# Author: ThymineC
# Date: 23/11/2013
# Python version: 2.7
# Description: Evaluates the behaviour of a miner AI using a greedy algorithm that
#              bases its behaviour at each tick on whatever action maximises
#              value/tick ratio

def valMineReturn(t, X, Y, Z):
    assert(t <= Y)

    return float(Z) / float(X + Y - t)

def valReturnNow(t, X, Y, Z):
    assert(t <= Y)

    return (Z / float(Y))*t / float(X)

def decision(t, X, Y, Z):
    if valMineReturn(t, X, Y, Z) > valReturnNow(t, X, Y, Z):
        return "Mines and returns"
    else:
        return "Returns now"

def main():
    xMax = 2
    yMax = 2
    zMax = 3

    print "Travel tick range: [1, %s]" % xMax
    print "Mining tick range: [1, %s]" % yMax
    print "Ore value range: [0, %s]\n\n" % zMax

    for X in range(1, xMax+1):
        for Y in range(1, yMax+1):
            for Z in range(0, zMax+1):
                for t in range(1, 2):
                    print "t: %s" % t
                    print "travelTicks: %s" % X
                    print "mineTicks: %s" % Y
                    print "oreValue: %s" % Z
                    print "V(mine+return): %s" % valMineReturn(t, X, Y, Z)
                    print "V(return now): %s" % valReturnNow(t, X, Y, Z)
                    print "decision: %s\n" % decision(t, X, Y, Z)

if __name__ == "__main__":
    main()