[英]How Do I put 2 matrix into scipy.optimize.minimize?
I work with the scipy.optimize.minimize
function. 我使用
scipy.optimize.minimize
函数。 My purpose is get w,z
which minimize f(w,z)
我的目的是得到
w,z
最小化f(w,z)
Both w
and z
are n by m matrices: w
和z
都是n × m矩阵:
[[1,1,1,1],
[2,2,2,2]]
f(w,z) is receive parameter w and z. f(w,z)是接收参数w和z。
I already tried the form given below: 我已经尝试过以下表格:
def f(x):
w = x[0]
z = x[1]
...
minimize(f, [w,z])
but, minimize does not work well. 但是,最小化不起作用。
What is the valid form to put two matrices ( n by m ) into scipy.optimize.minimize
? 将两个矩阵( n乘m )放入
scipy.optimize.minimize
的有效形式是什么?
Optimize needs a 1D vector to optimize. 优化需要1D向量来优化。 You are on the right track.
你走在正确的轨道上。 You need to flatten your argument to
minimize
and then in f
, start with x = np.reshape(x, (2, m, n))
then pull out w
and z
and you should be in business. 您需要将您的参数展平为
minimize
,然后在f
,从x = np.reshape(x, (2, m, n))
然后拉出w
和z
,您应该开展业务。
I've run into this issue before. 我之前遇到过这个问题。 For example, optimizing parts of vectors in multiple different classes at the same time.
例如,同时优化多个不同类中的向量部分。 I typically wind up with a function that maps things to a 1D vector and then another function that pulls the data back out into the objects so I can evaluate the cost function.
我通常最终得到一个函数,它将事物映射到一维矢量,然后另一个函数将数据拉回到对象中,这样我就可以评估成本函数。 As in:
如:
def toVector(w, z):
assert w.shape == (2, 4)
assert z.shape == (2, 4)
return np.hstack([w.flatten(), z.flatten()])
def toWZ(vec):
assert vec.shape == (2*2*4,)
return vec[:2*4].reshape(2,4), vec[2*4:].reshape(2,4)
def doOptimization(f_of_w_z, w0, z0):
def f(x):
w, z = toWZ(x)
return f_of_w_z(w, z)
result = minimize(f, toVec(w0, z0))
# Different optimize functions return their
# vector result differently. In this case it's result.x:
result.x = toWZ(result.x)
return result
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.