""" ___ """
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)
produces a
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
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?
The problem is that fsolve and root do not accept matrixes as return value of the objective function.
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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