如何在数组中正确使用numpy.c

Question

我有一个函数，我正在尝试矢量化。在函数内部，我有以下代码

A = np.c_[xdata, ydata, np.ones(len(zdata))]

其中x_data、y_data、z_data都是1x5数组，例如[1,2,3,4,5]。A的结果输出将是

array([[1.90155189, 1.64412979, 1.        ],
       [2.44148892, 1.73851717, 1.        ],
       [1.65259189, 2.10693759, 1.        ],
       [2.52045732, 2.30939049, 1.        ],
       [1.53516213, 2.39788003, 1.        ]])

我想将函数的这一部分转换为处理x、y、z的输入数组（例如1000行5列）。我天真地尝试将数组输入到这个函数中，第一行的输出如下

array([1.90155189, 2.44148892, 1.65259189, 2.52045732, 1.53516213,
       1.64412979, 1.73851717, 2.10693759, 2.30939049, 2.39788003,
       1.        ])

以下是第一个结果的输入示例：

x=[1.90155189 2.44148892 1.65259189 2.52045732 1.53516213]
y=[1.64412979 1.73851717 2.10693759 2.30939049 2.39788003]
z=[0.23273446 0.57301046 0.89755946 0.07169598 0.41394575]

假设现在我有第二种方法的以下数据：

x_array = [[1.90155189 2.44148892 1.65259189 2.52045732 1.53516213],
           [1.90155189 2.44148892 1.65259189 2.52045732 1.53516213],
           [1.90155189 2.44148892 1.65259189 2.52045732 1.53516213]]
y_array = [[1.64412979 1.73851717 2.10693759 2.30939049 2.39788003],
           [1.64412979 1.73851717 2.10693759 2.30939049 2.39788003],
           [1.64412979 1.73851717 2.10693759 2.30939049 2.39788003]]
z_array = [[0.23273446 0.57301046 0.89755946 0.07169598 0.41394575],
           [0.23273446 0.57301046 0.89755946 0.07169598 0.41394575],
           [0.23273446 0.57301046 0.89755946 0.07169598 0.41394575]]

预期产量为

     [[[1.90155189, 1.64412979, 1.        ],
       [2.44148892, 1.73851717, 1.        ],
       [1.65259189, 2.10693759, 1.        ],
       [2.52045732, 2.30939049, 1.        ],
       [1.53516213, 2.39788003, 1.        ]],
       [[1.90155189, 1.64412979, 1.        ],
       [2.44148892, 1.73851717, 1.        ],
       [1.65259189, 2.10693759, 1.        ],
       [2.52045732, 2.30939049, 1.        ],
       [1.53516213, 2.39788003, 1.        ]],
      [[1.90155189, 1.64412979, 1.        ],
       [2.44148892, 1.73851717, 1.        ],
       [1.65259189, 2.10693759, 1.        ],
       [2.52045732, 2.30939049, 1.        ],
       [1.53516213, 2.39788003, 1.        ]]]

Answer 1

您可以使用：

编辑：在下一行发表评论，添加下一行，感谢@hpaulj的评论

# new_A = np.stack((x,y,np.ones_like(z)), axis=1).swapaxes(1,2)
new_A = np.stack((x,y,np.ones_like(z)), axis=2)

测试：

THOUSAND = 6
x = np.random.randint(1,5,size=(THOUSAND,5))
y = np.random.randint(1,5,size=(THOUSAND,5))
z = np.random.randint(1,5,size=(THOUSAND,5))

print (x)
print (y)
print (z)

new_A = np.stack((x,y,np.ones_like(z)), axis=1).swapaxes(1,2)
print (new_A)

输出：

[[1 2 2 1 1]      # print(x)
 [4 4 4 4 4]
 [1 2 1 3 3]
 [2 3 1 4 4]
 [1 1 4 1 4]
 [4 1 3 3 2]]
[[2 2 3 4 4]      # print(y)
 [1 1 4 2 1]
 [3 3 1 1 2]
 [1 1 2 1 3]
 [3 2 1 4 3]
 [4 4 1 3 2]]
[[3 4 3 2 2]      # print(z)
 [4 2 4 3 3]
 [3 3 4 1 4]
 [4 3 3 3 1]
 [4 1 1 3 3]
 [4 1 4 3 3]]

# new_A output

[[[1 2 1]      # print(new_A)
  [2 2 1]
  [2 3 1]
  [1 4 1]
  [1 4 1]]

 [[4 1 1]
  [4 1 1]
  [4 4 1]
  [4 2 1]
  [4 1 1]]

 [[1 3 1]
  [2 3 1]
  [1 1 1]
  [3 1 1]
  [3 2 1]]

 [[2 1 1]
  [3 1 1]
  [1 2 1]
  [4 1 1]
  [4 3 1]]

 [[1 3 1]
  [1 2 1]
  [4 1 1]
  [1 4 1]
  [4 3 1]]

 [[4 4 1]
  [1 4 1]
  [3 1 1]
  [3 3 1]
  [2 2 1]]]