results_correlated.py 文件源码

def process(dic, p, s = 0, normalize = 1.0):
    #x = [5000, 10000, 20000, 40000, 80000, 150000]
    #x = [1000, 5000, 10000]
    a = ['vs_true', 'vs_false', 'tc', 'mv']


    data = {}
    for algo in a:
        y = zip(*dic[(p, algo)])[s]
        m = np.mean(y)
        sd = np.std(y)
        print p, algo, "%.4f" % (m/normalize) #, "%.2f" % sd
        data[algo] = np.asarray(y) * 1.0 / normalize
        #print data[algo]

    #print data['mv']
    print 'vsfalse', scipy.stats.ttest_1samp(data['tc'] - data['vs_false'], 0)
    print 'tc', scipy.stats.ttest_1samp(data['tc'] - data['vs_true'], 0)
    print 'mv', scipy.stats.ttest_1samp(data['mv'] - data['vs_true'], 0)