Python bindings

These are instruction for building the Python bindings and running Python code on Linux. For building on Mac OSX see https://github.com/beniz/libcmaes/wiki/Building-libcmaes-on-Mac-OSX

To build the Python bindings and use libcmaes from Python code:

• install 'boost-python', on Debian/Ubuntu systems:

sudo apt-get install libboost-python-dev

• the python bindings are yet not part of the production code, so move to the correct branch:

git checkout python

• build the libcmaes with support for Python bindings:

./autogen.sh
./configure --enable-python --with-prefix=/home/yourusername
make
make install

• test the bindings:

cd python
export LD_LIBRARY_PATH=/home/yourusername/lib
python ptest.py

Sample python code:

import lcmaes

# input parameters for a 10-D problem
x = [10]*10
olambda = 10 # lambda is a reserved keyword in python, using olambda instead.
seed = 0 # 0 for seed auto-generated within the lib.
sigma = 0.1
p = lcmaes.make_simple_parameters(x,sigma,olambda,seed)

# objective function.
def nfitfunc(x,n):
    val = 0.0
    for i in range(0,n):
        val += x[i]*x[i]
    return val

# generate a function object
objfunc = lcmaes.fitfunc_pbf.from_callable(nfitfunc);

# pass the function and parameter to cmaes, run optimization and collect solution object.
cmasols = lcmaes.pcmaes(objfunc,p)

# collect and inspect results
bcand = cmasols.best_candidate()
bx = lcmaes.get_candidate_x(bcand)
print "best x=",bx
print "distribution mean=",lcmaes.get_solution_xmean(cmasols)
cov = lcmaes.get_solution_cov(cmasols) # numpy array
print "cov=",cov
print "elapsed time=",cmasols.elapsed_time(),"ms"