def deterministic_solve(model, start_s, T=500):
# simulation is assumed to return to steady-state
dr = approximate_controls(model, order=1, )
final_s = model.calibration['states'].copy()
final_x = model.calibration['controls'].copy()
start_x = dr( numpy.atleast_2d( start_s.ravel()).T ).flatten()
final = numpy.concatenate( [final_s, final_x])
start = numpy.concatenate( [start_s, start_x])
print(final)
print(start)
n_s = len(final_s)
n_x = len(final_x)
initial_guess = numpy.concatenate( [start*(1-l) + final*l for l in linspace(0.0,1.0,T+1)] )
initial_guess = initial_guess.reshape( (-1, n_s + n_x)).T
# initial_guess = initial_guess[n_s:-n_x]
res = det_residual(model, initial_guess, start_s, final_x)
print(abs(res).max())
sh = initial_guess.shape
sh = initial_guess.shape
fobj = lambda vec: det_residual(model, vec.reshape(sh), start_s, final_x).ravel()
res = fobj( initial_guess.ravel() )
from scipy.optimize import newton_krylov, fsolve
import time
t = time.time()
sol = fsolve(fobj, initial_guess.ravel() )
sol = sol.reshape(sh)
print(fobj(sol))
# sol = newton_krylov(fobj, initial_guess)
s = time.time()
print('Elapsed : {}'.format(s-t))
return sol
def deterministic_solve(model, start_s, T=500):
# simulation is assumed to return to steady-state
dr = approximate_controls(model, order=1)
final_s = model.calibration["states"].copy()
final_x = model.calibration["controls"].copy()
start_x = dr(numpy.atleast_2d(start_s.ravel()).T).flatten()
final = numpy.concatenate([final_s, final_x])
start = numpy.concatenate([start_s, start_x])
print(final)
print(start)
n_s = len(final_s)
n_x = len(final_x)
initial_guess = numpy.concatenate([start * (1 - l) + final * l for l in linspace(0.0, 1.0, T + 1)])
initial_guess = initial_guess.reshape((-1, n_s + n_x)).T
# initial_guess = initial_guess[n_s:-n_x]
res = det_residual(model, initial_guess, start_s, final_x)
print(abs(res).max())
sh = initial_guess.shape
sh = initial_guess.shape
fobj = lambda vec: det_residual(model, vec.reshape(sh), start_s, final_x).ravel()
res = fobj(initial_guess.ravel())
from scipy.optimize import newton_krylov, fsolve
import time
t = time.time()
sol = fsolve(fobj, initial_guess.ravel())
sol = sol.reshape(sh)
print(fobj(sol))
# sol = newton_krylov(fobj, initial_guess)
s = time.time()
print("Elapsed : {}".format(s - t))
return sol
def test_eval_formula():
from dolo.compiler.eval_formula import eval_formula
from dolo import yaml_import, approximate_controls, simulate
model = yaml_import('examples/models/rbc.yaml')
dr = approximate_controls(model)
sim = simulate(model, dr)
rr = eval_formula("delta*k-i", sim, context=model.calibration)
rr = eval_formula("y(1) - y", sim, context=model.calibration)
sim['diff'] = model.eval_formula("delta*k-i", sim)
model.eval_formula("y(1) - y", sim)
sim['ddiff'] = model.eval_formula("diff(1)-diff(-1)", sim)
def test_simulations():
from dolo import yaml_import, approximate_controls
model = yaml_import('../../examples/models/rbc.yaml')
dr = approximate_controls(model)
parms = model.calibration['parameters']
sigma = model.covariances
import numpy
s0 = dr.S_bar
horizon = 50
import time
t1 = time.time()
simul = simulate(model, dr, s0, sigma, n_exp=1000, parms=parms, seed=1, horizon=horizon)
t2 = time.time()
print("Took: {}".format(t2-t1))
from dolo.numeric.discretization import gauss_hermite_nodes
N = 80
[x,w] = gauss_hermite_nodes(N, sigma)
t3 = time.time()
