def main():
"""test"""
test = [6, 5, 3, 1, 8, 7, 2, 4]
heap = list_to_min_heap(test)
show_tree(heap)
heapq.heapify(test)
show_tree(test)
print test
def main():
"""test"""
test = [6, 5, 3, 1, 8, 7, 2, 4]
heap = list_to_min_heap(test)
show_tree(heap)
heapq.heapify(test)
show_tree(test)
print test
def main():
heapq.heapify(data)
print("start ")
show_tree(data)
for n in [0, 13]:
smallest = heapq.heapreplace(data, n)
print(f"replace {smallest:>2} with {n:>2}:")
show_tree(data)
def main():
heap = []
print("random :", data)
print()
for n in data:
print(f"add {n:3}")
heapq.heappush(heap, n)
show_tree(heap)
def main():
print("random :", data)
heapq.heapify(data)
print("heapified :")
show_tree(data)
print
for i in range(2):
smallest = heapq.heappop(data)
print(f"pop {smallest:>3}:")
show_tree(data)
def main():
"""test"""
test = [6, 5, 3, 1, 8, 7, 2, 4]
heap = list_to_heap(test, 'min')
show_tree(heap)
heapq.heapify(test)
show_tree(test)
print heap
print test
print heap_sort(test, 'max')
print heap_sort(test, 'min')
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
heapq.heapify(data)
print 'start:'
show_tree(data)
for n in [0, 13]:
smallest = heapq.heapreplace(data, n)
print 'replace %2d with %2d:' % (smallest, n)
show_tree(data)
# child node has value larger than parent
if A[MaxChildIndex] > A[ParentIndex]:
swap(A, MaxChildIndex, ParentIndex)
# move down to largest child
ParentIndex = MaxChildIndex
MaxChildIndex = 2 * ParentIndex + 1
else:
#parent node has value greater than child node
#no need to percolate down - heapify on linear time!
return # force exit
def swap(A, x, y):
temp = A[x]
A[x] = A[y]
A[y] = temp
A = [15, 34, 111, 22, 63, 12, 77, 55, 2]
print(A, "changed to heap >>> ", end="")
BuildMaxHeap(A)
print(A)
#Print Heap
import sys
sys.path.append("./mylib")
from heapq_showtree import show_tree
show_tree(A)
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
heap = []
print('random: ', data)
print()
for n in data:
print('add {:>3}:'.format(n))
heapq.heappush(heap, n)
show_tree(heap)
def main():
print("random :", data)
heapq.heapify(data)
print("heapified :")
show_tree(data)
print(data)
result = []
for origin, cur_file in enumerate(ls_of_filedata):
if cur_file:
heappush(min_heap, (cur_file[0], origin, 1))
while min_heap:
nxt_min, origin, nxt_ndx = heappop(min_heap)
result.append(nxt_min)
nxt_file = ls_of_filedata[origin]
if nxt_ndx < len(nxt_file):
heappush(min_heap, (nxt_file[nxt_ndx], origin, nxt_ndx + 1))
return result
return min_heap
if __name__ == '__main__':
test_cases = [
([[1, 2], [2, 3, 4, 5]], [1, 2, 2, 3, 4, 5]),
([[1, 2], [2, 3, 4, 5], [3, 4, 5]], [1, 2, 2, 3, 3, 4, 4, 5, 5]),
]
for test_case, exp in test_cases:
show_tree(merge_sorted_file(test_case))
print(merge_sorted_file(test_case)) == exp
# right child exists and is value of right child larger than left child
if (MaxChildIndex < Size) and (A[MaxChildIndex] < A[MaxChildIndex + 1]):
MaxChildIndex += 1
# child node has value larger than parent
if A[MaxChildIndex] > A[ParentIndex]:
swap(A, MaxChildIndex, ParentIndex)
# move down to largest child
ParentIndex = MaxChildIndex
MaxChildIndex = 2 * ParentIndex + 1
else:
#parent node has value greater than child node
#no need to percolate down - heapify on linear time!
