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258 changes: 27 additions & 231 deletions hackerrank/problemsolving/sorting/fraudulent_activity_notification.py
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Original file line number Diff line number Diff line change
@@ -1,133 +1,11 @@
from curses import window
from queue import SimpleQueue
from typing import List
from unittest import expectedFailure
import random

# https://www.hackerrank.com/challenges/fraudulent-activity-notifications/problem
# fails due to timeout

def fraudulent_notifications(trailing_days: int, expenditure: List[int]) -> int:
notification_count = 0
for i in range(len(expenditure)):
if i < trailing_days:
continue

median_value = quickselect_median(expenditure[i - trailing_days:i])

if expenditure[i] >= 2 * median_value:
notification_count += 1
return notification_count


def fraudulent_notifications_v3(trailing_days, expenditures):
import queue
window = []
fifo = queue.SimpleQueue()
notification_count = 0

for i in range(len(expenditures)):
if i < trailing_days:
fifo.put(expenditures[i])
window.append(expenditures[i])
continue

if i == trailing_days:
window.sort()

median_value = median(window)
if expenditures[i] >= 2 * median_value:
notification_count += 1

to_remove = fifo.get()
to_remove_idx = binary_search(window, to_remove)
window[to_remove_idx] = expenditures[i]
partition_v3(window, to_remove_idx)
#window.sort()

return notification_count

def partition_v3(arr, pivot_idx):
pivot = arr[pivot_idx]
store_idx = 0
last_idx = len(arr) - 1
arr[last_idx], arr[pivot_idx] = arr[pivot_idx], arr[last_idx]

for i in range(len(arr)):
if arr[i] < pivot:
arr[store_idx], arr[i] = arr[i], arr[store_idx]
store_idx += 1

arr[store_idx], arr[last_idx] = arr[last_idx], arr[store_idx]

def binary_search(arr, x):
low = 0
high = len(arr) - 1
mid = 0

while low <= high:

mid = (high + low) // 2

# If x is greater, ignore left half
if arr[mid] < x:
low = mid + 1

# If x is smaller, ignore right half
elif arr[mid] > x:
high = mid - 1

# means x is present at mid
else:
return mid

# If we reach here, then the element was not present
return -1


def median(sorted_expenditure: List[int]) -> float:
length = len(sorted_expenditure)
half = length // 2
if length == 0:
return 0
if length % 2 == 0:
return (sorted_expenditure[half - 1] + sorted_expenditure[half]) / 2
return sorted_expenditure[half]

#!/bin/python3

def quickselect_median(expenditure):

def partition(arr, left_idx, right_idx, until_idx):
pivot_idx = random.randint(left_idx, right_idx)
pivot = arr[pivot_idx]
arr[right_idx], arr[pivot_idx] = arr[pivot_idx], arr[right_idx]

store_idx = left_idx
for i in range(left_idx, right_idx):
if arr[i] < pivot:
arr[store_idx], arr[i] = arr[i], arr[store_idx]
store_idx += 1

arr[store_idx], arr[right_idx] = arr[right_idx], arr[store_idx]

if store_idx == until_idx:
return arr[store_idx]
elif store_idx > until_idx:
return partition(arr, left_idx, store_idx - 1, until_idx)
else:
return partition(arr, store_idx + 1, right_idx, until_idx)

length = len(expenditure)
middle = length // 2
import math
import os
import random
import re
import sys

if length == 1:
return expenditure[0]
elif length % 2 == 0:
return (partition(expenditure, 0, length - 1, middle - 1) + \
partition(expenditure, middle, length - 1, middle)) / 2
else:
return partition(expenditure, 0, length - 1, middle)

import heapq
from collections import deque

Expand Down Expand Up @@ -179,21 +57,7 @@ def heap_median(expenditure):
median.push(i)
return median.median()

def fraudulent_notifications_heap(trailing_days: int, expenditure: List[int]) -> int:
notification_count = 0
median = Median()
for i in range(len(expenditure)):
if i < trailing_days:
continue

median_value = heap_median(expenditure[i - trailing_days:i])

if expenditure[i] >= 2 * median_value:
notification_count += 1
return notification_count


