Speed up viterbi (#93)

Author: gdalle

.github/workflows/benchmark.yml

@@ -8,6 +8,9 @@ on:
 jobs:
   Benchmark:
     runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: write
     if: contains(github.event.pull_request.labels.*.name, 'run benchmark')
     steps:
       - uses: actions/checkout@v2

Project.toml

@@ -1,7 +1,7 @@
 name = "HiddenMarkovModels"
 uuid = "84ca31d5-effc-45e0-bfda-5a68cd981f47"
 authors = ["Guillaume Dalle"]
-version = "0.4.1"
+version = "0.5.0"
 
 [deps]
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"

README.md

@@ -24,8 +24,8 @@ Then, you can create your first model as follows:
 
 ```julia
 using Distributions, HiddenMarkovModels
-init = [0.4, 0.6]
-trans = [0.9 0.1; 0.2 0.8]
+init = [0.6, 0.4]
+trans = [0.7 0.3; 0.2 0.8]
 dists = [Normal(-1.0), Normal(1.0)]
 hmm = HMM(init, trans, dists)
 ```

benchmark/Manifest.toml

@@ -169,7 +169,7 @@ version = "0.1.0"
 deps = ["ArgCheck", "ChainRulesCore", "DensityInterface", "DocStringExtensions", "FillArrays", "LinearAlgebra", "PrecompileTools", "Random", "SparseArrays", "StatsAPI", "StatsFuns"]
 path = ".."
 uuid = "84ca31d5-effc-45e0-bfda-5a68cd981f47"
-version = "0.4.0"
+version = "0.5.0"
 weakdeps = ["Distributions"]
 
     [deps.HiddenMarkovModels.extensions]

docs/src/api.md

@@ -94,14 +94,24 @@ HiddenMarkovModels.forward_backward!
 HiddenMarkovModels.baum_welch!
 ```
 
-## Misc
+## Miscellaneous
 
