def optimize(solver, target='rosen', **kwds):
if target == 'rosen': # 3d-rosenbrock
# Rosenbrock function
from dejong import rosen as the_model
ndim = 3
actual_coeffs = [1.0] * ndim
pprint = list
else: # 4th-order chebyshev
# Chebyshev cost function
from poly import chebyshev4cost as the_model
from poly import chebyshev4coeffs as actual_coeffs
ndim = len(actual_coeffs)
from mystic.math import poly1d as pprint
# number of trials
print("One trial:")
print("===============")
# initial guess
import random
x0 = [random.uniform(-100, 100) for i in range(ndim)]
# minimize the function
results = the_solver(the_model, x0, **kwds)
print("===============")
print("Actual params:\n %s" % pprint(actual_coeffs))
print("Solved params:\n %s" % pprint(results[0]))
print("Function value: %s" % results[1])
print("Total function evals: %s" % results[3])
return
def optimize(solver, target='rosen', **kwds):
if target == 'rosen': # 3d-rosenbrock
# Rosenbrock function
from dejong import rosen as the_model
ndim = 3
actual_coeffs = [1.0] * ndim
pprint = list
else: # 4th-order chebyshev
# Chebyshev cost function
from poly import chebyshev4cost as the_model
from poly import chebyshev4coeffs as actual_coeffs
ndim = len(actual_coeffs)
from mystic.math import poly1d as pprint
# number of trials
print("One trial:")
print("===============")
# initial guess
import random
x0 = [random.uniform(-100,100) for i in range(ndim)]
# minimize the function
results = the_solver(the_model, x0, **kwds)
print("===============")
print("Actual params:\n %s" % pprint(actual_coeffs))
print("Solved params:\n %s" % pprint(results[0]))
print("Function value: %s" % results[1])
print("Total function evals: %s" % results[3])
return
def powell_chebyshev(x0, *args, **kwds):
# Powell's Directonal solver
from optimize_helper import fmin_powell as the_solver
# Chebyshev cost function
from poly import chebyshev4cost as the_model
return the_solver(the_model, x0, monitor=False, *args, **kwds)
def powell_chebyshev(x0, *args, **kwds):
# Powell's Directonal solver
from optimize_helper import fmin_powell as the_solver
# Chebyshev cost function
from poly import chebyshev4cost as the_model
return the_solver(the_model, x0, monitor=False, *args, **kwds)
def powell_rosen(x0, *args, **kwds):
# Powell's Directonal solver
from optimize_helper import fmin_powell as the_solver
# Rosenbrock function
from dejong import rosen as the_model
return the_solver(the_model, x0, monitor=False, *args, **kwds)
def powell_rosen(x0, *args, **kwds):
# Powell's Directonal solver
from optimize_helper import fmin_powell as the_solver
# Rosenbrock function
from dejong import rosen as the_model
return the_solver(the_model, x0, monitor=False, *args, **kwds)
def diffev_chebyshev(x0, *args, **kwds):
# Differential Evolution solver
from optimize_helper import diffev as the_solver
# Chebyshev cost function
from poly import chebyshev4cost as the_model
return the_solver(the_model, x0, monitor=True, *args, **kwds)
def diffev_chebyshev(x0, *args, **kwds):
# Differential Evolution solver
from optimize_helper import diffev as the_solver
# Chebyshev cost function
from poly import chebyshev4cost as the_model
return the_solver(the_model, x0, monitor=True, *args, **kwds)