def test_sort_5():
A = random_array(5)
Acopy = A[:]
Acopy.sort()
heap.heapsort(A)
for x in range(0, len(Acopy)):
assert A[x] == Acopy[x]
def time_execute_heapsort_v100():
before = time.time()
heapsort(v100)
after = time.time()
total = ((after - before) * 1000)
print("time heapsort = (100 elements) %0.4f" % total)
def note(self):
'''
Summary
=
Print chapter6.4 note
Example
=
>>> Chapter6_4().note()
'''
print('6.4 堆排序算法')
print('设n = len(A)-1,堆排序算法先用BuildMaxHeap将输入数组 A[0..n]构造成一个最大堆')
print('因为数组中的最大元素在根A[0]，则可以通过把它与A[n]互换来达到最终正确的位置')
print('现在如果从堆中去掉结点n(通过减小heapsize[A]),可以很容易地将A[1..n-1]建成最大堆，',
'原来根的子女仍然是最大堆,而新的元素可能违背了最大堆的性质，这时调用MaxHeapify(A, 0)就可以保持这一个性质')
print('堆排序算法不断重复这个过程，堆的大小由n-1一直降到2')
print('堆排序算法的一个举例[7, 6, 5, 4, 3, 2, 1]',
heap.heapsort([1, 2, 3, 4, 5, 6, 7]))
print('HeapSort过程的时间代价O(nlgn)')
print(
'调用heap.buildmaxheap的时间为O(n),n-1次heap.maxheapify中每一次的时间代价为O(lgn)')
A = [5, 13, 2, 25, 7, 17, 20, 8, 4]
print('练习6.4-1 数组', _deepcopy(A), '的heapsort过程结果为：', heap.heapsort(A))
print('练习6.4-2 证明循环不变式的过程略')
print('练习6.4-3 按递增排序的数组A已经是一个最大堆，buildmaxheap的时间较少，但是交换元素花费时间较多')
print(' 若A的元素按降序排列，则buildmaxheap的花费时间较多，元素交换时间差不多')
print('练习6.4-4 略')
print('练习6.4-5 略')
# python src/chapter6/chapter6_4.py
# python3 src/chapter6/chapter6_4.py
return self
def test_heap_sort_correctness(self):
length = randint(100, 1000)
input = gen_random_input(length, 1000)
output = heapsort(input)
self.assertEqual(len(output), length)
self.assertTrue(is_sorted(output))
def createYoungTab(t):
for r in t:
build_max_heap(r)
heapsort(r)
nr = len(t[0]) #no of columns
nc = len(t)
a = []
for j in range(nc):
cs = []
for i in range(nr):
cs.append(t[i][j])
heapsort(cs)
a_n = np.array(a)
print(a_n)
print("=-")
print("\nTesting Heap\n")
#L = [16, 14, 10, 8, 7, 9, 3, 2, 4, 1]
L = [5, 14, 10, 8, 7, 9, 3, 2, 4, 1]
print("Initial L = ", L)
H = heap.Heap(L)
print("Heaped L = ", H)
H.build_heap()
print("Heaped L (should be equal)= ", H)
L = heap.heapsort(L)
print("Sorted L = ", L)
#### Priority queue test
print("\nTesting Priority queue\n")
S = Priority_queue([9, 8, 5, 6, 7, 1, 0, 4, 1, 3])
print("Original S: ", S)
M = S.extract_max()
print("Maximum extracted: ", M)
print("S after maximum extracted: ", S, "\n")
S = Priority_queue([12, 8, 5, 6, 7, 1, 0, 4, 1, 3])
title = "Insertion Sort"
generator = insertion_sort(vector)
elif input_is == "b":
title = "Bucket Sort"
generator = bucketsort(vector, bucketSize=DEFAULT_BUCKET_SIZE)
elif input_is == "m":
title = "Merge Sort"
generator = mergesort(vector, 0, number_interation - 1)
elif input_is == "s":
title = "Shell Sort"
generator = shellsort(vector)
elif input_is == "q":
title = "Quick Sort"
generator = quicksort_graph(vector, 0, number_interation - 1)
elif input_is == "h":
generator = heapsort(vector)
title = 'heapsort'
elif input_is == "r":
generator = radixsort(vector)
title = 'radix'
# Inicializando grafico
fig, ax = plt.subplots()
ax.set_title(title)
#bar
bar = ax.bar(range(len(vector)), vector, align="edge")
ax.set_xlim(0, number_interation)
ax.set_ylim(0, int(1.07 * number_interation))
