HBLR.py 文件源码

def computeXi(self):
        for m in range(self.n_tasks):
            task_X = self.task_dict[m]['X']
            for n in range(len(task_X)):
                inner_sum = 0
                for k in range(self.K):
                    # Note that transposes are different because we are using different notation than in the paper - specifically we use row vectors where they are using column vectors
                    inner_sum += self.phi[m,k]*np.dot((np.dot(np.atleast_2d(task_X[n,:]), 
                                                        (np.dot(np.atleast_2d(self.theta[k,:]).T, np.atleast_2d(self.theta[k,:])) + self.gamma[k]))),
                                                        np.atleast_2d(task_X[n,:]).T)
                assert inner_sum >= 0           # This number can't be negative since we are taking the square root

                self.xi[m][n] = np.sqrt(inner_sum[0,0])
                if self.xi[m][n]==0:
                    print m,n