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# Python program for weighted job scheduling using Dynamic
# Programming and Binary Search
# Class to represent a job
class Job:
def __init__(self, start, finish, profit):
self.start = start
self.finish = finish
self.profit = profit
# A Binary Search based function to find the latest job
# (before current job) that doesn't conflict with current
# job. "index" is index of the current job. This function
# returns -1 if all jobs before index conflict with it.
# The array jobs[] is sorted in increasing order of finish
# time.
def binarySearch(job, start_index):
# https://en.wikipedia.org/wiki/Binary_search_algorithm
# Initialize 'lo' and 'hi' for Binary Search
lo = 0
hi = start_index - 1
# Perform binary Search iteratively
while lo <= hi:
mid = (lo + hi) // 2
if job[mid].finish <= job[start_index].start:
if job[mid + 1].finish <= job[start_index].start:
lo = mid + 1
else:
return mid
else:
hi = mid - 1
return -1
# The main function that returns the maximum possible
# profit from given array of jobs
def schedule(job):
# Sort jobs according to start time
job = sorted(job, key = lambda j: j.start)
# Create an array to store solutions of subproblems. table[i]
# stores the profit for jobs till arr[i] (including arr[i])
n = len(job)
table = [0 for _ in range(n)]
table[0] = job[0].profit;
# Fill entries in table[] using recursive property
for i in range(1, n):
# Find profit including the current job
inclProf = job[i].profit
l = binarySearch(job, i)
if (l != -1):
inclProf += table[l];
# Store maximum of including and excluding
table[i] = max(inclProf, table[i - 1])
return table[n-1]
# Driver code to test above function
job = [Job(1, 2, 50), Job(3, 5, 20),
Job(6, 19, 100), Job(2, 100, 200)]
print("Optimal profit is"),
print(schedule(job))
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