def optimize(cost,lower,upper):
# generate arrays of points defining a grid in parameter space
grid_dimensions = len(lower)
bins = []
for i in range(grid_dimensions):
step = abs(upper[i] - lower[i])/nbins[i]
bins.append( [lower[i] + (j+0.5)*step for j in range(nbins[i])] )
# build a grid of starting points
initial_values = gridpts(bins)
# run optimizer for each grid point
lb = [lower for i in range(len(initial_values))]
ub = [upper for i in range(len(initial_values))]
cf = [cost for i in range(len(initial_values))]
# map:: params, energy, func_evals = local_optimize(cost,x0,lb,ub)
results = map(local_optimize, cf, initial_values, lb, ub)
#print "results = %s" % results
# get the results with the lowest energy
best = list(results[0][0]), results[0][1]
func_evals = results[0][2]
for result in results[1:]:
func_evals += result[2] # add function evaluations
if result[1] < best[1]: # compare energy
best = list(result[0]), result[1]
# return best
print "solved: %s" % best[0]
diameter_squared = -best[1] / scale #XXX: scale != 0
return diameter_squared, func_evals
def optimize(cost,lower,upper):
# generate arrays of points defining a grid in parameter space
grid_dimensions = len(lower)
bins = []
for i in range(grid_dimensions):
step = abs(upper[i] - lower[i])/nbins[i]
bins.append( [lower[i] + (j+0.5)*step for j in range(nbins[i])] )
# build a grid of starting points
initial_values = gridpts(bins)
# run optimizer for each grid point
lb = [lower for i in range(len(initial_values))]
ub = [upper for i in range(len(initial_values))]
cf = [cost for i in range(len(initial_values))]
# map:: params, energy, func_evals = local_optimize(cost,x0,lb,ub)
results = map(local_optimize, cf, initial_values, lb, ub)
#print "results = %s" % results
# get the results with the lowest energy
best = list(results[0][0]), results[0][1]
func_evals = results[0][2]
for result in results[1:]:
func_evals += result[2] # add function evaluations
if result[1] < best[1]: # compare energy
best = list(result[0]), result[1]
# return best
print "solved: %s" % best[0]
diameter_squared = -best[1] / scale #XXX: scale != 0
return diameter_squared, func_evals
def optimize(cost, lower, upper, nbins):
from mystic.tools import random_seed
from pyina.launchers import TorqueMpi as Pool
random_seed(123)
# generate arrays of points defining a grid in parameter space
grid_dimensions = len(lower)
bins = []
for i in range(grid_dimensions):
step = abs(upper[i] - lower[i]) / nbins[i]
bins.append([lower[i] + (j + 0.5) * step for j in range(nbins[i])])
# build a grid of starting points
from mystic.math.grid import gridpts
from pool_helper import local_optimize
from pool_helper import nnodes, queue, timelimit
initial_values = gridpts(bins)
# run optimizer for each grid point
lb = [lower for i in range(len(initial_values))]
ub = [upper for i in range(len(initial_values))]
cf = [cost for i in range(len(initial_values))]
# map:: params, energy, func_evals = local_optimize(cost,x0,lb,ub)
config = {'queue': queue, 'timelimit': timelimit}
results = Pool(nnodes, **config).map(local_optimize, cf, initial_values,
lb, ub)
#print("results = %s" % results)
# get the results with the lowest energy
best = list(results[0][0]), results[0][1]
func_evals = results[0][2]
for result in results[1:]:
func_evals += result[2] # add function evaluations
if result[1] < best[1]: # compare energy
best = list(result[0]), result[1]
# return best
print("solved: %s" % best[0])
scale = 1.0
diameter_squared = -best[1] / scale #XXX: scale != 0
return diameter_squared, func_evals
def optimize(cost,lower,upper,nbins):
from mystic.tools import random_seed
from pyina.launchers import TorqueMpi as Pool
random_seed(123)
# generate arrays of points defining a grid in parameter space
grid_dimensions = len(lower)
bins = []
for i in range(grid_dimensions):
step = abs(upper[i] - lower[i])/nbins[i]
bins.append( [lower[i] + (j+0.5)*step for j in range(nbins[i])] )
# build a grid of starting points
from mystic.math.grid import gridpts
from pool_helper import local_optimize
from pool_helper import nnodes, queue, timelimit
initial_values = gridpts(bins)
# run optimizer for each grid point
lb = [lower for i in range(len(initial_values))]
ub = [upper for i in range(len(initial_values))]
cf = [cost for i in range(len(initial_values))]
# map:: params, energy, func_evals = local_optimize(cost,x0,lb,ub)
config = {'queue':queue, 'timelimit':timelimit}
results = Pool(nnodes, **config).map(local_optimize,cf,initial_values,lb,ub)
#print "results = %s" % results
# get the results with the lowest energy
best = list(results[0][0]), results[0][1]
func_evals = results[0][2]
for result in results[1:]:
func_evals += result[2] # add function evaluations
if result[1] < best[1]: # compare energy
best = list(result[0]), result[1]
# return best
print "solved: %s" % best[0]
scale = 1.0
diameter_squared = -best[1] / scale #XXX: scale != 0
return diameter_squared, func_evals