ValueError:对象太深，无法放入所需的数组 [英] ValueError: object too deep for desired array

问题描述

""" ___ """
from scipy.optimize import root
import numpy as np


LENGTH = 3


def process(x):
    return x[0, 0] + x[0, 1] * 5


def draw(process, length):
    """ """
    X = np.matrix(np.random.normal(0, 10, (length, 2)))
    y = np.matrix([process(x) for x in X])
    y += np.random.normal(3, 1, len(y))
    return y.T, X.T


def maximum_likelyhood(y, X):
    def objective(b):
        return (X.T * (y - X * b.T))
    x0 = np.matrix([0, 0])
    res = root(objective, x0=x0)
    return res.x

y, X = draw(process, LENGTH)
X = X.transpose()
b = np.matrix([[0], [1]])
print maximum_likelyhood(y, X)

产生一个

  Traceback (most recent call last):
File "ml.py", line 33, in <module>
  maximum_likelyhood(y, X)
File "ml.py", line 26, in maximum_likelyhood
  res = root(objective, x0=x0)
File "/usr/local/lib/python2.7/dist-packages/scipy/optimize/_root.py", line 168, in root
  sol = _root_hybr(fun, x0, args=args, jac=jac, **options)
File "/usr/local/lib/python2.7/dist-packages/scipy/optimize/minpack.py", line 193, in    _root_hybr
ml, mu, epsfcn, factor, diag)

ValueError: object too deep for desired array 

我什至无法喘息，问题出在目标中的b 功能?还是它的输出中?

I can't even gasp what the problem is is it in the b which goes into the objective function? or is it in its output?

推荐答案

问题是fsolve和root不接受矩阵作为目标函数的返回值.

The problem is that fsolve and root do not accept matrixes as return value of the objective function.

例如，这是上述问题的解决方案:

For example this is a solution of above problem:

def maximum_likelyhood(y, X):
    def objective(b):
        b = np.matrix(b).T
        return np.transpose(np.array((X.T * (y - X * b))))[0]
    x0 = (1, 1)
    res = root(objective, x0=x0)
    return res.x

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