Vertex(node)
self.vertDictionary[node] = newVertex
return newVertex
def getVertex(self, node):
if node in self.vertDictionary:
return self.vertDictionary[node]
else:
return None
def addEdge(self, frm, to, cost = 0):
if frm not in self.vertDictionary:
self.addVertex(frm)
if to not in self.vertDictionary:
self.addVertex(to)
self.vertDictionary[frm].addNeighbor(self.vertDictionary[to], cost)
self.vertDictionary[to].addNeighbor(self.vertDictionary[frm], cost)
def getVertices(self):
return self.vertDictionary.keys()
def setPrevious(self, current):
self.previous = current
def getPrevious(self):
return self.previous
def getEdges(self):
edges = []
for v in G:
for w in v.getConnections():
v_id = v.getVertex_ID()
w_id = w.getVertex_ID()
edges.append((v_id, w_id, v.getWeight(w)))
return edges
def Dijkstra(G, s):
source = G.getVertex(s)
source.setDistance(0)
visited = {}
unvisitedQueue = [(v.getDistance(), v) for v in G]
heapq.heapify(unvisitedQueue)
while len(unvisitedQueue):
uv = heapq.heappop(unvisitedQueue)
currVert = uv[1]
visited[currVert] = True
for nbr in currVert.getConnections():
if currVert.getDistance() + currVert.getWeight(nbr) < nbr.getDistance():
nbr.setDistance(currVert.getDistance() + currVert.getWeight(nbr))
print("Updated: Current = %s Neighbour = %s New Distance = %s" %(currVert.getVertex_ID(), nbr.getVertex_ID(), nbr.getDistance()))
else:
print("Not Updated: Current = %s Neighbour = %s Distance = %s" %(currVert.getVertex_ID(), nbr.getVertex_ID(), nbr.getDistance()))
while len(unvisitedQueue):
heapq.heappop(unvisitedQueue)
unvisitedQueue = [(v.getDistance(), v) for v in G if v not in visited]
heapq.heapify(unvisitedQueue)
for v in G:
print(source.getVertex_ID(), "to", v.getVertex_ID(), "-->", v.getDistance())
ERROR -->
Traceback (most recent call last):
File "d:\Python\Final 450\Graph\Dijkstra's_Algo.py", line 124, in <module>
print(Dijkstra(G, "a"))
File "d:\Python\Final 450\Graph\Dijkstra's_Algo.py", line 86, in Dijkstra
heapq.heapify(unvisitedQueue)
TypeError: '<' not supported between instances of 'Vertex' and 'Vertex'
The error is happening because tuples are compared lexicographically. If two distances are the same, the comparison moves on to the Vertex objects themselves.
Two solutions readily come to mind. The first is simply to add a unique index to the tuple before the Vertex but after the distance. This is easy, and would work even if you didn't have access to the Vertex class:
unvisitedQueue = [(v.getDistance(), i, v) for i, v in enumerate(G) if v not in visited]
The second option is to modify Vertex to have a __lt__ magic method:
def __lt__(self, other):
return self.getDistance() < other.getDistance()
This is nice because you can heapify more directly now:
unvisitedQueue = [v for v in G if v not in visited]
This is my python code to make an Ordered Binary Decision Diagram (not very relevant for the context). So I just have a tree of a particular height, and I need to set some of the leaf nodes to one. So I have a variable path which involves an array of "decisions", to go left or right from that particular node. But my code is by mistake modifying multiple roots. I am fairly new to Python and I used to rely on pointers when I used C.
