def huffman(str):
freqdict = frequency(str)
pq = PriorityQueue()
for char in freqdict: # NOT for char in STRING!!
t = BinaryTree(char)
key = freqdict[char]
pq.add((key, t))
while pq.getSize() > 1:
u = pq.delMin()
v = pq.delMin()
t = BinaryTree(u[0] + v[0])
t.insertLeft(u[1]) # inserts left subtree T1
t.insertRight(v[1]) # inserts right subtree T2
pq.add((u[0] + v[0],
t)) # inserts T into PQ with key f1 + f2 (from u and v tuples)
result = pq.delMin() # tuple of (freq, T)
return result[1] # returns the final tree, T
def dijskstra(source):
global dist
global adjList
pq = PriorityQueue()
dist[source] = 0
pq.insertNode((source, 0))
while pq.getSize() > 1:
currPair = pq.getMin()
currNodeIndex = currPair[0]
currWeight = currPair[1]
if( currWeight > dist[currNodeIndex]):
continue
for neighbor in adjList[currNodeIndex]:
nextNodeIndex = neighbor[0]
nextWeight = neighbor[1]
if dist[currNodeIndex] + nextWeight < dist[nextNodeIndex]:
dist[nextNodeIndex] = dist[currNodeIndex] + nextWeight
pq.insertNode(neighbor)
from PriorityQueue import PriorityQueue
patient1 = [2, "John"]
patient2 = [1, "Jim"]
patient3 = [5, "Tim"]
patient4 = [7, "Cindy"]
priorityQueue = PriorityQueue()
priorityQueue.enqueue(patient1)
priorityQueue.enqueue(patient2)
priorityQueue.enqueue(patient3)
priorityQueue.enqueue(patient4)
while priorityQueue.getSize() > 0:
print(str(priorityQueue.dequeue()), end = " ")
from PriorityQueue import PriorityQueue
patient1 = [2, "John"]
patient2 = [1, "Jim"]
patient3 = [5, "Tim"]
patient4 = [7, "Cindy"]
priorityQueue = PriorityQueue()
priorityQueue.enqueue(patient1)
priorityQueue.enqueue(patient2)
priorityQueue.enqueue(patient3)
priorityQueue.enqueue(patient4)
while priorityQueue.getSize() > 0:
print(str(priorityQueue.dequeue()), end=" ")