Data Generators
The package offers (currently) two data generators, one for Multinomial priors, and the other for Gaussians.
DPMMSubClusters.generate_gaussian_data โ Function.generate_gaussian_data(N::Int64, D::Int64, K::Int64,MixtureVar::Number)Generate N observations, generated from K D dimensions Gaussians, with the Gaussian means sampled from a Normal distribution with mean 0 and MixtureVar variance.
Returns (Samples, Labels, Clusters_means, Clusters_cov)
Example
julia> x,y,clusters = generate_gaussian_data(10000,2,6,100.0)
[3644, 2880, 119, 154, 33, 3170]
...DPMMSubClusters.generate_mnmm_data โ Function. generate_mnmm_data(N::Int64, D::Int64, K::Int64, trials::Int64)Generate N observations, generated from K D features Multinomial vectors, with trials draws from each vector.
Returns (Samples, Labels, Vectors)
Example
julia> generate_mnmm_data(10000, 10, 5, 100)
...