Usage

fit(all_data::AbstractArray{Float32,2},local_hyper_params::distribution_hyper_params,α_param::Float32;
   iters::Int64 = 100, init_clusters::Int64 = 1,seed = nothing, verbose = true, save_model = false, burnout = 20, gt = nothing, max_clusters = Inf, outlier_weight = 0, outlier_params = nothing)

Run the model (basic mode).

Args and Kwargs

• all_data::AbstractArray{Float32,2} a DxN array containing the data
• local_hyper_params::distribution_hyper_params the prior hyperparams
• α_param::Float32 the concetration parameter
• iters::Int64 number of iterations to run the model
• init_clusters::Int64 number of initial clusters
• seed define a random seed to be used in all workers, if used must be preceeded with @everywhere using random.
• verbose will perform prints on every iteration.
• save_model will save a checkpoint every 25 iterations.
• burnout how long to wait after creating a cluster, and allowing it to split/merge
• gt Ground truth, when supplied, will perform NMI and VI analysis on every iteration.
• max_clusters limit the number of cluster
• outlier_weight constant weight of an extra non-spliting component
• outlier_params hyperparams for an extra non-spliting component

Return Values

• labels Labels assignments
• clusters Cluster parameters
• weights The cluster weights, does not sum to 1, but to 1 minus the weight of all uninstanistaed clusters.
• iter_count Timing for each iteration
• nmi_score_history NMI score per iteration (if gt suppled)
• likelihood_history Log likelihood per iteration.
• cluster_count_history Cluster counts per iteration.
• sub_labels Sub labels assignments

Example:

julia> x,y,clusters = generate_gaussian_data(10000,2,6,100.0)
...

julia> hyper_params = DPMMSubClusters.niw_hyperparams(1.0,
                  zeros(2),
                  5,
                  [1 0;0 1])
DPMMSubClusters.niw_hyperparams(1.0f0, Float32[0.0, 0.0], 5.0f0, Float32[1.0 0.0; 0.0 1.0])

julia> ret_values= fit(x,hyper_params,10.0, iters = 100, verbose=false)

...

julia> unique(ret_values[1])
6-element Array{Int64,1}:
 3
 6
 1
 2
 5
 4

fit(all_data::AbstractArray{Float32,2},α_param::Float32;
    iters::Int64 = 100, init_clusters::Int64 = 1,seed = nothing, verbose = true, save_model = false,burnout = 20, gt = nothing, max_clusters = Inf, outlier_weight = 0, outlier_params = nothing)

Run the model (basic mode) with default NIW prior.

Args and Kwargs

• all_data::AbstractArray{Float32,2} a DxN array containing the data
• α_param::Float32 the concetration parameter
• iters::Int64 number of iterations to run the model
• init_clusters::Int64 number of initial clusters
• seed define a random seed to be used in all workers, if used must be preceeded with @everywhere using random.
• verbose will perform prints on every iteration.
• save_model will save a checkpoint every 25 iterations.
• burnout how long to wait after creating a cluster, and allowing it to split/merge
• gt Ground truth, when supplied, will perform NMI and VI analysis on every iteration.
• outlier_weight constant weight of an extra non-spliting component
• outlier_params hyperparams for an extra non-spliting component

Return Values

• labels Labels assignments
• clusters Cluster parameters
• weights The cluster weights, does not sum to 1, but to 1 minus the weight of all uninstanistaed clusters.
• iter_count Timing for each iteration
• nmi_score_history NMI score per iteration (if gt suppled)
• likelihood_history Log likelihood per iteration.
• cluster_count_history Cluster counts per iteration.
• sub_labels Sub labels assignments

Example:

julia> x,y,clusters = generate_gaussian_data(10000,2,6,100.0)
...

julia> ret_values= fit(x,10.0, iters = 100, verbose=false)

...

julia> unique(ret_values[1])
6-element Array{Int64,1}:
 3
 6
 1
 2
 5
 4

dp_parallel(model_params::String; verbose = true, save_model = true,burnout = 5, gt = nothing)

Run the model in advanced mode.

Args and Kwargs

• model_params::String A path to a parameters file (see below)
• verbose will perform prints on every iteration.
• save_model will save a checkpoint every X iterations, where X is specified in the parameter file.
• burnout how long to wait after creating a cluster, and allowing it to split/merge
• gt Ground truth, when supplied, will perform NMI and VI analysis on every iteration.

Return values

dpmodel, itercount , nmiscorehistory, liklihoodhistory, clustercount_history

• dp_model The DPMM model inferred
• iter_count Timing for each iteration
• nmi_score_history NMI score per iteration (if gt suppled)
• likelihood_history Log likelihood per iteration.
• cluster_count_history Cluster counts per iteration.

run_model_from_checkpoint(filename)

Run the model from a checkpoint created by it, filename is the path to the checkpoint. Only to be run when using the advanced mode, note that the data must be in the same path as previously.

Example:

julia> dp = run_model_from_checkpoint("checkpoint__50.jld2")
Loading Model:
  1.073261 seconds (2.27 M allocations: 113.221 MiB, 2.60% gc time)
Including params
Loading data:
  0.000881 seconds (10.02 k allocations: 378.313 KiB)
Creating model:
Node Leaders:
Dict{Any,Any}(2=>Any[2, 3])
Running model:
...