def __update_from_symbolic__(self):
import numpy
# updates calibration according to the symbolic definitions
system = self.symbolic.calibration_dict
from dolo.compiler.triangular_solver import solve_triangular_system
self.calibration_dict = solve_triangular_system( system )
from dolo.compiler.misc import calibration_to_vector
self.calibration = calibration_to_vector(self.symbols, self.calibration_dict)
from symbolic_eval import NumericEval
evaluator = NumericEval(self.calibration_dict)
# read symbolic structure
covariances = evaluator.eval(self.symbolic.covariances)
if covariances is None:
self.covariances = covariances
else:
self.covariances = numpy.atleast_2d(numpy.array(covariances, dtype=float))
markov_chain = evaluator.eval(self.symbolic.markov_chain)
if markov_chain is None:
self.markov_chain = None
else:
self.markov_chain = [numpy.atleast_2d(numpy.array(tab, dtype=float)) for tab in markov_chain]
self.options = evaluator.eval(self.symbolic.options)
def __update_from_symbolic__(self):
import numpy
# updates calibration according to the symbolic definitions
system = self.symbolic.calibration_dict
from dolo.compiler.triangular_solver import solve_triangular_system
self.calibration_dict = solve_triangular_system( system )
from dolo.compiler.misc import CalibrationDict, calibration_to_vector
calib = calibration_to_vector(self.symbols, self.calibration_dict)
self.calibration = CalibrationDict(self.symbols, calib)
from .symbolic_eval import NumericEval
evaluator = NumericEval(self.calibration_dict)
# read symbolic structure
self.options = evaluator.eval(self.symbolic.options)
distribution = evaluator.eval(self.symbolic.distribution)
discrete_transition = evaluator.eval(self.symbolic.discrete_transition)
covariances = distribution
if distribution is None:
self.covariances = None
else:
self.covariances = numpy.atleast_2d(numpy.array(covariances, dtype=float))
markov_chain = discrete_transition
if markov_chain is None:
self.markov_chain = None
else:
self.markov_chain = [numpy.atleast_2d(numpy.array(tab, dtype=float)) for tab in markov_chain]
def calibration(self):
if self.__calibration__ is None:
calibration_dict = super(self.__class__, self).get_calibration()
from dolo.compiler.misc import CalibrationDict, calibration_to_vector
calib = calibration_to_vector(self.symbols, calibration_dict)
self.__calibration__ = CalibrationDict(self.symbols, calib) #
return self.__calibration__
def calibration(self):
if self.__calibration__ is None:
calibration_dict = super(self.__class__, self).get_calibration()
from dolo.compiler.misc import CalibrationDict, calibration_to_vector
calib = calibration_to_vector(self.symbols, calibration_dict)
self.__calibration__ = CalibrationDict(self.symbols, calib) #
return self.__calibration__
def __update_from_symbolic__(self):
import numpy
# updates calibration according to the symbolic definitions
system = self.symbolic.calibration_dict
from dolo.compiler.triangular_solver import solve_triangular_system
self.calibration_dict = solve_triangular_system(system)
from dolo.compiler.misc import CalibrationDict, calibration_to_vector
calib = calibration_to_vector(self.symbols, self.calibration_dict)
self.calibration = CalibrationDict(self.symbols, calib)
from .symbolic_eval import NumericEval
evaluator = NumericEval(self.calibration_dict)
# read symbolic structure
self.options = evaluator.eval(self.symbolic.options)
self.exogenous = self.get_exogenous()
self.domain = self.get_domain()
def __update_from_symbolic__(self):
import numpy
# updates calibration according to the symbolic definitions
system = self.symbolic.calibration_dict
from dolo.compiler.triangular_solver import solve_triangular_system
self.calibration_dict = solve_triangular_system( system )
from dolo.compiler.misc import CalibrationDict, calibration_to_vector
calib = calibration_to_vector(self.symbols, self.calibration_dict)
self.calibration = CalibrationDict(self.symbols, calib)
from .symbolic_eval import NumericEval
evaluator = NumericEval(self.calibration_dict)
# read symbolic structure
self.options = evaluator.eval(self.symbolic.options)
#
# distribution = evaluator.eval(self.symbolic.options.get('distribution'))
# discrete_transition = evaluator.eval(self.symbolic.options.get('discrete_transition'))
distribution = self.options.get('distribution')
discrete_transition = self.options.get('discrete_transition')
self.distribution = distribution
self.discrete_transition = discrete_transition
if distribution is None:
self.covariances = None
else:
self.covariances = numpy.atleast_2d(numpy.array(distribution.sigma, dtype=float))
markov_chain = discrete_transition
if markov_chain is None:
self.markov_chain = None
else:
markov_chain = [markov_chain.P, markov_chain.Q]
self.markov_chain = [numpy.atleast_2d(numpy.array(tab, dtype=float)) for tab in markov_chain]
def calibration(self):
if self.__calibration__ is None:
calibration_dict = self.__get_calibration__()
calib = calibration_to_vector(self.symbols, calibration_dict)
self.__calibration__ = CalibrationDict(self.symbols, calib) #
return self.__calibration__
z="zbar",
rk="1/β-1+δ",
w="(1-α)*exp(z)*(k/n)**(α)",
k="n/(rk/α)**(1/(1-α))",
y="exp(z)*k**α*n**(1-α)",
i="δ*k",
c="y - i",
V="log(c)/(1-β)",
u="c**(1-σ)/(1-σ) - χ*n**(1+η)/(1+η)",
m="β/c**σ*(1-δ+rk)")
from dolang.triangular_solver import solve_triangular_system
calibration_dict = solve_triangular_system(calibration_strings)
calibration_vector = calibration_to_vector(symbols, calibration_dict)
calibration = CalibrationDict(symbols, calibration_vector)
calibration
###
### Define functions
###
# the basic formulation of the functions (aka kernel)
# take and return "tuples" of floats because tuples are supposed
# to be easy to be optimzed away by the compiler
# after the definition of the function, there is some boilerplate
# to translate these functions into broadcastable ones, with
# optional numerical differentiation capabilities
