LogisticRegression.py 文件源码

def hypothesis(self,x,theta):
        l_theta = []
        for i in range(len(theta)):
            thetaX = x.dot(theta[i])    ## wx ##
            thetaX_exp = np.exp(thetaX)  ## exp(wx)##
            if np.isinf(thetaX_exp):
                print "overflow"
                ###prova a mettere una matrice con probabilita tutte uguali in caso di overflow
            l_theta.append(thetaX_exp)
        l_theta = np.array(l_theta)
        thetaX_exp_sum = np.sum(l_theta)  ## sum of exp(wx) ##
        p = l_theta.T / thetaX_exp_sum  ## 5xlen(x) predicted results ##
        #print np.sum(p)
        '''if np.isinf(p).any():  ## deal with overflow in results ##
            inf_idx = np.isinf(p)  ## idx where overflow occurs ##
            val = np.sum(p, 0) / np.sum(inf_idx, 0) * inf_idx ## values to be used to substitution ##
            p[inf_idx] = val[inf_idx]  ## substitute values ##'''
        return p.T

    ## Calcolo la Derivata della Funzione di Costo ##