models.py 文件源码

def P(self,x):
        """ calculate vector of P_a for each action a """
        indx = np.arange(len(x))
        ps = self.pX[x,indx] #probability of P(X_i = x_i) for each i given do()
        joint = ps.prod() # probability of x given do()
        pi = np.true_divide(joint,ps) # will be nan for elements for which ps is 0 
        for j in np.where(np.isnan(pi))[0]:
            pi[j] = np.prod(ps[indx != j]) 
        pij = np.vstack((pi,pi))
        pij[1-x,indx] = 0 # now this is the probability of x given do(x_i=j)
        pij = pij.reshape((len(x)*2,)) #flatten first N-1 will be px=0,2nd px=1
        result = np.hstack((pij,joint))
        return result