class MedianHeap(object):
def __init__(self):
self.minheap = MinHeap()
self.maxheap = MaxHeap()
def get_median(self):
if self.minheap.size == self.maxheap.size:
return (self.minheap.get_min() + self.maxheap.get_max()) / 2.0
elif self.minheap.size > self.maxheap.size:
return self.minheap.get_min()
else:
return self.maxheap.get_max()
def insert(self, value):
if self.minheap.size == 0:
self.minheap.insert(value)
elif value > self.minheap.get_min():
self.minheap.insert(value)
else:
self.maxheap.insert(value)
self.rebalance()
def rebalance(self):
if self.minheap.size < self.maxheap.size - 1:
self.minheap.insert(self.maxheap.delete_max())
elif self.maxheap.size < self.minheap.size - 1:
self.maxheap.insert(self.minheap.delete_min())
class MedianHeap(object):
def __init__(self):
self.minheap = MinHeap()
self.maxheap = MaxHeap()
def get_median(self):
if self.minheap.size == self.maxheap.size:
return (self.minheap.get_min() + self.maxheap.get_max()) / 2.0
elif self.minheap.size > self.maxheap.size:
return self.minheap.get_min()
else:
return self.maxheap.get_max()
def insert(self, value):
if self.minheap.size == 0:
self.minheap.insert(value)
elif value > self.minheap.get_min():
self.minheap.insert(value)
else:
self.maxheap.insert(value)
self.rebalance()
def rebalance(self):
if self.minheap.size < self.maxheap.size-1:
self.minheap.insert(self.maxheap.delete_max())
elif self.maxheap.size < self.minheap.size-1:
self.maxheap.insert(self.minheap.delete_min())
def test(self):
# test data
ob = MaxHeap()
ob.insert(10)
ob.insert(5)
ob.insert(6)
ob.insert(3)
ob.insert(8)
ob.insert(20)
self.assertEqual(ob.peek(), 20, msg="Max Element is not matched")
ob.pop()
ob.pop()
self.assertEqual(ob.peek(), 8, msg="Max Element is not matched")
ob.pop()
self.assertEqual(ob.peek(), 6, msg="Max Element is not matched")
ob.pop()
ob.pop()
ob.pop()
self.assertEqual(ob.peek(), None, msg="Max Element is not matched")
import text_tokenizer
import histogram
import hashtable
import Heap
from text_tokenizer import *
from hashtable import *
from histogram import *
from Heap import MaxHeap
# If we subtract the Unix dictionary's keys from our word count dict's keys,
# we can find specialized/jargon words in our corpus or words
# we didn't normalize. We can even use this to refine our regular expressions
# and normalization techniques!
if __name__ == '__main__':
user_input = "humannature.txt"
tokenized_text = text_Parser(user_input)
# TODO are we supposed to rewrite the histogram function to incorporate our hash table?
histogram = histogram(tokenized_text)
max_heap = MaxHeap()
for i in histogram:
max_heap.insert((i, histogram[i]))
print(max_heap.peek(10))
from Heap import MaxHeap
import Heapsort
# Program to test the heap class.
h = MaxHeap()
input_list = [25, 44, 55, 99, 30, 37, 15, 10, 2, 4]
for item in input_list:
h.insert(item)
print(' --> array: %s\n' % h.heap_array)
print(h)
h = MaxHeap(input_list)
print(h)
numbers = [82, 36, 49, 82, 34, 75, 18, 9, 23]
#numbers = [6, 5, 4, 3, 2, 1]
print("UNSORTED:", numbers)
Heapsort.heap_sort(numbers)
print("SORTED: ", numbers)
from dotenv import load_dotenv
import os
# If we subtract the Unix dictionary's keys from our word count dict's keys,
# we can find specialized/jargon words in our corpus or words
# we didn't normalize. We can even use this to refine our regular expressions
# and normalization techniques!
if __name__ == '__main__':
dotenv_path = join(dirname(__file__), '.env')
load_dotenv(dotenv_path, verbose=True)
api = twitter.Api(consumer_key=os.environ.get('CONSUMER_KEY'),
consumer_secret=os.environ.get('CONSUMER_SECRET'),
access_token_key=os.environ.get('ACCESS_TOKEN'),
access_token_secret=os.environ.get('ACCESS_TOKEN_SECRET'))
user_input = "obama_tweets.txt"
tokenized_text = text_Parser(user_input)
# TODO are we supposed to rewrite the histogram function to incorporate our hash table?
histogram = histogram(tokenized_text)
max_heap = MaxHeap()
for i in histogram:
max_heap.insert((i, histogram[i]))
words = "obama_tweets.txt"
tokenized_text = text_Parser(words)
sentence_generator = SentenceGenerator(10)
List = sentence_generator.build_dict(tokenized_text)
sentence = sentence_generator.generate_sentences(List)
# status = api.PostUpdate(sentence)
# print(status.text)
from Heap import MaxHeap
heap = MaxHeap(10)
heap.insert(20)
heap.insert(10)
heap.insert(1)
heap.insert(0)
heap.insert(50)
heap.printHeap()
print('\n')
print(heap.getMax())
print('\n')
heap.printHeap()
