I'm new to python, and I have the following problem: I am trying to minimize a python function that has a numpy array as one of its arguments. When I use scipy.optimize.fmin, it turns my array into a list (which results in the function failing to evaluate). Is there an optimization function that does accept numpy arrays as function arguments?
Thanks in advance!
-MB
Edit: Here is an example of what I'm talking about, courtesy of @EOL:
import scipy.optimize as optimize
import numpy as np
def rosen(x):
print x
x=x[0]
"""The Rosenbrock function"""
return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)
x0 = np.array([[1.3, 0.7, 0.8, 1.9, 1.2]])
xopt = optimize.fmin(rosen, x0, xtol=1e-8, disp=True)
#[ 1.3 0.7 0.8 1.9 1.2]
#(note that this used to be a numpy array of length 0,
#now it's "lost" a set of brackets")
Here is an example using optimize.fmin
which comes from the scipy tutorial:
import scipy.optimize as optimize
def rosen(x):
"""The Rosenbrock function"""
return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)
x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
xopt = optimize.fmin(rosen, x0, xtol=1e-8, disp=True)
# Optimization terminated successfully.
# Current function value: 0.000000
# Iterations: 339
# Function evaluations: 571
print(xopt)
# [ 1. 1. 1. 1. 1.]
Does this help? If not, can you modify this example to show what is turning into a list?
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