Simulator¶

This is the main class used to run the different algorithms using a user specified model class

class pyparticleest.simulator.Simulator(model, u, y)¶

Class interfacing filters/smoothers to assisst in solving estimation problem

Args:

model: object of class describing problem type
u (array-like): inputs, first dimension is the time index, the rest is specific to the particlar model class being used
y (array-like): measurements, first dimension is the time index, the rest is specific to the particlar model class being used

get_filtered_estimates()¶

Returns type (est, w) (must first have called ‘simulate’)

est: (T, N, D) array containing all particles
w: (T,D) array containing all particle weights

T is the length of the dataset, N is the number of particles and D is the dimension of each particle

get_filtered_mean()¶

Calculate mean of filtered estimates (must first have called ‘simulate’)

Returns:

(T, D) array

T is the length of the dataset, N is the number of particles and D is the dimension of each particle

get_smoothed_estimates()¶

Return smoothed estimates (must first have called ‘simulate’)

Returns:

(T, N, D) array

T is the length of the dataset, N is the number of particles D is the dimension of each particle

get_smoothed_mean()¶

Calculate mean of smoothed estimates (must first have called ‘simulate’)

Returns:

(T, D) array

T is the length of the dataset, N is the number of particles and D is the dimension of each particle

set_params(params)¶

Set the parameters of the model (if any)

Args:

params (array-like): Model specific paremeters

simulate(num_part, num_traj, filter='PF', filter_options=None, smoother='full', smoother_options=None, res=0.67, meas_first=False)¶

Solve the estimation problem

Args:

num_part (int): Number of particles used in the forward filter.
num_traj (int): Number of backward trajectories generated by the smoother.
filter (string): The filter algorithm to use
smooter (string): The smoothing algorithm to use
smoother_options (dict): algorithm specific smoother options
res (float): resampling threshold for the forward filter
meas_first (bool): Is the first measurement of the initial state (true) or after the first time update? (false)

Supported filters:

‘pf’: regular particle filter
‘apf’: auxilliary particle filter

Supported smoothers:

‘ancestor’: return forward trajectories from particle filtier (no extra smoothing step)
‘full’: Backward simulation evaluating all particle weights
‘rs’: Rejection sampling (with early stopping)
Options:
R: number of rejection sampling steps before falling back to ‘full’
‘rsas’: Rejection sampling with early stopping.
Options:
x1 (float): (default is 1.0)

P1 (float): (default is 1.0)

sv (float): (default is 1.0)

sw (float): (default is 1.0)

ratio (float): (default is 1.0)
‘mcmc’: Metropolis-Hastings FFBSi
Options:
R: number of iterations to run the Markov chain
‘mhips’: Metropolis-Hastings Improved Particle Smoother
Options:
R: number of passes of the dataset to run the algortithms
‘mhbp’: Metropolis-Hastings Backward Proposer
Options:
R: the number of iterations to run the Markov chain for each time step

Parameter Estimator¶

This is an extension of the Simulator class which allows parameter estimation

class pyparticleest.paramest.paramest.ParamEstimation(model, u, y)¶

Extension of the Simulator class to iterative perform particle smoothing combined with a gradienst search algorithms for maximizing the likelihood of the parameter estimates

maximize(param0, num_part, num_traj, max_iter=1000, tol=0.001, callback=None, callback_sim=None, meas_first=False, filter='pf', smoother='full', smoother_options=None)¶

Find the maximum likelihood estimate of the paremeters using an EM-algorihms combined with a gradient search algorithms

Args:

param0 (array-like): Initial parameter estimate
num_part (int/array-like): Number of particle to use in the forward filter if array each iteration takes the next element from the array when setting up the filter
num_traj (int/array-like): Number of smoothed trajectories to create if array each iteration takes the next element from the array when setting up the smoother
max_iter (int): Max number of EM-iterations to perform
tol (float): When the different in loglikelihood between two iterations is less that his value the algorithm is terminated
callback (function): Callback after each EM-iteration with new estimate
callback_sim (function): Callback after each simulation
bounds (array-like): Hard bounds on parameter estimates
meas_first (bool): If true, first measurement occurs before the first time update
smoother (string): Which particle smoother to use
smoother_options (dict): Extra options for the smoother
analytic_gradient (bool): Use analytic gradient (requires that the model implements ParamEstInterface_GradientSearch)