def dijkstra2(self, start):
unvisited = Priority_Queue()
unvisited.enqueue((0, start))
visited = set()
distance = dict()
infinity = 10000
prev = dict()
for label in self.vertexList:
distance[label] = infinity
prev[label] = None
distance[start] = 0
prev[start] = start
while unvisited:
initial_cost, label = unvisited.dequeue()
visited.add(label)
for neighbor, weight in self.vertexList[label].neighbors:
if neighbor not in visited:
cost = initial_cost + weight
if cost < distance[neighbor]:
distance[neighbor] = cost
prev[neighbor] = label
if neighbor not in unvisited:
unvisited.enqueue((cost, neighbor))
else:
unvisited[neighbor] = cost
return distance, prev
def dijkstra(self, start, end):
distance = dict()
infinity = 10000
for label in self.vertexList:
distance[label] = infinity
distance[start] = 0
visited = set()
minQueue = Priority_Queue()
minQueue.enqueue((0, (start, start)))
while minQueue:
w, edge = minQueue.dequeue()
if distance[edge[1]] > distance[edge[0]] + w:
distance[edge[1]] = distance[edge[0]] + w
if edge[1] not in visited:
for label, weight in self.vertexList[edge[1]].neighbors:
#print(label, weight, edge[1])
minQueue.enqueue((weight, (edge[1], label)))
visited.add(edge[1])
# if distance[end] < infinity:
# shorterpath = False
# for weight, item in minQueue:
# if item[0] < 12:
# print(item[0], distance[item[0]])
# if distance[item[0]] < distance[end]:
# shorterpath = True
# break
# if not shorterpath:
# return distance[end]
if distance[end] < infinity:
return distance[end]
else:
return False
print(f'Oi kaliteres klasikes taksinomimenes me vasi tin mesi vathmologia tous: {omades.retrieveBestClassics()}')
streaming1.setMeanRating(7.3)
streaming2.setMeanRating(8.7)
streaming3.setMeanRating(6.7)
streaming4.setMeanRating(9.2)
streaming5.setMeanRating(7.8)
mainstream1 = Mainstream_Movie('Panic Room','David Fincher','Thriller',['Jodie Foster','Forest Whitaker'],2002)
mainstream2 = Mainstream_Movie('Zodiac','David Fincher','Thriller',['Jake Gyllenhaal','Mark Ruffalo'],2007)
mainstream3 = Mainstream_Movie('The Hobbit: An unexpected Journey','Peter Jackson','Adventure',['Martin Freeman','Ian Mckellen'],2012)
mainstream1.setMeanRating(8.5)
mainstream2.setMeanRating(8.8)
mainstream3.setMeanRating(7.8)
omades.addMainstream(mainstream1)
omades.addMainstream(mainstream2)
omades.addMainstream(mainstream3)
print(f'Oi kalitera on demand movies taksinomimenes : {omades.retrieveBestMovies()}')
# i stili group periexei to cluster sto opoio anikei o kathe xristis-allazei me diaforetikes ekteleseis tou algorithmou
omades.Clustering()
omades.retrieveRecommendedMovies(8,667) # cluster 8 gia ton user 667
omades.retrieveRecommendedClassics(1,4) # cluster 4 gia user 1
omades.retrieveRecommendedGenres(1) # ta genres pou protima o user 1
priority_queue = Priority_Queue()
priority_queue.insertToPriorityQueue(client1)
priority_queue.insertToPriorityQueue(client2)
print(f'Pelates se oura anamonis: {priority_queue.getOnHold()}')
# estw thelei kratisi gia tainia stis 22:30
print(f'Xronos pou apomenei mexri tin dinatotita kratisis: {priority_queue.calculateReservationTime(22,30,00)}')
def __init__(self, directed=False):
self.vertexList = dict()
self.edgeList = Priority_Queue()
self.directed = directed
class Graph:
def __init__(self, directed=False):
self.vertexList = dict()
self.edgeList = Priority_Queue()
self.directed = directed
class Node:
def __init__(self, value):
self.value = value
class Vertex(Node):
def __init__(self, value):
super().__init__(value)
self.neighbors = set()
def addneighbor(self, vertex, weight):
self.neighbors.add((vertex, weight))
def removeneighbor(self, label):
for neighbor in self.neighbors:
if neighbor[0] == label:
self.neighbors.remove(neighbor)
class Edge:
def __init__(self, vertex1, vertex2, weight=0):
self.vertex1 = vertex1
self.vertex2 = vertex2
self.weight = weight
def __lt__(self, other):
return self.weight < other.weight
def __iter__(self):
yield self.vertex1
yield self.vertex2
