def TestBuildMinHeap(self):
input = [random.randrange(100) for _ in range(100)]
h = Heap(input)
sorted_input = sorted(input)
for i in range(len(input)):
self.assertEqual(h.peek_min(), sorted_input[i])
self.assertEqual(h.extract_min(), sorted_input[i])
def TestMinHeapWithTuples(self):
input = [(random.randrange(100), "Hullo!") for _ in range(100)]
h = Heap(key=lambda x: x[0])
for num in input:
h.insert(num)
sorted_input = sorted(input)
for i in range(len(input)):
self.assertEqual(h.extract_min(), sorted_input[i])
def TestMinHeapWithRandomInput(self):
input = [random.randrange(100) for _ in range(100)]
h = Heap()
for num in input:
h.insert(num)
sorted_input = sorted(input)
for i in range(len(input)):
self.assertEqual(h.extract_min(), sorted_input[i])
def test_extract_root(self):
# Arrange
h = Heap()
h.insert(6)
h.insert(5)
h.insert(3)
h.insert(4)
h.insert(2)
h.insert(1)
# Act
root = h.extract_root()
# Assert
self.assertEqual(1, root)
self.assertEqual(2, h._heap[0])
def TestSizeReducedAfterExtractMin(self):
input = [6, 1, 4, 5, 3, 2]
h = Heap()
for num in input:
h.insert(num)
initial_size = h.size
self.assertEqual(initial_size, len(h.a))
h.extract_min()
self.assertEqual(initial_size - 1, h.size)
def test_insert_minimum_at_the_root(self):
# Arrange
h = Heap()
# Act
h.insert(6)
h.insert(5)
h.insert(3)
h.insert(4)
h.insert(2)
h.insert(1)
# Assert
self.assertEqual(1, h._heap[0])
def calculate_medians_with_heaps(numbers):
"""Calculate the sum of all 10,000 medians, modulo 10000"""
max_heap = MaxHeap() # For storing the smaller half of numbers
min_heap = MinHeap() # For storing the larger half of numbers
medians = []
for number in numbers:
if max_heap.peek_max() is None or max_heap.peek_max() > number:
max_heap.insert(number)
if max_heap.size > min_heap.size + 1:
min_heap.insert(max_heap.extract_max())
else:
min_heap.insert(number)
if min_heap.size > max_heap.size + 1:
max_heap.insert(min_heap.extract_min())
if max_heap.size >= min_heap.size:
medians.append(max_heap.peek_max())
else:
medians.append(min_heap.peek_min())
return medians
def construct_tree(freqs): # constructs the tree given the frequencies of each character nodes = [HuffmanNode(letter, freqs[letter]) for letter in freqs] # nodes contains the HuffmanNodes trees = [HuffmanTree(node) for node in nodes] h = Heap() for tree in trees: h.insert(tree) # the heap contains all the trees while len(h.elems) > 2: # including 0 t1 = h.minimum() # extract the two least frequent letters h.delete() t2 = h.minimum() h.delete() newTree = combine(t2, t1) # create a new tree combining the two nodes h.insert(newTree) # insert the new tree back into the heap # at this point, the heap contains only one element return h.minimum()
def TestMinHeapProperty(self):
input = [6, 1, 4, 5, 3, 2]
h = Heap()
for num in input:
h.insert(num)
self.assertEqual(h.extract_min(), 1)
def _factory(l):
_h = Heap()
for item in l:
_h.insert(item)
return _h
"""Test driver to confirm build_heap method runs in O(n) time"""
import random
import time
from heaps import Heap
inputs = []
for x in range(1, 10):
inputs.append([random.randrange(10000) for _ in range(x * 100000)])
for i, input in enumerate(inputs):
start = time.time()
Heap(input)
ellapsed = time.time() - start
print("Took {} seconds to build a heap from {}00,000 "
"elements".format(ellapsed, i + 1))