simul_2 = simulate(model, dr, s0, sigma, n_exp=1000, parms=parms, horizon=horizon, seed=1, solve_expectations=True, nodes=x, weights=w)
t4 = time.time()
print("Took: {}".format(t4-t3))
from matplotlib.pyplot import hist, show, figure, plot, title
timevec = numpy.array(range(simul.shape[2]))
figure()
for k in range(10):
plot(simul[:,k,0] - simul_2[:,k,0])
title("Productivity")
show()
figure()
for k in range(10):
plot(simul[:,k,1] - simul_2[:,k,1])
title("Investment")
show()
#
# figure()
# plot(simul[0,0,:])
# plot(simul_2[0,0,:])
# show()
figure()
for i in range( horizon ):
hist( simul[i,:,0], bins=50 )
show()
def deterministic_solve_comp(model, start_s, T=10):
# simulation is assumed to return to steady-state
dr = approximate_controls(model, order=1, )
final_s = model.calibration['states'].copy()
final_x = model.calibration['controls'].copy()
start_x = dr( numpy.atleast_2d( start_s.ravel()).T ).flatten()
final = numpy.concatenate( [final_s, final_x])
start = numpy.concatenate( [start_s, start_x])
print(final)
print(start)
n_s = len(final_s)
n_x = len(final_x)
initial_guess = numpy.concatenate( [start*(1-l) + final*l for l in linspace(0.0,1.0,T+1)] )
initial_guess = initial_guess.reshape( (-1, n_s + n_x)).T
N = initial_guess.shape[1]
initial_guess = numpy.row_stack( [initial_guess, numpy.zeros( (n_x, N) ), numpy.zeros( (n_x, N) )] )
# initial_guess = initial_guess[n_s:-n_x]
lower_bound = initial_guess*0
lower_bound[:n_s+n_x,:] = -big_number
lower_bound[:,-1] = -big_number
upper_bound = lower_bound*0 + big_number
print( sum( (upper_bound - initial_guess)<=0 ) )
print( sum( (lower_bound - initial_guess)>0 ) )
res = det_residual_comp(model, initial_guess, start_s, final_x)
print(abs(res).max())
sh = initial_guess.shape
fobj = lambda vec: det_residual_comp(model, vec, start_s, final_x)
fobj = lambda vec: det_residual_comp(model, vec.reshape(sh), start_s, final_x).ravel()
from dolo.numeric.solver import MyJacobian
#
dres = MyJacobian(fobj)(initial_guess.ravel())
#
# print(dres.shape)
# print(numpy.linalg.matrix_rank(dres))
f = lambda x: [fobj(x), MyJacobian(fobj)(x)]
import time
t = time.time()
initial_guess = initial_guess.ravel()
lower_bound = lower_bound.ravel()
upper_bound = upper_bound.ravel()
from dolo.numeric.ncpsolve import ncpsolve
sol = ncpsolve( f, lower_bound, upper_bound, initial_guess, verbose=True)
# from dolo.numeric.solver import solver
# sol = solver(fobj, initial_guess, lb=lower_bound, ub=upper_bound, serial_problem=False, method='ncpsolve', verbose=True)
print(sol.shape)
s = time.time()
print('Elapsed : {}'.format(s-t))
return sol[:n_s+n_x,:]
if isinstance(plot_controls, str):
i = controls_names.index(plot_controls)
pyplot.plot(values, xvec[i, :], **kwargs)
else:
for cn in plot_controls:
i = controls_names.index(cn)
pyplot.plot(values, xvec[i, :], label=cn)
pyplot.legend()
pyplot.xlabel(state)
if __name__ == '__main__':
from dolo import yaml_import, approximate_controls
model = yaml_import('../../examples/global_models/capital.yaml')
dr = approximate_controls(model)
[y, x, parms] = model.read_calibration()
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,
i = controls_names.index(plot_controls)
pyplot.plot(values, xvec[i,:], **kwargs)
else:
for cn in plot_controls:
i = controls_names.index(cn)
pyplot.plot(values, xvec[i,:], label=cn)
pyplot.legend()
pyplot.xlabel(state)
if __name__ == '__main__':