return # force exit
def swap(A, x, y):
temp = A[x]
A[x] = A[y]
A[y] = temp
A = [ 15,34,111,22,63,12,77,55,2]
print(A, "changed to heap >>> ", end="")
BuildMaxHeap(A)
print(A)
#Print Heap
import sys
sys.path.append("./mylib")
from heapq_showtree import show_tree
show_tree(A)
# -*- coding: utf-8 -*-
#2.3 heapq模块实现了一个适用于Python列表的最小堆排序算法。
import heapq
from heapq_heapdata import data
from heapq_showtree import show_tree
#2.3.2创建堆,创建堆有两种基本方式:heappush()和heapify()
#使用heappush()中，从数据源增加新元素时会保持元素的堆顺序
heap = []
print 'random :', data
print
for n in data:
print 'add %3d:' % n
heapq.heappush(heap, n)
show_tree(heap)
#如果数据已经在内存中，使用heaoify()原地重新组织列表中的元素会更高效
from heapq_heapdata import data
print 'random :', data
heapq.heapify(data)
print 'heapified :'
show_tree(data)
print
#2.3.3访问堆的内容
#一旦堆已正确组织，就可以使用heappop()删除最小值的元素
from heapq_heapdata import data
heapq.heapify(data)
print 'heapified :'
show_tree(data)
print
for i in xrange(2):
smallest = heapq.heappop(data)
print 'pop %3d:' % smallest
#Source: https://github.com/careermonk/DataStructureAndAlgorithmicThinkingWithPython/blob/master/src/chapter07priorityqueues/KthSmallestWithExtraHeap.py
import sys
sys.path.append("./mylib")
import Heap #custom Heap module
from heapq_showtree import show_tree #custom module for printing Heap
#Initialize Max Heap
Something = Heap.MaxHeap()
#Insert into Heap
Something.Insert(13)
Something.Insert(5)
Something.Insert(6)
Something.Insert(1)
Something.Insert(2)
Something.Insert(4)
Something.Insert(17)
#Print Heap - linear
Something.printHeap()
#Print Heap - tree type
show_tree(Something.printHeap2())
#Delete node
for x in range (1,Something.size+1):
print("Delete node")
Something.Delete()
show_tree(Something.printHeap2())
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
print 'random :', data
heapq.heapify(data)
print 'heapified :'
show_tree(data)
print
for i in xrange(2):
smallest = heapq.heappop(data)
print 'pop %3d:' % smallest
show_tree(data)
import heapq
from heapq_showtree import show_tree
class Node:
def __init__(self, data):
self.data = data
self.right = None
self.left = None
def PrintTree(self):
print(self.data)
ls = [int(n) for n in input("Enter the Max heap").split()]
k = int(input("Enter the Value of K"))
h = []
print(heapq.nlargest(k, ls))
show_tree(ls)
# heapq_heappush.py
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
heap = []
print('casuali :', data)
print()
for n in data:
print('aggiungo {:>3}:'.format(n))
heapq.heappush(heap, n)
show_tree(heap)
# -*- coding: utf-8 -*- #2.3 heapq模块实现了一个适用于Python列表的最小堆排序算法。 import heapq from heapq_heapdata import data from heapq_showtree import show_tree #2.3.2创建堆,创建堆有两种基本方式:heappush()和heapify() #使用heappush()中，从数据源增加新元素时会保持元素的堆顺序 heap = [] print 'random :', data print for n in data: print 'add %3d:' % n heapq.heappush(heap, n) show_tree(heap) #如果数据已经在内存中，使用heaoify()原地重新组织列表中的元素会更高效 from heapq_heapdata import data print 'random :', data heapq.heapify(data) print 'heapified :' show_tree(data) print #2.3.3访问堆的内容 #一旦堆已正确组织，就可以使用heappop()删除最小值的元素 from heapq_heapdata import data heapq.heapify(data) print 'heapified :' show_tree(data) print for i in xrange(2): smallest = heapq.heappop(data) print 'pop %3d:' % smallest