from Generator.generator import generate
from Generator.generator import init as gen_init
from SchedulingAlgorithms.enumerate import enumerate as enum
from math import e
from random import random

def anneal(max_temp = 300, max_steps = 250, accept_prob=0.01):
    global problem
    gen_init(problem)
    temp = max_temp
    initial = enum(problem)
    current = generate(initial, problem.machines * problem.jobs * 10, 0) #Complete the iteration once fully.
    del initial
    for step in range(0, max_steps):
        new = generate(current, problem.machines * problem.jobs, accept_prob)
        new_end = rate(new)
        curr_end = rate(current)
        p = 1 / ( 1 + (e ** ((curr_end - new_end)/temp)))
        if (new_end < curr_end) or (p < random()):
            current = new
            print("Old: " + str(curr_end) + " New: " + str(new_end))
        temp = ((max_temp-1)/(max_steps**2))*(step-max_steps)**2+1
        print("Iteration: "+ str(step) + " Temperature: " + str(temp))
    return current

def rate(solution):
    global problem
    last_tasks = []
    for i in range(0,problem.jobs):
        last_tasks += [next(( x for x in solution[::-1] if x[1][0] == i), [])]
    end_times = [ problem[x[1]][0] + x[0] for x in last_tasks]
    return max(end_times)

def init(in_problem):
    global problem
    problem = in_problem