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Rename SIRParticleFilter to BootstrapFilter #44

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Oct 28, 2020
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Rename SIRParticleFilter to BootstrapFilter #44

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609b073
did renaming
zsunberg Oct 11, 2020
83682de
fixed notebooks
zsunberg Oct 11, 2020
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2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,7 +1,7 @@
name = "ParticleFilters"
uuid = "c8b314e2-9260-5cf8-ae76-3be7461ca6d0"
repo = "https://github.com/JuliaPOMDP/ParticleFilters.jl"
version = "0.5.1"
version = "0.5.2"
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I think this should be 0.6.0 now since it breaks existing code.

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According to SemVer this would be okay since all major version 0 releases may be incompatible with eachother. But in Julia there is the convention:

While the semver specification says that all versions with a major version of 0 are incompatible with each other, we have made that choice that a version given as 0.a.b is considered compatible with 0.a.c if a != 0 and c >= b.

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@zsunberg zsunberg Oct 27, 2020 •
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Yeah, I actually looked this up (https://semver.org/#how-should-i-handle-deprecating-functionality), and I am still not 100% sure if I understand it correctly it. According to that section, it looks like what you're actually supposed to do is deprecate with a minor version release, and then remove in the next major version (so in this case deprecate in 0.5.2 and remove in 0.6.0).

Before julia 1.5, deprecation warnings were on by default, so there was a big performance penalty for deprecating things, but now they are off by default except in tests. So, I think 0.5.2 is actually correct.


[deps]
POMDPLinter = "f3bd98c0-eb40-45e2-9eb1-f2763262d755"
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -26,7 +26,7 @@ dynamics(x, u, rng) = x + u + randn(rng)
y_likelihood(x_previous, u, x, y) = pdf(Normal(), y - x)

model = ParticleFilterModel{Float64}(dynamics, y_likelihood)
pf = SIRParticleFilter(model, 10)
pf = BootstrapFilter(model, 10)
```
Then the `update` function can be used to perform a particle filter update.
```julia
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4 changes: 2 additions & 2 deletions docs/src/basic.md
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Expand Up @@ -8,7 +8,7 @@ The basic particle filtering step in ParticleFilters.jl is implemented in the [`
2. Reweighting - an explicit measurement (observation) model is used to calculate a new weight
3. Resampling - a new collection of state particles is generated with particle frequencies proportional to the new weights

This is an example of [sequential importance resampling](https://en.wikipedia.org/wiki/Particle_filter#Sequential_Importance_Resampling_(SIR)), and the [`SIRParticleFilter`](@ref) constructor can be used to construct such a filter with a `model` that controls the prediction and reweighting steps, and a number of particles to create in the resampling phase.
This is an example of [sequential importance resampling](https://en.wikipedia.org/wiki/Particle_filter#Sequential_Importance_Resampling_(SIR)) using the state transition distribution as the proposal distribution, and the [`BootstrapFilter`](@ref) constructor can be used to construct such a filter with a `model` that controls the prediction and reweighting steps, and a number of particles to create in the resampling phase.

A more flexible structure for building a particle filter is the [`BasicParticleFilter`](@ref). It contains three models, one for each step:

