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StateVar.py
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Sat, Jul 5, 09:11

StateVar.py
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# -*- coding: utf-8 -*-
### Module import
import numpy as np
import scipy.stats as st
class StateVar:
"""
This class is used to store and compute all the information related to the
hidden variables (e.g. intial condition, transition matrix, domain).
"""
def __init__(self, name_variable, l_boundaries, nb_substates,
f_trans, l_parameters_f_trans, dt):
"""
Constructor of StateVar.
Parameters
----------
name_variable : string
Name of the current hidden variable.
l_boundaries : list
Tuple containing the boundaries of the variable domain.
nb_substates : integer
Number of states of the current hidden variable.
f_trans : function
Transition kernel of the current process.
l_parameters_f_trans : list
List of parameters needed to compute the transition kernel.
dt : float
Time resolution.
"""
self.name_variable = name_variable
self.l_boundaries = l_boundaries
self.nb_substates = nb_substates
self.increment = (l_boundaries[1]-l_boundaries[0])/nb_substates
self.domain = np.linspace(l_boundaries[0],l_boundaries[1],
nb_substates, endpoint=False)
self.f_trans = f_trans
self.l_parameters_f_trans = l_parameters_f_trans
self.TR = self.buildTransitionMatrix(dt)
self.pi = self.computeInitialDistribution(dt)
def buildTransitionMatrix(self, dt):
"""
Build the transition matrix for every state according to a gaussian
distribution.
Parameters
----------
dt : float
Time resolution.
Returns
-------
Transition matrix of the current variable.
"""
TR = np.zeros([self.nb_substates, self.nb_substates])
for i, xi in enumerate(self.domain):
mean, std = self.f_trans(xi, self.l_parameters_f_trans, dt)
#TR[i,:] = st.norm.cdf(self.domain+self.increment/2 , mean, std)
# - st.norm.cdf(self.domain-self.increment/2 , mean, std)
TR[i,:] = st.norm.pdf(self.domain, mean, std)
#TR[i,:] = TR[i,:]/np.sum(TR[i,:])
return TR
def computeInitialDistribution(self, dt):
"""
Compute the initial distribution of the current variable.
Parameters
----------
dt : float
Time resolution.
Returns
-------
Array containing the initial distribution of the current variable.
"""
#mean, std = self.f_trans(self.l_parameters_f_trans[0],
# self.l_parameters_f_trans, dt)
#pi = st.norm.pdf(self.domain, mean , std)
#pi = pi/np.sum(pi)
pi = np.array([1/self.nb_substates]*self.nb_substates)
return pi
def return_increment(self, xi, dt):
"""
Compute the random increment of the current process given the current
state (useful for stochastic simulations).
Parameters
----------
xi : float
Current value of the process.
dt : float
Time resolution.
Returns
-------
New value of the process after the random increment.
"""
mean, std = self.f_trans(xi, self.l_parameters_f_trans, dt)
xi2 = st.norm.rvs( mean, std)
return xi2

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