def fraudulent_notifications_heap_v2(trailing_days: int, expenditures: List[int]) -> int:
def fraudulent_notifications_heap(trailing_days, expenditures):
import queue
median = Median()
fifo = queue.SimpleQueue()
Expand All @@ -217,112 +81,44 @@ def fraudulent_notifications_heap_v2(trailing_days: int, expenditures: List[int]

return notification_count

class SlidingMedianCountingSort():

def __init__(self, window_size, max_value):
self._frequencies = [0 for i in range(max_value + 1)]
self._count = 0
self._window_size = window_size

def push(self, value):
self._frequencies[value] += 1
self._count += 1

def remove(self, value):
self._frequencies[value] -= 1

def median(self):
acc = 0
if self._window_size % 2 != 0:
for v, f in enumerate(self._frequencies):
acc += f
if acc > self._window_size // 2:
return v
else:
a = 0
b = 0
for v, f in enumerate(self._frequencies):
acc += f
if acc >= self._window_size // 2 and a == 0:
a = v
if acc >= (self._window_size // 2) + 1 and b == 0:
b = v
if a != 0 and b != 0:
break
return (a + b) / 2

def sorted(self):
sorted = []
for i in range(0, len(self._frequencies)):
if self._frequencies[i] != 0:
sorted[len(sorted):] = [i for j in range(0, self._frequencies[i])]

return sorted

def fraudulent_notifications_counting_sort(trailing_days, expenditures):
median = SlidingMedianCountingSort(trailing_days, 200)
def activityNotifications(expenditures, d):
import queue
median = Median()
fifo = queue.SimpleQueue()
notification_count = 0

for i in range(len(expenditures)):
if i < trailing_days:
if i < d:
fifo.put(expenditures[i])
median.push(expenditures[i])
continue

median_value = median.median()
if expenditures[i] >= 2 * median_value:
notification_count += 1

to_remove = fifo.get()
median.remove(to_remove)

median.remove(expenditures[i - trailing_days])
fifo.put(expenditures[i])
median.push(expenditures[i])

return notification_count

if __name__ == "__main__":
import time
import random

# expenditures = [random.randint(0, 200) for i in range(2 * 10**5 + 1)]

# t1 = time.time()
# res = fraudulent_notifications_counting_sort(10**4, expenditures)
# t2 = time.time()
# print(f"fraudulent_notifications_counting_sort : {t2 - t1} seconds - {res}")
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')

# t1 = time.time()
# res = fraudulent_notifications_heap(10**3, expenditures)
# t2 = time.time()
# print(f"fraudulent_notifications_heap : {t2 - t1} seconds {res}")
first_multiple_input = input().rstrip().split()

# t1 = time.time()
# res = fraudulent_notifications(10**3, expenditures)
# t2 = time.time()
# print(f"fraudulent_notifications : {t2 - t1} seconds - {res}")
n = int(first_multiple_input[0])

# t1 = time.time()
# res = fraudulent_notifications_heap_v2(10**4, expenditures)
# t2 = time.time()
# print(f"fraudulent_notifications_heap_v2 : {t2 - t1} seconds - {res}")
d = int(first_multiple_input[1])

# import timeit
# print(timeit.timeit(lambda: fraudulent_notifications_counting_sort(10**4, expenditures), number=1))
expenditure = list(map(int, input().rstrip().split()))

import matplotlib as mpl
import matplotlib.pyplot as plt
import timeit

expediture_sizes = [j*10**i for i in range(1, 6) for j in range(1, 10)]

times_a = []
times_b = []
linear = []
for size in expediture_sizes:
expenditures = [random.randint(0, 200) for x in range(size)]
window_size = len(expenditures) // 3
times_a.append(timeit.timeit(lambda: fraudulent_notifications_counting_sort(window_size, expenditures), number=1))
times_b.append(timeit.timeit(lambda: fraudulent_notifications_heap_v2(window_size, expenditures), number=1))
linear.append(size)
result = activityNotifications(expenditure, d)

fig, ax = plt.subplots() # Create a figure containing a single axes.
ax.plot(expediture_sizes, times_a)
ax.plot(expediture_sizes, times_b) # Plot some data on the axes.
fptr.write(str(result) + '\n')

fig.savefig("test.png")
fptr.close()