def makeCubes(arr):
ans = []
for ar in arr:
ar2 = [ar[i:i + 2] for i in range(0, len(ar), 2)]
#splitting into segments of 2 each
if not '00' in ar2:
ans += [ar2]
return ans
class Node:
def __init__(self,key):
self.key = key
self.left = None
self.right = None
def addLeft(self,node):
self.left = node
def addRight(self,node):
self.right = node
def makeTree(size):
if(size == 1):
leaf = Node('x0')
leaf.addLeft(Node('zero'))
leaf.addRight(Node('zero'))
return leaf
else:
node = Node('x'+str(size-1))
childNode = makeTree(size-1)
node.addLeft(childNode)
node.addRight(childNode)
return node
def inOrder(root):
if(root != None):
return inOrder(root.left) + [root.key] + inOrder(root.right)
return []
def makeOBDD(array):
maxLen = max([len(word) for word in array])
tree = makeTree(maxLen)
for cube in array:
tree = makeOne(tree,cube)
return tree
def makeOne(root,cube):
print("cube",cube)
if(cube == []):
print("updated")
root.key = 'one'
else:
element = cube[0]
if(element == '01'):
root.addLeft(makeOne(root.left,cube[1:]))
elif(element == '10'):
root.addRight(makeOne(root.right,cube[1:]))
return root
# ab + a'b'
'''
Expected output
x1
/ \
x0 x0
/ \ / \
1 0 0 1
'''
cubeSet = ['1010','0101']
cubes = makeCubes(cubeSet)
print(cubes)
obdd = makeOBDD(cubes)
print(inOrder(obdd))
I am learning python as one my project requirements is to print a binary tree.
I am trying out my code to print the tree path.
It just stops at root node.
I am trying to find where I messed up.
Tree:
A
/ \
B C
/ / \
D E F
The output I want is:
ABD
ACE
ACF
class Node(object):
def __init__(self, data):
self.data = data
self.children = []
self.val = data
self.left = None
self.right = None
r = Node('A')
r.left = Node('B')
r.right = Node('C')
r.left.left = Node('D')
r.right.right = Node('E')
The above should be r.right.left = Node('E)
r.right.right = Node('F')
def binaryTreePaths(root):
results = []
c = []
binary_tree_paths(root, c, results)
return results
def binary_tree_paths(root, cand, res):
if root is None:
return
else:
cand.append(str(root.val)+" ")
if root.left is None and root.right is None:
p = ''.join(map(str, cand))
res.append(p)
binary_tree_paths(root.left, cand, res)
binary_tree_paths(root.right, cand, res)
cand.pop()
print binaryTreePaths(r)
You should have r.right.left = Node('E'), instead of r.right.right = Node('E')
class QNode:
def __init__(self, num):
self.prev = None
self.next = None
self.pageNumber = num
class Queue:
def __init__(self, num):
self.count = 0
self.front = None
self.rear = None
self.numberOfFrames = num
class Hash:
def __init__(self, num):
self.capacity = num
self.array = dict()
class LRU:
#staticmethod
def are_all_frames_full(q):
"returns a boolean value"
return q.count == q.numberOfFrames
#staticmethod
def is_queue_empty(q):
return q.rear == None
def de_queue(self, q):
if self.is_queue_empty(q):
return
if q.front == q.rear:
q.front = None
temp = q.rear
q.rear = q.rear.prev
if q.rear != None:
q.rear.next = None
q.count -= 1
def enqueue(self, q, h, pnum):
if self.are_all_frames_full(q):
h.array[q.rear.pageNumber] = None
self.de_queue(q)
temp = QNode(pnum)
temp.next = q.front
if self.is_queue_empty(q):
q.rear = q.front = temp
else:
q.front.prev = temp
q.front = temp
h.array[pnum] = temp
q.count += 1
def referencePage(self, q, h, pnum):
reqPage = h.array.get(pnum, None)
if reqPage == None:
self.enqueue(q, h, pnum)
elif reqPage != q.front:
reqPage.prev.next = reqPage.next
if reqPage.next != None:
reqPage.next.prev = reqPage.prev
if reqPage == q.rear:
q.rear = q.front
q.rear.next = None
reqPage.next = q.front
reqPage.prev = None
reqPage.next.prev = reqPage
q.front = reqPage
#staticmethod
def print_queue(q):
temp = q.front
print 'In the print queue method'
while(temp != q.rear):
print temp.pageNumber
temp = temp.next
q = Queue(4)
h = Hash(10)
l = LRU()
l.referencePage(q, h, 1)
l.referencePage(q, h, 2)
l.referencePage(q, h, 3)
l.referencePage(q, h, 1)
l.referencePage(q, h, 4)
l.referencePage(q, h, 5)
LRU.print_queue(q)
I am trying to implement LRU algorithm in Python. I am using Doubly linked list to maintain a queue which maintain the frames in the least recently used order.
The function referencePage implements the algorithm. After executing the algorithm, when I am trying to print the queue, the print_queue function is not printing anything. Dont know where it is going wrong.