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pfilter

Basic Python particle filter. Plain SIR filtering, with various resampling algorithms. Written to be simple and clear; not necessarily most efficient or most flexible implementation. Depends on NumPy only.

Uses

This repo is useful for understanding how a particle filter works, or a quick way to develop a custom filter of your own from a relatively simple codebase.

Alternatives

There are more mature and sophisticated packages for probabilistic filtering in Python (especially for Kalman filtering) if you want an off-the-shelf solution:

Particle filtering

• particles Extensive particle filtering, including smoothing and quasi-SMC algorithms
• FilterPy Provides extensive Kalman filtering and basic particle filtering.
• pyfilter provides Unscented Kalman Filtering, Sequential Importance Resampling and Auxiliary Particle Filter models, and has a number of advanced algorithms implemented, with PyTorch backend.

Kalman filtering

• pykalman Easy to use Kalman Filter, Extended Kalman Filter and Unscented Kalman Filter implementations
• simdkalman Fast implmentations of plain Kalman filter banks.
• torch-kalman PyTorch implementation of Kalman filters, including Pandas dataframe support.

Installation

Available via PyPI:

pip install pfilter

Or install the git version:

pip install git+https://github.com/johnhw/pfilter.git

Usage

Create a ParticleFilter object, then call update(observation) with an observation array to update the state of the particle filter.

Calling update() without an observation will update the model without any data, i.e. perform a prediction step only.

Model

• Internal state space of d dimensions
• Observation space of h dimensions
• n particles estimating state in each time step

Particles are represented as an (n,d) matrix of states, one state per row. Observations are generated from this matrix into an (n,h) matrix of hypothesized observations via the observation function.

Functions

You need to specify at the minimum:

• an observation function observe_fn(state (n,d)) => observation matrix (n,h) which will return a predicted observation for an internal state.
• a function that samples from an initial distributions prior_fn => state matrix (n,d) for all of the internal state variables. These are usually distributions from scipy.stats. The utility function independent_sample makes it easy to concatenate sampling functions to sample the whole state vector.
• a weight function weight_fn(hyp_observed (n,h), real_observed (h,)) => weight vector (n,) which specifies how well each of the hyp_observed arrays match the real observation real_observed. This must produce a strictly positive weight value for each hypothesized observation, where larger means more similar. This is often an RBF kernel or similar.

Typically, you would also specify:

• dynamics a function dynamics_fn(state (n,d)) => predicted_state (n,d) to update the state based on internal (forward prediction) dynamics, and a
• diffusion a function noise_fn(predicted_state (n,d)) => noisy_state (n,d) to add diffusion into the sampling process (though you could also merge into the dynamics).

You might also specify:

• Internal weighting a function internal_weight_fn(state (n,d)) => weight vector (n,) which provides a weighting to apply on top of the weight function based on internal state. This is useful to impose penalties or to include learned inverse models in the inference.
• Post-processing transform function a function transform_fn(state (n,d), weights (n,)) => states(n, k) which can apply a post-processing transform and store the result in transformed_particles

Missing observations

If you want to be able to deal with partial missing values in the observations, the weight function should support masked arrays. The squared_error(a,b) function in pfilter.py does this, for example.

Passing values to functions

Sometimes it is useful to pass inputs to callback functions like dynamics_fn(x) at each time step. You can do this by giving keyword arguments to update().

If you call pf.update(y, t=5) all of the functions dynamics_fn, weight_fn, noise_fn, internal_weight_fn, observe_fn will receive the keyword argument t=5. ALl kwargs are forwarded to these calls. You can just ignore them if not used (e.g. define dynamics_fn = lambda x, **kwargs: real_dynamics(x)) but this can be useful for propagating inputs that are neither internal states nor observed states to the filter. If no kwargs are given to update, then no extra arguments are passed to any of callbacks.

Attributes

The ParticleFilter object will have the following useful attributes after updating:

• original_particles the (n,d) collection of particles in the last update step
• mean_state the (d,) expectation of the state
• mean_hypothesized the (h,) expectation of the hypothesized observations
• cov_state the (d,d) covariance matrix of the state
• map_state the (d,) most likely state
• map_hypothesized the (h,) most likely hypothesized observation
• weights the (n,) normalised weights of each particle

In equations

Example

For example, assuming we observe 32x32 images and want to track a moving circle. Assume the internal state we are estimating is the 4D vector (x, y, dx, dy), with 200 particles

        from pfilter import ParticleFilter, gaussian_noise, squared_error, independent_sample
        columns = ["x", "y", "radius", "dx", "dy"]
        from scipy.stats import norm, gamma, uniform 
        
        # prior sampling function for each variable
        # (assumes x and y are coordinates in the range 0-32)    
        prior_fn = independent_sample([uniform(loc=0, scale=32).rvs, 
                    uniform(loc=0, scale=32).rvs, 
                    gamma(a=2,loc=0,scale=10).rvs,
                    norm(loc=0, scale=0.5).rvs,
                    norm(loc=0, scale=0.5).rvs])
                                    
        # very simple linear dynamics: x += dx
        def velocity(x):
            xp = np.array(x)
            xp[0:2] += xp[3:5]        
        return xp
        
        # create the filter
        pf = pfilter.ParticleFilter(
                        prior_fn=prior_fn, 
                        observe_fn=blob,
                        n_particles=200,
                        dynamics_fn=velocity,
                        noise_fn=lambda x: 
                                    gaussian_noise(x, sigmas=[0.2, 0.2, 0.1, 0.05, 0.05]),
                        weight_fn=lambda x,y:squared_error(x, y, sigma=2),
                        resample_proportion=0.1,
                        column_names = columns)
                        
        # assuming image of the same dimensions/type as blob will produce
        pf.update(image) 

• blob (200, 4) -> (200, 1024) which draws a blob on an image of size 32x32 (1024 pixels) for each internal state, our observation function
• velocity (200, 4) -> (200, 4), our dynamics function, which just applies a single Euler step integrating the velocity
• prior_fn which generates a (200,4) initial random state
• gaussian_noise (200, 4) -> (200,4) which adds noise to the internal state
• squared_error ((200,1024), (1024,)) -> (200,) the similarity measurement

See the notebook examples/example_filter.py for a working example using skimage and OpenCV which tracks a moving white circle.