from dolo import yaml_import, approximate_controls
model = yaml_import('../../examples/global_models/capital.yaml')
dr = approximate_controls(model)
[y,x,parms] = model.read_calibration()
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)
def deterministic_solve_comp(model, start_s, T=10):
# simulation is assumed to return to steady-state
dr = approximate_controls(model, order=1)
final_s = model.calibration["states"].copy()
final_x = model.calibration["controls"].copy()
start_x = dr(numpy.atleast_2d(start_s.ravel()).T).flatten()
final = numpy.concatenate([final_s, final_x])
start = numpy.concatenate([start_s, start_x])
print(final)
print(start)
n_s = len(final_s)
n_x = len(final_x)
initial_guess = numpy.concatenate([start * (1 - l) + final * l for l in linspace(0.0, 1.0, T + 1)])
initial_guess = initial_guess.reshape((-1, n_s + n_x)).T
N = initial_guess.shape[1]
initial_guess = numpy.row_stack([initial_guess, numpy.zeros((n_x, N)), numpy.zeros((n_x, N))])
# initial_guess = initial_guess[n_s:-n_x]
lower_bound = initial_guess * 0
lower_bound[: n_s + n_x, :] = -big_number
lower_bound[:, -1] = -big_number
upper_bound = lower_bound * 0 + big_number
print(sum((upper_bound - initial_guess) <= 0))
print(sum((lower_bound - initial_guess) > 0))
res = det_residual_comp(model, initial_guess, start_s, final_x)
print(abs(res).max())
sh = initial_guess.shape
fobj = lambda vec: det_residual_comp(model, vec, start_s, final_x)
fobj = lambda vec: det_residual_comp(model, vec.reshape(sh), start_s, final_x).ravel()
from dolo.numeric.solver import MyJacobian
#
dres = MyJacobian(fobj)(initial_guess.ravel())
#
# print(dres.shape)
# print(numpy.linalg.matrix_rank(dres))
f = lambda x: [fobj(x), MyJacobian(fobj)(x)]
import time
t = time.time()
initial_guess = initial_guess.ravel()
lower_bound = lower_bound.ravel()
upper_bound = upper_bound.ravel()
from dolo.numeric.ncpsolve import ncpsolve
sol = ncpsolve(f, lower_bound, upper_bound, initial_guess, verbose=True)
# from dolo.numeric.solver import solver
# sol = solver(fobj, initial_guess, lb=lower_bound, ub=upper_bound, serial_problem=False, method='ncpsolve', verbose=True)
print(sol.shape)
s = time.time()
print("Elapsed : {}".format(s - t))
return sol[: n_s + n_x, :]
def test_simulations():
import time
from matplotlib.pyplot import hist, show, figure, plot, title
from dolo import yaml_import, approximate_controls
from dolo.numeric.discretization import gauss_hermite_nodes
model = yaml_import('../../examples/models/rbc.yaml')
dr = approximate_controls(model)
parms = model.calibration['parameters']
sigma = model.get_calibration().sigma
s0 = dr.S_bar
horizon = 50
t1 = time.time()
simul = simulate(model,
dr,
s0,
sigma,
n_exp=1000,
parms=parms,
seed=1,
horizon=horizon)
t2 = time.time()
print("Took: {}".format(t2 - t1))
N = 80
[x, w] = gauss_hermite_nodes(N, sigma)
t3 = time.time()
simul_2 = simulate(model,
dr,
s0,
sigma,
n_exp=1000,
parms=parms,
horizon=horizon,
seed=1,
solve_expectations=True,
nodes=x,
weights=w)
t4 = time.time()
print("Took: {}".format(t4 - t3))
timevec = numpy.array(range(simul.shape[2]))
figure()
for k in range(10):
plot(simul[:, k, 0] - simul_2[:, k, 0])
title("Productivity")
show()
figure()
for k in range(10):
plot(simul[:, k, 1] - simul_2[:, k, 1])
title("Investment")
show()
#
# figure()
# plot(simul[0,0,:])
# plot(simul_2[0,0,:])
# show()
figure()
for i in range(horizon):
hist(simul[i, :, 0], bins=50)
show()
