[英]<lambda>() takes 1 positional argument but 2 were given
我試圖在這里實現相同的 Sage 代碼:在 python 中找到向量中心,如下:
import numpy as np
from scipy.optimize import minimize
def norm(x):
return x/np.linalg.norm(x)
vectors = np.array([[1,2,3],[4,5,6],[7,8,9]])
unit_vectors = [np.divide(v,norm(v)) for v in vectors]
constraints = [lambda x: np.dot(x,u)-1 for u in unit_vectors]
target = lambda x: norm(x)
res = minimize(target,[3,3,3],constraints)
但我一直遇到同樣的問題:
TypeError: <lambda>() takes 1 positional argument but 2 were given
我不是數學家,我只是想寫一個可以找到多維向量中心的代碼。 我嘗試了很多方法來解決問題,但沒有任何效果。
謝謝。
您指出的答案的算法不是用 python 編寫的,因此考慮到我已經實現以下解決方案的官方文檔,這顯然可能會失敗:
import numpy as np
from scipy.optimize import minimize
x0 = 10, 10, 10
vectors = [
np.array([1, 2, 3]),
np.array([1, 0, 2]),
np.array([3, 2, 4]),
np.array([5, 2, -1]),
np.array([1, 1, -1]),
]
unit_vectors = [vector / np.linalg.norm(vector) for vector in vectors]
constraints = [
{"type": "ineq", "fun": lambda x, u=u: (np.dot(x, u) - 1)} for u in unit_vectors
]
target = lambda x: np.linalg.norm(x)
res = minimize(fun=target, x0=x0, constraints=constraints)
print(res.x)
輸出:
[1.38118173 0.77831221 0.42744313]
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