print("Powell's Method")
print("===============")
# dimensional information
from mystic.tools import random_seed
random_seed(12)
ndim = len(pointvec)
nbins = 1 #[2,1,2,1,2,1,2,1,1]
# configure monitor
stepmon = VerboseMonitor(1)
# use lattice-Powell to solve 8th-order Chebyshev coefficients
solver = LatticeSolver(ndim, nbins)
solver.SetNestedSolver(PowellDirectionalSolver)
solver.SetMapper(Pool().map)
solver.SetGenerationMonitor(stepmon)
solver.SetStrictRanges(min=[0]*ndim, max=[1]*ndim)
solver.SetConstraints(constraintfunc)
solver.Solve(test_obj, NCOG(1e+2), disp=1)
solution = solver.Solution()
# use pretty print for polynomials
print(solution)
for i in list(range(0,len(pointvec))):
if round(solution[i])==1:
print(namevec[i])
print(team_c(solution))
print("===============")
# dimensional information
from mystic.tools import random_seed
random_seed(123)
ndim = 9
nbins = 8 #[2,1,2,1,2,1,2,1,1]
# draw frame and exact coefficients
plot_exact()
# configure monitor
stepmon = VerboseMonitor(1)
# use lattice-Powell to solve 8th-order Chebyshev coefficients
solver = LatticeSolver(ndim, nbins)
solver.SetNestedSolver(PowellDirectionalSolver)
solver.SetMapper(Pool().map)
solver.SetGenerationMonitor(stepmon)
solver.SetStrictRanges(min=[-300] * ndim, max=[300] * ndim)
solver.Solve(chebyshev8cost, NCOG(1e-4), disp=1)
solution = solver.Solution()
# use pretty print for polynomials
print(poly1d(solution))
# compare solution with actual 8th-order Chebyshev coefficients
print("\nActual Coefficients:\n %s\n" % poly1d(chebyshev8coeffs))
# plot solution versus exact coefficients
plot_solution(solution)
if ide < 55:
g = proc.processDataForOptimizing
elif ide > 54 and ide < 109:
g = proc.processDataForOptimizingUpdate1
else:
g = proc.processDataForOptimizingBL
my_solver = PowellDirectionalSolver(
dim
) # initializes the PowellDirectionalSolver with the number of parameters
my_solver.SetStrictRanges(
lb, ub
) # sets the range that the solutions must be within for PowellDirectionalSolver
start = time.time()
solver = LatticeSolver(
dim, nbins=2
) # initalizes the LatticeSolver with the number of parameters and the number of bins for starting points
solver.SetStrictRanges(
lb, ub
) # sets the range that the solutions must be within for the LatticeSolver
solver.SetNestedSolver(
my_solver) # nests the PowellDirectionalSolver within the LatticeSolver
solver.SetMapper(Pool(nodes=100).map)
solver.Solve(g) # LatticeSolver optimizes on the function g
sol = solver.Solution(
) # sets sol to the list of values that each parameter is optimized to
stop = time.time()
lst = []
for j in range(len(sol)):
lst.append(
params[j] + "->" + str(sol[j])