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我正在通过包PyGPGO研究贝叶斯优化，它在引擎盖下使用 PyMC3，将高斯过程拟合到底层函数并提出运动以最大化奖励。

到目前为止，根据标量值定义函数似乎真的很容易，尤其是在参数有限的情况下。但是，我不确定是否有办法提供向量/数组参数。

from pyGPGO.covfunc import squaredExponential
from pyGPGO.acquisition import Acquisition
from pyGPGO.surrogates.GaussianProcess import GaussianProcess
from pyGPGO.GPGO import GPGO

def f(x):
    return (np.sin(x))


sexp = squaredExponential()
gp = GaussianProcess(sexp)
acq = Acquisition(mode='ExpectedImprovement')
param = {'x': ('cont', [0, 2 * np.pi])}

np.random.seed(23)
gpgo = GPGO(gp, acq, f, param)
gpgo.run(max_iter=20)

我想要的是类似的东西：

#        var:  type          , range
param = {'x': (np.array(3,10), [0, 2 * np.pi])}

如果这不可能，我可能会以 的形式设置多个变量，{x1: ..., x2: ...,}然后通过函数定义本身相应地访问它们，尽管当参数空间很大时这似乎不是最佳的。

编辑：这是我的方法，这是行不通的......

import numpy as np
from pyGPGO.covfunc import squaredExponential
from pyGPGO.acquisition import Acquisition
from pyGPGO.surrogates.GaussianProcess import GaussianProcess
from pyGPGO.GPGO import GPGO

param = {f'x_{i}': ('cont', [0, 1]) for i in range(3)}
param.update({f'y_{i}': ('cont', [0, 1]) for i in range(3)})

def f(p):
    v1 = np.array([p[f'x_{i}'] for i in range(3)])
    v2 = np.array([p[f'y_{i}'] for i in range(3)])
    return np.dot(v1,v2)


sexp = squaredExponential()
gp = GaussianProcess(sexp)
acq = Acquisition(mode='ExpectedImprovement')

np.random.seed(23)
gpgo = GPGO(gp, acq, f, param)
gpgo.run(max_iter=20)

和错误：

Evaluation   Proposed point       Current eval.      Best eval.
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-49-f58f602dca8c> in <module>()
     26 np.random.seed(23)
     27 gpgo = GPGO(gp, acq, f, param)
---> 28 gpgo.run(max_iter=20)

1 frames
/usr/local/lib/python3.7/dist-packages/pyGPGO/GPGO.py in _firstRun(self, n_eval)
     86             s_param_val = list(s_param.values())
     87             self.X[i] = s_param_val
---> 88             self.y[i] = self.f(**s_param)
     89         self.GP.fit(self.X, self.y)
     90         self.tau = np.max(self.y)

TypeError: f() got an unexpected keyword argument 'x_1'

因此，将参数包装到字典中似乎比我预期的要复杂一些，因为对象中的参数param必须由函数显式调用f，而不是作为字典接收，p。

以前有没有人解决过这个问题？

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