 ```@docs
+HiddenMarkovModels.valid_hmm
 HiddenMarkovModels.rand_prob_vec
 HiddenMarkovModels.rand_trans_mat
+HiddenMarkovModels.fit_in_sequence!
+```
+
+## Internals
+
+```@docs
 HiddenMarkovModels.LightDiagNormal
 HiddenMarkovModels.LightCategorical
-HiddenMarkovModels.fit_in_sequence!
+HiddenMarkovModels.log_initialization
+HiddenMarkovModels.log_transition_matrix
+HiddenMarkovModels.mul_rows_cols!
+HiddenMarkovModels.argmaxplus_transmul!
 ```
 
 ## Index

examples/basics.jl

@@ -19,16 +19,16 @@ rng = StableRNG(63);
 # ## Model
 
 #=
-The package provides a versatile [`HMM`](@ref) type with three attributes:
-- a vector of state initialization probabilities
-- a matrix of state transition probabilities
-- a vector of observation distributions, one for each state
+The package provides a versatile [`HMM`](@ref) type with three main attributes:
+- a vector `init` of state initialization probabilities
+- a matrix `trans` of state transition probabilities
+- a vector `dists` of observation distributions, one for each state
 
 Any scalar- or vector-valued distribution from [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) can be used for the last part, as well as [Custom distributions](@ref).
 =#
 
 init = [0.6, 0.4]
-trans = [0.7 0.3; 0.3 0.7]
+trans = [0.7 0.3; 0.2 0.8]
 dists = [MvNormal([-0.5, -0.8], I), MvNormal([0.5, 0.8], I)]
 hmm = HMM(init, trans, dists)
 
@@ -142,7 +142,7 @@ Since it is a local optimization procedure, it requires a starting point that is
 =#
 
 init_guess = [0.5, 0.5]
-trans_guess = [0.6 0.4; 0.4 0.6]
+trans_guess = [0.6 0.4; 0.3 0.7]
 dists_guess = [MvNormal([-0.4, -0.7], I), MvNormal([0.4, 0.7], I)]
 hmm_guess = HMM(init_guess, trans_guess, dists_guess);
 

examples/controlled.jl

@@ -32,14 +32,15 @@ struct ControlledGaussianHMM{T} <: AbstractHMM
 end
 
 #=
-In state $i$ with a vector of controls $u$, our observation is given by the linear model $y \sim \mathcal{N}(\beta_i^\top u, 1)$. 
+In state $i$ with a vector of controls $u$, our observation is given by the linear model $y \sim \mathcal{N}(\beta_i^\top u, 1)$.
+Controls must be provided to both `transition_matrix` and `obs_distributions` even if they are only used by one.
 =#
 
 function HMMs.initialization(hmm::ControlledGaussianHMM)
     return hmm.init
 end
 
-function HMMs.transition_matrix(hmm::ControlledGaussianHMM)
+function HMMs.transition_matrix(hmm::ControlledGaussianHMM, control::AbstractVector)
     return hmm.trans
 end
 
@@ -54,8 +55,8 @@ In this case, the transition matrix does not depend on the control.
 # ## Simulation
 
 d = 3
-init = [0.8, 0.2]
-trans = [0.7 0.3; 0.3 0.7]
+init = [0.6, 0.4]
+trans = [0.7 0.3; 0.2 0.8]
 dist_coeffs = [-ones(d), ones(d)]
 hmm = ControlledGaussianHMM(init, trans, dist_coeffs);
 
@@ -122,9 +123,9 @@ end
 Now we put it to the test.
 =#
 
-init_guess = [0.7, 0.3]
-trans_guess = [0.6 0.4; 0.4 0.6]
-dist_coeffs_guess = [-0.7 * ones(d), 0.7 * ones(d)]
+init_guess = [0.5, 0.5]
+trans_guess = [0.6 0.4; 0.3 0.7]
+dist_coeffs_guess = [-1.1 * ones(d), 1.1 * ones(d)]
 hmm_guess = ControlledGaussianHMM(init_guess, trans_guess, dist_coeffs_guess);
 
 #-
@@ -136,7 +137,7 @@ first(loglikelihood_evolution), last(loglikelihood_evolution)
 How did we perform?
 =#
 
-cat(transition_matrix(hmm_est), transition_matrix(hmm); dims=3)
+cat(hmm_est.trans, hmm.trans; dims=3)
 
 #-
 

examples/interfaces.jl

@@ -82,7 +82,7 @@ Let's put it to the test.
 =#
 
 init = [0.6, 0.4]
-trans = [0.7 0.3; 0.3 0.7]
+trans = [0.7 0.3; 0.2 0.8]
 dists = [StuffDist(-1.0), StuffDist(+1.0)]
 hmm = HMM(init, trans, dists);
 
@@ -104,8 +104,8 @@ If we implement `fit!`, Baum-Welch also works seamlessly.
 =#
 
 init_guess = [0.5, 0.5]
-trans_guess = [0.6 0.4; 0.4 0.6]
-dists_guess = [StuffDist(-0.7), StuffDist(+0.7)]
+trans_guess = [0.6 0.4; 0.3 0.7]
+dists_guess = [StuffDist(-1.1), StuffDist(+1.1)]
 hmm_guess = HMM(init_guess, trans_guess, dists_guess);
 
 #-

examples/temporal.jl

@@ -55,7 +55,7 @@ end
 # ## Simulation
 
 init = [0.6, 0.4]
-trans_per = ([0.7 0.3; 0.3 0.7], [0.3 0.7; 0.7 0.3])
+trans_per = ([0.7 0.3; 0.2 0.8], [0.3 0.7; 0.8 0.2])
 dists_per = ([Normal(-1.0), Normal(-2.0)], [Normal(+1.0), Normal(+2.0)])
 hmm = PeriodicHMM(init, trans_per, dists_per);
 
@@ -152,8 +152,8 @@ Now let's test our procedure with a reasonable guess.
 =#
 
 init_guess = [0.7, 0.3]
-trans_per_guess = ([0.6 0.4; 0.4 0.6], [0.4 0.6; 0.6 0.4])
-dists_per_guess = ([Normal(-0.7), Normal(-1.7)], [Normal(+0.7), Normal(+1.7)])
+trans_per_guess = ([0.6 0.4; 0.3 0.7], [0.4 0.6; 0.7 0.3])
+dists_per_guess = ([Normal(-1.1), Normal(-2.1)], [Normal(+1.1), Normal(+2.1)])
 hmm_guess = PeriodicHMM(init_guess, trans_per_guess, dists_per_guess);
 
 #=

examples/types.jl

@@ -6,6 +6,7 @@ Here we explain why playing with different number and array types can be useful
 
 using Distributions
 using HiddenMarkovModels
+using HiddenMarkovModels: log_transition_matrix  #src
 using HMMTest  #src
 using LinearAlgebra
 using LogarithmicNumbers
@@ -40,7 +41,7 @@ To give an example, let us first generate some data from a vanilla HMM.
 =#
 
 init = [0.6, 0.4]
-trans = [0.7 0.3; 0.3 0.7]
+trans = [0.7 0.3; 0.2 0.8]
 dists = [Normal(-1.0), Normal(1.0)]
 hmm = HMM(init, trans, dists)
 state_seq, obs_seq = rand(rng, hmm, 100);
@@ -57,6 +58,10 @@ hmm_uncertain = HMM(init, trans, dists_guess)
 Every quantity we compute with this new HMM will have propagated uncertainties around it.
 =#
 
+logdensityof(hmm, obs_seq)
+
+#-
+
 logdensityof(hmm_uncertain, obs_seq)
 
 #=
@@ -129,7 +134,7 @@ trans_guess = sparse([
     0 0.6 0.4
     0.4 0 0.6
 ])
-dists_guess = [Normal(1.2), Normal(2.2), Normal(3.2)]
+dists_guess = [Normal(1.1), Normal(2.1), Normal(3.1)]
 hmm_guess = HMM(init_guess, trans_guess, dists_guess);
 
 #-
@@ -149,10 +154,12 @@ Another useful array type is [StaticArrays.jl](https://github.com/JuliaArrays/St
 
 # ## Tests  #src
 
+@test nnz(log_transition_matrix(hmm)) == nnz(transition_matrix(hmm))  #src
+
 seq_ends = cumsum(rand(rng, 100:200, 100));  #src
-control_seqs = fill(nothing, length(seq_ends));  #src
+control_seq = fill(nothing, last(seq_ends));  #src
 test_identical_hmmbase(rng, hmm, 100; hmm_guess)  #src
-test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess, init=false)  #src
+test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess, init=false, atol=0.08)  #src
 test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)  #src
 # https://github.com/JuliaSparse/SparseArrays.jl/issues/469  #src
 @test_skip test_allocations(rng, hmm, control_seq; seq_ends, hmm_guess)  #src