import numpy
from binomialPoisson import *

hugeNumber = float("inf")
unused = -1000

stages = 10
seats = 50
fullFare = 500
discountFare = 250
costOccupied = 8

meanFullDemand = numpy.array([unused,
                              1.3, 1.4, 1.9, 2.0, 2.2,
                              2.8, 2.2, 2.4, 1.8, 3.7])

maxCanRelease = [unused, 10, 10, 10, 10, 10, 5, 5, 5, 5, 5]


f = numpy.zeros([stages+2, seats+1])
x = numpy.zeros([stages+1, seats+1], dtype=int)

# Iterate through stages
for t in range(stages, 0, -1):

    # Iterate through possible states (ie, number of seats occupied)
    for i in range(seats + 1):

        demandProbability = poisson(meanFullDemand[t], seats)

        value = -hugeNumber
        maxReleased = min(maxCanRelease[t], seats-i)

        for p in range(maxReleased+1):

            moveValue = -costOccupied*p + 250*p

            seats_unoccupied = seats - i - p

            for r in range(seats_unoccupied+1):
                j = seats_unoccupied - r
                moveValue += demandProbability[r]*(500*r + f[t+1, j])

            if moveValue > value:
                value = moveValue
                bestMove = p

        f[t, i] = value
        x[t, i] = bestMove