模块导入: NameError (未定义名称'A‘)

Question

我已经创建了一个计算QR分解和测试.It包含几个不同功能的模块。我已将此文件保存为Numla.py。我想在木星笔记本中导入这些函数，但是我一直有这个错误。我还尝试过其他导入方法，例如从Numla进口qr、从Numla import *、导入Numla as num。但我一直在说同一个名字的错误。

进口Numla Numla.qr(A)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [11], in <cell line: 2>()
      1 import Numla
----> 2 Numla.qr(A)

NameError: name 'A' is not defined

import numpy as np 


def qr(A):
  #source: #https://rosettacode.org/wiki/QR_decomposition#Python
    m, n = A.shape
    Q = np.eye(m)
    for i in range(n - (m == n)):
        H = np.eye(m)
        #calculates Householder matrix i: rows and i: columns from A i: rows and ith column
        H[i:, i:] = make_householder(A[i:, i])
        Q = Q@H
        A = H@A
    return Q, A

def make_householder(a):
    #finds orthogonal vector to mirror
    u = a / (a[0] + np.copysign(np.linalg.norm(a), a[0]))
    u[0] = 1
    H = np.eye(a.shape[0])
    #finds Householder projection
    H -= (2 / np.dot(u, u)) * u[:, None] @ u[None, :]

    return H

def compose(qr, make_householder):
    def comp(arg):
        return qr()(make_householder(arg))
    return comp

def test_compose(qr, make_householder):
    m=3
    n=3
    A = np.random.rand(m, n)
    Q, R = qr(A)
    Q.round(3)
    R.round(3)

    Q_np, R_np=np.linalg.qr(A)
    np.allclose(Q, Q_np)
    np.allclose(R, R_np)
    
    print (" QR-Decomposition and test ")
    print ('\n')
    print ("Q vs Q_np")
    print (Q.round(8))
    print (Q_np)
    print ('\n')
    print ("R vs R_np")
    print (R.round(8))
    print (R_np)
    print ('\n')
    
    if np.allclose(Q, Q_np) == True:
        print('ok')
        if np.allclose(R, R_np) == True:
            print ('ok')
            print('\n')
            
            return 
        
        
#test_compose(qr, make_householder)



def back_substitution(U, y):
    
    n = U.shape[0]
    x = np.zeros_like(y, dtype=np.double);
    x[-1] = y[-1] / U[-1, -1]
    
    for i in range(n-2, -1, -1):
        x[i] = (y[i] - np.dot(U[i,i:], x[i:])) / U[i,i]
    return x

def linsolve_qr():
    
    #QR decomposition with qr function 
    Q, R = np.linalg.qr(A)
    y = Q.T @ B
    #solve for x using back substitution method
    K=back_substitution(R, y)
    #using np solver to prove back substitution+QR method works
    L=np.linalg.solve(R, y)
    
    return 

def test_linsolve_qr():
    A = np.array([
        [6., 7., 8.],
        [2., 4., 3.],
        [8., 1., 2]])
    B = np.array([1., -2., 3.])
    
    #QR decomposition with qr function 
    Q, R = np.linalg.qr(A)
    y = Q.T @ B
    K=back_substitution(R, y)
    L=np.linalg.solve(R, y)

    print (" QR-Decomposition implementation to solve LGS ")
    print ('\n')
    print (back_substitution(R, y))
    print (np.linalg.solve(R, y))
    
    if np.allclose(K, L) == True:
        print('ok')
        return
    
#test_linsolve_qr()


if __name__ == "__main__":
    test_compose(qr, make_householder)
    test_linsolve_qr()

Answer 1

要导入函数，只需使用函数名而不带参数：

from Numla import qr

然后，您可以在笔记本中使用函数qr。

我已经在本地测试过了，而且效果很好。只要Numla.py位于Python中的一个目录中，它就能正常工作。如果您继续与Colab有问题，您可能需要咨询this answer。