我有一个要向量化的功能。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均为1x5数组,例如。 [1,2,3,4,5]。 A的最终输出为
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在x,y,z的输入数组(例如1000行5列)上工作。我天真地尝试将数组与第一行的以下输出一起馈入该函数。
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]
让我们说第二种方法有以下数据:
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]]
预期输出
[[[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. ]]]
答案 0 :(得分:0)
您可以使用此:
# 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)
进行测试:
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]]]