heterogeneous_solver.py 文件源码

def shift_polynomial(coef, d):
    # given polynomial sum_k a_k x^k -> sum_k a_k (x+d)^k
    coef2 = np.zeros(5, dtype="float64")
    for i in range(5):
        for j in range(i, 5):
            coef2[i] = coef2[i] + coef[j] * (d**(j - i)) * sp.binom(j, i)
    return coef2


# def shift_graph_2(graph1, graph2, d):
#     # numpy matrix implementation of shift_graph
#     A = np.zeros((5,5),dtype="float64")
#     for i in range(5):
#         for j in range(i,5):
#             A[j,i] = (d**(j-i)) * sp.binom(j,i)
#     graph2[:,3:] = graph2[:,3:].dot(A)
#     return graph2