Expand All @@ -23,7 +23,7 @@ To carry out the steps individually without the need for pre-allocating memory o
## Docstrings

```@docs
SIRParticleFilter
BootstrapFilter
BasicParticleFilter
update
predict!
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4 changes: 2 additions & 2 deletions docs/src/index.md
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Expand Up @@ -8,7 +8,7 @@ Additionally, an important requirement for a particle filter is efficient resamp

Dynamics and measurement models for the filters can be specified as a [`ParticleFilterModel`](@ref) or a [`POMDP`](https://github.com/JuliaPOMDP/POMDPs.jl) or a custom user-defined type.

The simplest sequential-importance-resampling Particle filter can be constructed with [`SIRParticleFilter`](@ref). [`BasicParticleFilter`](@ref) provides a more flexible structure.
The simplest Bootstrap Particle filter can be constructed with [`BootstrapFilter`](@ref). [`BasicParticleFilter`](@ref) provides a more flexible structure.

Basic setup of a model is as follows:
```julia
Expand All @@ -17,7 +17,7 @@ using ParticleFilters, Distributions
dynamics(x, u, rng) = x + u + randn(rng)
y_likelihood(x_previous, u, x, y) = pdf(Normal(), y - x)
model = ParticleFilterModel{Float64}(dynamics, y_likelihood)
pf = SIRParticleFilter(model, 10)
pf = BootstrapFilter(model, 10)
```
Then the [`update`](@ref) function can be used to perform a particle filter update.
```julia
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263 changes: 113 additions & 150 deletions notebooks/Filtering-a-Trajectory-or-Data-Series.ipynb
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39 changes: 14 additions & 25 deletions notebooks/Using-a-Particle-Filter-for-Feedback-Control.ipynb
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Expand Up @@ -160,12 +160,12 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"N = 1000\n",
"filter = SIRParticleFilter(model, N);"
"filter = BootstrapFilter(model, N);"
]
},
{
Expand All @@ -183,7 +183,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -193,28 +193,17 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Recompiling stale cache file /home/zach/.julia/compiled/v1.0/Plots/ld3vC.ji for Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]\n",
"└ @ Base loading.jl:1184\n",
"WARNING: StaticArrays.FixedSizeArrays is deprecated. Use StaticArrays directly.\n",
" likely near /home/zach/.julia/packages/Plots/Ufx0i/src/Plots.jl:8\n"
]
}
],
"outputs": [],
"source": [
"using Plots\n",
"rng = Random.GLOBAL_RNG;"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 15,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -253,7 +242,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -262,7 +251,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 17,
"metadata": {},
"outputs": [
{
Expand All @@ -278,7 +267,7 @@
"\"output.gif\""
]
},
"execution_count": 10,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -294,7 +283,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 18,
"metadata": {},
"outputs": [
{
Expand All @@ -306,7 +295,7 @@
"HTML{String}(\"<img src=\\\"output.gif\\\"/>\")"
]
},
"execution_count": 11,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -325,15 +314,15 @@
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.0.0",
"display_name": "Julia 1.5.1",
"language": "julia",
"name": "julia-1.0"
"name": "julia-1.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.0.0"
"version": "1.5.1"
}
},
"nbformat": 4,
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20 changes: 10 additions & 10 deletions notebooks/Using-a-Particle-Filter-with-POMDPs-jl.ipynb
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Expand Up @@ -11,7 +11,7 @@
},
{
"cell_type": "code",
"execution_count": 46,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -35,14 +35,14 @@
},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"pomdp = LightDark1D()\n",
"N=1000\n",
"\n",
"up = SIRParticleFilter(pomdp, N)\n",
"up = BootstrapFilter(pomdp, N)\n",
"\n",
"policy = FunctionPolicy(b->1)\n",
"b0 = POMDPModels.LDNormalStateDist(-15.0, 5.0)\n",
Expand All @@ -63,7 +63,7 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -73,7 +73,7 @@
},
{
"cell_type": "code",
"execution_count": 65,
"execution_count": 8,
"metadata": {},
"outputs": [
{
Expand All @@ -82,7 +82,7 @@
"\"hist.gif\""
]
},
"execution_count": 65,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -106,7 +106,7 @@
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 9,
"metadata": {},
"outputs": [
{
Expand All @@ -118,7 +118,7 @@
"HTML{String}(\"<img src=\\\"hist.gif\\\"/>\")"
]
},
"execution_count": 63,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -137,15 +137,15 @@
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.5.0",
"display_name": "Julia 1.5.1",
"language": "julia",
"name": "julia-1.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.5.0"
"version": "1.5.1"
}
},
"nbformat": 4,
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8 changes: 4 additions & 4 deletions src/ParticleFilters.jl
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Expand Up @@ -24,10 +24,10 @@ export
BasicParticleFilter,
ImportanceResampler,
LowVarianceResampler,
SIRParticleFilter,
UnweightedParticleFilter,
ParticleFilterModel,
PredictModel,
BootstrapFilter,
ReweightModel

export
Expand Down Expand Up @@ -57,16 +57,16 @@ export
support,
initialize_belief

# deprecated
export
SimpleParticleFilter

SIRParticleFilter

include("beliefs.jl")
include("basic.jl")
include("resamplers.jl")
include("sir.jl")
include("unweighted.jl")
include("models.jl")
include("bootstrap.jl")
include("pomdps.jl")
include("policies.jl")
include("runfilter.jl")
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12 changes: 5 additions & 7 deletions src/sir.jl → src/bootstrap.jl
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@@ -1,7 +1,9 @@
"""
SIRParticleFilter(model, n, [rng])
BootstrapFilter(model, n, [rng])

Construct a sequential importance resampling particle filter.
Construct a standard bootstrap particle filter.

