Question

每次我评估它们与预期值之间的差异时，我都会尝试弹出我附加到列表M的值。我在使用M.pop()之前和之后打印列表的结果。它是否在内存中使用了一个位置并弄乱了我在deltaL中的列表？

M=[]
delta = 3
while abs(delta) > 0.3:
    for num1 in range(450,800,20):
        best_config_per_numL = []
        delta_mL = []
        for config in ['FFF','FFS','FSF','FSS','SFF','SFS','SSF','SSS']:
            M.append(gen(num1,config[0]))
            M.append(gen(num1,config[1]))
            M.append(gen(num1,config[2]))

            xyL = []
            xL = range(400,801,1)
            for i in xL:
                xyL.append([geny(M,i),i])

            deltaL = []
            yL = range(400,801,1)
            for i in range(len(yL)):
                expected = yL[i]
                actual = xyL[i][0].real
                deltaL.append(abs(expected - actual))

            delta_mL.append([max(deltaL),M]) 
            print '\n'+str(delta_mL)+'\n' #<-------------------------- LINE 1
            M.pop()
            M.pop()
            M.pop()
        print '\n'+str(delta_mL)+'\n' #<-------------------------- LINE 2
        best_config_per_numL.append(delta_mL[0].sort()[0]) #best config for all lambda

    M.append(best_config_per_numL.sort()[0][1])
    delta = best_config_per_numL.sort()[0][0]

LINE 1的输出为： [[1.0，[[1.9736842105263157,57.0]，[1.9736842105263157,57.0]，[1.9736842105263157,57.0]]]

[[1.0，[[1.9736842105263157,57.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]]，[0.99749174811000929，[[1.9736842105263157,57.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]] ]

[[1.0，[[1.9736842105263157,57.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]，[0.99749174811000929，[[1.9736842105263157,57.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]] ]，[0.90639755394574695，[[1.9736842105263157,57.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]]

[[1.0，[[1.9736842105263157,57.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]，[0.99749174811000929，[[1.9736842105263157,57.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]] ]，[0.90639755394574695，[[1.9736842105263157,57.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]，[0.78984872616045532，[[1.9736842105263157,57.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]]]

[[1.0，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.9736842105263157,57.0]]，[0.99749174811000929，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.9736842105263157,57.0]] ]，[0.90639755394574695，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.9736842105263157,57.0]]，[0.78984872616045532，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.9736842105263157,57.0]]， [0.99749174811000885，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.9736842105263157,57.0]]]

[[1.0，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]]，[0.99749174811000929，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]] ]，[0.90639755394574695，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]]，[0.78984872616045532，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]]]， [0.99749174811000885，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]]，[0.77268527172444679，[[1.0514018691588785,107.0]，[1.9736842105263157,57.0]，[1.0514018691588785,107.0]]]]

[[1.0，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]，[0.99749174811000929，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]] ]，[0.90639755394574695，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]，[0.78984872616045532，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]]， [0.99749174811000885，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]，[0.77268527172444679，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]，[0.78984872616045532 ，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.9736842105263157,57.0]]]

[[1.0，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]，[0.99749174811000929，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]] ]，[0.90639755394574695，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]，[0.78984872616045532，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]]， [0.99749174811000885，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]，[0.77268527172444679，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]，[0.78984872616045532] ，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]，[1.0，[[1.0514018691588785,107.0]，[1.0514018691588785,107.0]，[1.0514018691588785,107.0]]]]

LINE 2的输出为： [[1.0，[]]，[0.99749174811000929，[]]，[0.90639755394574695，[]]，[0.78984872616045532，[]]，[0.99749174811000885，[]]，[0.77268527172444679，[]]，[0.78984872616045532，[]]， [1.0，[]]]

我期望得到与LINE 1相同的东西。

Answer 1

这一行：

delta_mL.append([max(deltaL),M])

...创建一个包含两个项目的列表，第二个是列表M的引用。当您稍后更改M时，您可以看到delta_mL中的更改，因为它引用了相同的名单。如果您想制作M的副本，请尝试：

delta_mL.append([max(deltaL),list(M)])

这将创建一个包含与M相同的项目的新列表，但这是一个单独的副本，以便在M中添加或删除项目时不会更改。

请记住，在Python中，变量将引用存储到对象中。如果要复制对象，则需要明确地执行此操作。当然，这只有当有问题的对象是 mutable 时才有用，就像列表一样。不可变对象 - 例如数字，元组和字符串 - 不是问题，因为即使许多变量存储对同一个不可变对象的引用，它们都不能修改它，因此共享不是问题。

Answer 2

是否在内存中使用了一个位置并弄乱了我在deltaL中的列表？

在某种程度上。

delta_mL.append([max(deltaL),M])

delta_mL对M有参考。不是M的副本。如果M发生变化，delta_mL也会发生变化。

您可能不想简单地追加M。您可能希望使用M[:]复制M。

Python列表变量 - 是使用内存位置还是其他东西？

2 个答案: