lifelonglearning.py 文件源码

def evalObjectiveFunction(clean_biascount,Pword_plus,Pword_minus,ratio,removing_words):
    mlog(evalObjectiveFunction.__name__,"call")
    import numpy as np
    import math
    #Obj = np.log(1)
    Obj = 0
    for doc in clean_biascount:
        Pdoc = calcProbabilityDocument(Pword_plus,Pword_minus,doc[1],ratio,removing_words)
        i = clean_biascount.index(doc) 
        if i == 0:
            mlog(evalObjectiveFunction.__name__,'Pdoc 0 ' + str(Pdoc) + ' type ' + str(type(Pdoc)))
            t00 = np.exp(np.float64(Pdoc[0]))
            t01 = np.exp(np.float64(Pdoc[1]))
            t1 = t00 - t01
            #print str(t1)
            t2 = abs(t1)
            Obj = np.log(t2)
            #print str(Obj)
        if i % 100 == 0:
            mlog(evalObjectiveFunction.__name__,"Pdoc + " + str(i) + "= " + str(Pdoc))
        if doc[0] == True:
            Obj = np.logaddexp(Obj,Pdoc[0])
            if i % 100 == 1:
                mlog(evalObjectiveFunction.__name__,"Obj+ " + str(i) + " after += " + str(Obj))
            Obj = np.log(np.exp(Obj) - np.exp(Pdoc[1]))
        elif doc[0] == False:
            Obj = np.log(np.exp(np.float64(Obj)) + np.exp(np.float64(Pdoc[1])))
            if i % 100 == 2:
                mlog(evalObjectiveFunction.__name__,"Obj- " + str(i) + " after += " + str(Obj))
            Obj = np.log(np.exp(Obj) - np.exp(Pdoc[0]))
        if Obj == 0.0:
            mlog(evalObjectiveFunction.__name__, "Obj=0 fuck " + str(i) + "")
    mlog(evalObjectiveFunction.__name__,"Obj = " + str(np.exp(Obj)))
    if math.isnan(np.exp(Obj))==False:
        print 'J = ' + str(np.exp(Obj))
    return Obj #type np.log