yield self.weight
def addVertex(self, label, value=None):
self.vertexList[label] = self.Vertex(value)
def removeVertex(self, label):
self.vertexList.pop(label)
for vertex in self.vertexList.keys():
if label in vertex.neighbors:
vertex.neighbors.remove(label)
def addEdge(self, edge):
(v1, v2, w) = edge
self.edgeList.enqueue((w, self.Edge(v1, v2, w)))
if v1 not in self.vertexList:
self.addVertex(v1)
if v2 not in self.vertexList:
self.addVertex(v2)
self.vertexList[v1].addneighbor(v2, w)
if self.directed is False:
self.vertexList[v2].addneighbor(v1, w)
def removeedge(self, label1, label2):
for item in self.edgeList:
w, edge = item
if edge[0] == label1 and edge[0] == label2:
self.edgeList.dequeue(item)
if self.directed is False:
self.removeedge(label2, label1)
def __repr__(self):
return "Vertices" + repr(__dict__[self.vertexList]) + '\n' \
+ "Edge"
def dijkstra2(self, start):
unvisited = Priority_Queue()
unvisited.enqueue((0, start))
visited = set()
distance = dict()
infinity = 10000
prev = dict()
for label in self.vertexList:
distance[label] = infinity
prev[label] = None
distance[start] = 0
prev[start] = start
while unvisited:
initial_cost, label = unvisited.dequeue()
visited.add(label)
for neighbor, weight in self.vertexList[label].neighbors:
if neighbor not in visited:
cost = initial_cost + weight
if cost < distance[neighbor]:
distance[neighbor] = cost
prev[neighbor] = label
if neighbor not in unvisited:
unvisited.enqueue((cost, neighbor))
else:
unvisited[neighbor] = cost
return distance, prev
def dijkstra(self, start, end):
distance = dict()
infinity = 10000
for label in self.vertexList:
distance[label] = infinity
distance[start] = 0
visited = set()
minQueue = Priority_Queue()
minQueue.enqueue((0, (start, start)))
while minQueue:
w, edge = minQueue.dequeue()
if distance[edge[1]] > distance[edge[0]] + w:
distance[edge[1]] = distance[edge[0]] + w
if edge[1] not in visited:
for label, weight in self.vertexList[edge[1]].neighbors:
#print(label, weight, edge[1])
minQueue.enqueue((weight, (edge[1], label)))
visited.add(edge[1])
# if distance[end] < infinity:
# shorterpath = False
# for weight, item in minQueue:
# if item[0] < 12:
# print(item[0], distance[item[0]])
# if distance[item[0]] < distance[end]:
# shorterpath = True
# break
# if not shorterpath:
# return distance[end]
if distance[end] < infinity:
return distance[end]
else:
return False
def bfs(self, start, end):
SENTINEL = object()
nodes_to_visit = [self.vertexList[start], SENTINEL]
cnt = 0
while nodes_to_visit:
node = nodes_to_visit.pop(0)
if node is SENTINEL:
cnt += 1
nodes_to_visit.append(SENTINEL)
elif node == self.vertexList[end]:
return cnt
else:
for vertex, _ in node.neighbors:
nodes_to_visit.append(self.vertexList[vertex])
return False
def bfs1(self, start, end):
SENTINEL = object()
nodes_to_visit = [start, SENTINEL]
cnt = 0
while nodes_to_visit:
node = nodes_to_visit.pop(0)
if node is SENTINEL:
cnt += 1
nodes_to_visit.append(SENTINEL)
elif node == end:
return cnt
else:
for vertex, _ in node.neighbors:
nodes_to_visit.append(vertex)
return False
def minSpanTree(self):
copyQueue = self.edgeList
copyQueue = [edge for edge in self.edgeList]
copyQueue.sort()
#w, edge = copyQueue.dequeue()
#print(edge.vertex1, edge.vertex2, edge.weight)
#return
self.partition = dict()
self.vertexTree = dict()
for vertex in self.vertexList:
self.partition[vertex] = vertex
self.vertexTree[vertex] = []
while copyQueue:
w, currentEdge = copyQueue.pop(0)
print(w)
v1, v2, _ = currentEdge
#print(v1,v2)
if self.sameSetAndUnion(v1, v2):
self.vertexTree[v1].append(v2)
#yield self.vertexTree
return self.vertexTree
def sameSetAndUnion(self, v1, v2):
#print('fn call', v1, v2)
if self.partition[self.root(v1)] != self.partition[self.root(v2)]:
#print(self.partition[v1], self.partition[v2])
self.partition[self.root(v2)] = self.partition[self.root(v1)]
return True
def root(self, vertex):
while self.partition[vertex] != vertex:
vertex = self.partition[vertex]
return vertex
