cmodel = CModel(model)
s0 = numpy.atleast_2d(dr.S_bar).T
horizon = 50
simul = simulate(cmodel,
dr,
s0,
sigma,
n_exp=500,
parms=parms,
seed=1,
horizon=horizon)
from dolo.numeric.quantization import quantization_nodes
N = 80
[x, w] = quantization_nodes(N, sigma)
simul_2 = simulate(cmodel,
dr,
s0,
sigma,
n_exp=500,
parms=parms,
horizon=horizon,
seed=1,
solve_expectations=True,
nodes=x,
weights=w)
from matplotlib.pyplot import hist, show, figure, plot
timevec = numpy.array(range(simul.shape[2]))
def global_solve(model,
bounds=None, verbose=False,
initial_dr=None, pert_order=2,
interp_type='smolyak', smolyak_order=3, interp_orders=None,
maxit=500, numdiff=True, polish=True, tol=1e-8,
integration='gauss-hermite', integration_orders=[],
compiler='numpy', memory_hungry=True,
T=200, n_s=2, N_e=40 ):
def vprint(t):
if verbose:
print(t)
[y, x, parms] = model.read_calibration()
sigma = model.read_covariances()
if initial_dr == None:
initial_dr = approximate_controls(model, order=pert_order)
if interp_type == 'perturbations':
return initial_dr
if bounds is not None:
pass
elif 'approximation' in model['original_data']:
vprint('Using bounds specified by model')
# this should be moved to the compiler
ssmin = model['original_data']['approximation']['bounds']['smin']
ssmax = model['original_data']['approximation']['bounds']['smax']
ssmin = [model.eval_string(str(e)) for e in ssmin]
ssmax = [model.eval_string(str(e)) for e in ssmax]
ssmin = [model.eval_string(str(e)) for e in ssmin]
ssmax = [model.eval_string(str(e)) for e in ssmax]
[y, x, p] = model.read_calibration()
d = {v: y[i] for i, v in enumerate(model.variables)}
d.update({v: p[i] for i, v in enumerate(model.parameters)})
smin = [expr.subs(d) for expr in ssmin]
smax = [expr.subs(d) for expr in ssmax]
smin = numpy.array(smin, dtype=numpy.float)
smax = numpy.array(smax, dtype=numpy.float)
bounds = numpy.row_stack([smin, smax])
bounds = numpy.array(bounds, dtype=float)
else:
vprint('Using bounds given by second order solution.')
from dolo.numeric.timeseries import asymptotic_variance
# this will work only if initial_dr is a Taylor expansion
Q = asymptotic_variance(initial_dr.A.real, initial_dr.B.real, initial_dr.sigma, T=T)
devs = numpy.sqrt(numpy.diag(Q))
bounds = numpy.row_stack([
initial_dr.S_bar - devs * n_s,
initial_dr.S_bar + devs * n_s,
])
smin = bounds[0, :]
smax = bounds[1, :]
if interp_orders == None:
interp_orders = [5] * bounds.shape[1]
if interp_type == 'smolyak':
from dolo.numeric.interpolation.smolyak import SmolyakGrid
dr = SmolyakGrid(bounds[0, :], bounds[1, :], smolyak_order)
elif interp_type == 'spline':
from dolo.numeric.interpolation.splines import MultivariateSplines
dr = MultivariateSplines(bounds[0, :], bounds[1, :], interp_orders)
elif interp_type == 'multilinear':
from dolo.numeric.interpolation.multilinear import MultilinearInterpolator
dr = MultilinearInterpolator(bounds[0, :], bounds[1, :], interp_orders)
elif interp_type == 'sparse_linear':
from dolo.numeric.interpolation.interpolation import SparseLinear
dr = SparseLinear(bounds[0, :], bounds[1, :], smolyak_order)
elif interp_type == 'linear':
from dolo.numeric.interpolation.interpolation import LinearTriangulation, TriangulatedDomain, RectangularDomain
rec = RectangularDomain(smin, smax, interp_orders)
domain = TriangulatedDomain(rec.grid)
dr = LinearTriangulation(domain)
from dolo.compiler.compiler_global import CModel
cm = CModel(model, solve_systems=True, compiler=compiler)
cm = cm.as_type('fg')
if integration == 'optimal_quantization':
from dolo.numeric.quantization import quantization_nodes
# number of shocks
[epsilons, weights] = quantization_nodes(N_e, sigma)
elif integration == 'gauss-hermite':
from dolo.numeric.quadrature import gauss_hermite_nodes
if not integration_orders:
integration_orders = [3] * sigma.shape[0]
[epsilons, weights] = gauss_hermite_nodes(integration_orders, sigma)
vprint('Starting time iteration')
from dolo.numeric.global_solution import time_iteration
from dolo.numeric.global_solution import stochastic_residuals_2, stochastic_residuals_3
xinit = initial_dr(dr.grid)
xinit = xinit.real # just in case...
dr = time_iteration(dr.grid, dr, xinit, cm.f, cm.g, parms, epsilons, weights, x_bounds=cm.x_bounds, maxit=maxit,
tol=tol, nmaxit=50, numdiff=numdiff, verbose=verbose)
if polish and interp_type == 'smolyak': # this works with smolyak only
vprint('\nStarting global optimization')
import time
t1 = time.time()
if cm.x_bounds is not None:
lb = cm.x_bounds[0](dr.grid, parms)
ub = cm.x_bounds[1](dr.grid, parms)
else:
lb = None
ub = None
xinit = dr(dr.grid)
dr.set_values(xinit)
shape = xinit.shape
if not memory_hungry:
fobj = lambda t: stochastic_residuals_3(dr.grid, t, dr, cm.f, cm.g, parms, epsilons, weights, shape, deriv=False)
dfobj = lambda t: stochastic_residuals_3(dr.grid, t, dr, cm.f, cm.g, parms, epsilons, weights, shape, deriv=True)[1]
else:
fobj = lambda t: stochastic_residuals_2(dr.grid, t, dr, cm.f, cm.g, parms, epsilons, weights, shape, deriv=False)
dfobj = lambda t: stochastic_residuals_2(dr.grid, t, dr, cm.f, cm.g, parms, epsilons, weights, shape, deriv=True)[1]
from dolo.numeric.solver import solver
x = solver(fobj, xinit, lb=lb, ub=ub, jac=dfobj, verbose=verbose, method='ncpsolve', serial_problem=False)
dr.set_values(x) # just in case
t2 = time.time()
# test solution
res = stochastic_residuals_2(dr.grid, x, dr, cm.f, cm.g, parms, epsilons, weights, shape, deriv=False)
if numpy.isinf(res.flatten()).sum() > 0:
raise ( Exception('Non finite values in residuals.'))
vprint('Finished in {} s'.format(t2 - t1))
return dr
sigma = model.read_covariances()
from dolo.compiler.compiler_global import CModel
import numpy
cmodel = CModel(model)
s0 = numpy.atleast_2d( dr.S_bar ).T
horizon = 50
simul = simulate(cmodel, dr, s0, sigma, n_exp=500, parms=parms, seed=1, horizon=horizon)
from dolo.numeric.quantization import quantization_nodes
N = 80
[x,w] = quantization_nodes(N, sigma)
simul_2 = simulate(cmodel, dr, s0, sigma, n_exp=500, parms=parms, horizon=horizon, seed=1, solve_expectations=True, nodes=x, weights=w)
from matplotlib.pyplot import hist, show, figure, plot
timevec = numpy.array(range(simul.shape[2]))
figure()
plot(simul[0,0,:] - simul_2[0,0,:])
show()
#
# figure()
# plot(simul[0,0,:])