Python scipy 模块，genfromtxt() 实例源码

def PlotMultipleRuns(Alg, nruns=20, fname=None):
    '''Plot "nruns" runs of a given algorithm to show performance
    and variability across runs.'''
    if fname:
        runs = scipy.genfromtxt(fname)
    else:
        runs = []
        for i in range(nruns):
            bestSol, fitHistory = tsp.TSP(200, Alg, 3000, 30, seed=None,
                                          coordfile='tmp.txt')
            runs.append(fitHistory)
        fname = 'MultRuns-' + str(Alg) + '.txt'
        runs = scipy.array(runs)
        scipy.savetxt(fname, runs)

    # plotting
    Xs = scipy.linspace(0, runs.shape[1] * 1000, runs.shape[1])
    for i in range(runs.shape[0]):
        pl.plot(Xs, runs[i, :])
    pl.show()

def LongMC3(fname=None):
    '''Plot a single long MC3 run to demonstrate high performance
    but slow convergence.'''
    if fname:
        run = scipy.genfromtxt(fname)
    else:
        bestSol, run = tsp.TSP(200, 'MC3', 20000, 10, seed=None,
                               coordfile='tmp.txt')
        fname = 'ExampleOutput/MC3-Long.txt'
        run = scipy.array(run)
        scipy.savetxt(fname, run)

    # plotting
    Xs = range(0, run.shape[0] * 1000, 1000)
    pl.plot(Xs, run)
    pl.show()

def LongSA(fname=None):
    '''Plot a single long SA run to demonstrate performance under slower
    cooling schedule.'''
    if fname:
        run = scipy.genfromtxt(fname)
    else:
        bestSol, run = tsp.TSP(200, 'SA', 20000, 'placeholder', seed=None,
                               coordfile='tmp.txt')
        fname = 'ExampleOutput/SA-Long.txt'
        run = scipy.array(run)
        scipy.savetxt(fname, run)

    # plotting
    Xs = range(0, run.shape[0] * 1000, 1000)
    pl.plot(Xs, run)
    pl.show()

def TSP(stops, Alg, steps, param, seed=None,
        coordfile='xycoords.txt'):
    '''A wrapper function that attempts to optimize the traveling
    salesperson problem using a specified algorithm. If coordfile
    exists, a preexisting set of coordinates will be used. Otherwise,
    a new set of "stops" coordinates will be generated for the person to
    traverse, and will be written to the specified file.'''

    # Create the distance matrix, which will be used to calculate
    # the fitness of a given path
    if os.path.isfile(coordfile):
        coords = scipy.genfromtxt(coordfile)
        distMat = DistanceMatrix(coords)
    else:
        distMat = GenerateMap(stops, fname=coordfile, seed=seed)

    if Alg == 'HC':
        # param is the number of solutions to try per step
        bestSol, fitHistory = hc.HillClimber(steps, param, distMat, seed)
    elif Alg == 'SA':
        # param is a placeholder
        bestSol, fitHistory = sa.SimulatedAnnealing(
            steps, param, distMat, seed)
    elif Alg == 'MC3':
        # param is the number of chains
        bestSol, fitHistory = mc3.MCMCMC(steps, param, distMat, seed)
    elif Alg == 'GA':
        # param is the population size
        bestSol, fitHistory = ga.GeneticAlgorithm(steps, param, distMat, seed)
    else:
        raise ValueError('Algorithm must be "HC", "SA", "MC3", or "GA".')

    outfname = coordfile + '-' + Alg + '-' + \
        str(steps) + '-' + str(param) + '.txt'
    scipy.savetxt(outfname, scipy.array(bestSol), fmt='%i')
    return bestSol, fitHistory