A user can write any Python function and optimize it using CrossEntropy. Here is template code:

#objective function for CrossEntropy

def MyObjective():

error = 0.0

#do something here, e.g. call tc_getSteadyState or tc_simulateDeterministic

return error

#optimization parameters

minimize = False

maxruns = 100

numPoints = 100

title = "My Optimization Function"

#optimize

result = CrossEntropy.OptimizeParameters(FitFormula_Objective, title, maxruns, numPoints, minimize)

mu = result[0]

sigma = result[1]

paramnames = result[2]

CrossEntropy.DoPCA(mu, sigma, paramnames)

#set the optimized parameters in the model if you want

n = len(mu)

params = tc_createMatrix(n, 1)

for i in range(0,n):

tc_setMatrixValue(params, i, 0, mu[i])

tc_setRowName(params, i, paramnames[i])

tc_setParameters(params,1)

CrossEntropy.DoPCA will generate a summary file such as the one below.

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