Simulated annealing calculator

simulatedAnnealingCalc.py

# Licensed under CRAPL - see http://matt.might.net/articles/crapl/CRAPL-LICENSE.txt

from math import exp
import matplotlib.pyplot as plt

def doplot(r,filename=None,lw=0.1,fancy=False):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    l = ax.plot(r)
    plt.setp(l, color='k', linewidth=lw,aa=True)
    plt.xlabel('Number of iterations')
    plt.ylabel('Execution time (cost)')
    plt.show()


badDelta = -0.06

def getSchedule(cooling,limit):
    tempt = 1
    for _ in range(limit):
        tempt = tempt*cooling
        yield tempt


merit = lambda delta,k,temp: exp(delta /(k*temp))


def old():

    #print [merit(-0.06,1,x) for x in getSchedule(0.5,15)][6]



    delta = -0.06
    delta = -0.01

    # k = 1
    # cf = 0.6

    # k = 0.1
    # cf = 0.8

    k = 1
    cf = 0.6

    k = 0.3
    cf = 0.7


    # cooling_factor = cf
    # merits = [merit(delta,k,x) for x in getSchedule(cooling_factor,15)]

    #print merits[8]

    # for x in merits:
    #     print x

    # from plot_progress import doplot
    # doplot(merits,lw=0.5)


if __name__ == "__main__":
    import sys
    delta = float(sys.argv[1])
    steps = int(sys.argv[2])
    k = float(sys.argv[3])
    cooling_factor = float(sys.argv[4])
    merits = [merit(delta,k,x) for x in getSchedule(cooling_factor,steps)]

    from plot_progress import doplot
    doplot(merits,lw=0.5)