The Bootstrap filter was first described in Gordon, N. J., Salmond, D. J., & Smith, A. F. M. "Novel approach to nonlinear / non-Gaussian Bayesian state estimation", with the added robustness of the LowVarianceResampler.

# Arguments
- `model`: a model for the prediction dynamics and likelihood reweighing, for example a `POMDP` or `ParticleFilterModel`
Expand All @@ -10,10 +12,6 @@ Construct a sequential importance resampling particle filter.

For a more flexible particle filter structure see [`BasicParticleFilter`](@ref).
"""
function SIRParticleFilter(model, n::Int, rng::AbstractRNG)
return BasicParticleFilter(model, LowVarianceResampler(n), n, rng)
end

function SIRParticleFilter(model, n::Int; rng::AbstractRNG=Random.GLOBAL_RNG)
function BootstrapFilter(model, n::Int, rng::AbstractRNG=Random.GLOBAL_RNG)
return BasicParticleFilter(model, LowVarianceResampler(n), n, rng)
end
3 changes: 2 additions & 1 deletion src/deprecated.jl
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@@ -1 +1,2 @@
@deprecate SimpleParticleFilter BasicParticleFilter
@deprecate SIRParticleFilter(model, n::Int, rng::AbstractRNG) BootstrapFilter(model, n, rng)
@deprecate SIRParticleFilter(model, n::Int; rng::AbstractRNG=Random.GLOBAL_RNG) BootstrapFilter(model, n, rng)
2 changes: 1 addition & 1 deletion src/unweighted.jl
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Expand Up @@ -28,7 +28,7 @@ function update(up::UnweightedParticleFilter, b::ParticleCollection, a, o)
warn("""
Particle Depletion!

The UnweightedParticleFilter generated no particles consistent with observation $o. Consider upgrading to a SIRParticleFilter or a BasicParticleFilter or creating your own domain-specific updater.
The UnweightedParticleFilter generated no particles consistent with observation $o. Consider upgrading to a BootstrapFilter or a BasicParticleFilter or creating your own domain-specific updater.
"""
)
end
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2 changes: 1 addition & 1 deletion test/example.jl
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Expand Up @@ -29,7 +29,7 @@ h(x, rng) = rand(rng, MvNormal(x[1:2], V))

@testset "example" begin
N = 1000
filter = SIRParticleFilter(model, N)
filter = BootstrapFilter(model, N)
Random.seed!(1)
rng = Random.GLOBAL_RNG
b = ParticleCollection([4.0*rand(4).-2.0 for i in 1:N])
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2 changes: 1 addition & 1 deletion test/lightdark.jl
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Expand Up @@ -7,7 +7,7 @@ pomdp = LightDark2D(init_dist=SymmetricNormal2([2.0, 2.0], 5.0))

policy = FunctionPolicy(s->-0.1*mean(s))

fnew = SIRParticleFilter(pomdp, 100, rng=MersenneTwister(42))
fnew = BootstrapFilter(pomdp, 100, rng=MersenneTwister(42))
ro = RolloutSimulator(rng=MersenneTwister(1), max_steps=10)
simulate(ro, pomdp, policy, fnew)

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6 changes: 3 additions & 3 deletions test/runtests.jl
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Expand Up @@ -24,7 +24,7 @@ include("domain_specific_resampler.jl")
struct ContinuousPOMDP <: POMDP{Float64, Float64, Float64} end
@testset "infer" begin
p = TigerPOMDP()
filter = SIRParticleFilter(p, 10000)
filter = BootstrapFilter(p, 10000)
Random.seed!(filter, 47)
b = @inferred initialize_belief(filter, initialstate(p))
@testset "sir" begin
Expand Down Expand Up @@ -64,7 +64,7 @@ struct ContinuousPOMDP <: POMDP{Float64, Float64, Float64} end
end

@testset "normal" begin
pf = SIRParticleFilter(ContinuousPOMDP(), 100)
pf = BootstrapFilter(ContinuousPOMDP(), 100)
ps = @inferred initialize_belief(pf, Normal())
end
end
Expand All @@ -75,7 +75,7 @@ POMDPs.observation(::TerminalPOMDP, a, sp) = Normal(sp)
POMDPs.transition(::TerminalPOMDP, s, a) = Deterministic(s+a)
@testset "pomdp terminal" begin
pomdp = TerminalPOMDP()
pf = SIRParticleFilter(pomdp, 100)
pf = BootstrapFilter(pomdp, 100)
bp = update(pf, initialize_belief(pf, Categorical([0.5, 0.5])), -1, 1.0)
@test all(particles(bp) .== 1)
end
Expand Down